From cd8839940b2b79682c58b95b1c75dd1704782dbd Mon Sep 17 00:00:00 2001 From: Dylan Bourgeois Date: Mon, 11 Feb 2019 11:51:15 -0800 Subject: [PATCH] Notebooks --- notebook/BERT-100k_epochs_top100.png | Bin 0 -> 61029 bytes notebook/BERT-freq-100k_epochs_top100.png | Bin 0 -> 54288 bytes notebook/BERT-freq-200k_epochs_top100.png | Bin 0 -> 54834 bytes notebook/Inspect Attention - Var Naming.ipynb | 1107 + notebook/Inspect Attention.ipynb | 20 +- notebook/Inspect Predictions - MLM.ipynb | 4409 ++++ .../Inspect Predictions - Var Naming.ipynb | 18760 ++++++++++++++-- notebook/Inspect Predictions.ipynb | 5307 ----- notebook/MAGRET-100k_epochs_top100.png | Bin 64895 -> 63112 bytes notebook/MAGRET-100k_epochs_top200.pdf | Bin 0 -> 30004 bytes notebook/MAGRET-200k_epochs_top200.pdf | Bin 0 -> 29988 bytes notebook/MAGRET-freq-100k_epochs_top100.png | Bin 55388 -> 54702 bytes 12 files changed, 22553 insertions(+), 7050 deletions(-) create mode 100644 notebook/BERT-100k_epochs_top100.png create 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+ "metadata": {}, + "source": [ + "Load vocabulary" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1155, 1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab = pd.read_csv(path+'vocab-code.txt', header=None)\n", + "vocab.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load predictions results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "0 x [PAD] [PAD] [PAD] NaN\n", + "1 is keras tensor [PAD] NaN\n", + "2 name [PAD] [PAD] [PAD] NaN\n", + "3 string types [PAD] [PAD] NaN\n", + "4 warn [PAD] [PAD] [PAD] NaN\n", + "5 cast [PAD] [PAD] [PAD] NaN\n", + "6 y true [PAD] [PAD] NaN\n", + "7 mean [PAD] [PAD] [PAD] NaN\n", + "8 mean [PAD] [PAD] [PAD] NaN\n", + "9 self [PAD] [PAD] [PAD] NaN" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_df = pd.read_csv(path+'sparse_split_magret_label_val.txt', header=None)\n", + "label_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the prediction specific to this attention matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 [CLS] for name [PAD] [PAD] [PAD] [PAD] name if compare name in name expr call attribute append name subscript name index\n", + "Label = x [PAD] [PAD] [PAD]\n", + "\n", + "1 [CLS] if call attribute input [PAD] [PAD] [PAD] name name assign name call layer subscript attribute keras history name index num if call name name name return call name keyword binop list name add name keyword attribute name name raise call name\n", + "Label = is keras tensor [PAD]\n", + "\n", + "2 [PAD] [CLS] if call name name name return call name keyword binop list name add name keyword attribute name [PAD] [PAD] [PAD] name raise call name\n", + "Label = name [PAD] [PAD] [PAD]\n", + "\n" + ] + } + ], + "source": [ + "n=3; i=3\n", + "preds = []\n", + "for idx, row in results_df.iterrows():\n", + " top_n = list(np.argsort(-row)[:n])\n", + " preds.append(top_n[:n])\n", + " if (idx % 63 == 0) and (idx > 0):\n", + " preds = np.asarray(preds)\n", + " k = np.nonzero(preds[:,0])[0]\n", + " last_idx=k[-2]+1 \n", + " pred = [vocab.loc[p][0] for p in preds[:last_idx,0]]\n", + " print(idx // 64, ' '.join(pred))\n", + " print(\"Label = \", ' '.join(list(label_df.loc[idx//64][:4])))\n", + " preds = []\n", + " print()\n", + " if idx > i*64:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "# Actual sentence length, unpadded\n", + "emb_len = len(pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emb_len" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read the attention values" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12, 4096)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "att = np.asarray(pd.read_csv(path+'cls_output/attention_results.tsv', sep=' ', header=None))[:,:-1]\n", + "att.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "nb_heads = 12\n", + "seq_len = 64\n", + "attention = att.reshape((nb_heads, seq_len, seq_len))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot attention matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(6, 2, sharex='col', sharey='row', figsize=(20,40))\n", + "#f.suptitle('Attention head probabilities (Layer #12)')\n", + "for i, ax in enumerate(axes.flatten()):\n", + " im = ax.imshow(attention[i, :emb_len, :emb_len])\n", + " ax.set_title(\"Attention head {}\".format(i+1))\n", + " im.set_clim(0, 1)\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes('right', size='5%', pad=0.05)\n", + " f.colorbar(im, cax=cax, orientation='vertical')\n", + " \n", + " plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load adjacency matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 1\n", + "with open(path+'sparse_split_magret_tk.txt') as f:\n", + " for i in range(idx):\n", + " s = f.readline()" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['[CLS]',\n", + " 'For',\n", + " 'Name',\n", + " '[MASK]',\n", + " '[MASK]',\n", + " '[MASK]',\n", + " '[MASK]',\n", + " 'Attribute',\n", + " 'input',\n", + " 'layers',\n", + " 'Name',\n", + " 'Assign',\n", + " 'Name',\n", + " 'input',\n", + " 'tensor',\n", + " 'Call',\n", + " 'Name',\n", + " 'keyword',\n", + " 'Attribute',\n", + " 'batch',\n", + " 'input',\n", + " 'shape',\n", + " 'Name',\n", + " 'keyword',\n", + " 'Attribute',\n", + " 'dtype',\n", + " 'Name',\n", + " 'keyword',\n", + " 'Attribute',\n", + " 'sparse',\n", + " 'Name',\n", + " 'keyword',\n", + " 'Attribute',\n", + " 'name',\n", + " 'Name',\n", + " 'Expr',\n", + " 'Call',\n", + " 'Attribute',\n", + " 'append',\n", + " 'Name',\n", + " 'Name',\n", + " 'Assign',\n", + " 'Name',\n", + " 'newly',\n", + " 'created',\n", + " 'input',\n", + " 'layer',\n", + " 'Subscript',\n", + " 'Attribute',\n", + " 'keras',\n", + " 'history',\n", + " 'Name',\n", + " 'Index',\n", + " 'Num',\n", + " 'Assign',\n", + " 'Subscript',\n", + " 'Name',\n", + " 'Index',\n", + " 'Name',\n", + " 'Name']" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = s.strip('\\n').split(' ')\n", + "emb_len = len(s)\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import io" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = io.mmread(path+'adj/'+str(idx-1)+'_sparse_split_magret_adj.mtx')\n", + "A = m.toarray()\n", + "np.max(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,15))\n", + "plt.imshow(A[:emb_len,:emb_len])\n", + "plt.xticks(range(emb_len), s, rotation=90);\n", + "plt.yticks(range(emb_len), s);" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "G=nx.from_numpy_matrix(A[:emb_len,:emb_len])\n", + "#G.remove_node(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "pos = nx.spring_layout(G) # positions for all nodes\n", + "# nodes\n", + "nx.draw_networkx_nodes(G, pos, node_size=700)\n", + "# edges\n", + "nx.draw_networkx_edges(G, pos, width=2)\n", + "# labels\n", + "nx.draw_networkx_labels(G, pos, labels=dict(zip(range(emb_len),pred[:emb_len])), font_size=20, font_family='sans-serif')\n", + "\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "pos = nx.spring_layout(G) # positions for all nodes\n", + "# nodes\n", + "nx.draw_networkx_nodes(G, pos, node_size=700, node_color=blue)\n", + "# edges\n", + "nx.draw_networkx_edges(G, pos, width=1, edge_color=blue)\n", + "# labels\n", + "nx.draw_networkx_labels(G, pos, labels=dict(zip(range(1,emb_len),pred[1:emb_len])), font_size=20, font_family='sans-serif')\n", + "\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(12, 2, sharex='col', figsize=(20,60))\n", + "\n", + "#G0 = nx.from_numpy_matrix(attention[0, 1:emb_len, 1:emb_len])\n", + "#pos = nx.spring_layout(G0) # positions for all nodes\n", + "labels = dict(zip(range(1,emb_len),pred[1:emb_len]))\n", + "\n", + "#f.suptitle('Attention head probabilities (Layer #12)')\n", + "for i, (ax0, ax1) in enumerate(axes):\n", + " # Attention map\n", + " im = ax0.imshow(attention[i, :emb_len, :emb_len])\n", + " ax0.set_title(\"Attention head {}\".format(i+1))\n", + " im.set_clim(0, 1)\n", + " divider = make_axes_locatable(ax0)\n", + " cax = divider.append_axes('right', size='5%', pad=0.05)\n", + " f.colorbar(im, cax=cax, orientation='vertical')\n", + " \n", + " # -----------------------------------------------------\n", + " # graph\n", + " Gi = nx.from_numpy_matrix(attention[i, :emb_len, :emb_len])\n", + " Gi.remove_node(0)\n", + " \n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if d['weight'] > 0.8]\n", + " elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.3) & (d['weight'] <= 0.8)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if d['weight'] <= 0.3]\n", + " \n", + " lg_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] > 0.5) &(u==v)]\n", + " sm_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] <= 0.5)&(u==v)]\n", + "\n", + " # nodes\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=lg_self_att, node_size=900, ax=ax1, node_color=red)\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=sm_self_att, node_size=200, ax=ax1, node_color=blue)\n", + "\n", + " # edges\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=evlarge, width=6, ax=ax1, edge_color=red)\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=elarge, width=2, ax=ax1, edge_color=red, style='dashed')\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=esmall, width=1, ax=ax1, alpha=0.5, edge_color=blue)\n", + "\n", + " # labels\n", + " nx.draw_networkx_labels(Gi, pos, labels=labels, font_size=20, font_family='sans-serif', ax=ax1)\n", + "\n", + " ax1.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/Inspect Attention.ipynb b/notebook/Inspect Attention.ipynb index f4bfa84..37e0128 100644 --- a/notebook/Inspect Attention.ipynb +++ b/notebook/Inspect Attention.ipynb @@ -31,7 +31,7 @@ "metadata": {}, "outputs": [], "source": [ - "path = '../funcname_magret/pretraining_output/eval_results_att.txt'" + "path = '../sparse/cls_output/attention_results.tsv'" ] }, { @@ -43,11 +43,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(1155, 1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "vocab = pd.read_csv('../funcname_magret/vocab-code.txt', header=None)" + "vocab = pd.read_csv('../sparse/vocab-code.txt', header=None)\n", + "vocab.shape" ] }, { diff --git a/notebook/Inspect Predictions - MLM.ipynb b/notebook/Inspect Predictions - MLM.ipynb new file mode 100644 index 0000000..6f15329 --- /dev/null +++ b/notebook/Inspect Predictions - MLM.ipynb @@ -0,0 +1,4409 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "path = \"../sparse/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n", + "0 53 53 11 2 6 111 127 \n", + "1 38 38 8 2 139 25 387 \n", + "2 206 206 9 2 6 44 426 \n", + "3 25 25 30 2 6 53 25 \n", + "4 289 289 11 2 6 44 25 \n", + "5 25 25 6 2 7 576 111 \n", + "6 1142 1142 16 2 53 1142 193 \n", + "7 25 25 14 2 398 253 426 \n", + "8 53 53 34 2 57 58 59 \n", + "9 25 25 12 2 398 38 38 \n", + "\n", + " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n", + "0 7 576 1142 ... 0 0 0 0 0 0 0 0 0 0 \n", + "1 81 38 25 ... 0 0 0 0 0 0 0 0 0 0 \n", + "2 104 25 237 ... 0 0 0 0 0 0 0 0 0 0 \n", + "3 25 426 237 ... 0 0 0 0 0 0 0 0 0 0 \n", + "4 140 43 237 ... 0 0 0 0 0 0 0 0 0 0 \n", + "5 678 53 4 ... 0 0 0 0 0 0 0 0 0 0 \n", + "6 25 253 426 ... 0 0 0 0 0 0 0 0 0 0 \n", + "7 426 426 426 ... 0 0 0 0 0 0 0 0 0 0 \n", + "8 107 59 42 ... 0 0 0 0 0 0 0 0 0 0 \n", + "9 53 1142 50 ... 0 0 0 0 0 0 0 0 0 0 \n", + "\n", + "[10 rows x 67 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df = pd.read_csv(path+'pretraining_output-100k/eval_results_masked_lm.txt')\n", + "results_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1156, 1)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab_df = pd.read_csv(path+'/vocab-code.txt', header=None)\n", + "vocab_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1146, 1)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab_df2 = pd.read_csv('../../bert-cmp/bert/vocab-code.txt', header=None)\n", + "vocab_df2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'[cls]',\n", + " 'accuracy',\n", + " 'batches',\n", + " 'categorical',\n", + " 'cw',\n", + " 'existing',\n", + " 'lengths',\n", + " 'modes',\n", + " 'ref',\n", + " 'score',\n", + " 'suffix'}" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(vocab_df[0]) - set(vocab_df2[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1156" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(vocab_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "accuracy = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "per_token_acc = {}; per_token_count = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " if per_token_acc.get(label, None) == None:\n", + " per_token_acc[label] = 0\n", + " per_token_count[label] = 0\n", + " per_token_acc[label] += int(prediction == label)\n", + " per_token_count[label] += 1\n", + " accuracy += int(prediction == label)\n", + " #print(\"Predicted --\", prediction)\n", + " #print(\"Label --\", label)\n", + " #print()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "total_per_token_accuracy = {}\n", + "per_token_freq = {}\n", + "for k,v in per_token_acc.items():\n", + " if per_token_count[k] > 0:\n", + " total_per_token_accuracy[k] = v / per_token_count[k]\n", + " per_token_freq[k] = per_token_count[k] / len(results_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('predictions', 1.0),\n", + " ('withitem', 1.0),\n", + " ('units', 1.0),\n", + " ('random', 1.0),\n", + " ('total', 1.0),\n", + " ('inferreddimension', 1.0),\n", + " ('toarray', 1.0),\n", + " ('stop', 1.0),\n", + " ('enqueuer', 1.0),\n", + " ('preprocess', 1.0),\n", + " ('subscript', 1.0),\n", + " ('val', 1.0),\n", + " ('get', 1.0),\n", + " ('with', 1.0),\n", + " 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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = Counter(total_per_token_accuracy)\n", + "c.most_common(1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(30,10))\n", + "labels, values = zip(*c.most_common(200))\n", + "\n", + "indexes = np.arange(len(labels))\n", + "width = 1\n", + "\n", + "freqs = [per_token_freq[l] for l in labels]\n", + "\n", + "mean_freq = np.mean(list(per_token_freq.values()))\n", + "mean_acc = (accuracy / len(results_df))\n", + "\n", + "plt.bar(indexes, values, width, label='Accuracy')\n", + "plt.bar(indexes, freqs, width, label='Frequency')\n", + "plt.xticks(indexes , labels, rotation=90)\n", + "plt.title('MAGRET (100k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('MAGRET-100k_epochs_top200.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[2,\n", + " 53,\n", + " 1142,\n", + " 309,\n", + " 310,\n", + " 25,\n", + " 655,\n", + " 53,\n", + " 1142,\n", + " 22,\n", + " 25,\n", + " 25,\n", + " 655,\n", + " 319,\n", + " 655,\n", + " 25,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = list(results_df.loc[10][3:])\n", + "pred" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "pred_str = [vocab_df.loc[i][0] for i in pred]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['[CLS]',\n", + " 'call',\n", + " 'attribute',\n", + " 'random',\n", + " 'normal',\n", + " 'name',\n", + " 'keyword',\n", + " 'call',\n", + " 'attribute',\n", + " 'shape',\n", + " 'name',\n", + " 'name',\n", + " 'keyword',\n", + " 'num',\n", + " 'keyword',\n", + " 'name',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " 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"output_type": "execute_result" + } + ], + "source": [ + "d = Counter(per_token_freq)\n", + "d.most_common(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,10))\n", + "labels, values = zip(*d.most_common(100))\n", + "\n", + "indexes = np.arange(len(labels))\n", + "width = 1\n", + "\n", + "accuracies = [c[tok] for tok in labels]\n", + "\n", + "plt.bar(indexes, accuracies, width, label='Accuracy')\n", + "plt.bar(indexes, values, width, label='Frequency')\n", + "plt.xticks(indexes , labels, rotation=90)\n", + "plt.title('MAGRET (200k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('MAGRET-freq-100k_epochs_top100.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "confusion = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " if confusion.get(label, None) == None:\n", + " confusion[label] = []\n", + " if prediction != label:\n", + " confusion[label].append(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "confusion_counter = {c: Counter(confusion[c]) for c in confusion}" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'1': Counter({'mask': 1}),\n", + " '2': Counter({'1': 3}),\n", + " '2d': 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'broadcast': Counter(),\n", + " 'broadcastable': Counter({'axes': 3}),\n", + " 'build': Counter({'append': 7}),\n", + " 'built': Counter({'layers': 1, 'trainable': 4}),\n", + " 'by': Counter(),\n", + " 'c': Counter({'add': 1, 'i': 2, 'name': 2, 'w': 2}),\n", + " 'cache': Counter({'log': 4}),\n", + " 'call': Counter({'attribute': 1, 'keyword': 4, 'tuple': 8}),\n", + " 'carry': Counter({'img': 1, 'rate': 1}),\n", + " 'cast': Counter(),\n", + " 'categorical': Counter({'binary': 2}),\n", + " 'cell': Counter(),\n", + " 'cells': Counter(),\n", + " 'channel': Counter(),\n", + " 'char': Counter({'classes': 4, 'dim': 2, 'start': 1}),\n", + " 'check': Counter({'index': 2}),\n", + " 'chunk': Counter(),\n", + " 'chunked': Counter(),\n", + " 'chunks': Counter({'words': 2}),\n", + " 'class': Counter({'layer': 3, 'sample': 1}),\n", + " 'classdef': Counter(),\n", + " 'classes': Counter({'constants': 2,\n", + " 'kernel': 2,\n", + " 'samples': 1,\n", + " 'stddev': 1,\n", + " 'train': 6,\n", + " 'words': 4}),\n", + " 'clip': Counter(),\n", + " 'clipnorm': Counter({'dtype': 3, 'verbose': 6}),\n", + " 'close': Counter({'append': 2, 'update': 3}),\n", + " 'closure': Counter({'masks': 1}),\n", + " 'cls': Counter({'config': 1, 'layer': 1, 'name': 1, 'self': 1, 'x': 4}),\n", + " 'cntk': Counter({'spatial': 1}),\n", + " 'co': Counter(),\n", + " 'col': Counter({'ndarray': 1}),\n", + " 'cols': Counter({'rows': 2}),\n", + " 'combine': Counter({'hstack': 3}),\n", + " 'compare': Counter(),\n", + " 'compile': Counter({'normal': 3}),\n", + " 'comprehension': Counter(),\n", + " 'compute': Counter(),\n", + " 'concatenate': Counter(),\n", + " 'config': Counter(),\n", + " 'constant': Counter(),\n", + " 'constraint': Counter({'initializer': 14}),\n", + " 'continue': Counter({'return': 6}),\n", + " 'conv': Counter(),\n", + " 'conv2d': Counter({'conv3d': 3, 'layer': 1}),\n", + " 'convert': Counter(),\n", + " 'converted': Counter({'padding': 1}),\n", + " 'convolution': Counter(),\n", + " 'cooldown': Counter({'phase': 3, 'sequence': 4}),\n", + " 'copy': Counter({'name': 2, 'square': 3}),\n", + " 'count': Counter(),\n", + " 'counter': Counter({'far': 3}),\n", + " 'create': Counter(),\n", + " 'cropping': Counter({'padding': 3}),\n", + " 'cropping3d': Counter({'gaussiandropout': 4}),\n", + " 'crossentropy': Counter(),\n", + " 'csv': Counter(),\n", + " 'ctype': Counter({'dtype': 4}),\n", + " 'cudnn': Counter(),\n", + " 'cudnnlstm': Counter({'constant': 6, 'variable': 2}),\n", + " 'custom': Counter(),\n", + " 'cw': Counter({'ref': 4}),\n", + " 'd': Counter({'c': 1, 'h': 1, 'w': 3}),\n", + " 'data': Counter({'filters': 1, 'id': 3, 'x': 1}),\n", + " 'dataset': Counter(),\n", + " 'decay': Counter(),\n", + " 'decode': Counter(),\n", + " 'deconv': Counter(),\n", + " 'default': Counter(),\n", + " 'delta': Counter(),\n", + " 'dense': Counter(),\n", + " 'densenet169': Counter({'inceptionv3': 2}),\n", + " 'depth': Counter({'name': 1}),\n", + " 'depthwise': Counter(),\n", + " 'device': Counter({'kwd': 3}),\n", + " 'devs': Counter({'num': 1}),\n", + " 'dict': Counter({'list': 1, 'name': 2, 'nameconstant': 3}),\n", + " 'dilation': Counter(),\n", + " 'dim': Counter(),\n", + " 'dims': Counter({'inputs': 1, 'l1': 1, 'ndim': 1}),\n", + " 'dimshuffle': Counter(),\n", + " 'distribution': Counter({'ndim': 1}),\n", + " 'div': Counter({'mult': 2}),\n", + " 'dot': Counter({'bias': 3}),\n", + " 'dropout': Counter(),\n", + " 'dset': Counter({'broadcast': 1}),\n", + " 'dtype': Counter({'name': 4}),\n", + " 'dumps': Counter({'serialize': 1}),\n", + " 'dynamic': Counter(),\n", + " 'edge': Counter({'get': 1, 'loss': 1}),\n", + " 'element': Counter(),\n", + " 'elemwise': Counter(),\n", + " 'embeddings': Counter({'attribute': 1,\n", + " 'histogram': 1,\n", + " 'output': 1,\n", + " 'update': 1,\n", + " 'validation': 3}),\n", + " 'end': Counter({'begin': 8, 'node': 2}),\n", + " 'enqueuer': Counter(),\n", + " 'epoch': Counter({'batch': 2, 'logs': 2}),\n", + " 'epochs': Counter({'monitor': 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Counter({'units': 4}),\n", + " 'final': Counter({'enqueuer': 1, 'metrics': 1}),\n", + " 'first': Counter({'outbound': 1}),\n", + " 'fit': Counter({'test': 1}),\n", + " 'float32': Counter(),\n", + " 'float64': Counter({'append': 5}),\n", + " 'floatx': Counter(),\n", + " 'floor': Counter({'items': 1, 'keys': 5}),\n", + " 'floordiv': Counter(),\n", + " 'flush': Counter({'update': 2}),\n", + " 'fn': Counter({'value': 1, 'x': 2}),\n", + " 'for': Counter(),\n", + " 'format': Counter({'join': 3}),\n", + " 'fpath': Counter({'file': 1, 'info': 1, 'inputlabels': 2}),\n", + " 'freedimension': Counter(),\n", + " 'freq': Counter({'data': 5, 'metadata': 1}),\n", + " 'from': Counter({'group': 1, 'name': 1, 'shape': 1, 'to': 3}),\n", + " 'frombuffer': Counter({'constant': 1}),\n", + " 'full': Counter({'asarray': 2}),\n", + " 'function': Counter({'outputs': 2, 'weights': 3}),\n", + " 'functiondef': Counter(),\n", + " 'functiontype': Counter({'parameter': 2}),\n", + " 'fused': Counter({'key': 1}),\n", + " 'gain': Counter(),\n", + " 'gamma': Counter({'alpha': 2,\n", + " 'depthwise': 3,\n", + " 'iterations': 1,\n", + " 'pointwise': 2}),\n", + " 'generator': Counter({'dropout': 1}),\n", + " 'generatorexp': Counter(),\n", + " 'get': Counter(),\n", + " 'global': Counter({'name': 1}),\n", + " 'go': Counter(),\n", + " 'gradients': Counter({'foldr': 2}),\n", + " 'graph': Counter({'config': 2}),\n", + " 'greater': Counter(),\n", + " 'group': Counter({'data': 1, 'name': 1}),\n", + " 'gt': Counter({'eq': 8, 'in': 5, 'noteq': 2}),\n", + " 'gte': Counter({'lt': 3}),\n", + " 'h': Counter({'batch': 1, 'r': 1, 'w': 2}),\n", + " 'h5py': Counter({'flag': 1, 'target': 1}),\n", + " 'has': Counter(),\n", + " 'hdf5': Counter({'group': 1, 'name': 2}),\n", + " 'header': Counter(),\n", + " 'histogram': Counter(),\n", + " 'hstack': Counter({'concatenate': 2}),\n", + " 'i': Counter({'num': 1, 'o': 2}),\n", + " 'id': Counter(),\n", + " 'identity': Counter(),\n", + " 'idx': Counter({'ins': 3, 'kernel': 1}),\n", + " 'if': Counter({'assert': 1, 'excepthandler': 7, 'return': 1, 'while': 4}),\n", + " 'ifexp': Counter({'call': 5}),\n", + " 'img': Counter(),\n", + " 'import': Counter(),\n", + " 'importfrom': Counter(),\n", + " 'in': Counter({'eq': 7,\n", + " 'isnot': 1,\n", + " 'noteq': 2,\n", + " 'notin': 1,\n", + " 'out': 3,\n", + " 'to': 1}),\n", + " 'inbound': Counter({'layer': 1, 'outbound': 3, 'weight': 1}),\n", + " 'index': Counter({'in': 1, 'on': 2, 'slice': 7}),\n", + " 'indices': Counter({'biases': 4}),\n", + " 'inferreddimension': Counter(),\n", + " 'info': Counter(),\n", + " 'init': Counter({'biases': 5, 'float32': 1, 'function': 2}),\n", + " 'initial': Counter(),\n", + " 'initializer': Counter(),\n", + " 'input': Counter({'output': 4, 'recurrent': 3}),\n", + " 'inputs': Counter({'call': 1,\n", + " 'dtype': 3,\n", + " 'feed': 1,\n", + " 'mask': 2,\n", + " 'masks': 7,\n", + " 'nameconstant': 1,\n", + " 'outputs': 4,\n", + " 'targets': 1,\n", + " 'trainable': 1}),\n", + " 'ins': Counter(),\n", + " 'int': Counter({'keras': 1}),\n", + " 'int32': Counter(),\n", + " 'is': Counter({'eq': 6, 'isnot': 2, 'steps': 1}),\n", + " 'isfile': Counter({'exists': 7}),\n", + " 'isnot': Counter({'is': 6}),\n", + " 'item': Counter(),\n", + " 'items': Counter({'keys': 2}),\n", + " 'iterations': Counter(),\n", + " 'j': Counter({'i': 2}),\n", + " 'join': Counter({'exists': 2}),\n", + " 'keepdims': Counter(),\n", + " 'kept': Counter(),\n", + " 'keras': Counter({'int': 3}),\n", + " 'kernel': Counter({'bias': 3}),\n", + " 'keys': Counter(),\n", + " 'keyword': Counter({'str': 1}),\n", + " 'known': Counter({'info': 2}),\n", + " 'kwargs': Counter(),\n", + " 'l': Counter({'layer': 2}),\n", + " 'lambda': Counter(),\n", + " 'last': Counter(),\n", + " 'layer': Counter({'model': 3, 'self': 1, 'x': 1}),\n", + " 'layers': Counter({'attrs': 1, 'inputs': 3, 'nodes': 3, 'shape': 1}),\n", + " 'learning': Counter(),\n", + " 'legacy': Counter(),\n", + " 'len': Counter({'padding': 1}),\n", + " 'length': Counter({'fn': 1}),\n", + " 'lengths': Counter({'index': 3}),\n", + " 'like': Counter(),\n", + " 'limit': Counter({'at': 1, 'header': 5}),\n", + " 'linalg': Counter({'nn': 1}),\n", + " 'list': Counter({'keyword': 1,\n", + " 'name': 19,\n", + " 'nameconstant': 9,\n", + " 'num': 3,\n", + " 'set': 6,\n", + " 'tuple': 6}),\n", + " 'listcomp': Counter({'generatorexp': 1}),\n", + " 'load': Counter({'group': 1, 'save': 1}),\n", + " 'log': Counter(),\n", + " 'log10': Counter({'readline': 1}),\n", + " 'logs': Counter(),\n", + " 'loop': Counter(),\n", + " 'loss': Counter({'x': 3}),\n", + " 'losses': Counter({'trainable': 2}),\n", + " 'lower': Counter(),\n", + " 'lr': Counter({'decay': 1, 'kwargs': 1}),\n", + " 'lstm': Counter({'gru': 4}),\n", + " 'lt': Counter({'eq': 5, 'noteq': 1}),\n", + " 'lte': Counter({'eq': 1, 'gt': 6, 'lt': 1}),\n", + " 'm': Counter({'output': 2}),\n", + " 'mask': Counter({'strides': 4}),\n", + " 'masking': Counter(),\n", + " 'masks': Counter(),\n", + " 'max': Counter({'abs': 4, 'bias': 2, 'pow': 4, 'sum': 1}),\n", + " 'maximum': Counter({'square': 1}),\n", + " 'maxlen': Counter({'self': 3}),\n", + " 'maxval': Counter({'seed': 3}),\n", + " 'mean': Counter({'max': 7, 'ones': 2, 'sum': 3}),\n", + " 'merge': Counter(),\n", + " 'methods': Counter({'pooling1d': 1}),\n", + " 'metric': Counter({'layer': 4, 'name': 1}),\n", + " 'metrics': Counter({'output': 5}),\n", + " 'min': Counter({'max': 14}),\n", + " 'minimum': Counter({'maximum': 2}),\n", + " 'minval': Counter({'seed': 4}),\n", + " 'mod': Counter({'sub': 5}),\n", + " 'mode': Counter(),\n", + " 'model': Counter({'layer': 6}),\n", + " 'module': Counter({'name': 2}),\n", + " 'momentum': Counter({'beta': 3}),\n", + " 'monitor': Counter(),\n", + " 'moves': Counter(),\n", + " 'mult': Counter({'add': 4}),\n", + " 'multiplier': Counter(),\n", + " 'multiply': Counter({'tile': 3}),\n", + " 'n': Counter(),\n", + " 'name': Counter({'arguments': 2,\n", + " 'dtype': 2,\n", + " 'expr': 4,\n", + " 'fn': 3,\n", + " 'layer': 2,\n", + " 'list': 2,\n", + " 'nameconstant': 4,\n", + " 'num': 48,\n", + " 'return': 37,\n", + " 'str': 28}),\n", + " 'nameconstant': Counter({'name': 34, 'num': 6, 'str': 16}),\n", + " 'names': Counter({'func': 1, 'initializer': 1}),\n", + " 'nb': Counter(),\n", + " 'ndarray': Counter({'axis': 3}),\n", + " 'ndim': Counter({'rho': 1, 'shape': 2}),\n", + " 'neg': Counter({'ins': 1, 'output': 1}),\n", + " 'negative': Counter({'inferreddimension': 1}),\n", + " 'neq': Counter({'values': 7}),\n", + " 'nesterov': Counter({'value': 4}),\n", + " 'network': Counter({'inbound': 1}),\n", + " 'new': Counter(),\n", + " 'nn': Counter({'cell': 1}),\n", + " 'nnet': Counter({'nn': 1}),\n", + " 'node': Counter(),\n", + " 'nodes': Counter(),\n", + " 'noise': Counter(),\n", + " 'non': Counter(),\n", + " 'nones': Counter({'axis': 3}),\n", + " 'norm': Counter({'g': 2, 'normalization': 6, 'relu': 1, 't': 1}),\n", + " 'normal': Counter({'uniform': 2}),\n", + " 'normalize': Counter(),\n", + " 'not': Counter(),\n", + " 'noteq': Counter({'eq': 3, 'gt': 14, 'lt': 3, 'notin': 3}),\n", + " 'notin': Counter({'in': 10, 'isnot': 7}),\n", + " 'num': Counter({'backward': 1, 'name': 44, 'nameconstant': 2, 'str': 14}),\n", + " 'numdigits': Counter({'output': 2}),\n", + " 'o': Counter({'add': 1, 'i': 1}),\n", + " 'object': Counter({'header': 4}),\n", + " 'objects': Counter(),\n", + " 'on': Counter(),\n", + " 'ones': Counter({'inputs': 1}),\n", + " 'oov': Counter({'start': 2}),\n", + " 'op': Counter({'elemwise': 2}),\n", + " 'ops': Counter(),\n", + " 'optimizer': Counter({'exists': 2, 'float32': 3, 'self': 1, 'verbose': 1}),\n", + " 'or': Counter({'and': 7}),\n", + " 'original': Counter(),\n", + " 'out': Counter({'name': 5, 'new': 1, 'x': 8}),\n", + " 'output': Counter({'keras': 8,\n", + " 'name': 2,\n", + " 'shape': 2,\n", + " 'state': 3,\n", + " 'states': 2,\n", + " 'x': 4}),\n", + " 'outputs': Counter({'kwargs': 3, 'output': 7, 'states': 8}),\n", + " 'override': Counter({'logs': 1}),\n", + " 'overwrite': Counter({'fpath': 1, 'inputlabels': 4}),\n", + " 'p': Counter({'s': 3, 'states': 1}),\n", + " 'pad': Counter({'img': 1, 'transpose': 2, 'width': 2}),\n", + " 'padding': Counter({'strides': 2}),\n", + " 'param': Counter(),\n", + " 'parameter': Counter({'constant': 3}),\n", + " 'params': Counter({'attrs': 5}),\n", + " 'path': Counter({'self': 2}),\n", + " 'pattern': Counter({'pad': 2, 'param': 1, 'x': 3}),\n", + " 'per': Counter(),\n", + " 'permutation': Counter(),\n", + " 'phase': Counter(),\n", + " 'placeholder': Counter({'dense': 2}),\n", + " 'pool': Counter({'constant': 2, 'conv': 3, 'kernel': 2, 'pool3d': 3}),\n", + " 'pool2d': Counter({'arange': 1}),\n", + " 'pooling': Counter(),\n", + " 'pooling1d': Counter({'spatialdropoutnd': 1}),\n", + " 'pop': Counter({'decode': 1, 'warn': 1}),\n", + " 'pow': Counter({'add': 3}),\n", + " 'pred': Counter({'train': 3}),\n", + " 'predictions': Counter(),\n", + " 'prefix': Counter({'self': 1}),\n", + " 'preprocess': Counter(),\n", + " 'probs': Counter({'biases': 3, 'sequence': 1}),\n", + " 'proceed': Counter({'logs': 4}),\n", + " 'prod': Counter({'append': 1, 'reshape': 1, 'sqrt': 4, 'sum': 3}),\n", + " 'py': Counter(),\n", + " 'queue': Counter(),\n", + " 'r': Counter({'o': 1, 'z': 1}),\n", + " 'raise': Counter({'name': 12, 'return': 1}),\n", + " 'random': Counter(),\n", + " 'rate': Counter({'self': 2}),\n", + " 'read': Counter(),\n", + " 'recurrent': Counter({'x': 4}),\n", + " 'reduce': Counter(),\n", + " 'reduction': Counter(),\n", + " 'ref': Counter(),\n", + " 'regularizer': Counter({'constraint': 4}),\n", + " 'relu': Counter({'parameter': 7}),\n", + " 'remove': Counter(),\n", + " 'repeats': Counter({'func': 1}),\n", + " 'required': Counter({'not': 1, 'out': 3}),\n", + " 'reraise': Counter({'stop': 2}),\n", + " 'reset': Counter(),\n", + " 'reshape': Counter({'log': 5, 'shape': 1}),\n", + " 'result': Counter({'axis': 3, 'x': 3}),\n", + " 'return': Counter({'name': 14, 'num': 7}),\n", + " 'rho': Counter({'decay': 4}),\n", + " 'rnn': Counter({'call': 2, 'parameter': 3}),\n", + " 'root': Counter(),\n", + " 'round': Counter({'abs': 2}),\n", + " 'row': Counter(),\n", + " 'rows': Counter(),\n", + " 'run': Counter({'function': 3}),\n", + " 's': Counter({'dict': 1, 'o': 2}),\n", + " 'sample': Counter(),\n", + " 'schedule': Counter(),\n", + " 'scope': Counter(),\n", + " 'score': Counter(),\n", + " 'seed': Counter({'self': 2}),\n", + " 'seen': Counter({'target': 2, 'weights': 5}),\n", + " 'select': Counter(),\n", + " 'self': Counter({'name': 1, 'x': 2}),\n", + " 'seq': Counter(),\n", + " 'sequence': Counter({'axis': 1, 'iterations': 4, 'ndim': 1}),\n", + " 'sequences': Counter({'state': 5}),\n", + " 'session': Counter(),\n", + " 'set': Counter({'inner': 1}),\n", + " 'setdefault': Counter({'get': 5}),\n", + " 'shape': Counter({'class': 1,\n", + " 'config': 1,\n", + " 'constraint': 1,\n", + " 'dtype': 2,\n", + " 'padding': 1,\n", + " 'regularizer': 1,\n", + " 'reshape': 2,\n", + " 'size': 1,\n", + " 'value': 1,\n", + " 'x': 2}),\n", + " 'shared': Counter({'placeholder': 4}),\n", + " 'sharedvar': Counter({'tensor': 4}),\n", + " 'shift': Counter({'gamma': 8}),\n", + " 'shuffle': Counter({'broadcast': 3}),\n", + " 'signal': Counter(),\n", + " 'simple': Counter({'max': 4}),\n", + " 'size': Counter({'self': 2}),\n", + " 'sizes': Counter({'size': 4}),\n", + " 'slice': Counter({'index': 3}),\n", + " 'slope': Counter(),\n", + " 'softmax': Counter({'relu': 1}),\n", + " 'softplus': Counter({'log': 2}),\n", + " 'sort': Counter({'placeholder': 3}),\n", + " 'source': Counter({'padding': 1}),\n", + " 'sparse': Counter({'dtype': 1, 'theano': 4}),\n", + " 'spatial': Counter(),\n", + " 'spatialdropout1d': Counter({'dim': 1}),\n", + " 'spec': Counter({'dim': 11}),\n", + " 'split': Counter({'attribute': 1, 'img': 1}),\n", + " 'sqrt': Counter({'mean': 2, 'pow': 2}),\n", + " 'square': Counter({'sqrt': 1}),\n", + " 'squared': Counter({'ops': 2}),\n", + " 'squeeze': Counter({'histogram': 1}),\n", + " 'stack': Counter({'concatenate': 2, 'reshape': 1}),\n", + " 'starred': Counter(),\n", + " 'start': Counter(),\n", + " 'startswith': Counter({'warn': 1}),\n", + " 'state': Counter({'layer': 3, 'output': 3, 'sequences': 1}),\n", + " 'stateful': Counter({'trainable': 1}),\n", + " 'states': Counter({'outputs': 1, 'shape': 1}),\n", + " 'stddev': Counter({'dtype': 1, 'seed': 1}),\n", + " 'step': Counter({'pooling': 1}),\n", + " 'steps': Counter({'bias': 4, 'multiprocessing': 8}),\n", + " 'stop': Counter(),\n", + " 'str': Counter({'arg': 1,\n", + " 'list': 1,\n", + " 'name': 18,\n", + " 'nameconstant': 10,\n", + " 'num': 9}),\n", + " 'strides': Counter({'padding': 2, 'xs': 1}),\n", + " 'string': Counter(),\n", + " 'strip': Counter(),\n", + " 'sub': Counter({'add': 7, 'mod': 2, 'mult': 6}),\n", + " 'subscript': Counter(),\n", + " 'sum': Counter(),\n", + " 'summary': Counter(),\n", + " 'support': Counter(),\n", + " 'svd': Counter({'conv2d': 2}),\n", + " 'sw': Counter({'ref': 2}),\n", + " 'swapaxes': Counter({'clip': 3, 'reshape': 3, 'transpose': 2}),\n", + " 't': Counter({'value': 1, 'x': 1}),\n", + " 'target': Counter({'new': 1}),\n", + " 'targets': Counter(),\n", + " 'tasks': Counter(),\n", + " 'tensor': Counter({'node': 5}),\n", + " 'tensorsharedvariable': Counter({'tensorvariable': 3}),\n", + " 'test': Counter({'train': 1}),\n", + " 'tf': Counter(),\n", + " 'threshold': Counter(),\n", + " 'tile': Counter(),\n", + " 'time': Counter({'queue': 1, 'value': 2}),\n", + " 'to': Counter(),\n", + " 'toarray': Counter(),\n", + " 'tolist': Counter({'item': 1}),\n", + " 'top': Counter({'idxs': 2}),\n", + " 'total': Counter(),\n", + " 'totals': Counter({'shape': 4}),\n", + " 'train': Counter({'model': 2}),\n", + " 'trainable': Counter({'stateful': 1}),\n", + " 'trainer': Counter({'initial': 3}),\n", + " 'training': Counter({'dtype': 1, 'kwargs': 1, 'shape': 1}),\n", + " 'transpose': Counter({'asarray': 3, 'convolution': 3, 'reshape': 2, 'x': 1}),\n", + " 'true': Counter(),\n", + " 'truncated': Counter(),\n", + " 'try': Counter({'if': 1}),\n", + " 'tuple': Counter({'assign': 5,\n", + " 'attribute': 2,\n", + " 'call': 4,\n", + " 'keyword': 3,\n", + " 'list': 17,\n", + " 'set': 1,\n", + " 'slice': 3}),\n", + " 'type': Counter(),\n", + " 'types': Counter(),\n", + " 'u': Counter({'mask': 1, 'mean': 1, 'out': 1}),\n", + " 'uid': Counter({'axis': 4}),\n", + " 'uint8': Counter({'frombuffer': 1}),\n", + " 'unaryop': Counter(),\n", + " 'unfinished': Counter(),\n", + " 'uniform': Counter({'arange': 5, 'constant': 1, 'mean': 1, 'normal': 4}),\n", + " 'units': Counter(),\n", + " 'unrelated': Counter({'state': 1}),\n", + " 'unroll': Counter({'axes': 1, 'headers': 2, 'outputs': 1}),\n", + " 'untar': Counter({'file': 1, 'negative': 1, 'num': 1}),\n", + " 'update': Counter({'loss': 1, 'v': 1, 'x': 1}),\n", + " 'updates': Counter({'float32': 1, 'kwargs': 3, 'losses': 7}),\n", + " 'upsampling1d': Counter({'gaussiandropout': 1}),\n", + " 'use': Counter({'validation': 6}),\n", + " 'uses': Counter(),\n", + " 'usub': Counter(),\n", + " 'v': Counter({'mean': 6, 'p': 1, 's': 1, 'value': 2}),\n", + " 'val': Counter(),\n", + " 'value': Counter({'freq': 2, 'k': 1, 'kwargs': 1, 's': 1, 'shape': 1}),\n", + " 'values': Counter(),\n", + " 'variable': Counter({'asarray': 1,\n", + " 'is': 4,\n", + " 'output': 1,\n", + " 'result': 1,\n", + " 'target': 1,\n", + " 'uid': 1,\n", + " 'weights': 6}),\n", + " 'variables': Counter({'x': 4}),\n", + " 'verbose': Counter(),\n", + " 'version': Counter(),\n", + " 'w': Counter({'b': 2, 'bias': 1, 'left': 1, 'x': 8}),\n", + " 'wait': Counter({'decay': 1, 'hstack': 9, 'value': 3}),\n", + " 'warn': Counter({'name': 3, 'pop': 1, 'rank': 1}),\n", + " 'weight': Counter(),\n", + " 'weights': Counter({'cls': 3, 'name': 3, 'sqrt': 1, 'value': 1, 'xs': 1}),\n", + " 'when': Counter({'config': 2, 'name': 2}),\n", + " 'where': Counter({'square': 1}),\n", + " 'while': Counter({'if': 4}),\n", + " 'width': Counter({'loss': 6}),\n", + " 'with': Counter(),\n", + " 'withitem': Counter(),\n", + " 'words': Counter({'constants': 1, 'samples': 1}),\n", + " 'workers': Counter({'kwargs': 1, 'seqs': 6, 'verbose': 1}),\n", + " 'write': Counter({'ctc': 1, 'default': 1, 'resize': 2}),\n", + " 'writer': Counter({'history': 1, 'seen': 4}),\n", + " 'x': Counter({'a': 3,\n", + " 'bias': 5,\n", + " 'inputs': 2,\n", + " 'k': 1,\n", + " 'kwargs': 2,\n", + " 'layer': 1,\n", + " 'name': 1,\n", + " 'recurrent': 1,\n", + " 'self': 4,\n", + " 'shape': 3,\n", + " 'slice': 1,\n", + " 'value': 5,\n", + " 'y': 9}),\n", + " 'xs': Counter(),\n", + " 'y': Counter({'kwargs': 1, 'output': 3, 'shape': 3, 'w': 4}),\n", + " 'z': Counter({'conv': 1, 'h': 1, 'r': 2}),\n", + " 'zeros': Counter({'dtype': 1, 'mean': 3, 'ones': 3})}" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_counter" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + 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slope\n", + "Preds -- \n", + "\n", + "Label -- legacy\n", + "Preds -- \n", + "\n", + "Label -- fan\n", + "Preds -- compare (2)\n", + "\n", + "Label -- initializer\n", + "Preds -- \n", + "\n", + "Label -- trainer\n", + "Preds -- initial (3)\n", + "\n", + "Label -- p\n", + "Preds -- s (3) states (1)\n", + "\n", + "Label -- fields\n", + "Preds -- \n", + "\n", + "Label -- for\n", + "Preds -- \n", + "\n", + "Label -- repeats\n", + "Preds -- func (1)\n", + "\n", + "Label -- output\n", + "Preds -- keras (8) x (4) state (3) states (2) shape (2)\n", + "\n", + "Label -- f\n", + "Preds -- k (3) o (2) c (2) dim (2) r (2)\n", + "\n", + "Label -- source\n", + "Preds -- padding (1)\n", + "\n", + "Label -- header\n", + "Preds -- \n", + "\n", + "Label -- sort\n", + "Preds -- placeholder (3)\n", + "\n", + "Label -- cls\n", + "Preds -- x (4) layer (1) self (1) config (1) name (1)\n", + "\n", + "Label -- permutation\n", + "Preds -- \n", + "\n", + "Label -- tuple\n", + "Preds -- list (17) assign (5) call (4) slice (3) keyword (3)\n", + "\n", + "Label -- nb\n", + "Preds -- \n", + "\n", + "Label -- compile\n", + "Preds -- normal (3)\n", + "\n", + "Label -- strip\n", + "Preds -- \n", + "\n", + "Label -- as\n", + "Preds -- is (2)\n", + "\n", + "Label -- row\n", + "Preds -- \n", + "\n", + "Label -- best\n", + "Preds -- \n", + "\n", + "Label -- lte\n", + "Preds -- gt (6) lt (1) eq (1)\n", + "\n", + "Label -- close\n", + "Preds -- update (3) append (2)\n", + "\n", + "Label -- initial\n", + "Preds -- \n", + "\n", + "Label -- metric\n", + "Preds -- layer (4) name (1)\n", + "\n", + "Label -- moves\n", + "Preds -- \n", + "\n", + "Label -- keepdims\n", + "Preds -- \n", + "\n", + "Label -- isnot\n", + "Preds -- is (6)\n", + "\n", + "Label -- mean\n", + "Preds -- max (7) sum (3) ones (2)\n", + "\n", + "Label -- string\n", + "Preds -- \n", + "\n", + "Label -- norm\n", + "Preds -- normalization (6) g (2) relu (1) t (1)\n", + "\n", + "Label -- gte\n", + "Preds -- lt (3)\n", + "\n", + "Label -- 1\n", + "Preds -- mask (1)\n", + "\n", + "Label -- item\n", + "Preds -- \n", + "\n", + "Label -- indices\n", + "Preds -- biases (4)\n", + "\n", + "Label -- sqrt\n", + "Preds -- mean (2) pow (2)\n", + "\n", + "Label -- load\n", + "Preds -- save (1) group (1)\n", + "\n", + "Label -- freedimension\n", + "Preds -- \n", + "\n", + "Label -- log10\n", + "Preds -- readline (1)\n", + "\n", + "Label -- attribute\n", + "Preds -- call (11) assign (1) tuple (1) num (1)\n", + "\n", + "Label -- append\n", + "Preds -- items (3) zeros (1)\n", + "\n", + "Label -- cudnnlstm\n", + "Preds -- constant (6) variable (2)\n", + "\n", + "Label -- like\n", + "Preds -- \n", + "\n", + "Label -- delta\n", + "Preds -- \n", + "\n", + "Label -- relu\n", + "Preds -- parameter (7)\n", + "\n", + "Label -- weight\n", + "Preds -- \n", + "\n", + "Label -- untar\n", + "Preds -- negative (1) file (1) num (1)\n", + "\n", + "Label -- graph\n", + "Preds -- config (2)\n", + "\n", + "Label -- fill\n", + "Preds -- concatenate (1)\n", + "\n", + "Label -- char\n", + "Preds -- classes (4) dim (2) start (1)\n", + "\n", + "Label -- cropping3d\n", + "Preds -- gaussiandropout (4)\n", + "\n", + "Label -- reduce\n", + "Preds -- \n", + "\n", + "Label -- time\n", + "Preds -- value (2) queue (1)\n", + "\n", + "Label -- new\n", + "Preds -- \n", + "\n", + "Label -- root\n", + "Preds -- \n", + "\n", + "Label -- ifexp\n", + "Preds -- call (5)\n", + "\n", + "Label -- keys\n", + "Preds -- \n", + "\n", + "Label -- expr\n", + "Preds -- name (7) raise (5) return (2)\n", + "\n", + "Label -- o\n", + "Preds -- add (1) i (1)\n", + "\n", + "Label -- shape\n", + "Preds -- reshape (2) dtype (2) x (2) value (1) constraint (1)\n", + "\n", + "Label -- inbound\n", + "Preds -- outbound (3) layer (1) weight (1)\n", + "\n", + "Label -- strides\n", + "Preds -- padding (2) xs (1)\n", + "\n", + "Label -- broadcast\n", + "Preds -- \n", + "\n", + "Label -- last\n", + "Preds -- \n", + "\n", + "Label -- ins\n", + "Preds -- \n", + "\n", + "Label -- xs\n", + "Preds -- \n", + "\n", + "Label -- filters\n", + "Preds -- units (4)\n", + "\n", + "Label -- cast\n", + "Preds -- \n", + "\n", + "Label -- notin\n", + "Preds -- in (10) isnot (7)\n", + "\n", + "Label -- read\n", + "Preds -- \n", + "\n", + "Label -- feature\n", + "Preds -- \n", + "\n", + "Label -- kwargs\n", + "Preds -- \n", + "\n", + "Label -- start\n", + "Preds -- \n", + "\n", + "Label -- compute\n", + "Preds -- \n", + "\n", + "Label -- shift\n", + "Preds -- gamma (8)\n", + "\n", + "Label -- float64\n", + "Preds -- append (5)\n", + "\n", + "Label -- unroll\n", + "Preds -- headers (2) axes (1) outputs (1)\n", + "\n", + "Label -- str\n", + "Preds -- name (18) nameconstant (10) num (9) arg (1) list (1)\n", + "\n", + "Label -- seq\n", + "Preds -- \n", + "\n", + "Label -- seed\n", + "Preds -- self (2)\n", + "\n", + "Label -- required\n", + "Preds -- out (3) not (1)\n", + "\n", + "Label -- abs\n", + "Preds -- square (5)\n", + "\n", + "Label -- cntk\n", + "Preds -- spatial (1)\n", + "\n", + "Label -- arange\n", + "Preds -- flatten (5)\n", + "\n", + "Label -- spec\n", + "Preds -- dim (11)\n", + "\n", + "Label -- stop\n", + "Preds -- \n", + "\n", + "Label -- known\n", + "Preds -- info (2)\n", + "\n", + "Label -- kernel\n", + "Preds -- bias (3)\n", + "\n", + "Label -- distribution\n", + "Preds -- ndim (1)\n", + "\n", + "Label -- z\n", + "Preds -- r (2) conv (1) h (1)\n", + "\n", + "Label -- placeholder\n", + "Preds -- dense (2)\n", + "\n", + "Label -- broadcastable\n", + "Preds -- axes (3)\n", + "\n", + "Label -- weights\n", + "Preds -- cls (3) name (3) sqrt (1) value (1) xs (1)\n", + "\n", + "Label -- to\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preds -- \n", + "\n", + "Label -- global\n", + "Preds -- name (1)\n", + "\n", + "Label -- tile\n", + "Preds -- \n", + "\n", + "Label -- monitor\n", + "Preds -- \n", + "\n", + "Label -- argmax\n", + "Preds -- mean (1)\n", + "\n", + "Label -- param\n", + "Preds -- \n", + "\n", + "Label -- session\n", + "Preds -- \n", + "\n", + "Label -- feed\n", + "Preds -- \n", + "\n", + "Label -- index\n", + "Preds -- slice (7) on (2) in (1)\n", + "\n", + "Label -- elemwise\n", + "Preds -- \n", + "\n", + "Label -- decode\n", + "Preds -- \n", + "\n", + "Label -- listcomp\n", + "Preds -- generatorexp (1)\n", + "\n", + "Label -- u\n", + "Preds -- mean (1) mask (1) out (1)\n", + "\n", + "Label -- col\n", + "Preds -- ndarray (1)\n", + "\n", + "Label -- write\n", + "Preds -- resize (2) ctc (1) default (1)\n", + "\n", + "Label -- h5py\n", + "Preds -- flag (1) target (1)\n", + "\n", + "Label -- model\n", + "Preds -- layer (6)\n", + "\n", + "Label -- nn\n", + "Preds -- cell (1)\n", + "\n", + "Label -- w\n", + "Preds -- x (8) b (2) bias (1) left (1)\n", + "\n", + "Label -- rows\n", + "Preds -- \n", + "\n", + "Label -- methods\n", + "Preds -- pooling1d (1)\n", + "\n", + "Label -- lambda\n", + "Preds -- \n", + "\n", + "Label -- final\n", + "Preds -- enqueuer (1) metrics (1)\n", + "\n", + "Label -- frombuffer\n", + "Preds -- constant (1)\n", + "\n", + "Label -- deconv\n", + "Preds -- \n", + "\n", + "Label -- update\n", + "Preds -- v (1) loss (1) x (1)\n", + "\n", + "Label -- run\n", + "Preds -- function (3)\n", + "\n", + "Label -- uniform\n", + "Preds -- arange (5) normal (4) mean (1) constant (1)\n", + "\n", + "Label -- normalize\n", + "Preds -- \n", + "\n", + "Label -- rho\n", + "Preds -- decay (4)\n", + "\n", + "Label -- limit\n", + "Preds -- header (5) at (1)\n", + "\n", + "Label -- dtype\n", + "Preds -- name (4)\n", + "\n", + "Label -- select\n", + "Preds -- \n", + "\n", + "Label -- gain\n", + "Preds -- \n", + "\n", + "Label -- losses\n", + "Preds -- trainable (2)\n", + "\n", + "Label -- items\n", + "Preds -- keys (2)\n", + "\n", + "Label -- path\n", + "Preds -- self (2)\n", + "\n", + "Label -- full\n", + "Preds -- asarray (2)\n", + "\n", + "Label -- hstack\n", + "Preds -- concatenate (2)\n", + "\n", + "Label -- nnet\n", + "Preds -- nn (1)\n", + "\n", + "Label -- logs\n", + "Preds -- \n", + "\n", + "Label -- learning\n", + "Preds -- \n", + "\n", + "Label -- sw\n", + "Preds -- ref (2)\n", + "\n", + "Label -- round\n", + "Preds -- abs (2)\n", + "\n", + "Label -- join\n", + "Preds -- exists (2)\n", + "\n", + "Label -- setdefault\n", + "Preds -- get (5)\n", + "\n", + "Label -- scope\n", + "Preds -- \n", + "\n", + "Label -- numdigits\n", + "Preds -- output (2)\n", + "\n", + "Label -- i\n", + "Preds -- o (2) num (1)\n", + "\n", + "Label -- axes\n", + "Preds -- shape (3) kwargs (1)\n", + "\n", + "Label -- x\n", + "Preds -- y (9) value (5) bias (5) self (4) shape (3)\n", + "\n", + "Label -- tensorsharedvariable\n", + "Preds -- tensorvariable (3)\n", + "\n", + "Label -- config\n", + "Preds -- \n", + "\n", + "Label -- recurrent\n", + "Preds -- x (4)\n", + "\n", + "Label -- sizes\n", + "Preds -- size (4)\n", + "\n", + "Label -- metrics\n", + "Preds -- output (5)\n", + "\n", + "Label -- layer\n", + "Preds -- model (3) self (1) x (1)\n", + "\n", + "Label -- module\n", + "Preds -- name (2)\n", + "\n", + "Label -- by\n", + "Preds -- \n", + "\n", + "Label -- noise\n", + "Preds -- \n", + "\n", + "Label -- custom\n", + "Preds -- \n", + "\n", + "Label -- axis\n", + "Preds -- seed (1)\n", + "\n", + "Label -- linalg\n", + "Preds -- nn (1)\n", + "\n", + "Label -- dict\n", + "Preds -- nameconstant (3) name (2) list (1)\n", + "\n", + "Label -- steps\n", + "Preds -- multiprocessing (8) bias (4)\n", + "\n", + "Label -- max\n", + "Preds -- pow (4) abs (4) bias (2) sum (1)\n", + "\n", + "Label -- functiondef\n", + "Preds -- \n", + "\n", + "Label -- eval\n", + "Preds -- \n", + "\n", + "Label -- depth\n", + "Preds -- name (1)\n", + "\n", + "Label -- dataset\n", + "Preds -- \n", + "\n", + "Label -- multiply\n", + "Preds -- tile (3)\n", + "\n", + "Label -- mode\n", + "Preds -- \n", + "\n", + "Label -- proceed\n", + "Preds -- logs (4)\n", + "\n", + "Label -- set\n", + "Preds -- inner (1)\n", + "\n", + "Label -- when\n", + "Preds -- config (2) name (2)\n", + "\n", + "Label -- is\n", + "Preds -- eq (6) isnot (2) steps (1)\n", + "\n", + "Label -- remove\n", + "Preds -- \n", + "\n", + "Label -- extend\n", + "Preds -- append (7)\n", + "\n", + "Label -- totals\n", + "Preds -- shape (4)\n", + "\n", + "Label -- equal\n", + "Preds -- square (2)\n", + "\n", + "Label -- s\n", + "Preds -- o (2) dict (1)\n", + "\n", + "Label -- sample\n", + "Preds -- \n", + "\n", + "Label -- backend\n", + "Preds -- \n", + "\n", + "Label -- count\n", + "Preds -- \n", + "\n", + "Label -- mult\n", + "Preds -- add (4)\n", + "\n", + "Label -- true\n", + "Preds -- \n", + "\n", + "Label -- info\n", + "Preds -- \n", + "\n", + "Label -- on\n", + "Preds -- \n", + "\n", + "Label -- swapaxes\n", + "Preds -- reshape (3) clip (3) transpose (2)\n", + "\n", + "Label -- try\n", + "Preds -- if (1)\n", + "\n", + "Label -- base\n", + "Preds -- shape (8) x (6)\n", + "\n", + "Label -- avg\n", + "Preds -- max (4)\n", + "\n", + "Label -- isfile\n", + "Preds -- exists (7)\n", + "\n", + "Label -- identity\n", + "Preds -- \n", + "\n", + "Label -- queue\n", + "Preds -- \n", + "\n", + "Label -- return\n", + "Preds -- name (14) num (7)\n", + "\n", + "Label -- tasks\n", + "Preds -- \n", + "\n", + "Label -- eq\n", + "Preds -- gt (2) notin (1)\n", + "\n", + "Label -- probs\n", + "Preds -- biases (3) sequence (1)\n", + "\n", + "Label -- result\n", + "Preds -- x (3) axis (3)\n", + "\n", + "Label -- fused\n", + "Preds -- key (1)\n", + "\n", + "Label -- svd\n", + "Preds -- conv2d (2)\n", + "\n", + "Label -- go\n", + "Preds -- \n", + "\n", + "Label -- pooling1d\n", + "Preds -- spatialdropoutnd (1)\n", + "\n", + "Label -- shuffle\n", + "Preds -- broadcast (3)\n", + "\n", + "Label -- sub\n", + "Preds -- add (7) mult (6) mod (2)\n", + "\n", + "Label -- seen\n", + "Preds -- weights (5) target (2)\n", + "\n", + "Label -- where\n", + "Preds -- square (1)\n", + "\n", + "Label -- uint8\n", + "Preds -- frombuffer (1)\n", + "\n", + "Label -- sequences\n", + "Preds -- state (5)\n", + "\n", + "Label -- non\n", + "Preds -- \n", + "\n", + "Label -- width\n", + "Preds -- loss (6)\n", + "\n", + "Label -- copy\n", + "Preds -- square (3) name (2)\n", + "\n", + "Label -- network\n", + "Preds -- inbound (1)\n", + "\n", + "Label -- epoch\n", + "Preds -- logs (2) batch (2)\n", + "\n", + "Label -- chunked\n", + "Preds -- \n", + "\n", + "Label -- functiontype\n", + "Preds -- parameter (2)\n", + "\n", + "Label -- wait\n", + "Preds -- hstack (9) value (3) decay (1)\n", + "\n", + "Label -- img\n", + "Preds -- \n", + "\n", + "Label -- py\n", + "Preds -- \n", + "\n", + "Label -- cooldown\n", + "Preds -- sequence (4) phase (3)\n", + "\n", + "Label -- padding\n", + "Preds -- strides (2)\n", + "\n", + "Label -- threshold\n", + "Preds -- \n", + "\n", + "Label -- maximum\n", + "Preds -- square (1)\n", + "\n", + "Label -- loop\n", + "Preds -- \n", + "\n", + "Label -- dim\n", + "Preds -- \n", + "\n", + "Label -- types\n", + "Preds -- \n", + "\n", + "Label -- prefix\n", + "Preds -- self (1)\n", + "\n", + "Label -- constant\n", + "Preds -- \n", + "\n", + "Label -- devs\n", + "Preds -- num (1)\n", + "\n", + "Label -- nones\n", + "Preds -- axis (3)\n", + "\n", + "Label -- len\n", + "Preds -- padding (1)\n", + "\n", + "Label -- t\n", + "Preds -- value (1) x (1)\n", + "\n", + "Label -- pool2d\n", + "Preds -- arange (1)\n", + "\n", + "Label -- unfinished\n", + "Preds -- \n", + "\n", + "Label -- edge\n", + "Preds -- loss (1) get (1)\n", + "\n", + "Label -- decay\n", + "Preds -- \n", + "\n", + "Label -- per\n", + "Preds -- \n", + "\n", + "Label -- cudnn\n", + "Preds -- \n", + "\n", + "Label -- dropout\n", + "Preds -- \n", + "\n", + "Label -- h\n", + "Preds -- w (2) batch (1) r (1)\n", + "\n", + "Label -- float32\n", + "Preds -- \n", + "\n", + "Label -- concatenate\n", + "Preds -- \n", + "\n", + "Label -- has\n", + "Preds -- \n", + "\n", + "Label -- verbose\n", + "Preds -- \n", + "\n", + "Label -- expand\n", + "Preds -- \n", + "\n", + "Label -- categorical\n", + "Preds -- binary (2)\n", + "\n", + "Label -- assign\n", + "Preds -- call (7) get (2) for (2) keyword (2) range (1)\n", + "\n", + "Label -- lower\n", + "Preds -- \n", + "\n", + "Label -- boolop\n", + "Preds -- \n", + "\n", + "Label -- argmin\n", + "Preds -- argmax (3)\n", + "\n", + "Label -- dset\n", + "Preds -- broadcast (1)\n", + "\n", + "Label -- truncated\n", + "Preds -- \n", + "\n", + "Label -- object\n", + "Preds -- header (4)\n", + "\n", + "Label -- d\n", + "Preds -- w (3) c (1) h (1)\n", + "\n", + "Label -- histogram\n", + "Preds -- \n", + "\n", + "Label -- csv\n", + "Preds -- \n", + "\n", + "Label -- stddev\n", + "Preds -- dtype (1) seed (1)\n", + "\n", + "Label -- stack\n", + "Preds -- concatenate (2) reshape (1)\n", + "\n", + "Label -- reduction\n", + "Preds -- \n", + "\n", + "Label -- min\n", + "Preds -- max (14)\n", + "\n", + "Label -- keyword\n", + "Preds -- str (1)\n", + "\n", + "Label -- crossentropy\n", + "Preds -- \n", + "\n", + "Label -- ndarray\n", + "Preds -- axis (3)\n", + "\n", + "Label -- rate\n", + "Preds -- self (2)\n", + "\n", + "Label -- objects\n", + "Preds -- \n", + "\n", + "Label -- alpha\n", + "Preds -- log (2) axis (2) l1 (1)\n", + "\n", + "Label -- minval\n", + "Preds -- seed (4)\n", + "\n", + "Label -- begin\n", + "Preds -- node (1)\n", + "\n" + ] + } + ], + "source": [ + "for key, c in confusion_counter.items():\n", + " print(\"Label -- \", key)\n", + " print('Preds -- ',' '.join([\"{} ({})\".format(c0, c1) for c0,c1 in c.most_common(5)]))\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "token_names = [\"Module\",\"Interactive\",\"Expression\",\"Suite\",\"FunctionDef\",\"AsyncFunctionDef\",\"ClassDef\",\"Return\",\"Delete\",\"Assign\",\"AugAssign\",\"For\",\"AsyncFor\",\"While\",\"If\",\"With\",\"AsyncWith\",\"Raise\",\"Try\",\"Assert\",\"Import\",\"ImportFrom\",\"Global\",\"Nonlocal\",\"Expr\",\"Pass\",\"Break\",\"Continue\",\"BoolOp\",\"BinOp\",\"UnaryOp\",\"Lambda\",\"IfExp\",\"Dict\",\"Set\",\"ListComp\",\"SetComp\",\"DictComp\",\"GeneratorExp\",\"Await\",\"Yield\",\"YieldFrom\",\"Compare\",\"Call\",\"Num\",\"Str\",\"FormattedValue\",\"JoinedStr\",\"Bytes\",\"NameConstant\",\"Ellipsis\",\"Constant\",\"Attribute\",\"Subscript\",\"Starred\",\"Name\",\"List\",\"Tuple\",\"Load\",\"Store\",\"Del\",\"AugLoad\",\"AugStore\",\"Param\",\"Slice\",\"ExtSlice\",\"Index\",\"And\",\"Or\",\"Add\",\"Sub\",\"Mult\",\"MatMult\",\"Div\",\"Mod\",\"Pow\",\"LShift\",\"RShift\",\"BitOr\",\"BitXor\",\"BitAnd\",\"FloorDiv\",\"Invert\",\"Not\",\"UAdd\",\"USub\",\"Eq\",\"NotEq\",\"Lt\",\"LtE\",\"Gt\",\"GtE\",\"Is\",\"IsNot\",\"In\",\"NotIn\",\"excepthandler\",\"ExceptHandler\",\"arguments\",\"arg\",\"keyword\",\"alias\",\"withitem\",\"comprehension\"]\n", + "token_names = [t.lower() for t in token_names]" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "def is_ast_token(t):\n", + " return t in token_names" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "mistaken = {}; tot_right = 0; tot_wrong = 0\n", + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " if mistaken.get(label, None) == None:\n", + " mistaken[label] = {'correct':0, 'wrong':0}\n", + " right = is_ast_token(prediction) == is_ast_token(label)\n", + " #if prediction != label:\n", + " if right:\n", + " mistaken[label]['correct'] += 1\n", + " tot_right += 1\n", + " else:\n", + " mistaken[label]['wrong'] += 1\n", + " tot_wrong += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9966995768688294, 0.003300423131170663)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tot_right / (tot_right + tot_wrong), tot_wrong / (tot_right + tot_wrong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "MOD_SYMBOLS = [\"Module\", \"Interactive\", \"Expression\", \"Suite\"]\n", + "STMT_SYMBOLS = [\"FunctionDef\", \"AsyncFunctionDef\", \"ClassDef\", \"Return\", \"Delete\", \\\n", + " \"Assign\", \"AugAssign\", \"For\", \"AsyncFor\", \"While\", \"If\", \"With\", \"AsyncWith\", \\\n", + " \"Raise\", \"Try\", \"Assert\", \"Import\", \"ImportFrom\", \"Global\", \"Nonlocal\", \\\n", + " \"Expr\", \"Pass\", \"Break\", \"Continue\"]\n", + "EXPR_SYMBOLS = [\"BoolOp\", \"BinOp\", \"UnaryOp\", \"Lambda\", \"IfExp\", \"Dict\", \"Set\", \"ListComp\", \\\n", + " \"SetComp\", \"DictComp\", \"GeneratorExp\", \"Await\", \"Yield\", \"YieldFrom\", \\\n", + " \"Compare\", \"Call\", \"Num\", \"Str\", \"FormattedValue\", \"JoinedStr\", \"Bytes\", \\\n", + " \"NameConstant\", \"Ellipsis\", \"Constant\", \"Attribute\", \"Subscript\", \\\n", + " \"Starred\", \"Name\", \"List\", \"Tuple\"]\n", + "EXPR_CONTENT_SYMBOLS = [\"Load\", \"Store\", \"Del\", \"AugLoad\", \"AugStore\", \"Param\"]\n", + "SLICE_SYMBOLS = [\"Slice\", \"ExtSlice\", \"Index\"]\n", + "BOOLOP_SYMBOLS = [\"And\", \"Or\"]\n", + "OPERATOR_SYMBOLS = [\"Add\", \"Sub\", \"Mult\", \"MatMult\", \"Div\", \"Mod\", \"Pow\", \"LShift\", \"RShift\", \\\n", + " \"BitOr\", \"BitXor\", \"BitAnd\", \"FloorDiv\"]\n", + "UNARYOP_SYMBOLS = [\"Invert\", \"Not\", \"UAdd\", \"USub\"]\n", + "CMPOP_SYMBOLS = [\"Eq\", \"NotEq\", \"Lt\", \"LtE\", \"Gt\", \"GtE\", \"Is\", \"IsNot\", \"In\", \"NotIn\"]\n", + "COMPREHENSION_SYMBOLS = [\"comprehension\"]\n", + "EXCEPT_SYMBOLS = [\"excepthandler\", \"ExceptHandler\"]\n", + "ARG_SYMBOLS = [\"arguments\", \"arg\", \"keyword\"]\n", + "IMPORT_SYMBOLS = [\"alias\", \"withitem\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "def to_lower(l):\n", + " return [l_.lower() for l_ in l]" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "MOD_SYMBOLS = to_lower(MOD_SYMBOLS)\n", + "STMT_SYMBOLS = to_lower(STMT_SYMBOLS)\n", + "EXPR_SYMBOLS = to_lower(EXPR_SYMBOLS)\n", + "EXPR_CONTENT_SYMBOLS = to_lower(EXPR_CONTENT_SYMBOLS)\n", + "SLICE_SYMBOLS = to_lower(SLICE_SYMBOLS)\n", + "BOOLOP_SYMBOLS = to_lower(BOOLOP_SYMBOLS)\n", + "OPERATOR_SYMBOLS = to_lower(OPERATOR_SYMBOLS)\n", + "UNARYOP_SYMBOLS = to_lower(UNARYOP_SYMBOLS)\n", + "CMPOP_SYMBOLS = to_lower(CMPOP_SYMBOLS)\n", + "COMPREHENSION_SYMBOLS = to_lower(COMPREHENSION_SYMBOLS)\n", + "EXCEPT_SYMBOLS = to_lower(EXCEPT_SYMBOLS)\n", + "ARG_SYMBOLS = to_lower(ARG_SYMBOLS)\n", + "IMPORT_SYMBOLS = to_lower(IMPORT_SYMBOLS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def get_token_class_id(t):\n", + " if t in MOD_SYMBOLS: return 0\n", + " if t in STMT_SYMBOLS: return 1\n", + " if t in EXPR_SYMBOLS: return 2\n", + " if t in EXPR_CONTENT_SYMBOLS: return 3\n", + " if t in SLICE_SYMBOLS: return 4\n", + " if t in BOOLOP_SYMBOLS: return 5\n", + " if t in OPERATOR_SYMBOLS: return 6\n", + " if t in UNARYOP_SYMBOLS: return 7\n", + " if t in CMPOP_SYMBOLS: return 8\n", + " if t in ARG_SYMBOLS: return 9\n", + " if t in EXCEPT_SYMBOLS: return 10\n", + " if t in COMPREHENSION_SYMBOLS: return 11\n", + " if t in IMPORT_SYMBOLS: return 12\n", + " else: return 13" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "def is_same_class(t0, t1):\n", + " return get_token_class_id(t0) == get_token_class_id(t1)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "mistaken = {}; tot_right = 0; tot_wrong = 0\n", + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " if mistaken.get(label, None) == None:\n", + " mistaken[label] = {'correct':0, 'wrong':0}\n", + " right = is_same_class(prediction, label)\n", + " #if prediction != label:\n", + " if right:\n", + " mistaken[label]['correct'] += 1\n", + " tot_right += 1\n", + " else:\n", + " mistaken[label]['wrong'] += 1\n", + " tot_wrong += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9928913963328632, 0.007108603667136813)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tot_right / (tot_right + tot_wrong), tot_wrong / (tot_right + tot_wrong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "classes = [\"MOD\", \"STMT\", \"EXPR\", \"EXPR_CONT\", \"SLICE\", \"BOOLOP\", \"OPERATOR\", \"UNARY\", \"CMPOP\", \"COMPR\", \"EXCEPT\", \"ARG\", \"IMPORT\", \"VAR\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "confusion_mat = np.zeros((14,14))\n", + "class_freqs = {str(i):0 for i in range(14)};\n", + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " t0 = get_token_class_id(prediction)\n", + " t1 = get_token_class_id(label)\n", + " confusion_mat[t0][t1] += 1\n", + " class_freqs[str(t1)] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[8.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 2.275e+03, 4.700e+01, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 4.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [2.000e+00, 4.900e+01, 2.305e+04, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 7.000e+00, 0.000e+00, 0.000e+00,\n", + " 1.000e+00, 5.800e+01],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 6.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.000e+00],\n", + " [0.000e+00, 0.000e+00, 4.000e+00, 0.000e+00, 1.117e+03, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 6.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.690e+02,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 9.200e+02, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 9.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.710e+02, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 5.450e+02, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 6.000e+00],\n", + " [0.000e+00, 2.000e+00, 1.100e+01, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 1.598e+03, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 7.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+01, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.380e+02,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 5.200e+01, 0.000e+00],\n", + " [0.000e+00, 4.000e+00, 9.000e+00, 2.000e+00, 2.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 5.000e+00, 1.000e+01, 0.000e+00, 0.000e+00,\n", + " 4.000e+00, 5.139e+03]])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_mat" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'0': 0.00028208744710860365,\n", + " '1': 0.06592383638928068,\n", + " '10': 0.00039492242595204514,\n", + " '11': 0.0038928067700987304,\n", + " '12': 0.0016078984485190409,\n", + " '13': 0.1472496473906911,\n", + " '2': 0.6522143864598026,\n", + " '3': 0.00022566995768688293,\n", + " '4': 0.03159379407616361,\n", + " '5': 0.004767277856135402,\n", + " '6': 0.025952045133991537,\n", + " '7': 0.004823695345557123,\n", + " '8': 0.015514809590973202,\n", + " '9': 0.045557122708039494}" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freqs = {k: v/len(results_df) for k,v in class_freqs.items()}\n", + "freqs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.0000e+01, 2.3370e+03, 2.3121e+04, 8.0000e+00, 1.1200e+03,\n", + " 1.6900e+02, 9.2000e+02, 1.7100e+02, 5.5000e+02, 1.6150e+03,\n", + " 1.4000e+01, 1.3800e+02, 5.7000e+01, 5.2200e+03])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(confusion_mat, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tcJbKFQAAwAiEKwAAgBFoCwQAAAbpxG6Bq1C5AgAAGIFwBQAAMAJtgQAAwGDLrU4zyzcCAAAwApWrPVD//JV88+VXLnoZe60XvYB9dOaxWxa9hL127Ae/ddFL2Ce3PuKfF70EYG/1Rv+/P4vSd35t0UuADUu4AgAAhumyW+AqtAUCAACMQLgCAAAYgXAFAAAwAs9cAQAAg3SS5XjmapbKFQAAwAiEKwAAgBFoCwQAAAazFfs8lSsAAIARCFcAAAAj0BYIAAAM0tEWuBqVKwAAgBEIVwAAACPQFggAAAymLXCeyhUAAMAIhCsAAIARaAsEAAAG6ZS2wFWoXAEAAIxAuAIAABiBcAUAADACz1wBAACDLcczV7N2W7mqqqWq2jF1nF9Vh1TVlVX1mKnr3l1VT538/cmquqaqrp6M32eV8f9RVQ/YzXvfp6ourqobJ+93aVU9ZHLupKq6vKqur6pPVNXLq6om586tquWqetjUXB+pquOr6kOTz/EPVfWZqc91/N58gQAAAMmetQXe3t1bpo5XdfdSkmcleX1VHVpV5yRZ7u4/mbrvjO5+WJLtSV66yvi2JC9b600nQekdSbZ194O7+9QkL0ly76o6IsklSV7V3ScmOSXJoyZr2unmJL88O293P7y7tyR5RZK3Tn2uT+7BdwEAALCqvW4L7O4PVdUHklyQ5OlJfmSNS9+b5HmrjH9gjfGdzkhyZ3e/aeo9r0qSqvoPSd7f3e+ejH+lqp6TlcD2hsnl70rymKo6sbuv39PPBQAA7EbHVuyr2JPK1REzbYE/NXXuJUmen+TN3X3DGvefleSaVcYfn+Sdu3jfk5Ncuca5k2bPdfeNSY6qqqMnQ8tJXp27Vs32WFWdV1Xbq2r7nbljb6YAAAA2kT2pXN0+aaNbzWOSfDErQWjWe6pqKcnVuWv733uq6l5Jvpzk5UMWuxfenOSXq+qBQ2/s7q1JtibJ0XWvHnthAADAwWWvt2Kvqm/JSmXosUm+o6p+bOaSMybPMj2zu78wPZ7kAUl2JPmVXbzFtUlOXePcdbPnqupBSb7c3V/aOdbdX0/ymiQv3oOPBAAA7IHOSlvgej4WYV9+5+oVSd7W3R/LykYSr62qw/fkxknoeX6SZ06qWKu5PMlhVXXezoGqelhV/VCSP07y6Kp63GT8iCSvy0rYm3VRkscl+fY9+lQAAAB7YW+euXpVVZ2U5MlJfi1JuvvDSS7LgApRd386yVuSPHuN8z15j8dNtmK/Nskrk/xjd9+e5IlJXlZV12flma4rkrx+lXm+lpXg9R17ujYAAIChdvvMVXcfssaph8xc97ypv49fY67jZ14/dzfvfWuSp61x7pokp69x7qKsVKx2vn5dVgLWmtcAAAB7zm6B8/alLRAAAICJvf6dq7FU1bcl+atVTv1wd3/2QK8HAABgbyw8XE0C1FpbvQMAAOtMZ3E78q1n2gIBAABGIFwBAACMQLgCAAAYwcKfuQIAADae9szVHJUrAACAEQhXAAAAI9AWCAAADLYcbYGzVK4AAABGIFwBAACMQFsgAAAwSHeybLfAOSpXAAAAIxCuAAAARqAtEAAAGMyPCM9TuQIAABiByhXsR7c+4p8XvYR9ctmtOxa9hH1y5rFbFr0EAGATEa4AAICBym6Bq9AWCAAAMALhCgAAYATaAgEAgMHsFjhP5QoAAGAEwhUAAMAIhCsAAIAReOYKAAAYpBNbsa9C5QoAAGAEwhUAAMAItAUCAADDdNK96EWsPypXAAAAIxCuAAAARqAtEAAAGGw5dgucpXIFAAAwAuEKAABgBNoCAQCAQTpJ+xHhOSpXAAAAIxCuAAAARqAtEAAAGKiyrC1wzrqvXFXVL1fVtVV1dVXtqKqHV9W2qjpt5rrTq+pdU69/tKq2V9V1VfXhqnrNZPyCqrplMtfO4x4H+nMBAAAHl3VduaqqRyY5K8n3d/cdVXVMkrvtwX0nJ3l9kh/v7o9V1SFJzpu65LXdfeF+WTQAALApretwleS+SW7r7juSpLtvS5Kq3ZYgfynJr3X3xyb3LSX5nf24TgAAYJNb722B705y/6r6eFW9sar+zR7ed3KSK3dx/gVTLYHv2fdlAgDA5tK9vo9FWNeVq+7+clWdmuSHkpyR5K1Vdf4IU++2LbCqzsuklfDwHDnCWwIAAAezdR2ukm+09G1Lsq2qrknyM3tw27VJTk1y1T6879YkW5Pk6LrXgrIvAACwUazrtsCqOrGqTpga2pLk7/fg1t9M8tKqeshknm+qqp/fH2sEAIDNqLvW9bEI6zpcJTkqyR9MtlO/OslDk1wwOfdnVXXz5PiT6Zu6++okz0/ylqr6aJKPJHnQ1CUvmNmK/fj9/UEAAICD27puC+zuK5M8apVTp69xy7ape9+V5F2zF3T3BfnXgAYAADCKdR2uAACA9WdlR77FtN6tZ+u9LRAAAGBDEK4AAABGoC0QAAAYbFlb4ByVKwAAgBEIVwAAACPQFggAAAzWvegVrD8qVwAAACMQrgAAAEagLRAAABjMjwjPU7kCAAAYgXAFAAAwAuEKAABgBJ65AgAABumUZ65WoXIFAAAwAuEKAABgBNoCAQCAwXrRC1iHVK4AAABGoHIFrOnMY7csegn75LJbdyx6Cftko3//ALDZCFcAAMAwHbsFrkJbIAAAwAiEKwAAgBFoCwQAAIazXeAclSsAAIARCFcAAAAj0BYIAAAMZrfAeSpXAAAAIxCuAAAARiBcAQAAjEC4AgAABute38fuVNXjq+r6qrqhqs5f45qnVdV1VXVtVb15d3Pa0AIAANhUquqQJG9I8iNJbk5yRVVd0t3XTV1zQpKXJPnB7v58VX3H7uZVuQIAADabH0hyQ3f/XXd/LcnFSZ44c83PJnlDd38+Sbr7n3Y3qcoVAAAwSGfDb8V+XJJPTb2+OcnDZ655SJJU1fuTHJLkgu7+811NKlwBAAAHo2OqavvU663dvXXA/d+c5IQkpye5X5L3VtX3dvcXdnUDAADAwea27j5tjXO3JLn/1Ov7Tcam3ZzkQ919Z5KbqurjWQlbV6z1hp65AgAAhukkXev72LUrkpxQVQ+sqrslOTvJJTPXvDMrVatU1TFZaRP8u11NKlwBAACbSnd/PclzklyW5KNJ3tbd11bVr1bVEyaXXZbks1V1XZL3JHlRd392V/NqCwQAADad7r40yaUzY6+Y+ruT/MLk2CPCFQAAMNie/FDvZnPA2gKraqmqdlTVVVX1t1X1qKlzJ1XV5ZNfSP5EVb28qmrq/JOq6uqq+mhVXVNVT5o6d1FVPWWV91tzzqo6t6o+M1nPdVX1s/v78wMAAAe3A/nM1e3dvaW7T8nKLx2/Mkmq6oisPDz2qu4+MckpSR6V5FmT86ckuTDJE7v7e5I8IcmFVfWwtd5od3NOvLW7t2TlIbVfr6p7j/lhAQCAzWVRG1ocneTzk7+fnuT93f3uJOnur2Tl4bLzJ+dfmOTXu/umyfmbshLMXrSL+Xc35zdMfmn5xiQP2MfPBAAAm0ev82MBDuQzV0dU1Y4khye5b5LHTsZPSnLl9IXdfWNVHVVVR0/OXzgz1/Ykz97Fe+1uzm+oqgcleVCSG2bGz0tyXpIcniN3/+kAAIBN7UCGq9snbXipqkcm+cOqOvkAvv+sn6qqRye5I8nPdffnpk9Ofr15a5IcXffyuB4AALBLC9ktsLs/MPkhrm9Pcl2Sx0yfn1STvtzdX5rsK39qkqumLjk1ybW7eIvdzZmsPHP1nH3+MAAAAFnQM1dV9d1JDkny2SR/nOTRVfW4ybkjkrwuyasnl1+Y5CVVdfzk/PFJXprkNbt4i93NCQAA7LVK9/o+FmERz1wlSSX5me5eSnJ7VT0xyW9X1RuyErr+KMnrk6S7d1TVi5P8aVUdmuTOJL/U3Tum5v7dqvqtyd+f6u5H7mpOAACAsR2wcNXdh+zi3DVZ2RJ9rfNvT/L2Nc6dO3TO7r4oyUVrvR8AAMBQC3nmCgAA2OBs+TZnUb9zBQAAcFARrgAAAEagLRAAABims7Ad+dYzlSsAAIARCFcAAAAj0BYIAAAMZ7fAOSpXAAAAIxCuAAAARqAtEAAA2At2C5ylcgUAADAC4QoAAGAE2gIBAIDh7BY4R+UKAABgBMIVAADACIQrAACAEXjmCgAAGM4zV3NUrgAAAEagcgUctM48dsuil7BPLrt1x6KXsE82+vcPAEMJVwAAwDCdpGvRq1h3tAUCAACMQLgCAAAYgbZAAABgsLZb4ByVKwAAgBEIVwAAACPQFggAAAynLXCOyhUAAMAIhCsAAIARaAsEAACG8yPCc1SuAAAARiBcAQAAjEC4AgAAGIFnrgAAgMHKVuxzVK4AAABGIFwBAACMQFsgAAAwTE8O7kLlCgAAYATCFQAAwAi0BQIAAANV0rXoRaw7e1W5qqr7VdX/U1WfqKobq+o/V9Xdqur0qvpiVe2oqo9W1X+cXD89vvN43OTc0uT1R6rqT6vqHjPv9fyq+mpV3X3y+sypOb5cVddP/v7DyflHV9XfVNXHJsd5U3NdUFW3TK6/rqrO2dsvDgAAYNrgcFVVleTtSd7Z3SckeUiSo5L82uSS93X3liSnJfnpqvr+6fGp4y8n47dPXp+c5HNJnj3zluckuSLJTyZJd1+2c44k25M8Y/L6mVV1nyRvTvLz3f3dSR6d5Oeq6sen5nvt5N4nJvndqjp06HcAAAAwa28qV49N8tXu/q9J0t1LSV6Q5N8nOXLnRd39L0muTPJdA+b+QJLjdr6oqgdnJbi9LCsha3eeneSi7v7byRpuS/JLSc6fvbC7P5HkK0nuOWB9AABA8q87Bq7XYwH2JlydlJXQ9A3d/aUk/5CpIFVV35bkEUmunQz90Exb4IOn56iqQ5L8cJJLpobPTnJxkvclObGq7j10bVmpbp00e+GkovaJ7v6n1SaqqvOqantVbb8zd+zmbQEAgM1uf+wW+ENV9eEk707yqu7eGa5m2wJvnIwfUVU7kvxjknsn+Yupuc5JcnF3Lyf570meOsL6XlBV1yb5UP61lXFOd2/t7tO6+7RDc9gIbwsAABzM9iZcXZfk1OmBqjo6yXcmuSErIer7uvvU7n7THsx3++QZqAckqUyeuaqq701yQpK/qKpPZqWKtbvWwLm1TV5fO/X6td19UpJ/m+T/qqrD92CNAADAtEW3/R0kbYF/leTIqnpm8o12vtckuSgrzzDtle7+SpLnJfnFqvrmrASpC7r7+MlxbJJjq+oBu5jmDUnOraotk7V9W5LfSPLqVd7vkqy0DP7M3q4ZAABgp8Hhqrs7yZOTPLWqPpHk40m+muSlu7l19pmrp6wy94eTXJ2VYHV2knfMXPKOyfhaa/t0kp9O8ntV9bEk/zPJ73f3n65xy68m+YWq8mPKAADAPtmrHxHu7k8l+YlVTm2bHLPXb0ty9zXmOmrm9c55/2iVa39h5vXpq1zz3iT/yxrvdcHM6yuTnLjatQAAwC4sqPVuPVOxAQAAGIFwBQAAMALhCgAAYAR79cwVAACwiXWSrkWvYt1RuQIAABiBcAUAADACbYEAAMBgZSv2OSpXAAAAIxCuAAAARqAtEAAAGE5b4ByVKwAAgBEIVwAAACMQrgAAAEYgXAEAAIxAuAIAABiB3QIBAIDB/IjwPJUrAACAEQhXAAAAI9AWCLBOnXnslkUvYZ9cduuORS9hn2z07x+AA0+4AgAAhuta9ArWHW2BAAAAIxCuAAAARqAtEAAAGKYnB3ehcgUAADAC4QoAAGAE2gIBAIDhtAXOUbkCAAAYgXAFAAAwAm2BAADAYKUtcI7KFQAAwAiEKwAAgBFoCwQAAIbTFjhH5QoAAGAEwhUAAMAIhCsAAIAReOYKAAAYzjNXc1SuAAAARiBcAQAAjEBbIAAAMEj1ysFdrYvKVVUdX1UfmRm7oKpeWFUXVdUtVXXYZPyYqvrkzLXPr6qvVtXdp8ZOr6ovVtWOqvpYVV04GT+pqj5eVUdMXftnVXXOfv2QAADAQW1dhKs9sJTk3+/i/DlJrkjykzPj7+vuLUm+L8lZVfWD3X1tkrcn+eUkqaonJTm0u98y/rIBAIDNYqOEq99K8oKqmmtjrKoHJzkqycuyErLh9+KBAAAgAElEQVTmdPftSXYkOW4y9KtJnlpVW5K8Ksmz98eiAQDgoNW1vo8F2Cjh6h+S/HWS/3WVc2cnuTjJ+5KcWFX3nr2gqu6Z5IQk702S7v5KkhdOXl/c3Z9Y5Z7zqmp7VW2/M3eM9kEAAICD03oJV2s9Djc9/sokL8r8ms/JSkBaTvLfkzx16twPVdVVSW5Jcll3/+M3Ju7+0yRfSPLGVd+4e2t3n9bdpx2awwZ9GAAAYPNZL7sFfjbJPWfG7pXkpp0vuvsTVbUjydN2jlXV92alIvUXVZUkd5vc8/rJJe/r7rOq6oFJPlhVb+vuHVPvsTw5AACAIewWOGddVK66+8tJPl1Vj02SqrpXksdnpRVw2q9lpZ1vp3OSXNDdx0+OY5McW1UPmJn/pqw8W/Xi/fUZAACAzW1dhKuJZyZ5+aQ6dXmSX+nuG6cvmOz097dTQ2cnecfMPO+YjM96U5LHVNXxYy0YAABgp/XSFpjuvi7JGauMnzvz+ien/n7QKtf/wtTLbVPjt+dfdwvcOXb83q4XAAA2Mz8iPG89Va4AAAA2LOEKAABgBOumLRAAANhAtAXOUbkCAAAYgXAFAAAwAuEKAABgBJ65AgAAhmlbsa9G5QoAAGAEwhUAAMAItAUCAADDaQuco3IFAAAwAuEKAABgBNoCAQCA4bQFzlG5AgAAGIFwBQAAMAJtgQAAwGB+RHieyhUAAMAIhCsAAIARaAsEYL8489gti17CPnnXLVcuegn75KzjTl30EgA2HZUrAACAEQhXAAAAIxCuAAAARuCZKwAAYDhbsc9RuQIAABiBcAUAADACbYEAAMAwnZS2wDkqVwAAACMQrgAAAEagLRAAABhOW+AclSsAAIARCFcAAAAj0BYIAAAMpy1wjsoVAADACIQrAACAEWgLBAAABqn4EeHVqFwBAACMQLgCAAAYgbZAAABgOG2Bc1SuAAAARrAuwlVV3aeqLq6qG6vqyqq6tKoeUlVdVf/H1HXHVNWdVfX6yesLquqWqtpRVR+pqidMXXteVX1scvxNVT166ty2qrq+qq6qqvdX1YkH9hMDAAAHm4WHq6qqJO9Isq27H9zdpyZ5SZJ7J7kpyY9PXf7UJNfOTPHa7t4yOff7VfVNVXVWkp9L8uju/u4kP5/kzVV1n6n7ntHdpyT5gyS/uT8+GwAAsHksPFwlOSPJnd39pp0D3X1Vkk8l+UqSj1bVaZNTP5XkbatN0t0fTfL1JMckeXGSF3X3bZNzf5uVEPXsVW59b5LvGuejAADAJtArW7Gv52MR1kO4OjnJlbs4f3GSs6vq/kmWkty62kVV9fAky0k+k+SkVebcPhmf9RNJrlllvvOqantVbb8zd+z2QwAAAJvbRtgt8M+T/Kck/1+St65y/gVV9dNJ/jnJT3V3r3Qa7tYfV9XtST6Z5LmzJ7t7a5KtSXJ03cteKAAAwC6th3B1bZKnrHWyu79WVVcm+cUkD03yhJlLXtvdF86MXZfk1CSXT42dmrs+r/WM7t6+16sGAIDNTPlhznpoC7w8yWFVdd7Ogap6WJL7T13zmiQv7u7P7eGcr07yG1X1bZP5tiQ5N8kbR1kxAADAjIVXriZtfE9O8ltV9eIkX81Kq97zp665NvO7BO5qzkuq6rgk/7OqOistgz/d3Z8edfEAAAATCw9XSdLdtyZ52iqnTl7l2ouSXDT5+4JdzPk7SX5njXOnD18lAADwDdoC56yHtkAAAIADqqoeX1XXV9UNVXX+Lq77t1XVUz8PtSbhCgAA2FSq6pAkb0jyo1nZNO+cqnroKtd9a5L/PcmH9mRe4QoAABhs0T8SvI8/IvwDSW7o7r/r7q9l5bd1n7jKdf8pyW9kZV+I3RKuAACAzea4JJ+aen3zZOwbqur7k9y/u/9sTyddFxtaAAAAjOyYqpr+Xdut3b11T26sqm9K8n9m5eec9phwBQAADLf+dwu8rbvX2oTiltz1d3XvNxnb6VuzsnP5tqpKkvskuaSqntDd04HtLrQFAgAAm80VSU6oqgdW1d2SnJ3kkp0nu/uL3X1Mdx/f3ccn+WCSXQarRLgCAAA2me7+epLnJLksyUeTvK27r62qX62qJ+ztvNoCAQCATae7L01y6czYK9a49vQ9mVO4AgAAhulshGeuDjhtgQAAACMQrgAAAEagLRAAABistAXOUbkCAAAYgXAFAAAwAm2BAADAcNoC56hcAQAAjEC4AgAAGIG2QABYxVnHnbroJeyTd91y5aKXsNc2+ncPm4XdAuepXAEAAIxAuAIAABiBtkAAAGA4bYFzVK4AAABGIFwBAACMQLgCAAAYgWeuAACAYTqeuVqFyhUAAMAIhCsAAIARaAsEAAAGqcnBXalcAQAAjEC4AgAAGIG2QAAAYDi7Bc5RuQIAABiBcAUAADACbYEAAMBgpS1wjsoVAADACIQrAACAEWgLBAAAhtMWOGfhlauquk9VXVxVN1bVlVV1aVU9pKpOqqrLq+r6qvpEVb28qmpyz7lV1VX1uKl5njQZe8rk9bbJvVdV1fur6sRVxq+oqi2L+eQAAMDBZKHhahKW3pFkW3c/uLtPTfKSJPdOckmSV3X3iUlOSfKoJM+auv2aJGdPvT4nyVUzb/GM7j4lyR8k+c1Vxt84Mw4AALBXFl25OiPJnd39pp0D3X1VkockeX93v3sy9pUkz0ly/tS970vyA1V1aFUdleS7kuxY433eOzk/6wNJjtvnTwEAAJtNr/NjARb9zNXJSa5cZfyk2fHuvrGqjqqqo3cOJfnLJGcmuXtWKl0PXON9fiIrla5Zj0/yzr1YNwAAwF0sOlztq4uTPC8r4eoXk7x05vwfV9XtST6Z5Lkz43dLclSSVZ+5qqrzkpyXJIfnyHFXDQAAHHQW3RZ4bZJTVxm/bna8qh6U5Mvd/aWdY939N0m+N8kx3f3xVeZ5Rndv6e4ndfenpseTPCgrz2L99moL6+6t3X1ad592aA4b9KEAAIDNZ9Hh6vIkh02qREmSqnpYkuuTPHrnboBVdUSS1yV59SpznJ/5itVudXcneXmSR1TVd+/F2gEAYHPqpNb5sQgLDVeTgPPkJI+bbMV+bZJXJvnHJE9M8rKquj4rz0tdkeT1q8zx/3b3e/by/W9P8pokL9rLjwAAAJBkHTxz1d23JnnaGqdPX+Oei5JctMr4uVN/r3Xv6TOvX7MHywQAANilhYcrAABgA1pQ6916tuhnrgAAAA4KwhUAAMAItAUCAACDLWpHvvVM5QoAAGAEwhUAAMAItAUCAADDaQuco3IFAAAwAuEKAABgBNoCAQCAwewWOE/lCgAAYATCFQAAwAiEKwAAgBF45goAABimYyv2VahcAQAAjEC4AgAAGIG2QAAAYDhtgXNUrgAAAEagcgUAB6Gzjjt10UvYa4duu++il7BP7jz904teArAgwhUAADBIJSltgXO0BQIAAIxAuAIAABiBtkAAAGA4bYFzVK4AAABGIFwBAACMQFsgAAAwWLW+wFkqVwAAACMQrgAAAEagLRAAABimY7fAVahcAQAAjEC4AgAAGIFwBQAAMALPXAEAAIOVZ67mqFwBAACMQLgCAAAYgbZAAABgOG2Bc1SuAAAARiBcAQAAjEBbIAAAMJjdAuctpHJVVUtVtWPqOL+qDqmqK6vqMVPXvbuqnjr5+6iq+t2qunFy3baqevha803Gt1XV9VV1VVW9v6pOrKp3TK65oaq+OHXPoxbxXQAAAAeHRVWubu/uLbODVfWsJL9XVacmeUqS5e7+k8np/5LkpiQndPdyVT0wyUN3Nd/EM7p7e1Wdl+Q3u/sJk/c6PckLu/us8T4WAACwWa2rtsDu/lBVfSDJBUmenuRHkqSqHpzk4VkJSsuTa2/KStjaU+9N8vxRFwwAAJuVtsA5iwpXR1TVjqnXr+zut07+fkmSTyX5re6+YTJ2UpId3b20F/Pt9BNJrtnXhQMAAKxmXbUFTjwmyReTnDzSfH9cVbcn+WSS5+7phJM2wvOS5PAcOWApAADAZrSu2gKr6luSvDrJY5P816r6se6+NMm1SU6pqkN2Ub1ayzO6e/vQtXT31iRbk+ToupeiJwAA7NR2C1zNevudq1ckeVt3fyzJs5K8tqoO7+4bk2xP8itVVUlSVcdX1Y8vcK0AAADfsF6eufrzJH+U5MlJTkmS7v5wVV2W5MVJfiXJ/5bkNUlumLT53ZbkRWvN193n7+fPAAAA8A0LCVfdfcgapx4yc93zpv7+UpKfHTJfd5++izVsS7Jt1ysFAADYM+vqmSsAAGCD8MzVnPX2zBUAAMCGJFwBAACMQFsgAAAwSMVW7KtRuQIAABiBcAUAADACbYEAAMBwrS9wlsoVAADACIQrAACAEWgLBAAABrNb4DyVKwAAgBEIVwAAACPQFggAAAzTk4O7ULkCAAAYgXAFAAAwAuEKAABgBJ65AgAABqvlRa9g/VG5AgAAGIHKFQCwrtx5+qcXvYR9ctmtOxa9hH1y5rFbFr0E2LCEKwAAYDhbsc/RFggAADAC4QoAAGAE2gIBAIDBSlvgHJUrAACAEQhXAAAAI9AWCAAADNNJWl/gLJUrAACAEQhXAAAAI9AWCAAADGa3wHkqVwAAACMQrgAAAEagLRAAABhOW+AclSsAAIARCFcAAAAjEK4AAABG4JkrAABgkIqt2FejcgUAADAC4QoAAGAE2gIBAIBhulcO7mJDV66q6klV1fX/t3fnYZaV1b3Hvz9QQCO2ImIEuUxifEQBsa9E1BgxuUJEiUEUlBiNisnVGBUNGOd5QC844AAJDkkEiQjiBERxjBFtCRobMSGCMTgyRExAHHrdP/Yu+vSpoet0n669d9X38zznYQ+nTq8qTp3aa7/vu1Zyz3Z/9yQ3Jbk0yWVJ3pfk1iPPv3+SzyT5tySXJPlYkvt09x1IkiRJWi4GnVwBRwNfaP8749+ran/gPsDdgMcCJLkLcBbwl1W1d1UdALwW2GtpQ5YkSZK0HA12WmCS2wEPAh4KfAR46ej5qvpVki8Du7SHngm8t6q+OPKcLyxRuJIkSdKyYrXA2YY8cnU4cH5V/StwbZL7jZ5Msh1wIHB+e2gf4JLFvniSY5OsSbLmF9w8rZglSZIkLVNDTq6OBs5st89k/dTAvZJcCvwQ+H5VfX2uL05ycZJvJnnzXOer6tSqWl1Vq2/NttOOXZIkSdIyM8hpgUl2AA4G7pOkgK2BAk6hXXOVZEfgH5M8qqrOA9YCBwAfBqiqA5M8Bjisk29CkiRJGjKnBc4y1JGrxwB/U1W7VdXuVbUrcCWw68wTquoa4ATgBe2hU4AnJTlo5HVuu1QBS5IkSVrehppcHQ2cM3bsbNYnUjPOBW6b5MFV9QPgccBrk1yR5Is0Sdrbtni0kiRJkpa9QU4LrKqHznHsLcBbxo4VsN/I/peAh2zxACVJkqRlzmqBsw115EqSJEmSesXkSpIkSZKmwORKkiRJkqZgkGuuJEmSJHWogHUuuhrnyJUkSZIkTYHJlSRJkiRNgdMCJUmSJE3OWYGzOHIlSZIkSVNgciVJkiRJU+C0QEmSJEkTi9MCZ3HkSpIkSZKmwORKkiRJkqbAaYGSJEmSJlfOCxznyJUkSZIkTYHJlSRJkiRNgdMCJUmSJE3MaoGzOXIlSZIkSVNgciVJkiRJU+C0QEmSpCl6+M77dx3CZnn5t7/adQib5aV73q/rEFaGah/agCNXkiRJkjQFJleSJEmSNAUmV5IkSZJWnCSHJPlWkiuSnDDH+ecmuSzJ15N8KsluG3tN11xJkiRJmkiA1HAXXSXZGjgF+F3gP4GvJDmvqi4bedo/A6ur6sYkfwq8AXjcQq/ryJUkSZKkleb+wBVV9e2q+jlwJnD46BOq6tNVdWO7+yXgbht7UZMrSZIkScvRjknWjDyOHTm3C/Ddkf3/bI/N5ynAJzb2DzotUJIkSdLk1nUdwEZdU1WrN/dFkhwDrAYesrHnmlxJkiRJWmmuBnYd2b9be2wDSX4HeCHwkKq6eWMv6rRASZIkSSvNV4C9k+yRZBvgKOC80SckuS/wLuBRVfWjxbyoI1eSJEmSJjbkaoFV9cskzwQuALYGTq+qtUleAaypqvOAE4HbAX+fBOA/qupRC72uyZUkSZKkFaeqPg58fOzYS0a2f2fS13RaoCRJkiRNgSNXkiRJkiZT7UMbcORKkiRJkqbA5EqSJEmSpsBpgZIkSZImVDDgaoFbiiNXkiRJkjQFJleSJEmSNAUmV5IkSZI0Ba65kiRJkjSxuORqlk5GrpL8d/vf3ZNUkleNnNsxyS+SvK3df1mSq5NcmuQbSR418txjk1zePr6c5EEj5z6T5FtJvpbkK0n2b49f3L7WfyT5cbt9aZLdl+r7lyRJkrT89GFa4JXAI0b2jwTWjj3npKravz13epKtkhwGPB14UFXdE/gT4P1Jfn3k655QVfsBbwdOBKiqA9vXegnwgarav31ctSW+OUmSJEkrQx+SqxuBbyZZ3e4/DjhrridW1TeBXwI7AscDz6+qa9pzlwDvBZ4xx5f+E7DLlOOWJEmSVq6qfj860IfkCuBM4KgkuwK/Ar4315OSHAisA34M7AN8dewpa9rj4w4Bzp0koHbK4Zoka37BzZN8qSRJkqQVqC8FLc4HXgn8EPjAHOefk+QY4KfA46qqkizmdf8uyTbA7YD9Jwmoqk4FTgW4fXZwuZ4kSZKkBfVi5Kqqfk4zCnUc8ME5nnJSuy7qwVX1+fbYZcD9xp53PzZcr/UEYE+a6YJvnW7UkiRJ0gpVkHX9fnShLyNXAG8CPltV1y1yVOoNwOuTHFJV17bVAJ8EHDj6pHaU68XAvye5Z1VdPu3AJUmSJKk3yVVVrWV2lcCFnn9ekl2ALyYpmimDx1TV9+d47k1J3gQ8H3jKtGKWJEmSpBmdJFdVdbv2v1cB957j/HuA97TbL1vgdd4BvGOec789tv+m+f4NSZIkSRPqqCJfn/VizZUkSZIkDZ3JlSRJkiRNQW/WXEmSJEkaEGcFzuLIlSRJkiRNgcmVJEmSJE2ByZUkSZIkTYFrriRJkiRNLJZin8WRK0mSJEmaApMrSZIkSZoCpwVKkiRJmpzTAmdx5EqSJEmSpsDkSpIkSZKmwGmBkiRJkiZTwLqug+gfR64kSZIkaQpMriRJkiRpCpwWKEmSJGkioWwiPAdHriRJkiRpCkyuJEmSJGkKnBYobUE3PvrArkPYLLc95+KuQ5C6s9XWXUeweWrAZbycatSpVx70e12HsFnOu/qjXYewWW67c9cRTMDf1VkcuZIkSZKkKTC5kiRJkqQpcFqgJEmSpMk5LXAWR64kSZIkaQpMriRJkiRpCkyuJEmSJGkKXHMlSZIkaTIFDLjjw5biyJUkSZIkTYHJlSRJkiRNgdMCJUmSJE0slmKfxZErSZIkSZoCkytJkiRJmgKnBUqSJEmanNMCZ3HkSpIkSZKmwORKkiRJkqbAaYGSJEmSJlROC5yDI1eSJEmSNAUmV5IkSZI0BU4LlCRJkjSZwmmBc3DkSpIkSZKmYLDJVZJPJ3n42LFnJ3nHyPbPkqwaOf/bSX6S5NIklyd541LHLUmSJGl5GmxyBZwBHDV27Kj2OMDRwFeAPxh7zueran/gvsBhSR64RaOUJEmStCIMObn6IPCIJNsAJNkd2Bn4fJK9gNsBL6JJsmapqpuAS4FdliJYSZIkaVlZ1/NHBwabXFXVdcCXgUPbQ0cBZ1VVtdtnAp8HfiPJXca/Pskdgb2Bz831+kmOTbImyZpfcPOW+BYkSZIkLSODTa5ao1MDx6cEnllV64CzgSNHvubBSb4GXA1cUFU/mOuFq+rUqlpdVatvzbZbJnpJkiRJy8bQS7F/GDgpyQHAbavqq0nuQzMi9Q9JALYBrgTe1n7N56vqsCR7AF9KclZVXdpF8JIkSdJQxVLsswx65Kqq/hv4NHA6G45avayqdm8fOwM7J9lt7GuvBF4HHL+UMUuSJElangadXLXOAPZjfXJ1FHDO2HPOYXZlQYB3Ar/VFsOQJEmSpE029GmBVNW5QEb295zjOc8d2f3MyPGbsFqgJEmSNDmnBc6yHEauJEmSJKlzJleSJEmSNAWDnxYoSZIkaYkVsM5pgeMcuZIkSZKkKTC5kiRJkqQpcFqgJEmSpAmV1QLn4MiVJEmSJE2ByZUkSZIkTYHTAiVJkiRNzmmBszhyJUmSJElTYHIlSZIkSVNgciVJkiRJU+CaK0mSJEmTc83VLI5cSZIkSdIUmFxJkiRJ0hQ4LVCSJEnSZApY57TAcY5cSZIkSdIUmFxJkiRJ0hQ4LXARfsr113yyPvidLfhP7AhcswVff0sz/vl86INb5GXH+PPvzpBjB+Nf2K+22CvP8OffrSHHv2Vj//4We+UZWzT+2+68pV75Flv6vbPbFnztKSqodV0H0TsmV4tQVXfekq+fZE1Vrd6S/8aWZPzdMv7uDDl2MP6uGX+3hhz/kGMH49fy5rRASZIkSZoCR64kSZIkTc4mwrM4ctUPp3YdwGYy/m4Zf3eGHDsYf9eMv1tDjn/IsYPxaxlLmXFKkiRJmsCqbe9SB9318V2HsaDzv3PyV5d6fZzTAiVJkiRNxibCc3JaoCRJkiRNgcmVJEmSJE2ByZU0IEn+V9cxbKokt1/g3CC+ryR3TrI6yR26jmWlSfK4rmPQcCX5za5jWKmG8vk+qeX6fWnzmVx1IMmtkjwyyfPbx2FJBrH+LcmFXcewJSS5Q5IXdh3HIpzbdQCb4TMzG0k+NXau999XkqcCa4G3ApcneVTHIU0sydZJdhzZ3ybJsUm+2WVci/SHSc5PsmfXgUwqyd5JPpzkG0nOSLJL1zFNQ5J7JDmt6zgW6e1dB7Apkuwz+lmT5KQkp7ePA7qMbQK9/3xfSJIHJHlMkp3a/X2TvB/4x45D64eqfj86YHK1xNo/qmuB44CdgV2A5wNrk+zcZWyLdOeuA9gcSXZNcmqSjyZ5apJfS/Im4F+BnbqObxHSdQCbYTT2HRY411fPBvapqgcABwEv6DieiSQ5CrgO+HqSzyb5P8C3gUOBJ3Qa3CJU1WHAO4GPJXlxkh2T7DDz6Dq+jTgd+ChwBHAJTYI+GO3F5IVtcviqJHdNcjZwEXBZ1/Etc68DrhnZfzjwMeDTwEs6iWhyQ/h8n1OSE2l+f4+g+ex5FXAhcDGwd5exqb8GMVqyzLwaeEdVnTx6MMmzgNcCf9RJVIu3KskfzHeyqj60lMFsgvcBnwXOBg4B1gCXAvtW1Q+6DGyRdknylvlOVtWzljKYCdU823Pt99HPq+rHAFX17STbdh3QhF4E3K+qrmjveP8T8Jiq+kjHcS1aVZ2b5Ergc8BTWP++KaDPI1rbV9XMCM+JSS7pNJrJnQa8g+Y9cwjNZ+Z7gSdU1c+6DGwCeyY5b76TVdXXkei7VtUXR/ZvqKqzAZI8vaOYJjXkv1uPAO5bVT9Lckfgu8C9q+qqbsNSn5lcLb3frKonjR+sqrck+VYH8UxqFXAYc9+JKqDvydUOVfWydvuCJEfSXCCs6zCmSdwEfLXrIDbRTkmeS/Pemdmm3R/CiOjdxi4QNtjv+QUCNMnhFQBVdUmSfxtSYtUmsy8CHkPzO/vRjkOaxHZJ7sv6z83bjO5XVd+TrW2r6j3t9reS/HlV/UWXAW2CHwNv6jqITbD96E5Vja4dG8JsCxj2362fzdxAqKrr28/NqzqOqV/slzuLydXSu2mBczcuWRSb7jtV9cddB7E52rtPMxc519KMxs1c5FzXWWCLc21VvbfrIDbRaay/UBjdBvirpQ9nYs8f2x/axcJoQgtwh9H9qvp/HcQ0ia/TjDgfUFULfY720feB0Z/vD0b2Czh4ySOazHhyePPAkkOAn1bVZ7sOYhN8L8mBVXXx6MG2QMf3OoppUkP+uzU+4rnH6H6PRzzVIZOrpTfftLoA81ZT65HBzp1uraJZ8zBqZr/vU4sAft51AJuqql7edQybY/TiIMnt2mP/3V1EExtPaMf3++7RVTXn+p4k/6uq/mOpA1qsqnpo1zFsptFkcHx/CMkhwFVdB7CJjgc+kOQ9rP9bdT+aJQRDqaA52L9bwOFj+0Mc/dQSSzmct6SSvHuh81X15KWKZVMk2aeq1nYdx0q1sdKvfb7AbBcGX1FV7xo7/nRgj6o6oZvIFi/Jn9IUsvi19tB/A6+vqkFWIhuaJA+gKQL0uar6UZJ9gROAB1fVrt1Gt7C20tgzgH3aQ2uBU6rqR91FtXIkuQdwIrAX8C/A86rq6m6jWpwkd2Hu984Pu4tq87X/T55fVU/rOpZJJdkVOKqqTuw6li6t2manOujO/c7xz//e275aVauX8t905GqJ9T15WoQvJRnNyENz5zJAVVWvR9/aC5y/BO5OM83odVV1Q7dRTeRjrP95zyiaNUs7AVt3EdQiHQzMtU7jNJr/F71OrpK8iKZK4G9X1bfbY3sCb06yQ1W9qtMAFyHJoTTJ4b3aQ2tpksOPdxfV4rTJ+WE0xRSOT3IB8FSaQkC9nqqc5IHA+4H30BTVgWb04ctJnlBVvS7pnGRvmsTk7gwsMRnx1zQ/+88Bj6Kp2DhvcaY+aZOoWZUBkzyw7+8daKpNAm+kqZB8LnAK8DbgQAY0EpTkzsCRwNE038s53UakvjK56kCSe9Os3xi9C/XGqvqX7qJatE8Bv05TuOLMPo+UzON9NGtl3kpzofYW4EldBjSJqrrP6H6S3WmmjfwO8JoOQprEtjXHUHlVrZtZ89ZzfwjsN1odra0a+Fjga0Cvk6skTwOeTpPgrmkPrwZel+RuVXVqZ8EtzpCrdr0J+P2q+ueRY+clOQd4F81FZp+dzkATkxGDrNiYZGvgsTQjtp+oqrVJDqO5SXgb4L5dxrdIg602mWR7mvf644F70Fz77FFVd+s0MPWaydUSS3I4zR2c17L+js1q4MnZ/qoAABN9SURBVENJnldVH+4suEWoqt9Psormw+a0JNsBH6BJtPpeDAKasrYzzYIvGMof2HHtneQXsv7O37Oq6hfdRrVRNyXZu6r+bfRg+70MoUBBzXUhUFU3JRlCtcnnAA8a+z29qB3N+gLQ9+RqyFW7bj+WWAFQVZe2F299N8jEZMxQKzb+NbAr8GXgrUm+R3PNcEJVDaU575CrTf6I5mf/IuALVVVJHt1xTP1RwLoh/PlbWiZXS+8VwO+OXRR8PclFwIfbR69V1U+Adyd5L3AUzejPdmy44Lm3xqoFbj263/cEsR31fCHNqOcbgKdU1a+6jWrRXgJ8om3COFNpbzXNNLVndxbV4l2d5GFV9anRg0kOpqkG13eZ6/1dVdcOY+Bw0FW7kuSOVXX92MEdgK06imkSQ01MRg21KMdqmj6M69qbmT8A9qqqazuOaxJDrjb5AprrnLcDZyT5QMfxaAAsaLHEkqytqn3mOXdZVd1rrnN9kuQgmjnHD6a54/2Bqvp8t1EtTpKrgHXM06erqnpdLTDJr2imQ30MmJVU9b3X0siU2Hu3h9YCJw5hSmySfWhufnyBDZPDBwKH973QS5KLgWOr6mtjx/cDTquq+3cT2eIkechC5/tcZjvJscDTgOexYcW31wOnjxd56Zskn2H+Rt9VVX1NTAYvySVVdcB8+0OQ5NMLnB7E+yfJXjTVGY8G9gZeCpxTVf/aaWAdW3XrneqgHY/sOowFnf+Dty95QQuTqyWW5GvAI8fXKiXZDfhIVe3bTWSLk+Q7wPXAmcBFwC9Hz/f8DtTgJfmjhc4PuJfIILR3jh/P+vWSlwF/1/d1AwBJHgT8HfBuNkwO/wg4pqq+0FVsm2MoVbvadTJ/QfPeKZr3zok1oEbOQ5bkL6rqDe32kVX19yPnXlNVf9lddPNLciNwxcwuTbXDK9rtdVW1X1exrQRJTgHeP1o4pL1JeDTwuKq6e2fB9cCqW+9UB93pMV2HsaDzf/gOk6vlLsnv00zneg0bXuCcABzf9znUY3cwZ1Wt6/sdqCRHVNXZcxzfhubn/8oOwtokQ+u1lOQjzH/3u+/TuhaU5B+r6oFdx7Exc5R0voympPMPuotqcnNV7aqq53Ub1cYl2bGqruk6jknN05vxFlX1oaWKZVONjvgMaTSovfE66zDNOqwXVNXvLXFIm2SorQiS/DnNtMC7AmfRJFqXdhtVf5hczc01V0usqs5NciVwHPBn7eHLgMeOT9fpqROq6ktdB7EZjk3yFOCZI+W0DwVOAs7vNLJFGu+1lGQovZbe2HUAW9CC/cf6IMl7qupJzFHSeQiGXLUrySNpKu79oi1+8tiq+mLHYU3igzQV3mYuKsdbQfQ+uWLDmMenhfd20WFVfWdmu12n9HiaGwtXArNuFPbRkFsRVNWbadpt7EaTZL07yW1ovp8zxgs0SWBy1Yk2iXpi13FsorcDvbzDtxhV9fAkRwP/kOT9NGt/dqKZVtT7u1FD7rW00JqYdpFwb9fMLMIQpgD0esrxIgy5ateraRodX57kQJrZCwuuIeuZP6C5sNyXZt3hGVV1xcJf0js1z/Zc+72RptHu0e3jGprqvKmqh3Ya2GSG3opgJsl9PfD6Nsk9nWbdVZ97S6ojJldLbKza1SwDmBrV2zt8EziLZmrCc4D/Ag4e0KLUQfdaWsADug5gYxaYGhWafjN9d9uxil0bGMB6ySFX7fplVV0OUFUXD6T8+i3a6ernJvk14HDgTUnuBLywz4VExuyX5Aba39d2m3Z/u+7C2qjLgc8Dh80ktEme021IExt6KwKS3Ao4lOYz6GHAZ4CXdRhSf7i8aBaTq6X3AJpqb2cAFzO8ZGWPhRLEvieH7aL+U4Av0sxZfwjwkfZC7dVVdXOX8S3C0HstDdkjFzj30SWLYtPtQnMHec5KmfS3FDUAVXUycHI7UnsUcC6wc5Lj6X/Vrp2SPHe+/aoaRBsL4GfAT4AbgN3od1Kygaoa6gjDzKjhp5OcT1NMamjXDYNtRZDkd2lGDX+PZuT8TJqqq//TaWDqNZOrpffrwMwv6+NpSmqf0fcyziN+zPrmx0N0MvC0qvpyu39ukgtphve/Btyzs8gWZ7C9lpLMN500wK2XMpZNUVVP7jqGzXRF3wvOLEY7HfY1wGvaql2PBz4O9Llq12nA9gvs91r7+XIUcH/gk8Cbq2pNt1FNJsn/Bnasqk+MHT8U+FFVfXXur+zWHKOGz6ZJzt9Bc1Phwk4DXJyTgAuTzNWK4KTOolqcF9CsrzpuPDmU5mO1wA4l2ZYmyToReHlVva3jkDYqyT9X1X27jmNTJdmqquYc4Ulyr6q6bKljmsSQey21vU5GK0yOfvgMYg3BSJ+u0YpXbxxIn65B/+6qO+2o+NdpPneKsTVKfe+vB5DkIuDJowUi2uO7Ae8e0o2HNI3vj6QpBf6wruNZDFsRLE+rbn3nOugOR3QdxoLOv+ZdVgtcCdqk6hE0idXuwFuAc7qMaQLXJ/n1mdLNSZ4IHAF8B3hZVV3XaXQb9zyaxeSzep0AxwC97HUyo6rWjtytn7nA/xzw9AH0Wjoe+G5VfR9u6dl1BHAVA5i7nuRwmoqHr2X96O1q4ENJnldVH+4suMU5fq6DA+oTdSVjCfnIflXVXksf1eIkectC5weQnAx91BZg+/HECppCBUl27CKgTdWOoJzaPgahqj6a5EtDbEUgTcrkaokleR9NhbqP04xWfaPjkCZ1B+DnAEl+C3gdTUn5/Wk+6Pvd8KCZ2vKGdvsFwGhydQg9T66S3B24S1WdPnb8gUl+UFX/3lFoi/FO4HfglvfOaxnWe+cVwO9W1VUjx77e3hH/cPvordHpQ3P1ieoqrgmM33ncCngszQ2TWYvle+ZPgG/QFNP5HgNbM1MjzcmH1l9vxB0XOHfbJYtiBVoGrQikiZhcLb1jgP8B/hx4VnLL39jQ3H29fVeBLdJWI6NTjwNObZvynp2k96XMGWivkxEn0ySF425ozy1UdKFrWw/8vXOrscQKgKq6Kknv14wNuU8UQFVdC83UXpqqmc+n6bv0iL5P56VpQHokzfv+lzTltD9YVf/VaVQTGHB/vRmfTPJq4EXVrodI8wf45cBFnUa2/A29FYHmUzDPSosVrddVWpajqtqqqrZvH7cfeWw/gMQK4FZtSVJoypGO/lEaQrI+yF4nI+4y1/qe9tjuSx/ORLYe+Hvnl0lmNQtu12z8soN4JvUj4I9pyvXvWVXH0Y5CD0GSWyd5Os1ajQfT9M05ZgCJFVV1bVW9s11X+GSaGQCXJfnDjkNblLa/3iNp+uvdqaruBDwUOLQ9NwTHAXsCVyQ5O8nZwL/R3Gg4rtPIlr8NWhEwoGIu0qYYwgWN+uUM4LNJrgFuoum/MTNd7SddBrZIQ+11MuMOC5zre6+lob93Xkpz9/s1bFhM5ATmWc/UM0PuEwVwJU0SezLwH8C+SW5pjFxVH+oqsMVqK2YeTVMx9hOsfx/13eD767Wls49uS/nfUpBmphm7tqjl0opAWhSTK02kql6d5FM001wunJleQTMK+mfdRbY4A+51MmNNkqdV1WmjB5M8lZ5fqC2D9865bVGF41gf72U06we+1l1kizPwPlEA/9D+d9/2ARtWnuxtcpXkFTRFjL5J0yfnBVU1hNHOGYPvr5fkiKo6u02mvj1yfBvg+Kp6ZXfRLXuDbkWgjVg3hEk/S8tS7NKAJLkLTfGBn7Ph6Mk2wKNnqjhqyxrwov4NjFSefGxV9blPFEmOY3Yp/x8DX6iqKzsLbBHaBORK4Mb20Hgp831nfVGPtDdFXjNHf72H0axhGkIbhQuAXwHPmHm/tD2uTgLOr6pndxmfNESrbnXnesDtf7/rMBZ0wfV/ZSl2SfOrqh8CByV5KE3VSYCPVdUGC7KT3NGGh9OX5P/STAMc6qL+DVTVN5K8mGYEru9uN8ex3YAXJnlZVZ251AFN4DeAuwDfHTu+KzCEGyLPAj6cZM7+ep1FNYGqeniSo2mm9r6f5vNzJ5o2BEMoqDNYy6AVgTQRkytpgKrq08CnF3jKp4ADliicFaFduH8QzaL+b7fH9gTenGSHqur1upMktweeAewCnEczze6ZwHNpGsT+bXfRbVxVvXyu40l2AD5JM92ur06imQo43sD29u25Plf5BLgZeBJN8YfR/nqnAX3vrzfqLJr4nwP8F3DwAKbDLgeDbkWgjXAG3CwmV9Ly5B+v6Rv6ov6/Aa4H/gl4Kk1Pt9BMJx3snfuqui4jPS16at4qn0l2X/pwJnYyTXI43l/vPvS/BQQASR4EnAJ8kWbE8CHAR9rCLq+uqpu7jG+ZG3wrAmkSlmKXlidvJU3fvIv6gSEs6t+zqp5UVe+iqVh3L+DhQ06sANopsn2fAjvkKp8w7BYQM04GnlZVf1pV11fVucB9gW1pbo5oCxl6KwJpUo5cSdLiXJ3kYXMs6j8Y+H5HMU3iFzMbVfWrJP85V7LYV0n+hdk3DXagmWb0xKWPaCKDrfLZGnpyCHD/Gut2WlU3AscneW9HMa0oA25FoPlUwboh3FtcWiZX0vLU92lSQzT0Rf0zPd5gwz5voRmV63sT88PG9gu4tu1f1HfPBs5J8gTmqPLZWVSLN/TkEOB5wBsAkhxZVX8/cu4Ymmmy2gKWQSsCaSKWYpeWgSR3oCkx/Op2f4equq7jsJadJNvRlC6fWdR/GfB3QxoBUnfGqnyuHa/y2VfLoQVEkkuq6oDx7bn2NV0LtCKYubHT61YEmt+qrXesB9zuUV2HsaALbni3pdglzS/JrsCLgZ1pmsCeAbyCptjCGTPPM7HaMtok6pZF/Ul2pKmk1ntJDp65mE+yx2hvqCR/UFW9bcK7XCyiymcvLbYFRM9lnu259jVde3QdgLSUTK6kYXkf8FngbOAQYA1wKbDvEO4eD1mS3wReB1wHvJKm+t6OwFZJnlhV53cZ3yK8kfXl+c9mw1L9LwJMrrSgoSaHrZpne659TdF4CwItM86Am8XkShqWHarqZe32BUmOBJ4wvlBbW8TbaNZlrAIuAg6tqi8luSfNqGHfkyvv3Gsl229kjeFtxtYfbtddWMtfkp8ydwI7lPWe0kRMrqSBSXJH1l8MXwusmunz43TALepWVXUhNAu0q+pLAFV1ef/bLAHeudcKVlVbdx3DSlVV23cdg7SUTK6kYVkFXDJ2bGa/gD2XNpwVZXR08Kaxc0NITvZMch5NYj6zTbvvmghJ0sTKUuyzmFxJA1JVu3cdwwo29GlFo+Xi3zh2bnxfkiRtApMraUCS7ESz7ufuwNeB11XVDQt/laZh6NOKquqzSfanee+srapvdh2TJEnLzVZdByBpIu8D/gd4K7A98JZuw9FQJHkJcBZwBPCxJE/rOCRJ0qBVUy2wz48OOHIlDctdq+qF7fYFScbXX0nzeRywf1XdmORONNUNT+s4JkmSlhWTK2lgxqoFbj26b7VALeDmqroRoKquTeLMBUmSpszkShqWVcBX2bAvkdUCtRijFQIB9hqpHlhV9aiO4pIkDVEB64ZQLHdpmVxJA2K1QG2GmWqBtwH2Bi4ErmB2WXlJkrSJnBYiDUiSI+Y5vk2SFy91PBqULwKPAP4WeDLwx8DfAIe15yRJ0mYyuZKG5dgkH09yy/S/JIfSlGW/U3dhaQDeANwR2KOqDqiqA4C9aKaanthpZJKkYap1/X50wGmB0oBU1cOTHA38Q5L3A/cGdgKOqqpLu41OPXcYcI+q9bVpq+qGJH8KXA48u7PIJElaJkyupOE5C9gHeA7wX8DBVfWv3YakAajRxGrk4K+SuCJZkqQpcFqgNCBJHkRTHfBOwK7AM4GPJHlFkm07DU59d1mSJ44fTHIMzciVJEnaTI5cScNyMvC0qvpyu39ukguBlwJfA+7ZWWTqu2cAH0ryxzTl/AFW01QPfHRnUUmSBqmAshT7LCZX0rDcv2rDFZptY9jjk7y3o5g0AFV1NXBgkoNpppUCfLyqPtVhWJIkLStOC5SG5XkzG0mOHDt3zBLHogGqqouq6q3tw8RKkqQpMrmShuWoke0XjJ07ZCkDkSRJK1hV96XWe1iK3eRKGpbMsz3XviRJkpaQyZU0LDXP9lz7kiRJWkIWtJCGZb8kN9CMUt2m3abd3667sCRJ0kpjtcDZTK6kAamqrbuOQZIkSXNzWqAkSZIkTYHJlSRJkqTJdV0NcDOrBSY5JMm3klyR5IQ5zm+b5APt+YuT7L6x1zS5kiRJkrSiJNkaOAU4FLgXcHSSe4097SnA9VV1d+Ak4PUbe12TK0mSJEkrzf2BK6rq21X1c+BM4PCx5xwOvLfd/iDwsCQLtr6xoIUkSZKkifyU6y/4ZH1wx67j2IjtkqwZ2T+1qk5tt3cBvjty7j+BA8e+/pbnVNUvk/wEuBNwzXz/oMmVJEmSpIlU1SFdx9BHTguUJEmStNJcDew6sn+39ticz0lyK2AVcO1CL2pyJUmSJGml+Qqwd5I9kmwDHAWcN/ac84A/arcfA1xUVQt2TnZaoCRJkqQVpV1D9UzgAmBr4PSqWpvkFcCaqjoP+Gvgb5JcAVxHk4AtKBtJviRJkiRJi+C0QEmSJEmaApMrSZIkSZoCkytJkiRJmgKTK0mSJEmaApMrSZIkSZoCkytJkiRJmgKTK0mSJEmagv8PDOd+mRu8ooQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,15))\n", + "n = np.sum(confusion_mat, axis=0)\n", + "plt.imshow(confusion_mat / n)\n", + "plt.xticks(range(14), classes, rotation=90)\n", + "plt.yticks(range(14), classes)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MOD 0.8 10 0.0\n", + "STMT 0.973 2337 0.066\n", + "EXPR 0.997 23121 0.652\n", + "EXPR_CONT 0.75 8 0.0\n", + "SLICE 0.997 1120 0.032\n", + "BOOLOP 1.0 169 0.005\n", + "OPERATOR 1.0 920 0.026\n", + "UNARY 1.0 171 0.005\n", + "CMPOP 0.991 550 0.016\n", + "COMPR 0.989 1615 0.046\n", + "EXCEPT 0.714 14 0.0\n", + "ARG 1.0 138 0.004\n", + "IMPORT 0.912 57 0.002\n", + "VAR 0.984 5220 0.147\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n = np.sum(confusion_mat, axis=0)\n", + "normed = confusion_mat / n\n", + "for i in range(14):\n", + " plt.bar(classes[i], np.around(normed[i][i],3), color='b', alpha=0.5)\n", + " plt.bar(classes[i], np.around(freqs[str(i)],3), color='orange')\n", + " print(classes[i], np.around(normed[i][i],3), class_freqs[str(i)], np.around(freqs[str(i)],3))\n", + "plt.xticks(range(14), [str(class_freqs[str(i)])+\" - \"+c + \" (\" + str(np.around(normed[i][i],3)) + \")\" for i,c in enumerate(classes)], rotation=90);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.00022566995768688293,\n", + " 0.06417489421720733,\n", + " 0.6502115655853314,\n", + " 0.0001692524682651622,\n", + " 0.03150916784203103,\n", + " 0.004767277856135402,\n", + " 0.025952045133991537,\n", + " 0.004823695345557123,\n", + " 0.015373765867418902,\n", + " 0.045077574047954865,\n", + " 0.0002820874471086037,\n", + " 0.0038928067700987304,\n", + " 0.001466854724964739,\n", + " 0.14496473906911142]" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[freqs[str(i)]*normed[i][i] for i in range(14)]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "preds = []; labels = []\n", + "for i in range(len(results_df)):\n", + " snippet = [results_df[str(_)][i] for _ in range(64)]\n", + " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", + " masked_tk = snippet[msk_idx]\n", + " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", + " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", + " t0 = get_token_class_id(prediction)\n", + " t1 = get_token_class_id(label)\n", + " preds.append(prediction)\n", + " labels.append(label)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9444005641748943" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_score(labels, preds, average='micro')" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5440963611536793" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_score(labels, preds, average='macro')" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9405293562210962" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_score(labels, preds, average='weighted')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/Inspect Predictions - Var Naming.ipynb b/notebook/Inspect Predictions - Var Naming.ipynb index d0c965f..f75519a 100644 --- a/notebook/Inspect Predictions - Var Naming.ipynb +++ b/notebook/Inspect Predictions - Var Naming.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -86,7 +86,6 @@ " 8\n", " 9\n", " ...\n", - " 1145\n", " 1146\n", " 1147\n", " 1148\n", @@ -96,319 +95,308 @@ " 1152\n", " 1153\n", " 1154\n", + " 1155\n", " \n", " \n", " \n", " \n", " 0\n", - " 4.151907e-08\n", - " 5.628823e-08\n", - " 9.999172e-01\n", - " 7.325633e-08\n", - " 9.239561e-08\n", - " 4.873208e-08\n", - " 1.642496e-07\n", - " 1.452978e-07\n", - " 1.074618e-07\n", - " 6.745368e-08\n", + " 0.000019\n", + " 8.099375e-06\n", + " 0.992974\n", + " 4.954076e-06\n", + " 6.187318e-06\n", + " 0.000003\n", + " 0.000003\n", + " 0.000020\n", + " 0.000007\n", + " 4.155489e-06\n", " ...\n", - " 8.291661e-08\n", - " 1.296739e-07\n", - " 4.569179e-08\n", - " 4.191060e-08\n", - " 8.301711e-08\n", - " 1.137941e-07\n", - " 2.012223e-07\n", - " 8.063271e-08\n", - " 5.108569e-08\n", - " 9.891638e-08\n", + " 0.000005\n", + " 0.000008\n", + " 0.000005\n", + " 0.000006\n", + " 5.796720e-06\n", + " 0.000004\n", + " 5.334173e-06\n", + " 0.000002\n", + " 1.165616e-05\n", + " 4.242901e-06\n", " \n", " \n", " 1\n", - " 7.158369e-05\n", - " 2.448824e-06\n", - " 2.393499e-05\n", - " 4.848149e-06\n", - " 3.204651e-06\n", - " 3.384589e-06\n", - " 6.336205e-06\n", - " 5.442391e-06\n", - " 1.003000e-05\n", - 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"

10 rows × 1155 columns

\n", + "

10 rows × 1156 columns

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" 5 6 7 8 9 \\\n", - "0 4.873208e-08 1.642496e-07 1.452978e-07 1.074618e-07 6.745368e-08 \n", - "1 3.384589e-06 6.336205e-06 5.442391e-06 1.003000e-05 5.537219e-06 \n", - "2 9.561894e-08 1.337540e-07 1.967287e-07 1.087677e-07 1.455602e-07 \n", - "3 8.282342e-06 1.344740e-05 2.215210e-05 3.453083e-05 6.336294e-06 \n", - "4 4.044596e-06 5.064339e-06 7.379301e-06 1.020063e-05 2.979773e-06 \n", - "5 7.400549e-07 1.066420e-06 1.270991e-06 1.887615e-06 5.901628e-07 \n", - "6 3.144652e-07 4.064599e-07 4.340328e-07 7.060059e-07 2.666402e-07 \n", - "7 5.254100e-08 7.292620e-08 1.063418e-07 7.733028e-08 7.707759e-08 \n", - "8 1.954238e-07 2.184539e-07 9.659458e-08 1.855055e-07 1.107843e-07 \n", - "9 4.345751e-07 4.449176e-07 2.669912e-07 1.743257e-07 1.609540e-07 \n", + " 6 7 8 9 ... 1146 1147 \\\n", + "0 0.000003 0.000020 0.000007 4.155489e-06 ... 0.000005 0.000008 \n", + "1 0.000778 0.000117 0.000053 7.151416e-05 ... 0.000073 0.000032 \n", + "2 0.000029 0.000060 0.000021 5.586033e-05 ... 0.000013 0.000009 \n", + "3 0.000002 0.000002 0.000001 8.076341e-07 ... 0.000002 0.000002 \n", + "4 0.001189 0.000361 0.000147 6.393947e-04 ... 0.000382 0.000244 \n", + "5 0.000088 0.000133 0.000013 2.924057e-05 ... 0.000024 0.000029 \n", + "6 0.000088 0.000133 0.000013 2.924057e-05 ... 0.000024 0.000029 \n", + "7 0.000088 0.000133 0.000013 2.924057e-05 ... 0.000024 0.000029 \n", + "8 0.000002 0.000002 0.000001 7.745513e-07 ... 0.000002 0.000002 \n", + "9 0.000824 0.000630 0.000105 2.750462e-04 ... 0.000306 0.000344 \n", "\n", - " ... 1145 1146 1147 1148 \\\n", - "0 ... 8.291661e-08 1.296739e-07 4.569179e-08 4.191060e-08 \n", - "1 ... 9.639001e-06 6.007896e-06 7.697829e-06 7.630359e-06 \n", - "2 ... 3.787783e-07 4.365191e-07 1.805067e-07 2.630779e-07 \n", - "3 ... 2.347830e-04 1.565076e-04 2.208807e-04 3.370915e-04 \n", - "4 ... 4.932042e-05 5.367909e-05 8.097535e-05 1.174679e-04 \n", - "5 ... 1.099861e-05 1.067407e-05 1.452648e-05 2.872214e-05 \n", - "6 ... 2.792366e-06 2.791264e-06 3.289019e-06 7.347874e-06 \n", - "7 ... 2.236410e-07 2.754898e-07 1.090885e-07 1.523410e-07 \n", - "8 ... 3.071913e-07 1.419670e-07 1.589839e-07 1.939485e-07 \n", - "9 ... 8.198891e-08 1.758069e-07 4.082163e-07 1.506922e-07 \n", + " 1148 1149 1150 1151 1152 1153 \\\n", + "0 0.000005 0.000006 5.796720e-06 0.000004 5.334173e-06 0.000002 \n", + "1 0.000085 0.000048 3.426078e-05 0.000144 1.159608e-04 0.000032 \n", + "2 0.000011 0.000013 3.160681e-05 0.000021 1.222850e-05 0.000009 \n", + "3 0.000003 0.000001 1.046853e-06 0.000004 9.981476e-07 0.000002 \n", + "4 0.000298 0.000313 3.891494e-04 0.000766 6.712895e-04 0.000225 \n", + "5 0.000040 0.000027 4.187433e-05 0.000097 5.295289e-05 0.000039 \n", + "6 0.000040 0.000027 4.187433e-05 0.000097 5.295289e-05 0.000039 \n", + "7 0.000040 0.000027 4.187433e-05 0.000097 5.295289e-05 0.000039 \n", + "8 0.000003 0.000002 9.622887e-07 0.000003 9.946224e-07 0.000001 \n", + "9 0.000374 0.000232 2.463795e-04 0.000259 4.406933e-04 0.000323 \n", "\n", - " 1149 1150 1151 1152 1153 \\\n", - "0 8.301711e-08 1.137941e-07 2.012223e-07 8.063271e-08 5.108569e-08 \n", - "1 1.561647e-05 1.636067e-05 1.220249e-05 5.898523e-06 6.381560e-06 \n", - "2 3.730678e-07 3.902348e-07 2.172894e-07 7.039162e-07 4.030064e-07 \n", - "3 9.896093e-05 3.423343e-04 1.780007e-04 2.048518e-04 1.265149e-04 \n", - "4 3.820025e-05 9.707937e-05 5.995100e-05 7.805443e-05 4.389494e-05 \n", - "5 7.801125e-06 2.119086e-05 1.109383e-05 1.449823e-05 8.827585e-06 \n", - "6 2.994278e-06 7.040510e-06 3.313725e-06 3.428768e-06 2.653228e-06 \n", - "7 2.271158e-07 2.007640e-07 1.144374e-07 4.364957e-07 2.360500e-07 \n", - "8 1.798385e-07 1.201070e-07 2.359045e-07 2.056562e-07 2.111698e-07 \n", - "9 2.406298e-07 2.539705e-07 1.909478e-07 4.005177e-07 2.267066e-07 \n", + " 1154 1155 \n", + "0 1.165616e-05 4.242901e-06 \n", + "1 4.544693e-05 3.708797e-05 \n", + "2 1.069300e-05 1.085431e-05 \n", + "3 1.038728e-06 8.094075e-07 \n", + "4 5.968320e-04 2.373134e-04 \n", + "5 5.497635e-05 6.087103e-05 \n", + "6 5.497635e-05 6.087103e-05 \n", + "7 5.497635e-05 6.087103e-05 \n", + "8 9.516547e-07 8.291184e-07 \n", + "9 2.994540e-04 2.892509e-04 \n", "\n", - " 1154 \n", - "0 9.891638e-08 \n", - "1 5.234640e-06 \n", - "2 8.200368e-07 \n", - "3 2.657415e-04 \n", - "4 1.641166e-04 \n", - "5 1.889048e-05 \n", - "6 3.494964e-06 \n", - "7 6.529726e-07 \n", - "8 1.819394e-07 \n", - "9 9.915669e-07 \n", - "\n", - "[10 rows x 1155 columns]" + "[10 rows x 1156 columns]" ] }, - "execution_count": 28, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -420,16 +408,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(39936, 1155)" + "(39744, 1156)" ] }, - "execution_count": 29, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -494,39 +482,39 @@ " \n", " \n", " 6\n", - " for\n", + " if\n", " \n", " \n", " 7\n", - " name\n", + " unaryop\n", " \n", " \n", " 8\n", - " [mask]\n", + " not\n", " \n", " \n", " 9\n", - " attribute\n", + " call\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 0\n", - "0 [PAD]\n", - "1 [UNK]\n", - "2 [CLS]\n", - "3 [SEP]\n", - "4 [MASK]\n", - "5 [cls]\n", - "6 for\n", - "7 name\n", - "8 [mask]\n", - "9 attribute" + " 0\n", + "0 [PAD]\n", + "1 [UNK]\n", + "2 [CLS]\n", + "3 [SEP]\n", + "4 [MASK]\n", + "5 [cls]\n", + "6 if\n", + "7 unaryop\n", + "8 not\n", + "9 call" ] }, - "execution_count": 30, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -538,452 +526,7 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 [CLS] for name [PAD] [PAD] [PAD] [PAD] name if compare name in name expr call attribute append name subscript name index name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n", - "Label = x [PAD] [PAD] [PAD]\n", - "\n", - "2 if call attribute input [PAD] [PAD] [PAD] name name assign name call layer subscript attribute keras history name index num if call name name name return call name keyword binop list name add name keyword attribute name name raise call name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = is keras tensor [PAD]\n", - "\n", - "3 if call name name name return call name keyword binop list name add name keyword attribute name [PAD] [PAD] [PAD] name raise call name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = name [PAD] [PAD] [PAD]\n", - "\n", - "4 if call name name attribute format [PAD] [PAD] [PAD] name assign name binop call name name return call name name if call name name if call name name name expr call attribute warn name call attribute format str keyword attribute name attribute class name return name raise call name str name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = string types [PAD] [PAD]\n", - "\n", - "5 if call name name name expr call attribute append [PAD] [PAD] [PAD] name call attribute format str keyword attribute name attribute class name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = warn [PAD] [PAD] [PAD]\n", - "\n", - "6 call attribute cast [PAD] [PAD] [PAD] name call attribute equal name call attribute argmax name name keyword unaryop usub num call attribute argmax name name keyword unaryop num call attribute floatx name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = cast [PAD] [PAD] [PAD]\n", - "\n", - "7 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] arg y pred arg k num return call attribute mean name call attribute in top k name name call attribute argmax name name keyword unaryop usub num name keyword unaryop num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = y true [PAD] [PAD]\n", - "\n", - "8 call attribute mean [PAD] [PAD] [PAD] name call attribute in top k name name call attribute argmax name name keyword unaryop usub num name keyword unaryop num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = mean [PAD] [PAD] [PAD]\n", - "\n", - "9 return call attribute mean [PAD] [PAD] [PAD] name call attribute in top k name name call attribute cast name call attribute flatten name name str name keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = mean [PAD] [PAD] [PAD]\n", - "\n", - "10 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] arg params for name callback attribute callbacks name expr call attribute set params name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "11 boolop and compare attribute recurrent [PAD] [PAD] [PAD] name gt num boolop compare name binop num mult attribute delta t batch name compare name num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = delta t batch [PAD]\n", - "\n", - "12 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] arg logs nameconstant expr str assign name logs boolop or name dict for name callback attribute callbacks name expr call attribute on train end name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "13 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] assign attribute validation data name nameconstant assign attribute model name nameconstant [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "14 if name assign attribute input [PAD] [PAD] [PAD] name call name name assign attribute stateful metrics name call name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = stateful metrics [PAD] [PAD]\n", - "\n", - "15 binop str mod tuple binop name add num attribute shape [PAD] [PAD] [PAD] name attribute best name name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = monitor [PAD] [PAD] [PAD]\n", - "\n", - "16 tuple binop name add num attribute shape [PAD] [PAD] [PAD] name attribute best name name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = monitor [PAD] [PAD] [PAD]\n", - "\n", - "17 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] arg logs nameconstant if boolop and compare attribute stopped epoch name gt num compare attribute verbose name num expr call name binop str mod binop attribute stopped epoch name add num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "18 call attribute append [PAD] [PAD] [PAD] name binop attribute root name add attribute path name keyword name keyword attribute headers name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = post [PAD] [PAD] [PAD]\n", - "\n", - "19 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] arg schedule arg verbose num expr call attribute init call name name name assign attribute schedule name name assign attribute verbose name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "20 assign name [PAD] [PAD] [PAD] [PAD] call name call attribute get value name attribute lr attribute optimizer attribute model name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = lr [PAD] [PAD] [PAD]\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "21 assign name [PAD] output [PAD] [PAD] call attribute reshape name name list num subscript name index num num num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = w img [PAD] [PAD]\n", - "\n", - "22 if unaryop not call name attribute [PAD] [PAD] [PAD] [PAD] name name assign name embeddings metadata attribute embeddings metadata name assign name embeddings shape augassign name attribute embeddings metadata name comprehension name layer name call attribute keys name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = embeddings metadata [PAD] [PAD]\n", - "\n", - "23 expr call attribute [PAD] [PAD] [PAD] [PAD] name dict attribute batch attribute name attribute step name name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = update [PAD] [PAD] [PAD]\n", - "\n", - "24 call attribute [PAD] [PAD] [PAD] [PAD] attribute arg name attribute sess name call attribute join attribute path name attribute log dir name str name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = save [PAD] [PAD] [PAD]\n", - "\n", - "25 if compare name gte attribute [PAD] [PAD] [PAD] [PAD] name expr call attribute write logs name name attribute lt seen name assign attribute lt seen at listcomp write name attribute lt seen name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = update freq [PAD] [PAD]\n", - "\n", - "26 boolop and call name name attribute append [PAD] [PAD] [PAD] name compare attribute ndim name eq num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = ndarray [PAD] [PAD] [PAD]\n", - "\n", - "27 if attribute [PAD] [PAD] data [PAD] attribute model name assign name logs call name listcomp tuple name ifexp compare name in name subscript name index name str comprehension name k attribute keys name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = stop training [PAD] [PAD]\n", - "\n", - "28 if attribute [PAD] [PAD] [PAD] [PAD] name augassign name regularization add call attribute sum name binop attribute l1 name mult call attribute abs name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = l1 [PAD] [PAD] [PAD]\n", - "\n", - "29 assign name x [PAD] [PAD] [PAD] call attribute sqrt name call attribute sum name call attribute square name name keyword attribute axis name keyword nameconstant [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = norms [PAD] [PAD] [PAD]\n", - "\n", - "30 call attribute cast [PAD] [PAD] [PAD] name call attribute greater equal name name num call attribute floatx name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = cast [PAD] [PAD] [PAD]\n", - "\n", - "31 binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute clip name name attribute min value name attribute max value name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = rate [PAD] [PAD] [PAD]\n", - "\n", - "32 functiondef arguments arg [PAD] [PAD] [PAD] [PAD] if compare name is nameconstant return nameconstant if call name name name return call name name if call name name attribute string types name assign name config dict str str call name name dict return call name name if call name name return name raise call name binop str add call name name [PAD] [PAD] [PAD] [CLS]\n", - "Label = identifier [PAD] [PAD] [PAD]\n", - "\n", - "33 return dict str str str attribute [PAD] [PAD] [PAD] [PAD] name attribute maxval name attribute seed name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = minval [PAD] [PAD] [PAD]\n", - "\n", - "34 return dict str str str str attribute [PAD] [PAD] [PAD] [PAD] name attribute mode name attribute attribute name attribute seed name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = scale [PAD] [PAD] [PAD]\n", - "\n", - "35 return binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute concatenate name binop list call attribute identity name subscript name index num binop subscript name index num floordiv subscript name index num keyword num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = gain [PAD] [PAD] [PAD]\n", - "\n", - "36 binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute concatenate name binop list call attribute identity name subscript name index num binop subscript name index num floordiv subscript name index num keyword num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = gain [PAD] [PAD] [PAD]\n", - "\n", - "37 return binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute concatenate name binop list call attribute identity name subscript name index num binop subscript name index num floordiv subscript name index num keyword num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = gain [PAD] [PAD] [PAD]\n", - "\n", - "38 if call name name name return call name name if call name name attribute [PAD] [PAD] [PAD] [PAD] name assign name config dict str str call name name dict return call name name if call name name return name raise call name binop str add call name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = string types [PAD] [PAD]\n", - "\n", - "39 call attribute [PAD] [PAD] [PAD] [PAD] name binop binop name sub name div call attribute clip name call attribute abs name name call attribute epsilon name nameconstant [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = abs [PAD] [PAD] [PAD]\n", - "\n", - "40 binop num mult call attribute mean [PAD] [PAD] [PAD] name name keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = mean [PAD] [PAD] [PAD]\n", - "\n", - "41 assign name [PAD] [PAD] [PAD] [PAD] call attribute log name binop call attribute clip name name call attribute epsilon name nameconstant add num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = second log [PAD] [PAD]\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "42 call attribute [PAD] [PAD] [PAD] [PAD] name call attribute maximum name binop num sub binop name mult name num keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = mean [PAD] [PAD] [PAD]\n", - "\n", - "43 call attribute [PAD] [PAD] [PAD] [PAD] name binop binop num sub name mult name keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = max [PAD] [PAD] [PAD]\n", - "\n", - "44 return call attribute [PAD] [PAD] [PAD] [PAD] name num binop binop name sub name add num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = maximum [PAD] [PAD] [PAD]\n", - "\n", - "45 call attribute mean [PAD] [PAD] [PAD] name call attribute binary crossentropy name name name keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = mean [PAD] [PAD] [PAD]\n", - "\n", - "46 call attribute mean [PAD] [PAD] [PAD] name binop name mult call attribute log name binop name div name keyword unaryop usub num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = sum [PAD] [PAD] [PAD]\n", - "\n", - "47 call attribute append [PAD] [PAD] [PAD] name call name listcomp call attribute sum name call attribute square name name comprehension name g name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = sqrt [PAD] [PAD] [PAD]\n", - "\n", - "48 listcomp call attribute append [PAD] [PAD] [PAD] name call attribute square name name comprehension name g name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = sum [PAD] [PAD] [PAD]\n", - "\n", - "49 arguments arg [PAD] [PAD] [PAD] [PAD] arg lr arg momentum arg decay arg nesterov arg kwargs num num num nameconstant [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "50 with withitem call attribute add [PAD] [PAD] [PAD] name attribute name attribute class name assign attribute iterations name call attribute variable name num keyword str keyword str assign attribute lr name call attribute variable name name keyword str assign attribute momentum name call attribute variable name name keyword str assign attribute decay name call attribute variable name name keyword str [PAD] [PAD] [PAD] [CLS]\n", - "Label = name scope [PAD] [PAD]\n", - "\n", - "51 binop num div binop num add binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n", - "52 binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n", - "53 assign name output [PAD] [PAD] [PAD] listcomp call attribute zeros name call attribute int shape name name keyword call attribute dtype name name comprehension name p name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = accumulators [PAD] [PAD] [PAD]\n", - "\n", - "54 binop attribute decay [PAD] [PAD] [PAD] name mult call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n", - "55 binop binop attribute [PAD] [PAD] [PAD] [PAD] name mult name add binop binop num sub attribute rho name call attribute square name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = rho [PAD] [PAD] [PAD]\n", - "\n", - "56 binop name mult binop num div binop num add binop attribute decay [PAD] [PAD] [PAD] name call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n", - "57 dict str str str call name call attribute [PAD] [PAD] [PAD] [PAD] name attribute lr name call name call attribute get value name attribute decay name attribute epsilon name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = get value [PAD] [PAD]\n", - "\n", - "58 binop num div binop num add binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n", - "59 binop binop attribute [PAD] [PAD] [PAD] [PAD] name mult name add binop binop num sub attribute rho name call attribute square name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = rho [PAD] [PAD] [PAD]\n", - "\n", - "60 binop binop name mult call attribute [PAD] [PAD] [PAD] [PAD] name binop name add attribute epsilon name div call attribute sqrt name binop name attribute epsilon name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = sqrt [PAD] [PAD] [PAD]\n", - "\n", - "61 assign name x [PAD] [PAD] [PAD] dict str str str str call name call attribute get value name attribute lr name attribute rho name call name call attribute get value name attribute decay name attribute epsilon name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = config [PAD] [PAD] [PAD]\n", - "\n", - "62 return call name binop call name call attribute items [PAD] [PAD] [PAD] name add call name call attribute items name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = items [PAD] [PAD] [PAD]\n", - "\n", - "63 call name binop call name call attribute items [PAD] [PAD] [PAD] name add call name call attribute items name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = items [PAD] [PAD] [PAD]\n", - "\n", - "64 binop num div binop num add binop attribute [PAD] [PAD] [PAD] [PAD] name mult call attribute cast name attribute iterations name call attribute dtype name attribute decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = decay [PAD] [PAD] [PAD]\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "65 call attribute output [PAD] [PAD] [PAD] name call attribute int shape name name keyword call attribute dtype name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = zeros [PAD] [PAD] [PAD]\n", - "\n", - "66 assign name [PAD] [PAD] [PAD] [PAD] listcomp call attribute zeros name call attribute int shape name name keyword call attribute dtype name name comprehension name p name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = vhats [PAD] [PAD] [PAD]\n", - "\n", - "67 dict str str str str str str call name call attribute append [PAD] [PAD] [PAD] name attribute lr name call name call attribute get value name attribute beta 1 name call name call attribute get value name attribute beta 2 name call name call attribute get value name attribute decay name attribute epsilon name attribute name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = get value [PAD] [PAD]\n", - "\n", - "68 assign name [PAD] [PAD] [PAD] [PAD] binop binop attribute beta 1 name mult name add binop binop num sub attribute beta 1 name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = m t [PAD] [PAD]\n", - "\n", - "69 assign attribute add [PAD] [PAD] [PAD] name call attribute variable name num keyword str keyword str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = iterations [PAD] [PAD] [PAD]\n", - "\n", - "70 assign name [PAD] [PAD] [PAD] [PAD] binop binop attribute beta 1 name mult name add binop binop num sub attribute beta 1 name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = m t [PAD] [PAD]\n", - "\n", - "71 dict str str str str str call name call attribute append [PAD] [PAD] [PAD] name attribute lr name call name call attribute get value name attribute beta 1 name call name call attribute get value name attribute beta 2 name attribute epsilon name attribute schedule decay name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = get value [PAD] [PAD]\n", - "\n", - "72 if compare call attribute append [PAD] [PAD] [PAD] subscript name index str in name assign subscript name index str call attribute lower subscript name index str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = lower [PAD] [PAD] [PAD]\n", - "\n", - "73 arguments arg [PAD] [PAD] [PAD] [PAD] arg return sequences arg return state arg go backwards arg stateful arg unroll arg bias arg kwargs nameconstant nameconstant nameconstant nameconstant nameconstant num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = self [PAD] [PAD] [PAD]\n", - "\n", - "74 tuple subscript name index num subscript name index num attribute shape [PAD] [PAD] [PAD] name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = units [PAD] [PAD] [PAD]\n", - "\n", - "75 assign name output [PAD] [PAD] [PAD] listcomp tuple subscript name index num attribute units name comprehension name attribute states name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = state shape [PAD] [PAD]\n", - "\n", - "76 binop binop binop binop str add call name call name attribute name [PAD] [PAD] [PAD] name str call name call name name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = states [PAD] [PAD] [PAD]\n", - "\n", - "77 binop binop binop str add call name call name attribute [PAD] [PAD] name [PAD] name str call name call name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = states [PAD] [PAD] [PAD]\n", - "\n", - "78 expr call attribute append [PAD] [PAD] [PAD] name tuple subscript attribute states name index name subscript name index name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = append [PAD] [PAD] [PAD]\n", - "\n", - "79 if attribute [PAD] [PAD] [PAD] [PAD] name assign name states call name name keyword nameconstant return binop list name add name return name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = return state [PAD] [PAD]\n", - "\n", - "80 call name binop binop binop binop binop binop binop str add call name name str attribute shape [PAD] [PAD] [PAD] name str call name tuple name attribute units name str call name attribute shape name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = name [PAD] [PAD] [PAD]\n", - "\n", - "81 assign name shape [PAD] [PAD] [PAD] tuple subscript name index num subscript name index num name name attribute filters name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = output shape [PAD] [PAD]\n", - "\n", - "82 assign name [PAD] [PAD] [PAD] [PAD] tuple subscript name index num name name attribute filters name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = output shape [PAD] [PAD]\n", - "\n", - "83 assign name [PAD] [PAD] [PAD] [PAD] dict str str str str str str str str str attribute filters name attribute kernel size name attribute strides name attribute padding name attribute data format name attribute dilation rate name attribute return sequences name attribute go backwards name attribute stateful name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = config [PAD] [PAD] [PAD]\n", - "\n", - "84 dict str str str str str str str str str attribute format [PAD] [PAD] [PAD] name attribute kernel size name attribute strides name attribute padding name attribute data format name attribute dilation rate name attribute return sequences name attribute go backwards name attribute stateful name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = filters [PAD] [PAD] [PAD]\n", - "\n", - "85 if compare name is nameconstant assign name output [PAD] [PAD] [PAD] nameconstant assign name inputs list args nameconstant [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = check positional args [PAD]\n", - "\n", - "86 assign name legacy support support [PAD] call name keyword list str keyword list tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = legacy dense support [PAD]\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "87 functiondef arguments arg [PAD] x [PAD] [PAD] arg kwargs assign name converted list if compare str in name expr call attribute pop name str expr call attribute warn name str keyword num return tuple name name name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = args [PAD] [PAD] [PAD]\n", - "\n", - "88 assign name legacy support support [PAD] call name keyword list str keyword list tuple str str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = legacy prelu support [PAD]\n", - "\n", - "89 assign name legacy support support [PAD] call name keyword list str str keyword list tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str tuple str str keyword name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = legacy conv1d support [PAD]\n", - "\n", - "90 assign name x [PAD] [PAD] [PAD] tuple call attribute pop name str call attribute pop name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = kernel size [PAD] [PAD]\n", - "\n", - "91 call name listcomp call name name name comprehension name [PAD] [PAD] [PAD] [PAD] subscript name slice num num [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = x [PAD] [PAD] [PAD]\n", - "\n", - "92 assign name x [PAD] [PAD] [PAD] tuple call attribute pop name str call attribute pop name str call attribute pop name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - 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"Label = start [PAD] [PAD] [PAD]\n", - "\n", - "99 assign name shape [PAD] [PAD] [PAD] listcomp name comprehension name x name compare call name name gt name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = bad attributes [PAD] [PAD]\n", - "\n", - "100 listcomp name comprehension name x [PAD] [PAD] [PAD] name compare call name name gt name [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [CLS]\n", - "Label = x [PAD] [PAD] [PAD]\n", - "\n" - ] - } - ], - "source": [ - "n=3; i=100\n", - "preds = []\n", - "for idx, row in results_df.iterrows():\n", - " top_n = list(np.argsort(-row)[:n])\n", - " preds.append(top_n[:n])\n", - " if (idx % 64 == 0) and (idx > 0):\n", - " preds = np.asarray(preds)\n", - " k,idj = np.where(preds == 0)\n", - " last_idx=64\n", - " if len(k) > 4:\n", - " last_idx=k[4]\n", - " else:\n", - " last_idx=k[-1]\n", - " print(idx // 64, ' '.join([vocab_label_df.loc[p][0] for p in preds[:64,0]]))\n", - " print(\"Label = \", ' '.join(list(label_df.loc[idx//64 -1][:4])))\n", - " preds = []\n", - " print()\n", - " if idx > i*64:\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 2, 6, 55, 25, 25, 25, 397, 55, 25, 654, 40,\n", - " 459, 25, 241, 25, 654, 1140, 25, 0, 0, 0, 25,\n", - " 11, 55, 25, 425, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preds = np.asarray(preds)\n", - "preds[64:128,0]" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['x', '[PAD]', '[PAD]', '[PAD]']" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(label_df.loc[idx//64][:4])" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CLS] if call name name name return call name keyword binop list name add name keyword attribute name [PAD] [PAD] [PAD] name raise call name str [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n" - ] - } - ], - "source": [ - "s = 1\n", - "print(' '.join([vocab_label_df.loc[p][0] for p in preds[s*64:(s+1)*64,0]]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1025,15 +568,15 @@ " \n", " \n", " 1\n", - " is\n", - " keras\n", - " tensor\n", + " mean\n", + " [PAD]\n", + " [PAD]\n", " [PAD]\n", " NaN\n", " \n", " \n", " 2\n", - " name\n", + " cast\n", " [PAD]\n", " [PAD]\n", " [PAD]\n", @@ -1041,15 +584,15 @@ " \n", " \n", " 3\n", - " string\n", - " types\n", + " mean\n", + " [PAD]\n", " [PAD]\n", " [PAD]\n", " NaN\n", " \n", " \n", " 4\n", - " warn\n", + " append\n", " [PAD]\n", " [PAD]\n", " [PAD]\n", @@ -1057,23 +600,23 @@ " \n", " \n", " 5\n", - " cast\n", - " [PAD]\n", - " [PAD]\n", + " delta\n", + " t\n", + " batch\n", " [PAD]\n", " NaN\n", " \n", " \n", " 6\n", - " y\n", - " true\n", + " self\n", + " [PAD]\n", " [PAD]\n", " [PAD]\n", " NaN\n", " \n", " \n", " 7\n", - " mean\n", + " k\n", " [PAD]\n", " [PAD]\n", " [PAD]\n", @@ -1081,7 +624,7 @@ " \n", " \n", " 8\n", - " mean\n", + " progbar\n", " [PAD]\n", " [PAD]\n", " [PAD]\n", @@ -1100,20 +643,20 @@ "" ], "text/plain": [ - " 0 1 2 3 4\n", - "0 x [PAD] [PAD] [PAD] NaN\n", - "1 is keras tensor [PAD] NaN\n", - "2 name [PAD] [PAD] [PAD] NaN\n", - "3 string types [PAD] [PAD] NaN\n", - "4 warn [PAD] [PAD] [PAD] NaN\n", - "5 cast [PAD] [PAD] [PAD] NaN\n", - "6 y true [PAD] [PAD] NaN\n", - "7 mean [PAD] [PAD] [PAD] NaN\n", - "8 mean [PAD] [PAD] [PAD] NaN\n", - "9 self [PAD] [PAD] [PAD] NaN" + " 0 1 2 3 4\n", + "0 x [PAD] [PAD] [PAD] NaN\n", + "1 mean [PAD] [PAD] [PAD] NaN\n", + "2 cast [PAD] [PAD] [PAD] NaN\n", + "3 mean [PAD] [PAD] [PAD] NaN\n", + "4 append [PAD] [PAD] [PAD] NaN\n", + "5 delta t batch [PAD] NaN\n", + "6 self [PAD] [PAD] [PAD] NaN\n", + "7 k [PAD] [PAD] [PAD] NaN\n", + "8 progbar [PAD] [PAD] [PAD] NaN\n", + "9 self [PAD] [PAD] [PAD] NaN" ] }, - "execution_count": 34, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1125,1063 +668,16802 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "labels= []; labels_str =[]\n", - "for idx, row in label_df.iterrows():\n", - " labels.append(vocab_label_df.index[vocab_label_df[0]==row[0]][0])\n", - " labels_str.append(row[0])" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 182, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['selu',\n", - " 'on_epoch_begin',\n", - " '__init__',\n", - " '__init__',\n", - " '__call__',\n", - " '__init__',\n", - " '__call__',\n", - " 'get',\n", - " 'hinge',\n", - " 'conv1d_args_preprocessor',\n", - " '__iter__',\n", - " '__init__',\n", - " '_get_executor_init',\n", - " '_run',\n", - " '__init__',\n", - " 'VGG19',\n", - " 'DenseNet201',\n", - " 'build',\n", - " '_merge_function',\n", - " 'average',\n", - " 'losses',\n", - " 'trainable',\n", - " 'losses',\n", - " 'constraints',\n", - " '__init__',\n", - " 'call',\n", - " 'compute_mask',\n", - " 'from_config',\n", - " '_generate_dropout_mask',\n", - " 'call',\n", - " 'get_config',\n", - " 'set_floatx',\n", - " 'eval',\n", - " 'eye',\n", - " 'var',\n", - " 'argmax',\n", - " 'binary_crossentropy',\n", - " 'update_add',\n", - " '_reshape_batch',\n", - " 'forward',\n", - " 'forward',\n", - " 'get_uid',\n", - " '_get_available_gpus',\n", - " 'gather',\n", - " 'argmax',\n", - " 'tile',\n", - " 'function',\n", - " 'dropout',\n", - " 'random_normal',\n", - " 'random_uniform',\n", - " 'foldr',\n", - " 'ones',\n", - " 'cumprod',\n", - " 'mean',\n", - " 'stack',\n", - " 'elu',\n", - " '_preprocess_conv2d_image_shape',\n", - " 'int_or_none',\n", - " 'predict_generator',\n", - " 'iter_sequence_infinite']" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 9, + "metadata": {}, + "outputs": [], "source": [ - "labels_str" + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 77, "metadata": {}, - "outputs": [ + "outputs": [], + "source": [ + "n=7; i=621\n", + "preds_all = []; preds = []\n", + "for idx, row in results_df.iterrows():\n", + " top_n = list(np.argsort(-row)[:n])\n", + " preds.append(top_n[:n])\n", + " #pred = np.asarray(top_n[:n])\n", + " if (idx % 64 == 63) and (idx > 0):\n", + " preds = np.asarray(preds)\n", + " last_idx=64\n", + " #print(idx // 64, ' '.join([vocab_label_df.loc[p][0] for p in preds[:64,0]]))\n", + " #print(\"Label = \", ' '.join(list(label_df.loc[idx//64][:4])))\n", + " preds_all.append(preds)\n", + " preds = []\n", + " #print()\n", + " if idx > i*64:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(621, 64, 7)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.asarray(preds_all).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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'[PAD]'],\n", + " ['key', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['tile', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['tile', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['where', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['negative', 'part', '[PAD]', '[PAD]'],\n", + " ['cast', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['where', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['padding', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['left', 'pad', '[PAD]', '[PAD]'],\n", + " ['left', 'pad', '[PAD]', '[PAD]'],\n", + " ['convolution', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['convolution', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['output', 'shape', '[PAD]', '[PAD]'],\n", + " ['force', 'transpose', '[PAD]', '[PAD]'],\n", + " ['output', 'shape', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['strides', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['strides', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['random', 'uniform', '[PAD]', '[PAD]'],\n", + " ['current', 'input', '[PAD]', '[PAD]'],\n", + " ['to', 'int32', '[PAD]', '[PAD]'],\n", + " ['expand', 'dims', '[PAD]', '[PAD]'],\n", + " ['decoded', 'dense', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['ndim', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['normal', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['a', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['dtype', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['keras', 'shape', '[PAD]', '[PAD]'],\n", + " ['a', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['max', 'value', '[PAD]', '[PAD]'],\n", + " ['gamma', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['beta', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['ndim', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['ndim', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['axis', '1', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['output', 'shape', '[PAD]', '[PAD]'],\n", + " ['n', 'size', '[PAD]', '[PAD]'],\n", + " ['ndim', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['axis', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['result', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['keras', 'shape', '[PAD]', '[PAD]'],\n", + " ['keras', 'shape', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['set', 'value', '[PAD]', '[PAD]'],\n", + " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['axes', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['unbroadcast', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['unbroadcast', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['module', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['negative', 'part', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['format', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['format', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['norm', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['image', 'shape', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['conv', 'out', '[PAD]', '[PAD]'],\n", + " ['left', 'pad', '[PAD]', '[PAD]'],\n", + " ['spatial', 'start', 'dim', '[PAD]'],\n", + " ['conv', 'out', '[PAD]', '[PAD]'],\n", + " ['pool', '2d', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['w', 'pad', '[PAD]', '[PAD]'],\n", + " ['h', 'pad', '[PAD]', '[PAD]'],\n", + " ['expected', 'height', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['maximum', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['set', 'subtensor', '[PAD]', '[PAD]'],\n", + " ['f', 'active', '[PAD]', '[PAD]'],\n", + " ['idxs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['initializer', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['slice', 'col', '[PAD]', '[PAD]'],\n", + " ['path', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['pad', 'sequences', '[PAD]', '[PAD]'],\n", + " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['inputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['stateful', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['layer', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['class', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['layer', 'data', '[PAD]', '[PAD]'],\n", + " ['attrs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['load', 'weights', 'from', 'hdf5'],\n", + " ['sort', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['count', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['count', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['do', 'validation', '[PAD]', '[PAD]'],\n", + " ['count', 'mode', '[PAD]', '[PAD]'],\n", + " ['val', 'outs', '[PAD]', '[PAD]'],\n", + " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['val', 'outs', '[PAD]', '[PAD]'],\n", + " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['progbar', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['is', 'sparse', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['stateful', 'metric', 'indices', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['layers', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['inputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['config', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['encode', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['unique', 'name', '[PAD]', '[PAD]'],\n", + " ['layer', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['close', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['model', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['state', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['bad', 'attributes', '[PAD]', '[PAD]'],\n", + " ['encode', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['attrs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['kernel', 'size', '[PAD]', '[PAD]'],\n", + " ['kernel', 'size', '[PAD]', '[PAD]'],\n", + " ['transpose', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['transpose', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['concatenate', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['source', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['target', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['model', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['batch', 'size', '[PAD]', '[PAD]'],\n", + " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['steps', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['model', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['concatenate', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['dtype', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['variable', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['input', 'spec', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['m', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['inbound', 'nodes', '[PAD]', '[PAD]'],\n", + " ['inputs', 'hash', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['output', 'names', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['metric', 'fn', '[PAD]', '[PAD]'],\n", + " ['metric', 'fn', '[PAD]', '[PAD]'],\n", + " ['suffix', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', 'scope', '[PAD]', '[PAD]'],\n", + " ['function', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['expects', 'training', 'arg', '[PAD]'],\n", + " ['ndarray', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['ndarray', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['all', 'inputs', '[PAD]', '[PAD]'],\n", + " ['compile', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['image', 'data', 'format', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['split', 'at', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['uses', 'dynamic', 'learning', 'phase'],\n", + " ['evaluate', 'generator', '[PAD]', '[PAD]'],\n", + " ['[PAD]', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['[PAD]', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['nested', 'metrics', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", + " ['is', 'tensor', '[PAD]', '[PAD]']]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels_str" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'[PAD]'" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab_label_df.loc[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(621, 1)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snippet = pd.read_csv(path+'sparse_split_magret_tk_val.txt', header=None)\n", + "snippet.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Name Tuple Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "1\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Name Call Attribute round Name Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "2\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute argmax Name Name keyword UnaryOp Num Call Attribute floatx Name\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items cast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "3\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute in top k Name Name Call Attribute cast Name Call Attribute flatten Name Name Str Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "4\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute delta ts batch end Name BinOp Call Attribute time Name Sub Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "5\n", + "[CLS] If BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num BoolOp Compare Name BinOp Num Mult Attribute delta t batch Name Compare Name Num Expr Call Attribute warn Name BinOp Str Mod Name\n", + "Label = ['delta', 't', 'batch', '[PAD]']\n", + "Pred =\n", + "data delta\n", + "[PAD] t\n", + "[PAD] batch\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "6\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Attribute validation data Name NameConstant Assign Attribute model Name NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "7\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name v Call Attribute items Name If Compare Name In Attribute stateful metrics Name Assign Subscript Attribute totals Name Index Name Name If Compare Name Attribute totals Name AugAssign Subscript Attribute totals Name Index Name Add BinOp Name Mult Name Assign Subscript Attribute totals Name Index Name BinOp Name Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "8\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Attribute target Name keyword Attribute verbose Name keyword Attribute stateful metrics Name\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output progbar\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "9\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg batch arg logs NameConstant If Compare Attribute seen Name Lt Attribute target Name Assign Attribute log values Name List\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "10\n", + "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute log values Name Tuple Name Subscript Name Index Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "11\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute filepath Name keyword BinOp Name Add Num keyword Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "12\n", + "[CLS] BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name Name Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "13\n", + "[CLS] Call Name BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "14\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Attribute verbose Name Gt Num Expr Call Name Str Expr Call Attribute set weights Attribute model Name Attribute best weights Name\n", + "Label = ['restore', 'best', 'weights', '[PAD]']\n", + "Pred =\n", + "name restore\n", + "[PAD] best\n", + "[PAD] weights\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "15\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Attribute root Name Add Attribute path Name Dict Attribute field Name Call Attribute dumps Name Name keyword Attribute headers Name\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append post\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "16\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute root Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "17\n", + "[CLS] If UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute model Name Str Raise Call Name Str\n", + "Label = ['optimizer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call optimizer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "18\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg epoch arg logs NameConstant Assign Name logs BoolOp Or Name Dict Assign Subscript Name Index Str Call Attribute get value Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "19\n", + "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['get', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append get\n", + "[PAD] value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "20\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name List Num Subscript Name Index Num Subscript Name Index Num Num\n", + "Label = ['w', 'img', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output w\n", + "[PAD] img\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "21\n", + "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Attribute name Name comprehension Name layer Attribute layers Attribute model Name Compare Attribute name Call Name Name Eq Str\n", + "Label = ['embeddings', 'layer', 'names', '[PAD]']\n", + "Pred =\n", + "output embeddings\n", + "[PAD] layer\n", + "[PAD] names\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "22\n", + "[CLS] ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name layer Attribute layers Attribute model Name Compare Attribute name Call Name Name Eq Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "23\n", + "[CLS] ListComp Subscript Name Slice Name BinOp Name Add Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "24\n", + "[CLS] BinOp Str Mod Tuple Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute join Str Call Name Call Attribute keys Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "25\n", + "[CLS] Tuple Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute join Str Call Name Call Attribute keys Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "26\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Attribute get value Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['old', 'lr', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output old\n", + "[PAD] lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "27\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute open Name Attribute filename Name BinOp Name Add Attribute file flags Name keyword Attribute open args Name\n", + "Label = ['csv', 'file', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias csv\n", + "[PAD] file\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "28\n", + "[CLS] BoolOp And Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute ndim Name Eq Num\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "29\n", + "[CLS] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name [MASK] [MASK] [MASK] [MASK] Attribute keys Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "30\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name fieldnames ListComp Call Name Name comprehension Name x Name\n", + "Label = ['PY2', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data PY2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "31\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Mult BinOp Name Div BinOp Call Attribute epsilon Name Add Name\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x w\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "32\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg min value arg max value arg rate arg axis Num Num Num Num Assign Attribute min value Name Name Assign Attribute max value Name Name Assign Attribute rate Name Name Assign Attribute axis Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "33\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "34\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Name Is NameConstant Return NameConstant If Call Name Name Name Return Call Name Name If Call Name Name Attribute string types Name Assign Name config Dict Str Str Call Name Name Dict Return Call Name Name If Call Name Name Return Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['identifier', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self identifier\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "35\n", + "[CLS] ClassDef Name Expr Str FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape arg dtype NameConstant Return Call Attribute constant Name Num keyword Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "36\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute minval Name Attribute maxval Name keyword Name keyword Attribute seed Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call random\n", + "[PAD] uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "37\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Num Name keyword Name keyword Attribute seed Name\n", + "Label = ['truncated', 'normal', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape truncated\n", + "[PAD] normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "38\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name UnaryOp USub Name Name keyword Name keyword Attribute seed Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call random\n", + "[PAD] uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "39\n", + "[CLS] Return BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute identity Name Subscript Name Index Num\n", + "Label = ['gain', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape gain\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "40\n", + "[CLS] BinOp List Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Mult BinOp Subscript Name Index Num FloorDiv Subscript Name Index Num\n", + "Label = ['identity', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "append identity\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "41\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num Assign Name fan in BinOp Subscript Name Index UnaryOp Num Mult Name Assign Name fan out BinOp Subscript Name Index UnaryOp Num Name Raise Call Name BinOp Str Add Name\n", + "Label = ['receptive', 'field', 'size', '[PAD]']\n", + "Pred =\n", + "x receptive\n", + "[PAD] field\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "42\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Return Call Attribute mean Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self y\n", + "[PAD] true\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "43\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name Name Call Attribute epsilon Name NameConstant\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append clip\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "44\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Call Attribute clip Name Name Call Attribute epsilon Name NameConstant Add Num\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append log\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "45\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Name keyword UnaryOp USub Num\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "46\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp Num Sub Name Mult Name keyword UnaryOp USub Num\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append max\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "47\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num BinOp BinOp Name Sub Name Add Num\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append maximum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "48\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute binary crossentropy Name Name Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "49\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name y true Call Attribute l2 normalize Name Name keyword UnaryOp USub Num Assign Name y pred Call Attribute l2 normalize Name Name keyword UnaryOp Num Return UnaryOp Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp Num\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self y\n", + "[PAD] true\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "50\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "51\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "52\n", + "[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape clipnorm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "53\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute rho Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "[PAD] scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "54\n", + "[CLS] BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "55\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "[PAD] p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "56\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "57\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "[PAD] scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "58\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Assign Name lr BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['initial', 'decay', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data initial\n", + "[PAD] decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "59\n", + "[CLS] BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "60\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "[PAD] scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "61\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "[PAD] p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "62\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute rho Name Mult Name Add BinOp BinOp Num Sub Attribute rho Name Call Attribute square Name Name\n", + "Label = ['new', 'd', 'a', '[PAD]']\n", + "Pred =\n", + "output new\n", + "[PAD] d\n", + "[PAD] a\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "63\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "64\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Call Attribute sqrt Name Name Add Attribute epsilon Name\n", + "Label = ['p', 't', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output p\n", + "[PAD] t\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "65\n", + "[CLS] BinOp BinOp Name Mult Name Div BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Add Attribute epsilon Name\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "66\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "[PAD] p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "67\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "68\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Assign Name lr BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['initial', 'decay', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data initial\n", + "[PAD] decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "69\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "70\n", + "[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 1 Name Name\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape pow\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "71\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 1 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", + "Label = ['m', 't', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output m\n", + "[PAD] t\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "72\n", + "[CLS] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name epsilon\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "73\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Call Name Call Attribute get value Name Attribute lr Name Call Name Call Attribute get value Name Attribute beta 1 Name Call Name Call Attribute get value Name Attribute beta 2 Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "74\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute beta 1 Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp Name Attribute schedule decay Name\n", + "Label = ['momentum', 'cache', 't', '[PAD]']\n", + "Pred =\n", + "output momentum\n", + "[PAD] cache\n", + "[PAD] t\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "75\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp List Attribute iterations Name Add Name Name\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape weights\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "76\n", + "[CLS] BinOp BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", + "Label = ['beta', '1', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name beta\n", + "[PAD] 1\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "77\n", + "[CLS] BinOp BinOp Num Sub Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute square Name Name\n", + "Label = ['beta', '2', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name beta\n", + "[PAD] 2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "78\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Str Str Name Name Name Name Name Name Name Name\n", + "Label = ['all', 'classes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output all\n", + "[PAD] classes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "79\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name Tuple Attribute nb feature Name Attribute output dim Name keyword Str keyword Str keyword Attribute b regularizer Name keyword Attribute b constraint Name\n", + "Label = ['b', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape b\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "80\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Assert BoolOp And Name Compare Call Name Name Eq Num Return Tuple Subscript Name Index Num Attribute output dim Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "81\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg init arg activation arg weights arg W regularizer arg b regularizer arg activity regularizer arg W constraint arg b constraint arg bias arg input dim arg kwargs Str NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "82\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg return sequences arg return state arg go backwards arg stateful arg unroll arg implementation arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant Num\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "83\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name outputs Name states Call Attribute rnn Name Attribute step Name Name Name keyword Attribute go backwards Name keyword Name keyword Name keyword Attribute unroll Name keyword Name\n", + "Label = ['last', 'output', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x last\n", + "[PAD] output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "84\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Expr Call Attribute append Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "85\n", + "[CLS] If Compare Num Lt BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Attribute recurrent dropout Name Assign Attribute uses learning phase Name NameConstant Assign Attribute uses learning phase Name NameConstant\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "86\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "87\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg filters arg kernel size arg strides arg padding arg data format arg dilation rate arg return sequences arg go backwards arg stateful arg kwargs Tuple Num Num Str NameConstant Tuple Num Num NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "88\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "89\n", + "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name If Compare Name Subscript Name Index Name Assign Subscript Name Index Name Subscript Subscript Name Index Name Index Name\n", + "Label = ['old', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output old\n", + "[PAD] value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "90\n", + "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name str val Str Assign Name str val Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "91\n", + "[CLS] If Compare Call Name Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Name Slice Num Add Str\n", + "Label = ['str', 'val', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x str\n", + "[PAD] val\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "92\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Str Add Name Str Str Name keyword Num\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "93\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str keyword List Tuple Str Str Tuple Str Str Tuple Str Str keyword Name\n", + "Label = ['legacy', 'embedding', 'support', '[PAD]']\n", + "Pred =\n", + "x legacy\n", + "[PAD] embedding\n", + "[PAD] support\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "94\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant\n", + "Label = ['legacy', 'pooling3d', 'support', '[PAD]']\n", + "Pred =\n", + "output legacy\n", + "[PAD] pooling3d\n", + "[PAD] support\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "95\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output kernel\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "96\n", + "[CLS] If BoolOp And Compare Str In Name Compare Str Name Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Attribute pop Name Str Call Attribute pop Name Str Assign Subscript Name Index Str Name Expr Call Attribute append Name Tuple Str Str\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output kernel\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "97\n", + "[CLS] If Call Name Subscript Name Index Num Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num Expr Call Attribute append Name Tuple Str NameConstant\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "98\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List Subscript Name Index Num Subscript Name Index Num Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "99\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword NameConstant keyword List Tuple Str Str\n", + "Label = ['legacy', 'input', 'support', '[PAD]']\n", + "Pred =\n", + "output legacy\n", + "[PAD] input\n", + "[PAD] support\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "100\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Name Index Num Add Subscript Name Slice Num\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "101\n", + "[CLS] Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute replace Call Attribute lower Name Str Str Str Slice UnaryOp USub Num\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "102\n", + "[CLS] BinOp List Str Add ListComp BinOp Str Mod Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "103\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Mod Tuple Name Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "104\n", + "[CLS] If Name With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name model Call Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "105\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute inputs Name With withitem Call Attribute device Name Attribute device Name Assign Name input shape Subscript Call Attribute int shape Name Name Slice Num Assign Name slice i Call Call Name Name keyword Name keyword Dict Str Str Name Name Name Expr Call Attribute append Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "106\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute output names Name If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name n BinOp Str Mod Subscript Name Index Name Expr Call Attribute append Name Name\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x n\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "107\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Name Str BinOp Str Mod Subscript Name Index Num Assign Name merged List For Tuple Name name Name outputs Call Name Name Name Expr Call Attribute append Name Call Name Name keyword Num keyword Name Return Call Name Attribute inputs Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "108\n", + "[CLS] If Compare Name Is NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Attribute data Name Index Num Assign Attribute end Name Name\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape end\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "109\n", + "[CLS] If Compare BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute end Name Assign Name idx BinOp Name Attribute start Name Raise Name\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name start\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "110\n", + "[CLS] If Compare BinOp Call Name Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute end Name Assign Name idx ListComp BinOp Name Attribute start Name comprehension Name x Name Raise Name\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name start\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "111\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg attr FunctionDef arguments arg f FunctionDef arguments arg args arg kwargs Assign Name out Call Name Starred Name keyword Name If Call Name Attribute data Name Call Name Name Return Call Name Name Return Name Return Name Return Call Name Call Name Attribute data Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "112\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Name BinOp Name Mult Name comprehension Name p Name\n", + "Label = ['positions', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output positions\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "113\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Subscript Name Index Name Sub Call Name Name\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name line\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "114\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Assign Name output shape Attribute output shape Name ExceptHandler Name Assign Name output shape Str Assign Name name Attribute name Name Assign Name cls name Attribute name Attribute class Name Assign Name fields List BinOp BinOp BinOp Name Add Str Name Str Name Call Attribute count params Name Expr Call Name Name Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "115\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp BinOp Name Add Str Call Name Name Str Call Name Name Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "116\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name Assign Name fields List Str Str Str Subscript Name Index Name Expr Call Name Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "117\n", + "[CLS] While NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute read Name Name AugAssign Name count Add Num If Compare Name IsNot NameConstant Expr Call Name Name Name Name If Name Expr Yield Name Break\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output chunk\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "118\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Attribute path Name Call Attribute expanduser Attribute path Name Str Str\n", + "Label = ['cache', 'dir', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output cache\n", + "[PAD] dir\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "119\n", + "[CLS] If BoolOp And Compare Name IsNot NameConstant Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Assign Name hash algorithm Str\n", + "Label = ['file', 'hash', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output file\n", + "[PAD] hash\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "120\n", + "[CLS] If Compare Name Is NameConstant Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute Value Name Str Num ExceptHandler Name Assign Name SEQUENCE COUNTER Num\n", + "Label = ['SEQUENCE', 'COUNTER', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output SEQUENCE\n", + "[PAD] COUNTER\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "121\n", + "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute Value Name Str Num ExceptHandler Name Assign Name SEQUENCE COUNTER Num\n", + "Label = ['SEQUENCE', 'COUNTER', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output SEQUENCE\n", + "[PAD] COUNTER\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "122\n", + "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal Name\n", + "Label = ['stop', 'signal', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data stop\n", + "[PAD] signal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "123\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Call Attribute is set Attribute stop signal Name Return Expr Call Attribute put Attribute queue Name Call Attribute apply async Name Name Tuple Attribute uid Name Name keyword NameConstant\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "124\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute queue Name Call Attribute apply async Name Name Tuple Attribute uid Name keyword NameConstant\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call put\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "125\n", + "[CLS] Call Name Call Name Lambda arguments arg [MASK] [MASK] [MASK] [MASK] Call Attribute wait Name Name\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self f\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "126\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute get Name comprehension Name future Name Call Attribute successful Name\n", + "Label = ['last', 'ones', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output last\n", + "[PAD] ones\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "127\n", + "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Name Str Call Name Name Str Call Name Name\n", + "Label = ['value', 'tuple', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x value\n", + "[PAD] tuple\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "128\n", + "[CLS] If UnaryOp Not Compare Num LtE Attribute [MASK] [MASK] [MASK] [MASK] Name Num Raise Call Name Str Attribute shape Name\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "129\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Name Sub BinOp BinOp Name Add Name Num\n", + "Label = ['dim', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dim\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "130\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Subscript Name Index Name keyword Num keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "131\n", + "[CLS] If Call Name Name Name If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute build Name\n", + "Label = ['built', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape built\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "132\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute name Name Add Str Call Name Name\n", + "Label = ['node', 'key', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name node\n", + "[PAD] key\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "133\n", + "[CLS] If Call Name Name Str Return Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Call Attribute get config Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "134\n", + "[CLS] If Call Name Name Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "135\n", + "[CLS] BoolOp And Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Attribute isatty Attribute stdout Name\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append stdout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "136\n", + "[CLS] Assign Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name List BinOp Name Mult BinOp Name Sub Attribute seen so far Name BinOp Name Attribute seen so far Name\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape values\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "137\n", + "[CLS] If BoolOp And Compare BinOp Name Sub Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute interval Name Compare Attribute target Name IsNot NameConstant Compare Name Attribute target Name Return\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name last\n", + "[PAD] update\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "138\n", + "[CLS] If Compare Name GtE Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mod Name If Compare Name Num AugAssign Name info BinOp Str BinOp Name Mult Num AugAssign Name info BinOp Str BinOp Name Num\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x info\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "139\n", + "[CLS] If Compare Name GtE Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mod BinOp Name Mult Num AugAssign Name info BinOp Str BinOp Name Num\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x info\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "140\n", + "[CLS] Call Name Num Subscript Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Index Num\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape values\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "141\n", + "[CLS] ListComp IfExp Compare Name Is NameConstant NameConstant Subscript Name Index Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "142\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x test Call Attribute reshape Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num Call Name Name Num Num\n", + "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append open\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "143\n", + "[CLS] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name comprehension Name x Name\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x w\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "144\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name Assign Name xs ListComp ListComp Name comprehension Name w Name Compare Name Name Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "145\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "146\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name labels test Tuple Subscript Name Index Str Subscript Name Index Str\n", + "Label = ['x', 'test', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] test\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "147\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name If Name Assign Name xs ListComp ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "148\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "149\n", + "[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name labels Call Name Name Name Name If UnaryOp Not Name Raise Call Name BinOp BinOp Str Add Call Name Name Str\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "150\n", + "[CLS] Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Name Call Attribute array Name Subscript Name Slice Name\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append array\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "151\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name Tuple Call Name Name Num\n", + "Label = ['y', 'test', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "[PAD] test\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "152\n", + "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name x train Call Attribute transpose Name Num Num Num Num Assign Name x test Call Attribute transpose Name Num Num Num Num\n", + "Label = ['image', 'data', 'format', '[PAD]']\n", + "Pred =\n", + "shape image\n", + "[PAD] data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "153\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Call Name BinOp Call Name Name Mult BinOp Num Sub Name\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append array\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "154\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute call Name keyword Call Attribute filter sk params Name Attribute call Name\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "155\n", + "[CLS] BoolOp And Compare Name Eq Str Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "156\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute preprocess input Name Starred Name keyword Name Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "157\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute MobileNet Name Starred Name keyword Name Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "158\n", + "[CLS] If Compare NameConstant In List Name Name Return NameConstant If Compare Call Name Name Lt Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] [MASK] Name Name Name If UnaryOp Not Name Return Name\n", + "Label = ['compute', 'elemwise', 'op', 'output']\n", + "Pred =\n", + "shape compute\n", + "[PAD] elemwise\n", + "[PAD] op\n", + "[PAD] output\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "159\n", + "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "160\n", + "[CLS] If BoolOp And Compare NameConstant NotIn Name Compare Call Name Call Name Call Name Name Name Eq Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name NameConstant Assign Attribute reshape required Name NameConstant\n", + "Label = ['reshape', 'required', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape reshape\n", + "[PAD] required\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "161\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Name Call Name Num Name Add List Num\n", + "Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dims\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "162\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute concatenate Name Name keyword Num keyword Num keyword NameConstant\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append all\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "163\n", + "[CLS] ClassDef Name Expr Str FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Subscript Name Index Num For Name i Call Name Num Call Name Name AugAssign Name output Add Subscript Name Index Name Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "164\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Expr Call Attribute build Call Name Name Name Name If Compare Call Name Name NotEq Num Raise Call Name Str\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "165\n", + "[CLS] AugAssign Subscript Name Index Attribute [MASK] [MASK] [MASK] [MASK] Name Add Subscript Name Index Attribute axis Name\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "166\n", + "[CLS] If Call Name ListComp Compare Name Is NameConstant comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return NameConstant\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x m\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "167\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute axis Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "168\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Attribute axes Name Mod Call Name Name BinOp Attribute axes Name Call Name Name\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "169\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "170\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Call Attribute serialize Name Attribute alpha initializer Name Call Attribute serialize Name Attribute alpha regularizer Name Call Attribute serialize Name Attribute alpha constraint Name Attribute shared axes Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "171\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "172\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "173\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg kwargs UnaryOp USub Num Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute supports masking Name NameConstant Assign Attribute axis Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "174\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute softmax Name Name keyword Attribute axis Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "175\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "176\n", + "[CLS] If Compare Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name inner inputs Subscript Attribute input map Name Index Name\n", + "Label = ['input', 'map', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "[PAD] map\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "177\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute backward layer Name Subscript Name Slice BinOp Name FloorDiv Num\n", + "Label = ['set', 'weights', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init set\n", + "[PAD] weights\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "178\n", + "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "179\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Call Attribute reset states Attribute forward layer Name Expr Call Attribute reset states Attribute backward layer Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "180\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute forward layer Name Str Return BinOp Attribute non trainable weights Attribute forward layer Name Add Attribute non trainable weights Attribute backward layer Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "181\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute updates Attribute forward layer Name Add Attribute updates Attribute backward layer Name\n", + "Label = ['forward', 'layer', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape forward\n", + "[PAD] layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "182\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name constraints Dict If Call Name Attribute forward layer Name Str Expr Call Attribute update Name Attribute constraints Attribute forward layer Name Expr Call Attribute update Name Attribute constraints Attribute backward layer Name Return Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "183\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "184\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "185\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "186\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name BinOp Attribute units Name Mult Num keyword Str keyword Attribute recurrent initializer Name keyword Attribute recurrent regularizer Name keyword Attribute recurrent constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "187\n", + "[CLS] List Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute bias z i Name Attribute bias h i Name Attribute bias r Name Attribute bias z Name Attribute bias h Name\n", + "Label = ['bias', 'r', 'i', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bias bias\n", + "[PAD] r\n", + "[PAD] i\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "188\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute CudnnLSTM Name keyword Num keyword Attribute units Name keyword Name keyword Str\n", + "Label = ['cudnn', 'lstm', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape cudnn\n", + "[PAD] lstm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "189\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "kernel kernel\n", + "[PAD] c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "190\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "191\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['recurrent', 'kernel', 'c', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "[PAD] kernel\n", + "[PAD] c\n", + "[PAD] [PAD]\n", + " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "192\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Slice Attribute units Name BinOp Attribute units Name Mult Num\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "193\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['bias', 'o', 'i', '[PAD]']\n", + "Pred =\n", + "bias bias\n", + "[PAD] o\n", + "[PAD] i\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "194\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pooling function Name keyword Name keyword BinOp Attribute pool size Name Add Tuple Num keyword BinOp Attribute strides Name Tuple Num keyword Attribute padding Name keyword Attribute data format Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "195\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "196\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Subscript Name Index Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "197\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "198\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "199\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", + "Label = ['len', 'dim2', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output len\n", + "[PAD] dim2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "200\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Subscript Name Index Num Name Name Name If Compare Attribute data format Name Str Return Tuple Subscript Name Index Num Name Name Name Subscript Name Index Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "201\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs Str Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "202\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Eq Str Assign Name ch dim Num If Compare Attribute data format Attribute cell Name Str Assign Name ch dim Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "203\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Attribute shape Name comprehension Name spec Attribute state spec Name Attribute state size Attribute cell Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "204\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Try Assign Name shape Call Attribute int shape Name Name ExceptHandler Name Assign Name shape Call Name GeneratorExp NameConstant comprehension Name Call Name Call Attribute ndim Name Name Expr Call Attribute append Attribute state spec Name Call Name keyword Name\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x state\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "205\n", + "[CLS] If Compare Name IsNot NameConstant Assign Subscript Name Index Str Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Name Assign Attribute constants spec Name ListComp Call Name keyword Call Attribute int shape Name Name comprehension Name constant Name Assign Attribute num constants Name Call Name Name AugAssign Name additional specs Attribute constants spec Name\n", + "Label = ['additional', 'inputs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x additional\n", + "[PAD] inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "206\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute call Call Name Name Name Name keyword Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "207\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output mask\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "208\n", + "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Call Name Attribute states Name Str Call Name Call Name Name Str\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "209\n", + "[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name comprehension Name dim Attribute state size Attribute cell Name\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append zeros\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "210\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute zeros Name Call Name Attribute state size Attribute cell Name\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "211\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "212\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "213\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "214\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice Attribute filters Name BinOp Attribute filters Name Mult Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "215\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", + "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "216\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", + "Label = ['kernel', 'o', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "217\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", + "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape bias\n", + "[PAD] c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "218\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input conv Name Name Attribute kernel o Name Attribute bias o Name keyword Attribute padding Name\n", + "Label = ['x', 'o', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output x\n", + "[PAD] o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "219\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Name keyword Attribute data format Name\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "[PAD] out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "220\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "221\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state NameConstant NameConstant NameConstant Return Call Attribute call Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "222\n", + "[CLS] Call Name keyword BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Num keyword Dict Name Name\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name rank\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "223\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d Name Name Attribute kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "224\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", + "Label = ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call conv2d\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "225\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "[PAD] bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "226\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "227\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "228\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name channel axis Num Assign Name channel axis UnaryOp USub Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "229\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name Name Attribute padding Name Name Subscript Attribute dilation rate Name Index Num\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape deconv\n", + "[PAD] length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "230\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "[PAD] bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "231\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "232\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "233\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name out pad Call Name Attribute strides Name Attribute output padding Name If Compare Name GtE Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute strides Name Str Call Name Attribute output padding Name\n", + "Label = ['stride', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x stride\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "234\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "235\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Attribute data format Name Eq Str Num UnaryOp USub Num\n", + "Label = ['channel', 'axis', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output channel\n", + "[PAD] axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "236\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute bias Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name Assign Attribute bias Name NameConstant\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "[PAD] bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "237\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple BinOp Name Mult Attribute depth multiplier Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "238\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Num keyword Dict Name Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "[PAD] spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "239\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Name\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "240\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Call Attribute get config Call Name Name Name Assign Subscript Name Index Str Subscript Attribute size Name Index Num Expr Call Attribute pop Name Str Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "241\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Subscript Attribute padding Name Index Num\n", + "Label = ['temporal', 'padding', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape temporal\n", + "[PAD] padding\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "242\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "243\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num Tuple Num Num NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "244\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n", + "Label = ['dim1', 'cropping', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dim1\n", + "[PAD] cropping\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "245\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "246\n", + "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "[PAD] length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "247\n", + "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "[PAD] length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "248\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute kernel shape Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "249\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "250\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Call Name Name keyword Dict Attribute axis Name Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items input\n", + "[PAD] spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "251\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute gamma initializer Name keyword Attribute gamma regularizer Name keyword Attribute gamma constraint Name\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape gamma\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "252\n", + "[CLS] Assign Subscript Name Index Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Attribute axis Name\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "253\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name broadcast beta Call Attribute reshape Name Attribute beta Name Name Assign Name broadcast beta NameConstant\n", + "Label = ['center', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape center\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "254\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute moving mean Name Attribute moving variance Name Attribute beta Name Attribute gamma Name keyword Attribute axis Name keyword Attribute epsilon Name\n", + "Label = ['batch', 'normalization', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias batch\n", + "[PAD] normalization\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "255\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast Name Name keyword Call Attribute dtype Name Name\n", + "Label = ['sample', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x sample\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "256\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute cells Name Index UnaryOp USub Num Index Num\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape state\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "257\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice Num Assign Name input shape Subscript Name Index Num\n", + "Label = ['constants', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output constants\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "258\n", + "[CLS] If Call Name Name Name If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute build Name BinOp List Name Add Name Expr Call Attribute build Name Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "259\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "260\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Attribute weights Name Assign Name weights Subscript Name Slice Name For Tuple Name sw Name w Call Name Attribute weights Name Name Expr Call Attribute append Name Tuple Name Name Assign Name weights Subscript Name Slice Name\n", + "Label = ['num', 'param', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output num\n", + "[PAD] param\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "261\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Attribute losses Name AugAssign Name losses Add Name Return Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "262\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cell arg return sequences arg return state arg go backwards arg stateful arg unroll arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "263\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name constants shape Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name constants shape NameConstant\n", + "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape num\n", + "[PAD] constants\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "264\n", + "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "265\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Attribute is keras tensor Name Name NotEq Name Raise Call Name Str\n", + "Label = ['tensor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x tensor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "266\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name initial state Attribute states Name Assign Name initial state Call Attribute get initial state Name Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "267\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name updates List For Name i Call Name Call Name Name Expr Call Attribute append Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name Expr Call Attribute add update Name Name Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "268\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "269\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute cell Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "270\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return BinOp Attribute losses Attribute cell Name Add Name\n", + "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape cell\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "271\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "272\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "273\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "274\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name BinOp Attribute units Name Mult Num keyword Str keyword Attribute recurrent initializer Name keyword Attribute recurrent regularizer Name keyword Attribute recurrent constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "275\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name\n", + "Label = ['kernel', 'z', '[PAD]', '[PAD]']\n", + "Pred =\n", + "kernel kernel\n", + "[PAD] z\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "276\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "277\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", + "Label = ['recurrent', 'kernel', 'h', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "[PAD] kernel\n", + "[PAD] h\n", + "[PAD] [PAD]\n", + " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "278\n", + "[CLS] BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant\n", + "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data recurrent\n", + "[PAD] dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "279\n", + "[CLS] If Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num AugAssign Name inputs Mult Subscript Name Index Num\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "280\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n", + "Label = ['x', 'r', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] r\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "281\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['matrix', 'inner', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x matrix\n", + "[PAD] inner\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "282\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dot\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "283\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape recurrent\n", + "[PAD] kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "284\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg config If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Num Assign Subscript Name Index Str Num Return Call Name keyword Name Name\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self cls\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "285\n", + "[CLS] ExtSlice Slice Slice BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name units\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "286\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n", + "Label = ['recurrent', 'kernel', 'i', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "[PAD] kernel\n", + "[PAD] i\n", + "[PAD] [PAD]\n", + " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "287\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "288\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", + "Label = ['recurrent', 'activation', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items recurrent\n", + "[PAD] activation\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "289\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name z Call Attribute bias add Name Name Attribute bias Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "[PAD] bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "290\n", + "[CLS] Subscript Name ExtSlice Slice Slice BinOp Num Mult Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Attribute units Name\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name units\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "291\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "292\n", + "[CLS] If Compare Name Gt Num Return ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name comprehension Name Call Name Name\n", + "Label = ['in', 'train', 'phase', '[PAD]']\n", + "Pred =\n", + "shape in\n", + "[PAD] train\n", + "[PAD] phase\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "293\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "294\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant Assign Name output mask Call Attribute any Name Call Attribute not equal Name Name Attribute mask value Name keyword UnaryOp USub Num Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "295\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute any Name Call Attribute not equal Name Name Attribute mask value Name keyword UnaryOp USub Num\n", + "Label = ['output', 'mask', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] mask\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "296\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Name Is NameConstant Subscript Name Index Name Name comprehension Tuple Name axis Name shape Call Name Attribute noise shape Name\n", + "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output noise\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "297\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name Assign Name noise shape Tuple Subscript Name Index Num Num Subscript Name Index Num Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "298\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg rate arg data format arg kwargs NameConstant Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute data format Name Call Attribute normalize data format Name Name Assign Attribute input spec Name Call Name keyword Num Attribute legacy spatialdropoutNd support Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "299\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Num Num\n", + "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output noise\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "300\n", + "[CLS] If Compare Name Lt Num If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str AugAssign Name known Mult Name\n", + "Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x unknown\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "301\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "302\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "303\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute repeat Name Name Attribute n Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "304\n", + "[CLS] If Call Name Name Name Return ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name x elem Name Return Call Attribute int shape Name Name\n", + "Label = ['int', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append int\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "305\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant Assign Name arguments Attribute arguments Name If Call Name Attribute function Name Str Assign Subscript Name Index Str Name Return Call Attribute function Name Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "306\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant If Call Name Attribute mask Name Return Call Attribute mask Name Name Name Return Attribute mask Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "307\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute LambdaType Name Assign Name function Call Name Attribute function Name Assign Name function type Str Assign Name function Attribute name Attribute function Name Assign Name function type Str\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "308\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Num keyword Dict UnaryOp USub Num Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "[PAD] spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "309\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Assert BoolOp And Name Compare Call Name Name GtE Num Assert Subscript Name Index UnaryOp USub Num Assign Name output shape Call Name Name Assign Subscript Name Index UnaryOp Num Attribute units Name Return Call Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "310\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "311\n", + "[CLS] If BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant Assign Name alt Call Name\n", + "Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call Function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "312\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Name Attribute Function Attribute cntk py Name Return Call Attribute eval Name If BoolOp Or Call Name Name Attribute Constant Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name Return Attribute value Name Raise Call Name BinOp Str Mod Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "313\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Attribute shape Name If Compare Subscript Name Index BinOp Name Add Name Is NameConstant Expr Call Attribute append Name Subscript Attribute shape Name Index Name Expr Call Attribute append Name Subscript Name Index BinOp Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "314\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name a Attribute dynamic axes Name Assign Name shape BinOp Call Name Name Add Name\n", + "Label = ['dynamic', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dynamic\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "315\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output seed\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "316\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "317\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute float32 Name Assign Name dtype Call Name Name\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "318\n", + "[CLS] Return Call Name keyword Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword Name\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape zeros\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "319\n", + "[CLS] While Compare Name Lt BinOp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Num Assign Name x Call Attribute swapaxes Name Name Name BinOp Name Add Num AugAssign Name i Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "320\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Name BinOp Name Add Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "321\n", + "[CLS] While Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Name BinOp Name Sub Num AugAssign Name i Num\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "322\n", + "[CLS] IfExp Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num BinOp Call Name Attribute shape Name Sub Num Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "323\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Eq Attribute FreeDimension Name Attribute InferredDimension Name Name comprehension Name Name\n", + "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x new\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "324\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name Add Name\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output n\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "325\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name keyword Tuple keyword Name keyword BinOp Name Add Num\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "326\n", + "[CLS] BoolOp And Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Call Name Name Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "327\n", + "[CLS] BinOp BinOp Num Sub Name Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Name\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append log\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "328\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name BinOp BinOp Name Mult Name Add BinOp Name BinOp Num Sub Name\n", + "Label = ['assign', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape assign\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "329\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constant Name Num keyword Attribute shape Name keyword Str\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output g\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "330\n", + "[CLS] Call Name ListComp IfExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute InferredDimension Name Name comprehension Name Name\n", + "Label = ['FreeDimension', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data FreeDimension\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "331\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute slice Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output current\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "332\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "333\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute slice Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['mask', 'slice', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output mask\n", + "[PAD] slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "334\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "335\n", + "[CLS] If Compare Call Name Name Eq Num If Call Name Name Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute to batch Name Name Expr Call Attribute append Name Call Attribute user function Name Call Name Name Expr Call Attribute append Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "336\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Attribute dynamic axes Name comprehension Name Name\n", + "Label = ['place', 'holders', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output place\n", + "[PAD] holders\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "337\n", + "[CLS] If BoolOp And Compare Call Name Name Eq Num Compare Call Name Name Num If Call Name Name Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute unpack batch Name Name Expr Call Attribute append Name Call Attribute user function Name Call Name Name keyword Subscript Attribute shape Name Index Num Expr Call Attribute append Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "338\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Subscript Attribute shape Name Index Num\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items user\n", + "[PAD] function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "339\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute reduce sum Name Call Attribute square Name Name keyword Subscript Name Index Num\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "340\n", + "[CLS] BinOp Name Mult BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Sub Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "341\n", + "[CLS] BoolOp And Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Name NotEq Num\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape type\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "342\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg depthwise kernel arg pointwise kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "343\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Tuple Num Num Num keyword List NameConstant\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "344\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", + "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output depthwise\n", + "[PAD] kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "345\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Subscript Name Index Num keyword List NameConstant Name Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "346\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute MAX POOLING Name Name Name keyword List Name\n", + "Label = ['pooling', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "call pooling\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "347\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Name Eq UnaryOp USub Num UnaryOp Num BinOp Name Sub Num\n", + "Label = ['axis', 'without', 'batch', '[PAD]']\n", + "Pred =\n", + "output axis\n", + "[PAD] without\n", + "[PAD] batch\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "348\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Div Call Attribute reduce sum Name Name keyword UnaryOp USub Num\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "349\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Attribute shape Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "350\n", + "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Expr Call Attribute append Name Name Raise Call Name BinOp Str Mod Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "351\n", + "[CLS] BoolOp And Compare Name NotEq Name Compare Name Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Attribute FreeDimension Name\n", + "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape InferredDimension\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "352\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute constant Name keyword Num keyword Name keyword Name\n", + "Label = ['splice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape splice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "353\n", + "[CLS] Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Num Sub IfExp Compare Name Gt Num Num Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "354\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If BoolOp Or Call Name Name Attribute Parameter Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Return Attribute value Name Return Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "355\n", + "[CLS] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name If Call Name Name Tuple Name Name Assign Name value Call Attribute full Name Attribute shape Name Name keyword Call Name Assign Attribute value Name Name Raise Name\n", + "Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call Parameter\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "356\n", + "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute full Name Attribute shape Name Name keyword Call Name\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "357\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x Call Name Name\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append user\n", + "[PAD] function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "358\n", + "[CLS] If Compare Call Name Name Gt Num Return Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name Return NameConstant\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "[PAD] axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "359\n", + "[CLS] Return Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "[PAD] axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "360\n", + "[CLS] Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "[PAD] axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "361\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute as shape Call Attribute data Name BinOp Tuple Name Add Attribute target shape Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "362\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Call Attribute as shape Name BinOp Tuple Name Add Attribute from shape Name\n", + "Label = ['Value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items Value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "363\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Subscript Attribute inputs Name Index Num Slice Num Attribute dtype Subscript Attribute inputs Name Index Num List Name\n", + "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append output\n", + "[PAD] variable\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "364\n", + "[CLS] Return BoolOp And Compare Name IsNot NameConstant Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Call Attribute upper Name\n", + "Label = ['device', 'type', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data device\n", + "[PAD] type\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "365\n", + "[CLS] FunctionDef arguments Expr Str Global If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute list devices Call Name Return ListComp Attribute name Name comprehension Name x Name Compare Attribute device type Name Eq Str\n", + "Label = ['LOCAL', 'DEVICES', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x LOCAL\n", + "[PAD] DEVICES\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "366\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute list devices Call Name\n", + "Label = ['LOCAL', 'DEVICES', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output LOCAL\n", + "[PAD] DEVICES\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "367\n", + "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute keras shape Name Attribute shape Name If Call Name Name Str Assign Attribute keras shape Name Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "368\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str Return Call Attribute zeros like Name Name keyword Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "369\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Call Attribute random normal initializer Name Name Name keyword Name keyword Name Name\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "370\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name List UnaryOp USub Num Subscript Name Index UnaryOp Num\n", + "Label = ['xt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output xt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "371\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name keyword Name List Subscript Name Index UnaryOp USub Num UnaryOp Num\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append reshape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "372\n", + "[CLS] Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "373\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute multiply Name Name Name Subscript Name Index Num\n", + "Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append reduce\n", + "[PAD] sum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "374\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute not equal Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "375\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name var Call Attribute moments Attribute nn Name Name Name NameConstant NameConstant NameConstant\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "376\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name If Compare Call Name Name Gt Num Assign Name beta Call Attribute reshape Name Name UnaryOp USub Num\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output beta\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "377\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute fused batch norm Attribute nn Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword NameConstant\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "378\n", + "[CLS] If Compare Name Eq Str Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name cols Tuple Num Num Assign Tuple Name rows Name cols Tuple Num Num\n", + "Label = ['rows', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x rows\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "379\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Mult Call Attribute constant Name Call Attribute array Name List Name Name keyword Str\n", + "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x new\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "380\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Name keyword Tuple Num Num\n", + "Label = ['set', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "381\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num Assign Name output Call Name Name Name keyword Num Assign Name output Call Name Name Name keyword Num Return Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "382\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n Expr Str Assert Compare Call Name Name Eq Num Assign Name x Call Attribute expand dims Name Name Num Assign Name pattern Call Attribute stack Name List Num Name Num Return Call Attribute tile Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "383\n", + "[CLS] If Compare Name NotEq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "384\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Expr Str Return Call Attribute expand dims Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "385\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis Expr Str Return Call Attribute squeeze Name Name List Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "386\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg start arg size Expr Str Return Call Attribute slice Name Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "387\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute as numpy dtype Call Attribute as dtype Name Attribute dtype Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "388\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute as numpy dtype Call Attribute as dtype Name Attribute dtype Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "389\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute concatenate Name Tuple Call Attribute expand dims Name Attribute row Name Num Call Attribute expand dims Name Attribute col Name Num Num\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output indices\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "390\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute run Name keyword Name keyword Name keyword Attribute session kwargs Name\n", + "Label = ['updated', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output updated\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "391\n", + "[CLS] Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "392\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If UnaryOp Not BoolOp Or Call Name Attribute run Attribute Session Name Name NameConstant Call Name Attribute init Name Name NameConstant Assign Name msg BinOp Str Mod Name Raise Call Name Name\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x key\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "393\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute stack Name List Num Subscript Call Attribute shape Name Name Index Num\n", + "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape tile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "394\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute stack Name List Num Subscript Call Attribute shape Name Name Index Num\n", + "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape tile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "395\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Gt Num Name Call Attribute ones like Name Name\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape where\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "396\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute relu Attribute nn Name BinOp UnaryOp USub Name Add Name\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x negative\n", + "[PAD] part\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "397\n", + "[CLS] BinOp Name Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute greater Name Name Name Call Name\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items cast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "398\n", + "[CLS] If Compare Name Eq Num Return Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Gt Num Name BinOp Name Mult Name\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape where\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "399\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute softplus Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "400\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute softsign Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "401\n", + "[CLS] If Compare Call Name Name GtE Num Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Call Attribute shape Name Name Slice UnaryOp USub Num Return Name\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape reshape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "402\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute log Name BinOp Name Div BinOp Num Sub Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "403\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute sigmoid Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "404\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg level arg noise shape arg seed NameConstant NameConstant Expr Str Assign Name retain prob BinOp Num Sub Name If Compare Name Is NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute dropout Attribute nn Name BinOp Name Mult Num Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "405\n", + "[CLS] If BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str Assign Name x Call Attribute cast Name Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "406\n", + "[CLS] BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "407\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x padding\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "408\n", + "[CLS] If Compare Name Eq Str If Compare Name NotEq Str Raise Call Name Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num Assign Name x Call Name Name Tuple Name Num Assign Name padding Str\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x left\n", + "[PAD] pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "409\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output left\n", + "[PAD] pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "410\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "411\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "412\n", + "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute stack Name Name\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "413\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name NotEq Tuple Num Num Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name force transpose NameConstant\n", + "Label = ['force', 'transpose', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output force\n", + "[PAD] transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "414\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "415\n", + "[CLS] BinOp Tuple Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Index Num Add Call Name Subscript Name Slice Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "416\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute atrous conv2d transpose Attribute nn Name Name Name Name Subscript Name Index Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "417\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Num Num Add BinOp Name Mult Num\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output strides\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "418\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "419\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Tuple Num Add Name Tuple Num Assign Name strides BinOp Tuple Num Num Name\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output strides\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "420\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "421\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg pool mode Tuple Num Num Str NameConstant Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "422\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg pool mode Tuple Num Num Num Str NameConstant Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "423\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute avg pool3d Attribute nn Name Name Name Name keyword Name keyword Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "424\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "425\n", + "[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Attribute nn Name Name Name keyword Str AugAssign Name x Add Call Name Name BinOp Tuple Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "426\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape random\n", + "[PAD] uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "427\n", + "[CLS] FunctionDef arguments arg arg [MASK] [MASK] [MASK] [MASK] Return Compare Call Attribute expand dims Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n", + "Label = ['current', 'input', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self current\n", + "[PAD] input\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "428\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute squeeze Name Name keyword UnaryOp USub Num\n", + "Label = ['to', 'int32', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "items to\n", + "[PAD] int32\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "429\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute ctc loss Name keyword Name keyword Name keyword Name Num\n", + "Label = ['expand', 'dims', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append expand\n", + "[PAD] dims\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "430\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute sparse to dense Name Attribute indices Name Attribute dense shape Name Attribute values Name keyword UnaryOp USub Num comprehension Name st Name\n", + "Label = ['decoded', 'dense', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output decoded\n", + "[PAD] dense\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "431\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Global Expr Call Attribute append Name Name Expr Yield Expr Call Attribute pop Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "432\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name shape Call Name ListComp NameConstant comprehension Name Call Name Name\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "433\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Return Call Attribute ones like Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "434\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Num keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "435\n", + "[CLS] If Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Str Str Call Name Name\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "436\n", + "[CLS] BoolOp Or Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute dtype Name Eq Str\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "437\n", + "[CLS] IfExp Name BinOp Tuple Num Mult Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Num\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "438\n", + "[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Num For Name a Subscript Name Slice UnaryOp USub Num Expr Call Attribute pop Name Name If UnaryOp Not Name Assign Name keras shape list Tuple Num\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "439\n", + "[CLS] If BoolOp And Compare Name IsNot NameConstant Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['max', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x max\n", + "[PAD] value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "440\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name gamma Call Name Name\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x gamma\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "441\n", + "[CLS] If Compare Name Is NameConstant If Compare Name NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name beta Call Name Name\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output beta\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "442\n", + "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Attribute ndim Name Gt Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "443\n", + "[CLS] BinOp BinOp List Str Mult BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Num Add List Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "444\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name axis 2 Num Raise Call Name Str Name\n", + "Label = ['axis', '1', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x axis\n", + "[PAD] 1\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "445\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Attribute keras shape Name Expr Call Attribute insert Name Num Name Assign Attribute keras shape Name Call Name Name\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "446\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name j Call Name Attribute keras shape Name Name If Compare Name Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Name Mult Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "447\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Attribute keras shape Name Slice UnaryOp USub Num Add Tuple NameConstant\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "448\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute keras shape Name Index Num Assign Name output shape BinOp Subscript Attribute keras shape Name Slice UnaryOp USub Name Add BinOp Tuple NameConstant Mult Name Assign Name output shape BinOp Tuple NameConstant Attribute ndim Name\n", + "Label = ['n', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output n\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "449\n", + "[CLS] If Compare Name Lt Num If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute type Name Eq Num Assign Name axis Num Assign Name axis BinOp BinOp Name Mod Attribute ndim Attribute type Name Add Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "450\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mod Attribute ndim Attribute type Name Add Num\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "451\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute set subtensor Name Subscript Name ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Slice Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "452\n", + "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "453\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num NameConstant\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "454\n", + "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute keras shape Name Index Num Name Name\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "455\n", + "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute keras shape Name Index Num Name Name Name\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "456\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If UnaryOp Not Call Name Name Str Raise Call Name Str Return Call Attribute get value Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "457\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute dtype Name\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "[PAD] value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "458\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assert Call Name Name Tuple Name Name Return Call Attribute function Name Starred Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "459\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List Num Num Add Call Name Call Name Num Name\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "460\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute stack Name Starred ListComp Subscript Name Index Name comprehension Name states at step Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "461\n", + "[CLS] If Compare Call Name Name Gt Num Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Num Num\n", + "Label = ['unbroadcast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append unbroadcast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "462\n", + "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Num Num\n", + "Label = ['unbroadcast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append unbroadcast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "463\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg alt arg training NameConstant Expr Str Return Call Name Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "464\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func If UnaryOp Not Call Name Name Name Raise Call Name Str\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self module\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "465\n", + "[CLS] If Compare Name NotEq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute relu Attribute nnet Name BinOp UnaryOp USub Name Add Name Assign Name negative part Call Attribute relu Attribute nnet Name UnaryOp Name\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x negative\n", + "[PAD] part\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "466\n", + "[CLS] If Compare Name NotEq Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call Name Assign Name x Call Attribute relu Attribute nnet Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "467\n", + "[CLS] If BoolOp And Compare Name NotEq UnaryOp USub Num Compare Name NotIn Name Raise Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Call Name Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "468\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Call Name Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "469\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name Call Attribute maximum Name Name Call Name\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output norm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "470\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", + "Label = ['image', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output image\n", + "[PAD] shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "471\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "472\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "473\n", + "[CLS] ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "474\n", + "[CLS] Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "475\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "476\n", + "[CLS] If Compare BinOp Subscript Name Index Num Mod Num Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "[PAD] out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "477\n", + "[CLS] If Compare Name Eq Str If UnaryOp Not Name Raise Call Name Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num Assign Name x Call Name Name Tuple Name Num Assign Name padding Str\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x left\n", + "[PAD] pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "478\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name spatial start dim Num\n", + "Label = ['spatial', 'start', 'dim', '[PAD]']\n", + "Pred =\n", + "x spatial\n", + "[PAD] start\n", + "[PAD] dim\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "479\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d Attribute nnet Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Subscript Name Index Num\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "[PAD] out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "480\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str\n", + "Label = ['pool', '2d', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape pool\n", + "[PAD] 2d\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "481\n", + "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "482\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", + "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output w\n", + "[PAD] pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "483\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", + "Label = ['h', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output h\n", + "[PAD] pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "484\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['expected', 'height', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output expected\n", + "[PAD] height\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "485\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Subscript Name Index Num Num Num Num AugAssign Name x Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "486\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "487\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "488\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Subscript Name Index Num Num AugAssign Name x Call Name Name Tuple Num Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "489\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Subscript Name Index Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "490\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Num BinOp BinOp Call Attribute max Name Call Attribute concatenate Name List Name List UnaryOp USub Num Num Num\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append maximum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "491\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Name BinOp Subscript Name Slice Name Add Name\n", + "Label = ['set', 'subtensor', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "[PAD] subtensor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "492\n", + "[CLS] Assign Tuple List Name [MASK] [MASK] [MASK] [MASK] Name log f probs Name b active Name log b probs Name Call Attribute scan Name Name keyword List Name Subscript Name ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num keyword List Call Attribute int32 Name Num Name Call Attribute int32 Name Num Name\n", + "Label = ['f', 'active', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x f\n", + "[PAD] active\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "493\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Call Attribute arange Name Subscript Attribute shape Name Index Num Str Num\n", + "Label = ['idxs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output idxs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "494\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name elems Subscript Name Slice Num\n", + "Label = ['initializer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output initializer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "495\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Slice Tuple Num UnaryOp USub Num Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "496\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name BinOp Name Mult Name BinOp BinOp Name Name Add Subscript Name Index Num\n", + "Label = ['slice', 'col', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output slice\n", + "[PAD] col\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "497\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg data format arg file format arg scale arg kwargs NameConstant NameConstant NameConstant If Compare Name Is NameConstant Assign Name data format Call Attribute image data format Name Return Call Attribute save img Name Name Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self path\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "498\n", + "[CLS] Module Expr Str ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias Assign Name [MASK] [MASK] [MASK] [MASK] Attribute pad sequences Name Assign Name make sampling table Attribute make sampling table Name Assign Name skipgrams Attribute skipgrams Name Assign Name remove long seq Attribute remove long seq Name ClassDef Attribute TimeseriesGenerator Name Attribute Sequence Name Expr Str Pass\n", + "Label = ['pad', 'sequences', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output pad\n", + "[PAD] sequences\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "499\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "500\n", + "[CLS] Call Name keyword Name keyword List keyword List keyword List keyword Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute outputs Name keyword ListComp NameConstant comprehension Name Attribute inputs Name keyword ListComp NameConstant comprehension Name Attribute outputs Name keyword ListComp Attribute keras shape Name comprehension Name x Attribute inputs Name keyword ListComp Attribute keras shape Name comprehension Name x Attribute outputs Name\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "501\n", + "[CLS] Call Name GeneratorExp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name UnaryOp Not Call Name Name Tuple Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "502\n", + "[CLS] ListComp BoolOp And Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name layer Attribute layers Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "503\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Name Str List If Compare Attribute input spec Name Is NameConstant Expr Call Attribute append Name NameConstant If UnaryOp Not Call Name Attribute input spec Name Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Attribute input spec Name AugAssign Name specs Attribute input spec Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "504\n", + "[CLS] If BoolOp And Call Name Name Str Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute keras shape Name Expr Call Attribute append Name Name Assign Name output shapes NameConstant\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "505\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Name kept nodes Num Assign Name kept nodes Num\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape class\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "506\n", + "[CLS] If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "507\n", + "[CLS] While Name For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name layer Subscript Name Index Subscript Name Index Str If Compare Name In Name For Name node data Call Attribute pop Name Name Expr Call Name Name Name\n", + "Label = ['layer', 'data', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "[PAD] data\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "508\n", + "[CLS] If BoolOp And Compare Str NotIn Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Str In Name Assign Name f Subscript Name Index Str\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data attrs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "509\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] [MASK] [MASK] [MASK] Name Name Attribute layers Name keyword Name keyword Name\n", + "Label = ['load', 'weights', 'from', 'hdf5']\n", + "Pred =\n", + "append load\n", + "[PAD] weights\n", + "[PAD] from\n", + "[PAD] hdf5\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "510\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Lambda arguments arg x Subscript Name Index Name\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sort\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "511\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape count\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "512\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape count\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "513\n", + "[CLS] If BoolOp And Name Name Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant If BoolOp Name Name Call Name Subscript Name Index Num Str Call Name Subscript Name Index Num Str Expr Call Name BinOp Str Mod Tuple Subscript Attribute shape Subscript Name Index Num Index Num Subscript Attribute shape Subscript Name Index Num Index Num\n", + "Label = ['do', 'validation', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output do\n", + "[PAD] validation\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "514\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name count mode Str\n", + "Label = ['count', 'mode', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output count\n", + "[PAD] mode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "515\n", + "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num Assign Name val outs Call Name Name For Tuple Name l Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x val\n", + "[PAD] outs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "516\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "517\n", + "[CLS] If Compare Name Eq BinOp Call Name Name Sub Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num Assign Name val outs Call Name Name For Tuple Name l Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x val\n", + "[PAD] outs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "518\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "519\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name Assign Name progbar Call Name keyword Name\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output progbar\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "520\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Attribute feed inputs Name If BoolOp And Call Name Subscript Name Index Name UnaryOp Not Call Attribute is sparse Name Subscript Attribute feed inputs Name Index Name Expr Call Attribute append Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "521\n", + "[CLS] BoolOp And Call Name Subscript Name Index Name UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute feed inputs Name Index Name\n", + "Label = ['is', 'sparse', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append is\n", + "[PAD] sparse\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "522\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name batch out Call Name Name Expr Call Attribute append Subscript Name Index Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "523\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Call Attribute toarray Subscript Name Index Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "524\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Name comprehension Tuple Name i Name name Call Name Attribute metrics names Name Compare Call Name Name In Attribute stateful metric names Name\n", + "Label = ['stateful', 'metric', 'indices', '[PAD]']\n", + "Pred =\n", + "output stateful\n", + "[PAD] metric\n", + "[PAD] indices\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "525\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name batch out Call Name Name If Compare Name In Name Assign Subscript Name Index Name Call Name Name AugAssign Subscript Name Index Name Add Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "526\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name If Compare Name NotIn Name AugAssign Subscript Name Index Name Div Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "527\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name keyword Name keyword BinOp Attribute name Name Add Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "528\n", + "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute outputs Name NameConstant Assign Attribute inputs Name NameConstant If Attribute outputs Name Assign Attribute outbound nodes Subscript Attribute layers Name Index UnaryOp USub Num List Assign Attribute outputs Name List Attribute output Subscript Attribute layers Name Index UnaryOp Num Expr Call Attribute build Name\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape layers\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "529\n", + "[CLS] Call Name keyword Name keyword Name keyword BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "530\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute init graph network Name Attribute inputs Name Attribute outputs Name keyword Attribute name Name Assign Attribute built Name NameConstant\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "531\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Attribute name Name Call Attribute deepcopy Name Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "532\n", + "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute dumps Name Dict Str Str Str Str Str Dict Str Str Attribute name Attribute class Attribute optimizer Name Call Attribute get config Attribute optimizer Name Attribute loss Name Attribute metrics Name Attribute sample weight mode Name Attribute loss weights Name keyword Name Str\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init encode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "533\n", + "[CLS] While Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Add Str Call Name Name AugAssign Name idx Num\n", + "Label = ['unique', 'name', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output unique\n", + "[PAD] name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "534\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Name weights Attribute weights Name If Name Expr Call Attribute append Name Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "535\n", + "[CLS] If Compare Call Name Name NotEq Call Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "536\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "537\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "538\n", + "[CLS] Try Expr Call Name Name Name Name If Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append close\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "539\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name d Dict Assign Name f Call Name Name Expr Call Name Name Name Return Name\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "540\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name f Call Name Name keyword Str Return Call Name Name\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self state\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "541\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Name comprehension Name x Name Compare Call Name Name Gt Name\n", + "Label = ['bad', 'attributes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x bad\n", + "[PAD] attributes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "542\n", + "[CLS] Expr Call Name Name Str ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str comprehension Name layer Name\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init encode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "543\n", + "[CLS] Assign Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Str Call Attribute encode Call Attribute backend Name Str\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape attrs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "544\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Tuple Name w Name val Call Name Call Name Name Name If BoolOp And Call Name Name Str Attribute name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Add Call Name Name Expr Call Attribute append Name Call Attribute encode Name Str\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "545\n", + "[CLS] Compare Subscript Name Slice Num NotEq Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "546\n", + "[CLS] Compare Subscript Name Slice Num Eq Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "547\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "548\n", + "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "549\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num keyword UnaryOp USub Num\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append concatenate\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "550\n", + "[CLS] If Compare Name Eq Tuple Num BinOp Name Mult Name Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple BinOp Name Name Assign Name source Str Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x source\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "551\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Attribute reset after Name Assign Name target Str Assign Name target Str\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output target\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "552\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "553\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "554\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Call Attribute format Str Attribute name Name Add Call Attribute format Str Attribute shape Subscript Name Index Name Attribute shape Subscript Name Index Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "555\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Subscript Name Index Name Call Attribute format Str Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "556\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps per epoch arg epochs arg verbose arg callbacks arg validation data arg validation steps arg class weight arg max queue size arg workers arg use multiprocessing arg shuffle arg initial epoch NameConstant Num Num NameConstant NameConstant NameConstant NameConstant Num Num NameConstant NameConstant Num\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "557\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Subscript Call Name Call Attribute values Name Index Num Index Num Assign Name batch size Subscript Attribute shape Name Index Num\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output batch\n", + "[PAD] size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "558\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "559\n", + "[CLS] ListComp Name comprehension Tuple Name [MASK] [MASK] [MASK] [MASK] Name name Call Name Attribute metrics names Name Compare Call Name Name In Attribute stateful metric names Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "560\n", + "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n", + "Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x steps\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "561\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute average Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "562\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps arg max queue size arg workers arg use multiprocessing arg verbose NameConstant Num Num NameConstant Num\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "563\n", + "[CLS] If Compare Call Name Name Eq Num If Compare Name Num Return Subscript Subscript Name Index Num Index Num Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append concatenate\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "564\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get Name Str\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "565\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name trainable Call Name Name Str NameConstant If Name Return Attribute trainable weights Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "566\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name keyword Name\n", + "Label = ['variable', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append variable\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "567\n", + "[CLS] If UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name Assign Name input spec Call Name Attribute input spec Name Assign Name input spec Attribute input spec Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append input\n", + "[PAD] spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "568\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "569\n", + "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute ndim Name Str Call Name Call Attribute ndim Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "570\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name Str Call Name Call Attribute ndim Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "571\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute dtype Name Str Call Name Call Attribute dtype Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "572\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute dtype Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "573\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "574\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "575\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name Str Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "576\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "577\n", + "[CLS] If UnaryOp Not Call Name Name If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str If Compare Str NotIn Name Assign Subscript Name Index Str Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "578\n", + "[CLS] If Call Name Name Name If Call Name GeneratorExp Compare Name IsNot NameConstant comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Name Raise Call Name BinOp BinOp BinOp Str Attribute name Name Str Call Name Name\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x m\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "579\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return Call Attribute get node attribute at index Name Name Str Str\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "580\n", + "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Raise Call Name BinOp BinOp Str Add Attribute name Name Str If UnaryOp Not Attribute inbound nodes Name Raise Call Name BinOp BinOp Str Attribute name Name Str\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inbound\n", + "[PAD] nodes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "581\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name inputs hash NameConstant\n", + "Label = ['inputs', 'hash', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output inputs\n", + "[PAD] hash\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "582\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "583\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name intermediate Call Attribute sub Name Str Str Name Assign Name insecure Call Attribute lower Call Attribute sub Name Str Str Name If Compare Subscript Name Index Num NotEq Str Return Name Return BinOp Str Add Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "584\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Try Expr Call Attribute append Name Call Attribute int shape Name Name ExceptHandler Name Expr Call Attribute append Name NameConstant\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "585\n", + "[CLS] BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape output\n", + "[PAD] names\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "586\n", + "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute outputs Name Str Call Name Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "587\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "588\n", + "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "589\n", + "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "590\n", + "[CLS] Raise Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "591\n", + "[CLS] Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "592\n", + "[CLS] If Compare Name Eq Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Expr Call Attribute append Name Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "593\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "594\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute metrics names Name BinOp Subscript Attribute output names Name Index Name Add Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "595\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Attribute binary accuracy Name If Compare Name Tuple Str Str Assign Name metric fn Attribute binary crossentropy Name\n", + "Label = ['metric', 'fn', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output metric\n", + "[PAD] fn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "596\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Attribute categorical accuracy Name If Compare Name Tuple Str Str Assign Name metric fn Attribute categorical crossentropy Name\n", + "Label = ['metric', 'fn', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output metric\n", + "[PAD] fn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "597\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple Str Str Assign Name suffix Str\n", + "Label = ['suffix', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x suffix\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "598\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Name metric result Call Name Name Name keyword Name keyword Subscript Name Index Name\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape name\n", + "[PAD] scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "599\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name BinOp List Attribute total loss Name Add Attribute metrics tensors Name keyword Name keyword Str keyword Attribute function kwargs Name\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "600\n", + "[CLS] If Compare Name Is NameConstant If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute call Name Call Name Attribute inputs Name keyword Name Assign Name outputs Call Attribute call Name Call Name Attribute inputs Name\n", + "Label = ['expects', 'training', 'arg', '[PAD]']\n", + "Pred =\n", + "shape expects\n", + "[PAD] training\n", + "[PAD] arg\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "601\n", + "[CLS] Call Name GeneratorExp BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute is tensor Name Name comprehension Name v Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "602\n", + "[CLS] If Call Name Name Name Raise Call Name Str If BoolOp And UnaryOp Not Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Call Attribute is tensor Name Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "603\n", + "[CLS] If Compare Name IsNot NameConstant AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name keyword NameConstant\n", + "Label = ['all', 'inputs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x all\n", + "[PAD] inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "604\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute optimizer Name keyword Attribute loss Name keyword Attribute metrics Name keyword Attribute loss weights Name keyword Name\n", + "Label = ['compile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append compile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "605\n", + "[CLS] BoolOp And Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Compare Call Name Name In List Num Num\n", + "Label = ['image', 'data', 'format', '[PAD]']\n", + "Pred =\n", + "shape image\n", + "[PAD] data\n", + "[PAD] format\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "606\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Tuple Subscript Name Index Num Num Add Subscript Name Slice Num\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "607\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Name Slice UnaryOp USub Num Add Tuple Num\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "608\n", + "[CLS] If BoolOp Or UnaryOp Not Call Name Name Str Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name NameConstant Is NameConstant Expr Call Attribute append Name NameConstant Expr Call Attribute append Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "609\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name BinOp Call Name Subscript Attribute shape Subscript Name Index Num Index Num Mult BinOp Num Sub Name\n", + "Label = ['split', 'at', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output split\n", + "[PAD] at\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "610\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Str Call Name Name Str\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "611\n", + "[CLS] If Call Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name ins BinOp BinOp BinOp Name Add Name Name List Num Assign Name ins BinOp BinOp Name Name Name\n", + "Label = ['uses', 'dynamic', 'learning', 'phase']\n", + "Pred =\n", + "append uses\n", + "[PAD] dynamic\n", + "[PAD] learning\n", + "[PAD] phase\n", + " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "\n", + "612\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['evaluate', 'generator', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape evaluate\n", + "[PAD] generator\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "\n" + ] + } + ], + "source": [ + "n=1\n", + "pred_str = []; score = 0; score_no_pad=0; rank =[]\n", + "for idx in range(613):\n", + " print(idx)\n", + " print(snippet.loc[idx][0])\n", + " print(\"Label =\", labels_str[idx])\n", + " msk_idx = snippet.loc[idx][0].split(\" \").index('[MASK]')\n", + " preds_ = []\n", + " print(\"Pred =\")\n", + " r = preds_all[idx]\n", + " for i in range(n):\n", + " p = [vocab_label_df.loc[r[msk_idx+j][i]][0] for j in range(4)]\n", + " for k,p_ in enumerate(p):\n", + " print(p_, labels_str[idx][k])\n", + " if p_==labels_str[idx][k]:\n", + " score +=1\n", + " if p_ != '[PAD]':\n", + " score_no_pad +=1\n", + " print(\" {}. {}\".format(i,p))\n", + " preds_.append(p)\n", + " pred_str.append(preds_)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Name Tuple Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "1\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Name Call Attribute round Name Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "items mean\n", + "add mean\n", + "init mean\n", + "bias mean\n", + "kernel mean\n", + "call mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "2\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute argmax Name Name keyword UnaryOp Num Call Attribute floatx Name\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items cast\n", + "append cast\n", + "init cast\n", + "add cast\n", + "sqrt cast\n", + "get cast\n", + "reshape cast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "3\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute in top k Name Name Call Attribute cast Name Call Attribute flatten Name Name Str Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "items mean\n", + "add mean\n", + "kernel mean\n", + "bias mean\n", + "init mean\n", + "recurrent mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "4\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute delta ts batch end Name BinOp Call Attribute time Name Sub Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "5\n", + "[CLS] If BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num BoolOp Compare Name BinOp Num Mult Attribute delta t batch Name Compare Name Num Expr Call Attribute warn Name BinOp Str Mod Name\n", + "Label = ['delta', 't', 'batch', '[PAD]']\n", + "Pred =\n", + "data delta\n", + "shape delta\n", + "append delta\n", + "output delta\n", + "bias delta\n", + "kernel delta\n", + "recurrent delta\n", + "[PAD] t\n", + "shape t\n", + "kernel t\n", + "format t\n", + "size t\n", + "output t\n", + "i t\n", + "[PAD] batch\n", + "shape batch\n", + "kernel batch\n", + "format batch\n", + "size batch\n", + "output batch\n", + "i batch\n", + "[PAD] [PAD]\n", + "\n", + "6\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Attribute validation data Name NameConstant Assign Attribute model Name NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "7\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name v Call Attribute items Name If Compare Name In Attribute stateful metrics Name Assign Subscript Attribute totals Name Index Name Name If Compare Name Attribute totals Name AugAssign Subscript Attribute totals Name Index Name Add BinOp Name Mult Name Assign Subscript Attribute totals Name Index Name BinOp Name Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "i k\n", + "output k\n", + "new k\n", + "self k\n", + "shape k\n", + "y k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "8\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Attribute target Name keyword Attribute verbose Name keyword Attribute stateful metrics Name\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output progbar\n", + "shape progbar\n", + "bias progbar\n", + "kernel progbar\n", + "recurrent progbar\n", + "data progbar\n", + "keras progbar\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "9\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg batch arg logs NameConstant If Compare Attribute seen Name Lt Attribute target Name Assign Attribute log values Name List\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "10\n", + "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute log values Name Tuple Name Subscript Name Index Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "11\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute filepath Name keyword BinOp Name Add Num keyword Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call format\n", + "init format\n", + "append format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "12\n", + "[CLS] BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name Name Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape monitor\n", + "data monitor\n", + "name monitor\n", + "output monitor\n", + "bias monitor\n", + "units monitor\n", + "append monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "13\n", + "[CLS] Call Name BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data monitor\n", + "name monitor\n", + "shape monitor\n", + "output monitor\n", + "append monitor\n", + "bias monitor\n", + "format monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "14\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Attribute verbose Name Gt Num Expr Call Name Str Expr Call Attribute set weights Attribute model Name Attribute best weights Name\n", + "Label = ['restore', 'best', 'weights', '[PAD]']\n", + "Pred =\n", + "name restore\n", + "data restore\n", + "shape restore\n", + "kernel restore\n", + "bias restore\n", + "append restore\n", + "return restore\n", + "[PAD] best\n", + "shape best\n", + "kernel best\n", + "format best\n", + "size best\n", + "output best\n", + "i best\n", + "[PAD] weights\n", + "shape weights\n", + "kernel weights\n", + "format weights\n", + "size weights\n", + "output weights\n", + "i weights\n", + "[PAD] [PAD]\n", + "\n", + "15\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Attribute root Name Add Attribute path Name Dict Attribute field Name Call Attribute dumps Name Name keyword Attribute headers Name\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append post\n", + "items post\n", + "add post\n", + "init post\n", + "kernel post\n", + "bias post\n", + "sqrt post\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "16\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute root Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "items warn\n", + "name warn\n", + "init warn\n", + "warn warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "17\n", + "[CLS] If UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute model Name Str Raise Call Name Str\n", + "Label = ['optimizer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call optimizer\n", + "init optimizer\n", + "append optimizer\n", + "format optimizer\n", + "get optimizer\n", + "output optimizer\n", + "add optimizer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "18\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg epoch arg logs NameConstant Assign Name logs BoolOp Or Name Dict Assign Subscript Name Index Str Call Attribute get value Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "19\n", + "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['get', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append get\n", + "add get\n", + "items get\n", + "bias get\n", + "output get\n", + "init get\n", + "kernel get\n", + "[PAD] value\n", + "shape value\n", + "size value\n", + "kernel value\n", + "format value\n", + "output value\n", + "length value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "20\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name List Num Subscript Name Index Num Subscript Name Index Num Num\n", + "Label = ['w', 'img', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output w\n", + "x w\n", + "shape w\n", + "i w\n", + "kernel w\n", + "input w\n", + "batch w\n", + "[PAD] img\n", + "shape img\n", + "kernel img\n", + "output img\n", + "format img\n", + "size img\n", + "out img\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "21\n", + "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Attribute name Name comprehension Name layer Attribute layers Attribute model Name Compare Attribute name Call Name Name Eq Str\n", + "Label = ['embeddings', 'layer', 'names', '[PAD]']\n", + "Pred =\n", + "output embeddings\n", + "x embeddings\n", + "shape embeddings\n", + "i embeddings\n", + "self embeddings\n", + "input embeddings\n", + "kernel embeddings\n", + "[PAD] layer\n", + "shape layer\n", + "output layer\n", + "kernel layer\n", + "size layer\n", + "out layer\n", + "format layer\n", + "[PAD] names\n", + "shape names\n", + "output names\n", + "kernel names\n", + "size names\n", + "out names\n", + "format names\n", + "[PAD] [PAD]\n", + "\n", + "22\n", + "[CLS] ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name layer Attribute layers Attribute model Name Compare Attribute name Call Name Name Eq Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "23\n", + "[CLS] ListComp Subscript Name Slice Name BinOp Name Add Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "24\n", + "[CLS] BinOp Str Mod Tuple Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute join Str Call Name Call Attribute keys Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name monitor\n", + "append monitor\n", + "data monitor\n", + "output monitor\n", + "shape monitor\n", + "decay monitor\n", + "items monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "25\n", + "[CLS] Tuple Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute join Str Call Name Call Attribute keys Name\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data monitor\n", + "append monitor\n", + "name monitor\n", + "output monitor\n", + "shape monitor\n", + "format monitor\n", + "bias monitor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "26\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Attribute get value Name Attribute lr Attribute optimizer Attribute model Name\n", + "Label = ['old', 'lr', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output old\n", + "x old\n", + "i old\n", + "shape old\n", + "input old\n", + "kernel old\n", + "self old\n", + "[PAD] lr\n", + "shape lr\n", + "kernel lr\n", + "output lr\n", + "size lr\n", + "format lr\n", + "out lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "27\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute open Name Attribute filename Name BinOp Name Add Attribute file flags Name keyword Attribute open args Name\n", + "Label = ['csv', 'file', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias csv\n", + "output csv\n", + "recurrent csv\n", + "shape csv\n", + "kernel csv\n", + "data csv\n", + "keras csv\n", + "[PAD] file\n", + "shape file\n", + "kernel file\n", + "size file\n", + "format file\n", + "output file\n", + "i file\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "28\n", + "[CLS] BoolOp And Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute ndim Name Eq Num\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "append ndarray\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data ndarray\n", + "output ndarray\n", + "call ndarray\n", + "kernel ndarray\n", + "input ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "29\n", + "[CLS] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name [MASK] [MASK] [MASK] [MASK] Attribute keys Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "i k\n", + "output k\n", + "self k\n", + "shape k\n", + "new k\n", + "state k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "30\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name fieldnames ListComp Call Name Name comprehension Name x Name\n", + "Label = ['PY2', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data PY2\n", + "kernel PY2\n", + "bias PY2\n", + "recurrent PY2\n", + "shape PY2\n", + "append PY2\n", + "return PY2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "31\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Mult BinOp Name Div BinOp Call Attribute epsilon Name Add Name\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x w\n", + "i w\n", + "output w\n", + "name w\n", + "y w\n", + "w w\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "32\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg min value arg max value arg rate arg axis Num Num Num Num Assign Attribute min value Name Name Assign Attribute max value Name Name Assign Attribute rate Name Name Assign Attribute axis Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "33\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items sqrt\n", + "append sqrt\n", + "sqrt sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "34\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Name Is NameConstant Return NameConstant If Call Name Name Name Return Call Name Name If Call Name Name Attribute string types Name Assign Name config Dict Str Str Call Name Name Dict Return Call Name Name If Call Name Name Return Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['identifier', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self identifier\n", + "x identifier\n", + "args identifier\n", + "y identifier\n", + "shape identifier\n", + "data identifier\n", + "input identifier\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "35\n", + "[CLS] ClassDef Name Expr Str FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape arg dtype NameConstant Return Call Attribute constant Name Num keyword Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "36\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute minval Name Attribute maxval Name keyword Name keyword Attribute seed Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call random\n", + "shape random\n", + "bias random\n", + "add random\n", + "kernel random\n", + "append random\n", + "recurrent random\n", + "[PAD] uniform\n", + "shape uniform\n", + "format uniform\n", + "kernel uniform\n", + "size uniform\n", + "output uniform\n", + "length uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "37\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Num Name keyword Name keyword Attribute seed Name\n", + "Label = ['truncated', 'normal', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape truncated\n", + "call truncated\n", + "append truncated\n", + "add truncated\n", + "bias truncated\n", + "kernel truncated\n", + "recurrent truncated\n", + "[PAD] normal\n", + "shape normal\n", + "format normal\n", + "kernel normal\n", + "size normal\n", + "output normal\n", + "length normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "38\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name UnaryOp USub Name Name keyword Name keyword Attribute seed Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call random\n", + "shape random\n", + "append random\n", + "add random\n", + "kernel random\n", + "bias random\n", + "output random\n", + "[PAD] uniform\n", + "shape uniform\n", + "kernel uniform\n", + "format uniform\n", + "size uniform\n", + "output uniform\n", + "length uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "39\n", + "[CLS] Return BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute identity Name Subscript Name Index Num\n", + "Label = ['gain', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape gain\n", + "append gain\n", + "data gain\n", + "name gain\n", + "bias gain\n", + "kernel gain\n", + "recurrent gain\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "40\n", + "[CLS] BinOp List Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Mult BinOp Subscript Name Index Num FloorDiv Subscript Name Index Num\n", + "Label = ['identity', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append identity\n", + "shape identity\n", + "add identity\n", + "bias identity\n", + "init identity\n", + "reshape identity\n", + "items identity\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "41\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num Assign Name fan in BinOp Subscript Name Index UnaryOp Num Mult Name Assign Name fan out BinOp Subscript Name Index UnaryOp Num Name Raise Call Name BinOp Str Add Name\n", + "Label = ['receptive', 'field', 'size', '[PAD]']\n", + "Pred =\n", + "x receptive\n", + "output receptive\n", + "shape receptive\n", + "i receptive\n", + "kernel receptive\n", + "input receptive\n", + "self receptive\n", + "[PAD] field\n", + "shape field\n", + "kernel field\n", + "output field\n", + "size field\n", + "out field\n", + "format field\n", + "[PAD] size\n", + "shape size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "\n", + "42\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Return Call Attribute mean Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self y\n", + "x y\n", + "args y\n", + "y y\n", + "[PAD] true\n", + "shape true\n", + "kernel true\n", + "size true\n", + "name true\n", + "format true\n", + "output true\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "43\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name Name Call Attribute epsilon Name NameConstant\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append clip\n", + "items clip\n", + "init clip\n", + "sqrt clip\n", + "get clip\n", + "output clip\n", + "add clip\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "44\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Call Attribute clip Name Name Call Attribute epsilon Name NameConstant Add Num\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append log\n", + "items log\n", + "sqrt log\n", + "init log\n", + "name log\n", + "kernel log\n", + "mean log\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "45\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Name keyword UnaryOp USub Num\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sum\n", + "items sum\n", + "add sum\n", + "kernel sum\n", + "bias sum\n", + "init sum\n", + "concatenate sum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "46\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp Num Sub Name Mult Name keyword UnaryOp USub Num\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append max\n", + "items max\n", + "kernel max\n", + "add max\n", + "init max\n", + "bias max\n", + "concatenate max\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "47\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num BinOp BinOp Name Sub Name Add Num\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append maximum\n", + "items maximum\n", + "shape maximum\n", + "init maximum\n", + "name maximum\n", + "output maximum\n", + "kernel maximum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "48\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute binary crossentropy Name Name Name keyword UnaryOp USub Num\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append mean\n", + "items mean\n", + "add mean\n", + "init mean\n", + "bias mean\n", + "kernel mean\n", + "get mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "49\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name y true Call Attribute l2 normalize Name Name keyword UnaryOp USub Num Assign Name y pred Call Attribute l2 normalize Name Name keyword UnaryOp Num Return UnaryOp Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp Num\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self y\n", + "x y\n", + "args y\n", + "y y\n", + "[PAD] true\n", + "shape true\n", + "size true\n", + "kernel true\n", + "name true\n", + "format true\n", + "output true\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "50\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x k\n", + "i k\n", + "name k\n", + "output k\n", + "shape k\n", + "y k\n", + "new k\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "51\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "52\n", + "[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape clipnorm\n", + "output clipnorm\n", + "data clipnorm\n", + "kernel clipnorm\n", + "bias clipnorm\n", + "recurrent clipnorm\n", + "keras clipnorm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "53\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute rho Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "items name\n", + "name name\n", + "[PAD] scope\n", + "shape scope\n", + "size scope\n", + "kernel scope\n", + "format scope\n", + "output scope\n", + "weight scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "54\n", + "[CLS] BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append decay\n", + "name decay\n", + "decay decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "55\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "x new\n", + "shape new\n", + "i new\n", + "self new\n", + "kernel new\n", + "input new\n", + "[PAD] p\n", + "shape p\n", + "kernel p\n", + "output p\n", + "size p\n", + "format p\n", + "out p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "56\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "57\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "items name\n", + "name name\n", + "[PAD] scope\n", + "shape scope\n", + "size scope\n", + "kernel scope\n", + "format scope\n", + "output scope\n", + "weight scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "58\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Assign Name lr BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['initial', 'decay', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data initial\n", + "append initial\n", + "output initial\n", + "shape initial\n", + "kernel initial\n", + "bias initial\n", + "recurrent initial\n", + "[PAD] decay\n", + "shape decay\n", + "kernel decay\n", + "size decay\n", + "format decay\n", + "output decay\n", + "i decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "59\n", + "[CLS] BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append decay\n", + "name decay\n", + "decay decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "60\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute name Attribute class Name Assign Attribute lr Name Call Attribute variable Name Name keyword Str Assign Attribute decay Name Call Attribute variable Name Name keyword Str Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append name\n", + "items name\n", + "name name\n", + "[PAD] scope\n", + "shape scope\n", + "size scope\n", + "kernel scope\n", + "format scope\n", + "output scope\n", + "weight scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "61\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "x new\n", + "shape new\n", + "i new\n", + "self new\n", + "kernel new\n", + "input new\n", + "[PAD] p\n", + "shape p\n", + "kernel p\n", + "output p\n", + "size p\n", + "format p\n", + "out p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "62\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute rho Name Mult Name Add BinOp BinOp Num Sub Attribute rho Name Call Attribute square Name Name\n", + "Label = ['new', 'd', 'a', '[PAD]']\n", + "Pred =\n", + "output new\n", + "x new\n", + "i new\n", + "name new\n", + "shape new\n", + "kernel new\n", + "config new\n", + "[PAD] d\n", + "shape d\n", + "kernel d\n", + "size d\n", + "output d\n", + "out d\n", + "format d\n", + "[PAD] a\n", + "shape a\n", + "kernel a\n", + "size a\n", + "output a\n", + "out a\n", + "format a\n", + "[PAD] [PAD]\n", + "\n", + "63\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output lr\n", + "x lr\n", + "i lr\n", + "name lr\n", + "shape lr\n", + "kernel lr\n", + "config lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "64\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Call Attribute sqrt Name Name Add Attribute epsilon Name\n", + "Label = ['p', 't', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output p\n", + "name p\n", + "x p\n", + "i p\n", + "w p\n", + "y p\n", + "new p\n", + "[PAD] t\n", + "shape t\n", + "name t\n", + "kernel t\n", + "size t\n", + "output t\n", + "out t\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "65\n", + "[CLS] BinOp BinOp Name Mult Name Div BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Add Attribute epsilon Name\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name sqrt\n", + "append sqrt\n", + "items sqrt\n", + "init sqrt\n", + "shape sqrt\n", + "sqrt sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "66\n", + "[CLS] If Compare Call Name Name Str NameConstant IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constraint Name Name\n", + "Label = ['new', 'p', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output new\n", + "x new\n", + "shape new\n", + "i new\n", + "self new\n", + "kernel new\n", + "input new\n", + "[PAD] p\n", + "shape p\n", + "kernel p\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output p\n", + "size p\n", + "format p\n", + "out p\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "67\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "68\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Assign Name lr BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['initial', 'decay', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data initial\n", + "append initial\n", + "output initial\n", + "shape initial\n", + "kernel initial\n", + "bias initial\n", + "recurrent initial\n", + "[PAD] decay\n", + "shape decay\n", + "kernel decay\n", + "size decay\n", + "format decay\n", + "output decay\n", + "i decay\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "69\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output lr\n", + "x lr\n", + "i lr\n", + "name lr\n", + "shape lr\n", + "kernel lr\n", + "config lr\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "70\n", + "[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 1 Name Name\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape pow\n", + "append pow\n", + "name pow\n", + "output pow\n", + "init pow\n", + "kernel pow\n", + "recurrent pow\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "71\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 1 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", + "Label = ['m', 't', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output m\n", + "x m\n", + "i m\n", + "name m\n", + "shape m\n", + "kernel m\n", + "input m\n", + "[PAD] t\n", + "shape t\n", + "kernel t\n", + "size t\n", + "output t\n", + "i t\n", + "format t\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "72\n", + "[CLS] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name epsilon\n", + "data epsilon\n", + "sqrt epsilon\n", + "append epsilon\n", + "init epsilon\n", + "overwrite epsilon\n", + "input epsilon\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "73\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Call Name Call Attribute get value Name Attribute lr Name Call Name Call Attribute get value Name Attribute beta 1 Name Call Name Call Attribute get value Name Attribute beta 2 Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "x config\n", + "name config\n", + "i config\n", + "config config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "74\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute beta 1 Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp Name Attribute schedule decay Name\n", + "Label = ['momentum', 'cache', 't', '[PAD]']\n", + "Pred =\n", + "output momentum\n", + "x momentum\n", + "i momentum\n", + "shape momentum\n", + "kernel momentum\n", + "input momentum\n", + "name momentum\n", + "[PAD] cache\n", + "shape cache\n", + "kernel cache\n", + "output cache\n", + "size cache\n", + "out cache\n", + "format cache\n", + "[PAD] t\n", + "shape t\n", + "kernel t\n", + "output t\n", + "size t\n", + "out t\n", + "format t\n", + "[PAD] [PAD]\n", + "\n", + "75\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp List Attribute iterations Name Add Name Name\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape weights\n", + "output weights\n", + "kernel weights\n", + "bias weights\n", + "name weights\n", + "recurrent weights\n", + "append weights\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "76\n", + "[CLS] BinOp BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", + "Label = ['beta', '1', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name beta\n", + "shape beta\n", + "data beta\n", + "recurrent beta\n", + "bias beta\n", + "append beta\n", + "kernel beta\n", + "[PAD] 1\n", + "shape 1\n", + "kernel 1\n", + "size 1\n", + "format 1\n", + "output 1\n", + "i 1\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "77\n", + "[CLS] BinOp BinOp Num Sub Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute square Name Name\n", + "Label = ['beta', '2', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name beta\n", + "append beta\n", + "shape beta\n", + "items beta\n", + "data beta\n", + "sqrt beta\n", + "output beta\n", + "[PAD] 2\n", + "shape 2\n", + "kernel 2\n", + "size 2\n", + "format 2\n", + "name 2\n", + "output 2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "78\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Str Str Name Name Name Name Name Name Name Name\n", + "Label = ['all', 'classes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output all\n", + "x all\n", + "name all\n", + "shape all\n", + "i all\n", + "self all\n", + "input all\n", + "[PAD] classes\n", + "shape classes\n", + "output classes\n", + "kernel classes\n", + "name classes\n", + "size classes\n", + "out classes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "79\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name Tuple Attribute nb feature Name Attribute output dim Name keyword Str keyword Str keyword Attribute b regularizer Name keyword Attribute b constraint Name\n", + "Label = ['b', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape b\n", + "output b\n", + "bias b\n", + "recurrent b\n", + "kernel b\n", + "data b\n", + "keras b\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "80\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Assert BoolOp And Name Compare Call Name Name Eq Num Return Tuple Subscript Name Index Num Attribute output dim Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "81\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg init arg activation arg weights arg W regularizer arg b regularizer arg activity regularizer arg W constraint arg b constraint arg bias arg input dim arg kwargs Str NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "82\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg return sequences arg return state arg go backwards arg stateful arg unroll arg implementation arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant Num\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "83\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name outputs Name states Call Attribute rnn Name Attribute step Name Name Name keyword Attribute go backwards Name keyword Name keyword Name keyword Attribute unroll Name keyword Name\n", + "Label = ['last', 'output', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x last\n", + "i last\n", + "output last\n", + "shape last\n", + "self last\n", + "y last\n", + "layer last\n", + "[PAD] output\n", + "shape output\n", + "kernel output\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "84\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Expr Call Attribute append Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "85\n", + "[CLS] If Compare Num Lt BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Attribute recurrent dropout Name Assign Attribute uses learning phase Name NameConstant Assign Attribute uses learning phase Name NameConstant\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name dropout\n", + "data dropout\n", + "shape dropout\n", + "append dropout\n", + "input dropout\n", + "self dropout\n", + "output dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "86\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "87\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg filters arg kernel size arg strides arg padding arg data format arg dilation rate arg return sequences arg go backwards arg stateful arg kwargs Tuple Num Num Str NameConstant Tuple Num Num NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "88\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "89\n", + "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name If Compare Name Subscript Name Index Name Assign Subscript Name Index Name Subscript Subscript Name Index Name Index Name\n", + "Label = ['old', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output old\n", + "x old\n", + "shape old\n", + "i old\n", + "self old\n", + "input old\n", + "kernel old\n", + "[PAD] value\n", + "shape value\n", + "output value\n", + "kernel value\n", + "size value\n", + "out value\n", + "format value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "90\n", + "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name str val Str Assign Name str val Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "output ndarray\n", + "data ndarray\n", + "call ndarray\n", + "add ndarray\n", + "input ndarray\n", + "keras ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "91\n", + "[CLS] If Compare Call Name Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Name Slice Num Add Str\n", + "Label = ['str', 'val', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x str\n", + "output str\n", + "shape str\n", + "name str\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i str\n", + "kernel str\n", + "input str\n", + "[PAD] val\n", + "shape val\n", + "kernel val\n", + "output val\n", + "size val\n", + "format val\n", + "out val\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "92\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Str Add Name Str Str Name keyword Num\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "items warn\n", + "init warn\n", + "name warn\n", + "add warn\n", + "shape warn\n", + "kernel warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "93\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str keyword List Tuple Str Str Tuple Str Str Tuple Str Str keyword Name\n", + "Label = ['legacy', 'embedding', 'support', '[PAD]']\n", + "Pred =\n", + "x legacy\n", + "output legacy\n", + "shape legacy\n", + "i legacy\n", + "kernel legacy\n", + "self legacy\n", + "input legacy\n", + "[PAD] embedding\n", + "shape embedding\n", + "output embedding\n", + "kernel embedding\n", + "format embedding\n", + "size embedding\n", + "out embedding\n", + "[PAD] support\n", + "shape support\n", + "output support\n", + "kernel support\n", + "format support\n", + "size support\n", + "out support\n", + "[PAD] [PAD]\n", + "\n", + "94\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant\n", + "Label = ['legacy', 'pooling3d', 'support', '[PAD]']\n", + "Pred =\n", + "output legacy\n", + "x legacy\n", + "shape legacy\n", + "i legacy\n", + "name legacy\n", + "input legacy\n", + "self legacy\n", + "[PAD] pooling3d\n", + "shape pooling3d\n", + "output pooling3d\n", + "kernel pooling3d\n", + "size pooling3d\n", + "format pooling3d\n", + "name pooling3d\n", + "[PAD] support\n", + "shape support\n", + "output support\n", + "kernel support\n", + "size support\n", + "format support\n", + "name support\n", + "[PAD] [PAD]\n", + "\n", + "95\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output kernel\n", + "shape kernel\n", + "x kernel\n", + "kernel kernel\n", + "[PAD] size\n", + "shape size\n", + "kernel size\n", + "output size\n", + "format size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "96\n", + "[CLS] If BoolOp And Compare Str In Name Compare Str Name Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Attribute pop Name Str Call Attribute pop Name Str Assign Subscript Name Index Str Name Expr Call Attribute append Name Tuple Str Str\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output kernel\n", + "x kernel\n", + "shape kernel\n", + "i kernel\n", + "name kernel\n", + "kernel kernel\n", + "[PAD] size\n", + "shape size\n", + "kernel size\n", + "output size\n", + "format size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "97\n", + "[CLS] If Call Name Subscript Name Index Num Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num Expr Call Attribute append Name Tuple Str NameConstant\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "x args\n", + "shape args\n", + "i args\n", + "kernel args\n", + "self args\n", + "input args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "98\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List Subscript Name Index Num Subscript Name Index Num Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "shape args\n", + "x args\n", + "i args\n", + "kernel args\n", + "self args\n", + "input args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "99\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword NameConstant keyword List Tuple Str Str\n", + "Label = ['legacy', 'input', 'support', '[PAD]']\n", + "Pred =\n", + "output legacy\n", + "x legacy\n", + "shape legacy\n", + "i legacy\n", + "kernel legacy\n", + "self legacy\n", + "input legacy\n", + "[PAD] input\n", + "shape input\n", + "output input\n", + "kernel input\n", + "format input\n", + "size input\n", + "out input\n", + "[PAD] support\n", + "shape support\n", + "output support\n", + "kernel support\n", + "format support\n", + "size support\n", + "out support\n", + "[PAD] [PAD]\n", + "\n", + "100\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Name Index Num Add Subscript Name Slice Num\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output args\n", + "shape args\n", + "x args\n", + "i args\n", + "kernel args\n", + "input args\n", + "config args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "101\n", + "[CLS] Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute replace Call Attribute lower Name Str Str Str Slice UnaryOp USub Num\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init split\n", + "items split\n", + "append split\n", + "name split\n", + "call split\n", + "format split\n", + "get split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "102\n", + "[CLS] BinOp List Str Add ListComp BinOp Str Mod Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "103\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Mod Tuple Name Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x device\n", + "i device\n", + "name device\n", + "output device\n", + "shape device\n", + "y device\n", + "new device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "104\n", + "[CLS] If Name With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name model Call Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append device\n", + "items device\n", + "name device\n", + "add device\n", + "init device\n", + "sqrt device\n", + "self device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "105\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute inputs Name With withitem Call Attribute device Name Attribute device Name Assign Name input shape Subscript Call Attribute int shape Name Name Slice Num Assign Name slice i Call Call Name Name keyword Name keyword Dict Str Str Name Name Name Expr Call Attribute append Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "106\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute output names Name If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name n BinOp Str Mod Subscript Name Index Name Expr Call Attribute append Name Name\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x n\n", + "i n\n", + "name n\n", + "output n\n", + "shape n\n", + "kernel n\n", + "state n\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "107\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Name Str BinOp Str Mod Subscript Name Index Num Assign Name merged List For Tuple Name name Name outputs Call Name Name Name Expr Call Attribute append Name Call Name Name keyword Num keyword Name Return Call Name Attribute inputs Name Name\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append device\n", + "items device\n", + "init device\n", + "add device\n", + "shape device\n", + "name device\n", + "reshape device\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "108\n", + "[CLS] If Compare Name Is NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Attribute data Name Index Num Assign Attribute end Name Name\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape end\n", + "bias end\n", + "recurrent end\n", + "output end\n", + "kernel end\n", + "data end\n", + "keras end\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "109\n", + "[CLS] If Compare BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute end Name Assign Name idx BinOp Name Attribute start Name Raise Name\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name start\n", + "data start\n", + "shape start\n", + "self start\n", + "input start\n", + "init start\n", + "append start\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "110\n", + "[CLS] If Compare BinOp Call Name Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute end Name Assign Name idx ListComp BinOp Name Attribute start Name comprehension Name x Name Raise Name\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name start\n", + "append start\n", + "data start\n", + "shape start\n", + "decay start\n", + "self start\n", + "input start\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "111\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg attr FunctionDef arguments arg f FunctionDef arguments arg args arg kwargs Assign Name out Call Name Starred Name keyword Name If Call Name Attribute data Name Call Name Name Return Call Name Name Return Name Return Name Return Call Name Call Name Attribute data Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "112\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Name BinOp Name Mult Name comprehension Name p Name\n", + "Label = ['positions', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output positions\n", + "x positions\n", + "i positions\n", + "name positions\n", + "shape positions\n", + "kernel positions\n", + "input positions\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "113\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Subscript Name Index Name Sub Call Name Name\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name line\n", + "x line\n", + "output line\n", + "i line\n", + "shape line\n", + "y line\n", + "input line\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "114\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Assign Name output shape Attribute output shape Name ExceptHandler Name Assign Name output shape Str Assign Name name Attribute name Name Assign Name cls name Attribute name Attribute class Name Assign Name fields List BinOp BinOp BinOp Name Add Str Name Str Name Call Attribute count params Name Expr Call Name Name Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self layer\n", + "x layer\n", + "args layer\n", + "name layer\n", + "shape layer\n", + "y layer\n", + "output layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "115\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp BinOp Name Add Str Call Name Name Str Call Name Name Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name append\n", + "items append\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "116\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name Assign Name fields List Str Str Str Subscript Name Index Name Expr Call Name Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "117\n", + "[CLS] While NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute read Name Name AugAssign Name count Add Num If Compare Name IsNot NameConstant Expr Call Name Name Name Name If Name Expr Yield Name Break\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output chunk\n", + "x chunk\n", + "i chunk\n", + "shape chunk\n", + "kernel chunk\n", + "self chunk\n", + "input chunk\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "118\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Attribute path Name Call Attribute expanduser Attribute path Name Str Str\n", + "Label = ['cache', 'dir', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output cache\n", + "x cache\n", + "i cache\n", + "self cache\n", + "kernel cache\n", + "input cache\n", + "config cache\n", + "[PAD] dir\n", + "shape dir\n", + "kernel dir\n", + "output dir\n", + "size dir\n", + "out dir\n", + "format dir\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "119\n", + "[CLS] If BoolOp And Compare Name IsNot NameConstant Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Assign Name hash algorithm Str\n", + "Label = ['file', 'hash', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output file\n", + "x file\n", + "shape file\n", + "i file\n", + "kernel file\n", + "self file\n", + "input file\n", + "[PAD] hash\n", + "shape hash\n", + "kernel hash\n", + "output hash\n", + "size hash\n", + "format hash\n", + "out hash\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "120\n", + "[CLS] If Compare Name Is NameConstant Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute Value Name Str Num ExceptHandler Name Assign Name SEQUENCE COUNTER Num\n", + "Label = ['SEQUENCE', 'COUNTER', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output SEQUENCE\n", + "x SEQUENCE\n", + "shape SEQUENCE\n", + "i SEQUENCE\n", + "kernel SEQUENCE\n", + "self SEQUENCE\n", + "input SEQUENCE\n", + "[PAD] COUNTER\n", + "shape COUNTER\n", + "kernel COUNTER\n", + "output COUNTER\n", + "size COUNTER\n", + "out COUNTER\n", + "format COUNTER\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "121\n", + "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute Value Name Str Num ExceptHandler Name Assign Name SEQUENCE COUNTER Num\n", + "Label = ['SEQUENCE', 'COUNTER', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output SEQUENCE\n", + "x SEQUENCE\n", + "shape SEQUENCE\n", + "i SEQUENCE\n", + "kernel SEQUENCE\n", + "self SEQUENCE\n", + "input SEQUENCE\n", + "[PAD] COUNTER\n", + "shape COUNTER\n", + "kernel COUNTER\n", + "output COUNTER\n", + "size COUNTER\n", + "out COUNTER\n", + "format COUNTER\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "122\n", + "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal Name\n", + "Label = ['stop', 'signal', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data stop\n", + "shape stop\n", + "bias stop\n", + "recurrent stop\n", + "kernel stop\n", + "output stop\n", + "append stop\n", + "[PAD] signal\n", + "shape signal\n", + "kernel signal\n", + "format signal\n", + "size signal\n", + "output signal\n", + "state signal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "123\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Call Attribute is set Attribute stop signal Name Return Expr Call Attribute put Attribute queue Name Call Attribute apply async Name Name Tuple Attribute uid Name Name keyword NameConstant\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "124\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute queue Name Call Attribute apply async Name Name Tuple Attribute uid Name keyword NameConstant\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call put\n", + "init put\n", + "append put\n", + "format put\n", + "get put\n", + "items put\n", + "add put\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "125\n", + "[CLS] Call Name Call Name Lambda arguments arg [MASK] [MASK] [MASK] [MASK] Call Attribute wait Name Name\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self f\n", + "x f\n", + "args f\n", + "y f\n", + "data f\n", + "input f\n", + "axis f\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "126\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute get Name comprehension Name future Name Call Attribute successful Name\n", + "Label = ['last', 'ones', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output last\n", + "x last\n", + "i last\n", + "kernel last\n", + "shape last\n", + "self last\n", + "input last\n", + "[PAD] ones\n", + "shape ones\n", + "kernel ones\n", + "output ones\n", + "size ones\n", + "format ones\n", + "out ones\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "127\n", + "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Name Str Call Name Name Str Call Name Name\n", + "Label = ['value', 'tuple', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x value\n", + "output value\n", + "name value\n", + "i value\n", + "shape value\n", + "input value\n", + "y value\n", + "[PAD] tuple\n", + "shape tuple\n", + "name tuple\n", + "output tuple\n", + "kernel tuple\n", + "out tuple\n", + "size tuple\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "128\n", + "[CLS] If UnaryOp Not Compare Num LtE Attribute [MASK] [MASK] [MASK] [MASK] Name Num Raise Call Name Str Attribute shape Name\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndim\n", + "data ndim\n", + "output ndim\n", + "bias ndim\n", + "append ndim\n", + "kernel ndim\n", + "recurrent ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "129\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Name Sub BinOp BinOp Name Add Name Num\n", + "Label = ['dim', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dim\n", + "x dim\n", + "name dim\n", + "shape dim\n", + "i dim\n", + "y dim\n", + "input dim\n", + "[PAD] size\n", + "shape size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "130\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Subscript Name Index Name keyword Num keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "init normal\n", + "append normal\n", + "format normal\n", + "bias normal\n", + "add normal\n", + "get normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "131\n", + "[CLS] If Call Name Name Name If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute build Name\n", + "Label = ['built', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape built\n", + "data built\n", + "append built\n", + "bias built\n", + "name built\n", + "recurrent built\n", + "items built\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "132\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute name Name Add Str Call Name Name\n", + "Label = ['node', 'key', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name node\n", + "output node\n", + "x node\n", + "shape node\n", + "i node\n", + "[PAD] node\n", + "config node\n", + "[PAD] key\n", + "shape key\n", + "name key\n", + "output key\n", + "kernel key\n", + "size key\n", + "format key\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "133\n", + "[CLS] If Call Name Name Str Return Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Call Attribute get config Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "134\n", + "[CLS] If Call Name Name Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "135\n", + "[CLS] BoolOp And Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Attribute isatty Attribute stdout Name\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append stdout\n", + "shape stdout\n", + "data stdout\n", + "call stdout\n", + "add stdout\n", + "init stdout\n", + "output stdout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "136\n", + "[CLS] Assign Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name List BinOp Name Mult BinOp Name Sub Attribute seen so far Name BinOp Name Attribute seen so far Name\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape values\n", + "keras values\n", + "kernel values\n", + "recurrent values\n", + "output values\n", + "bias values\n", + "data values\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "137\n", + "[CLS] If BoolOp And Compare BinOp Name Sub Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Attribute interval Name Compare Attribute target Name IsNot NameConstant Compare Name Attribute target Name Return\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name last\n", + "data last\n", + "shape last\n", + "output last\n", + "append last\n", + "self last\n", + "dtype last\n", + "[PAD] update\n", + "shape update\n", + "kernel update\n", + "format update\n", + "size update\n", + "output update\n", + "i update\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "138\n", + "[CLS] If Compare Name GtE Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mod Name If Compare Name Num AugAssign Name info BinOp Str BinOp Name Mult Num AugAssign Name info BinOp Str BinOp Name Num\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x info\n", + "output info\n", + "i info\n", + "shape info\n", + "name info\n", + "self info\n", + "input info\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "139\n", + "[CLS] If Compare Name GtE Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mod BinOp Name Mult Num AugAssign Name info BinOp Str BinOp Name Num\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x info\n", + "output info\n", + "i info\n", + "shape info\n", + "name info\n", + "self info\n", + "batch info\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "140\n", + "[CLS] Call Name Num Subscript Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Index Num\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape values\n", + "keras values\n", + "output values\n", + "data values\n", + "kernel values\n", + "bias values\n", + "recurrent values\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "141\n", + "[CLS] ListComp IfExp Compare Name Is NameConstant NameConstant Subscript Name Index Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "142\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x test Call Attribute reshape Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num Call Name Name Num Num\n", + "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append open\n", + "add open\n", + "items open\n", + "init open\n", + "kernel open\n", + "data open\n", + "bias open\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "143\n", + "[CLS] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name comprehension Name x Name\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x w\n", + "i w\n", + "shape w\n", + "new w\n", + "output w\n", + "name w\n", + "self w\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "144\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name Assign Name xs ListComp ListComp Name comprehension Name w Name Compare Name Name Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "output xs\n", + "shape xs\n", + "i xs\n", + "self xs\n", + "kernel xs\n", + "batch xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "145\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "output xs\n", + "shape xs\n", + "i xs\n", + "self xs\n", + "kernel xs\n", + "batch xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "146\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name labels test Tuple Subscript Name Index Str Subscript Name Index Str\n", + "Label = ['x', 'test', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] test\n", + "shape test\n", + "kernel test\n", + "output test\n", + "out test\n", + "size test\n", + "format test\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "147\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name If Name Assign Name xs ListComp ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "output xs\n", + "shape xs\n", + "i xs\n", + "self xs\n", + "kernel xs\n", + "input xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "148\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "output xs\n", + "shape xs\n", + "i xs\n", + "self xs\n", + "kernel xs\n", + "input xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "149\n", + "[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name labels Call Name Name Name Name If UnaryOp Not Name Raise Call Name BinOp BinOp Str Add Call Name Name Str\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x xs\n", + "i xs\n", + "output xs\n", + "shape xs\n", + "y xs\n", + "self xs\n", + "new xs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "150\n", + "[CLS] Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Name Call Attribute array Name Subscript Name Slice Name\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append array\n", + "add array\n", + "shape array\n", + "items array\n", + "bias array\n", + "init array\n", + "output array\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "151\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name Tuple Call Name Name Num\n", + "Label = ['y', 'test', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "output y\n", + "shape y\n", + "i y\n", + "kernel y\n", + "input y\n", + "batch y\n", + "[PAD] test\n", + "shape test\n", + "kernel test\n", + "output test\n", + "format test\n", + "size test\n", + "out test\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "152\n", + "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name x train Call Attribute transpose Name Num Num Num Num Assign Name x test Call Attribute transpose Name Num Num Num Num\n", + "Label = ['image', 'data', 'format', '[PAD]']\n", + "Pred =\n", + "shape image\n", + "append image\n", + "call image\n", + "add image\n", + "bias image\n", + "data image\n", + "recurrent image\n", + "[PAD] data\n", + "shape data\n", + "format data\n", + "kernel data\n", + "size data\n", + "output data\n", + "length data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "\n", + "153\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Call Name BinOp Call Name Name Mult BinOp Num Sub Name\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append array\n", + "items array\n", + "shape array\n", + "add array\n", + "init array\n", + "kernel array\n", + "bias array\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "154\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute call Name keyword Call Attribute filter sk params Name Attribute call Name\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output model\n", + "bias model\n", + "shape model\n", + "kernel model\n", + "recurrent model\n", + "data model\n", + "append model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "155\n", + "[CLS] BoolOp And Compare Name Eq Str Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "156\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute preprocess input Name Starred Name keyword Name Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self args\n", + "x args\n", + "args args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "157\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute MobileNet Name Starred Name keyword Name Name\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self args\n", + "x args\n", + "args args\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "158\n", + "[CLS] If Compare NameConstant In List Name Name Return NameConstant If Compare Call Name Name Lt Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] [MASK] Name Name Name If UnaryOp Not Name Return Name\n", + "Label = ['compute', 'elemwise', 'op', 'output']\n", + "Pred =\n", + "shape compute\n", + "append compute\n", + "init compute\n", + "output compute\n", + "add compute\n", + "bias compute\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "call compute\n", + "[PAD] elemwise\n", + "shape elemwise\n", + "format elemwise\n", + "kernel elemwise\n", + "size elemwise\n", + "output elemwise\n", + "i elemwise\n", + "[PAD] op\n", + "shape op\n", + "format op\n", + "kernel op\n", + "size op\n", + "output op\n", + "i op\n", + "[PAD] output\n", + "shape output\n", + "format output\n", + "kernel output\n", + "size output\n", + "output output\n", + "\n", + "159\n", + "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "160\n", + "[CLS] If BoolOp And Compare NameConstant NotIn Name Compare Call Name Call Name Call Name Name Name Eq Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name NameConstant Assign Attribute reshape required Name NameConstant\n", + "Label = ['reshape', 'required', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape reshape\n", + "output reshape\n", + "append reshape\n", + "bias reshape\n", + "kernel reshape\n", + "data reshape\n", + "recurrent reshape\n", + "[PAD] required\n", + "shape required\n", + "kernel required\n", + "format required\n", + "size required\n", + "output required\n", + "i required\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "161\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Name Call Name Num Name Add List Num\n", + "Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dims\n", + "x dims\n", + "shape dims\n", + "name dims\n", + "i dims\n", + "input dims\n", + "config dims\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "162\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute concatenate Name Name keyword Num keyword Num keyword NameConstant\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append all\n", + "add all\n", + "kernel all\n", + "bias all\n", + "init all\n", + "shape all\n", + "call all\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "163\n", + "[CLS] ClassDef Name Expr Str FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Subscript Name Index Num For Name i Call Name Num Call Name Name AugAssign Name output Add Subscript Name Index Name Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "164\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Expr Call Attribute build Call Name Name Name Name If Compare Call Name Name NotEq Num Raise Call Name Str\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "165\n", + "[CLS] AugAssign Subscript Name Index Attribute [MASK] [MASK] [MASK] [MASK] Name Add Subscript Name Index Attribute axis Name\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "shape axis\n", + "output axis\n", + "kernel axis\n", + "input axis\n", + "append axis\n", + "data axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "166\n", + "[CLS] If Call Name ListComp Compare Name Is NameConstant comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return NameConstant\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x m\n", + "i m\n", + "shape m\n", + "output m\n", + "self m\n", + "state m\n", + "new m\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "167\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute axis Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "168\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Attribute axes Name Mod Call Name Name BinOp Attribute axes Name Call Name Name\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axes\n", + "x axes\n", + "name axes\n", + "i axes\n", + "shape axes\n", + "input axes\n", + "config axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "169\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "170\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Call Attribute serialize Name Attribute alpha initializer Name Call Attribute serialize Name Attribute alpha regularizer Name Call Attribute serialize Name Attribute alpha constraint Name Attribute shared axes Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "x config\n", + "config config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "171\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "172\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "173\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg kwargs UnaryOp USub Num Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute supports masking Name NameConstant Assign Attribute axis Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "174\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute softmax Name Name keyword Attribute axis Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "175\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "176\n", + "[CLS] If Compare Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name inner inputs Subscript Attribute input map Name Index Name\n", + "Label = ['input', 'map', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "data input\n", + "bias input\n", + "output input\n", + "kernel input\n", + "keras input\n", + "recurrent input\n", + "[PAD] map\n", + "shape map\n", + "format map\n", + "kernel map\n", + "size map\n", + "output map\n", + "length map\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "177\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute backward layer Name Subscript Name Slice BinOp Name FloorDiv Num\n", + "Label = ['set', 'weights', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init set\n", + "call set\n", + "append set\n", + "items set\n", + "get set\n", + "add set\n", + "format set\n", + "[PAD] weights\n", + "shape weights\n", + "kernel weights\n", + "size weights\n", + "format weights\n", + "output weights\n", + "length weights\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "178\n", + "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "179\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Call Attribute reset states Attribute forward layer Name Expr Call Attribute reset states Attribute backward layer Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "180\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute forward layer Name Str Return BinOp Attribute non trainable weights Attribute forward layer Name Add Attribute non trainable weights Attribute backward layer Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "181\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute updates Attribute forward layer Name Add Attribute updates Attribute backward layer Name\n", + "Label = ['forward', 'layer', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape forward\n", + "call forward\n", + "add forward\n", + "append forward\n", + "output forward\n", + "data forward\n", + "kernel forward\n", + "[PAD] layer\n", + "shape layer\n", + "format layer\n", + "kernel layer\n", + "size layer\n", + "output layer\n", + "state layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "182\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name constraints Dict If Call Name Attribute forward layer Name Str Expr Call Attribute update Name Attribute constraints Attribute forward layer Name Expr Call Attribute update Name Attribute constraints Attribute backward layer Name Return Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "183\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "items states\n", + "name states\n", + "output states\n", + "states states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "184\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "185\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "186\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name BinOp Attribute units Name Mult Num keyword Str keyword Attribute recurrent initializer Name keyword Attribute recurrent regularizer Name keyword Attribute recurrent constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "shape weight\n", + "kernel weight\n", + "format weight\n", + "size weight\n", + "output weight\n", + "weight weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "187\n", + "[CLS] List Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute bias z i Name Attribute bias h i Name Attribute bias r Name Attribute bias z Name Attribute bias h Name\n", + "Label = ['bias', 'r', 'i', '[PAD]']\n", + "Pred =\n", + "bias bias\n", + "[PAD] r\n", + "shape r\n", + "kernel r\n", + "size r\n", + "format r\n", + "output r\n", + "i r\n", + "[PAD] i\n", + "shape i\n", + "kernel i\n", + "size i\n", + "format i\n", + "output i\n", + "i i\n", + "[PAD] [PAD]\n", + "\n", + "188\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute CudnnLSTM Name keyword Num keyword Attribute units Name keyword Name keyword Str\n", + "Label = ['cudnn', 'lstm', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape cudnn\n", + "output cudnn\n", + "bias cudnn\n", + "kernel cudnn\n", + "recurrent cudnn\n", + "data cudnn\n", + "keras cudnn\n", + "[PAD] lstm\n", + "shape lstm\n", + "kernel lstm\n", + "size lstm\n", + "format lstm\n", + "output lstm\n", + "i lstm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "189\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "kernel kernel\n", + "[PAD] c\n", + "shape c\n", + "kernel c\n", + "size c\n", + "format c\n", + "output c\n", + "i c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "190\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "191\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['recurrent', 'kernel', 'c', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "bias recurrent\n", + "recurrent recurrent\n", + "[PAD] kernel\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] c\n", + "shape c\n", + "kernel c\n", + "size c\n", + "format c\n", + "output c\n", + "i c\n", + "[PAD] [PAD]\n", + "\n", + "192\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Slice Attribute units Name BinOp Attribute units Name Mult Num\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape bias\n", + "kernel bias\n", + "bias bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "193\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['bias', 'o', 'i', '[PAD]']\n", + "Pred =\n", + "bias bias\n", + "[PAD] o\n", + "shape o\n", + "kernel o\n", + "format o\n", + "size o\n", + "output o\n", + "i o\n", + "[PAD] i\n", + "shape i\n", + "kernel i\n", + "format i\n", + "size i\n", + "output i\n", + "i i\n", + "[PAD] [PAD]\n", + "\n", + "194\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pooling function Name keyword Name keyword BinOp Attribute pool size Name Add Tuple Num keyword BinOp Attribute strides Name Tuple Num keyword Attribute padding Name keyword Attribute data format Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "195\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "196\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Subscript Name Index Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape data\n", + "data data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "197\n", + "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "198\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "199\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", + "Label = ['len', 'dim2', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output len\n", + "x len\n", + "shape len\n", + "i len\n", + "kernel len\n", + "self len\n", + "input len\n", + "[PAD] dim2\n", + "shape dim2\n", + "output dim2\n", + "kernel dim2\n", + "size dim2\n", + "out dim2\n", + "format dim2\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "200\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Subscript Name Index Num Name Name Name If Compare Attribute data format Name Str Return Tuple Subscript Name Index Num Name Name Name Subscript Name Index Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "201\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs Str Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "202\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Eq Str Assign Name ch dim Num If Compare Attribute data format Attribute cell Name Str Assign Name ch dim Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name data\n", + "data data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "203\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Attribute shape Name comprehension Name spec Attribute state spec Name Attribute state size Attribute cell Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "204\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Try Assign Name shape Call Attribute int shape Name Name ExceptHandler Name Assign Name shape Call Name GeneratorExp NameConstant comprehension Name Call Name Call Attribute ndim Name Name Expr Call Attribute append Attribute state spec Name Call Name keyword Name\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x state\n", + "i state\n", + "shape state\n", + "output state\n", + "name state\n", + "state state\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "205\n", + "[CLS] If Compare Name IsNot NameConstant Assign Subscript Name Index Str Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Name Assign Attribute constants spec Name ListComp Call Name keyword Call Attribute int shape Name Name comprehension Name constant Name Assign Attribute num constants Name Call Name Name AugAssign Name additional specs Attribute constants spec Name\n", + "Label = ['additional', 'inputs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x additional\n", + "output additional\n", + "i additional\n", + "shape additional\n", + "name additional\n", + "state additional\n", + "config additional\n", + "[PAD] inputs\n", + "shape inputs\n", + "output inputs\n", + "kernel inputs\n", + "size inputs\n", + "out inputs\n", + "format inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "206\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute call Call Name Name Name Name keyword Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "207\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output mask\n", + "x mask\n", + "shape mask\n", + "i mask\n", + "kernel mask\n", + "self mask\n", + "input mask\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "208\n", + "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Call Name Attribute states Name Str Call Name Call Name Name Str\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "output states\n", + "call states\n", + "data states\n", + "states states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "209\n", + "[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name comprehension Name dim Attribute state size Attribute cell Name\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append zeros\n", + "items zeros\n", + "add zeros\n", + "bias zeros\n", + "init zeros\n", + "get zeros\n", + "reshape zeros\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "210\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute zeros Name Call Name Attribute state size Attribute cell Name\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape states\n", + "output states\n", + "bias states\n", + "kernel states\n", + "recurrent states\n", + "data states\n", + "keras states\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "211\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "212\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "213\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "214\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice Attribute filters Name BinOp Attribute filters Name Mult Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "215\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", + "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] c\n", + "shape c\n", + "kernel c\n", + "size c\n", + "format c\n", + "output c\n", + "state c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "216\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", + "Label = ['kernel', 'o', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] o\n", + "shape o\n", + "kernel o\n", + "size o\n", + "format o\n", + "output o\n", + "i o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "217\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", + "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape bias\n", + "bias bias\n", + "[PAD] c\n", + "shape c\n", + "kernel c\n", + "format c\n", + "size c\n", + "output c\n", + "state c\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "218\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input conv Name Name Attribute kernel o Name Attribute bias o Name keyword Attribute padding Name\n", + "Label = ['x', 'o', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output x\n", + "x x\n", + "[PAD] o\n", + "shape o\n", + "kernel o\n", + "output o\n", + "size o\n", + "out o\n", + "format o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "219\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Name keyword Attribute data format Name\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "x conv\n", + "shape conv\n", + "i conv\n", + "kernel conv\n", + "self conv\n", + "input conv\n", + "[PAD] out\n", + "shape out\n", + "kernel out\n", + "output out\n", + "size out\n", + "format out\n", + "out out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "220\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "221\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state NameConstant NameConstant NameConstant Return Call Attribute call Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "222\n", + "[CLS] Call Name keyword BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Num keyword Dict Name Name\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name rank\n", + "shape rank\n", + "append rank\n", + "data rank\n", + "items rank\n", + "sqrt rank\n", + "input rank\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "223\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d Name Name Attribute kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "x outputs\n", + "i outputs\n", + "kernel outputs\n", + "shape outputs\n", + "input outputs\n", + "batch outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "224\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", + "Label = ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call conv2d\n", + "add conv2d\n", + "shape conv2d\n", + "bias conv2d\n", + "kernel conv2d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recurrent conv2d\n", + "output conv2d\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "225\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "shape use\n", + "kernel use\n", + "data use\n", + "recurrent use\n", + "add use\n", + "output use\n", + "[PAD] bias\n", + "shape bias\n", + "kernel bias\n", + "format bias\n", + "size bias\n", + "output bias\n", + "state bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "226\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "x outputs\n", + "i outputs\n", + "shape outputs\n", + "kernel outputs\n", + "input outputs\n", + "self outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "227\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "228\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name channel axis Num Assign Name channel axis UnaryOp USub Num\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data data\n", + "[PAD] format\n", + "shape format\n", + "kernel format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "229\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name Name Attribute padding Name Name Subscript Attribute dilation rate Name Index Num\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape deconv\n", + "call deconv\n", + "data deconv\n", + "output deconv\n", + "add deconv\n", + "bias deconv\n", + "recurrent deconv\n", + "[PAD] length\n", + "shape length\n", + "format length\n", + "size length\n", + "kernel length\n", + "output length\n", + "length length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "230\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "shape use\n", + "kernel use\n", + "data use\n", + "recurrent use\n", + "add use\n", + "output use\n", + "[PAD] bias\n", + "shape bias\n", + "kernel bias\n", + "format bias\n", + "size bias\n", + "output bias\n", + "state bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "231\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "x outputs\n", + "i outputs\n", + "shape outputs\n", + "kernel outputs\n", + "input outputs\n", + "self outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "232\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "233\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name out pad Call Name Attribute strides Name Attribute output padding Name If Compare Name GtE Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute strides Name Str Call Name Attribute output padding Name\n", + "Label = ['stride', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x stride\n", + "i stride\n", + "output stride\n", + "shape stride\n", + "y stride\n", + "new stride\n", + "self stride\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "234\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output outputs\n", + "x outputs\n", + "i outputs\n", + "shape outputs\n", + "kernel outputs\n", + "input outputs\n", + "self outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "235\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Attribute data format Name Eq Str Num UnaryOp USub Num\n", + "Label = ['channel', 'axis', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output channel\n", + "x channel\n", + "shape channel\n", + "i channel\n", + "kernel channel\n", + "self channel\n", + "input channel\n", + "[PAD] axis\n", + "shape axis\n", + "kernel axis\n", + "output axis\n", + "size axis\n", + "format axis\n", + "out axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "236\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute bias Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name Assign Attribute bias Name NameConstant\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "shape use\n", + "kernel use\n", + "data use\n", + "recurrent use\n", + "add use\n", + "output use\n", + "[PAD] bias\n", + "shape bias\n", + "kernel bias\n", + "format bias\n", + "size bias\n", + "output bias\n", + "i bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "237\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple BinOp Name Mult Attribute depth multiplier Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "238\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Num keyword Dict Name Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "output input\n", + "bias input\n", + "kernel input\n", + "recurrent input\n", + "data input\n", + "keras input\n", + "[PAD] spec\n", + "shape spec\n", + "kernel spec\n", + "format spec\n", + "size spec\n", + "output spec\n", + "i spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "239\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Name\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape data\n", + "data data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "240\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Call Attribute get config Call Name Name Name Assign Subscript Name Index Str Subscript Attribute size Name Index Num Expr Call Attribute pop Name Str Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "241\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Subscript Attribute padding Name Index Num\n", + "Label = ['temporal', 'padding', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape temporal\n", + "call temporal\n", + "add temporal\n", + "append temporal\n", + "output temporal\n", + "bias temporal\n", + "kernel temporal\n", + "[PAD] padding\n", + "shape padding\n", + "format padding\n", + "kernel padding\n", + "size padding\n", + "output padding\n", + "weight padding\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "242\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "243\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num Tuple Num Num NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "244\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n", + "Label = ['dim1', 'cropping', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dim1\n", + "x dim1\n", + "shape dim1\n", + "i dim1\n", + "kernel dim1\n", + "self dim1\n", + "input dim1\n", + "[PAD] cropping\n", + "shape cropping\n", + "kernel cropping\n", + "output cropping\n", + "size cropping\n", + "format cropping\n", + "out cropping\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "245\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init init\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "246\n", + "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "output input\n", + "append input\n", + "data input\n", + "init input\n", + "input input\n", + "[PAD] length\n", + "shape length\n", + "format length\n", + "output length\n", + "size length\n", + "kernel length\n", + "length length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "247\n", + "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "output input\n", + "append input\n", + "data input\n", + "init input\n", + "input input\n", + "[PAD] length\n", + "shape length\n", + "format length\n", + "output length\n", + "size length\n", + "kernel length\n", + "length length\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "248\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute kernel shape Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "shape weight\n", + "kernel weight\n", + "format weight\n", + "size weight\n", + "output weight\n", + "weight weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "249\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "shape axis\n", + "append axis\n", + "output axis\n", + "items axis\n", + "init axis\n", + "states axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "250\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Call Name Name keyword Dict Attribute axis Name Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items input\n", + "shape input\n", + "output input\n", + "kernel input\n", + "bias input\n", + "input input\n", + "[PAD] spec\n", + "shape spec\n", + "size spec\n", + "kernel spec\n", + "output spec\n", + "format spec\n", + "i spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "251\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute gamma initializer Name keyword Attribute gamma regularizer Name keyword Attribute gamma constraint Name\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape gamma\n", + "output gamma\n", + "bias gamma\n", + "kernel gamma\n", + "recurrent gamma\n", + "data gamma\n", + "input gamma\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "252\n", + "[CLS] Assign Subscript Name Index Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Attribute axis Name\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name axis\n", + "shape axis\n", + "output axis\n", + "data axis\n", + "append axis\n", + "input axis\n", + "kernel axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "253\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name broadcast beta Call Attribute reshape Name Attribute beta Name Name Assign Name broadcast beta NameConstant\n", + "Label = ['center', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape center\n", + "kernel center\n", + "data center\n", + "bias center\n", + "recurrent center\n", + "append center\n", + "ndim center\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "254\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute moving mean Name Attribute moving variance Name Attribute beta Name Attribute gamma Name keyword Attribute axis Name keyword Attribute epsilon Name\n", + "Label = ['batch', 'normalization', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias batch\n", + "call batch\n", + "kernel batch\n", + "shape batch\n", + "append batch\n", + "add batch\n", + "recurrent batch\n", + "[PAD] normalization\n", + "shape normalization\n", + "kernel normalization\n", + "format normalization\n", + "size normalization\n", + "output normalization\n", + "i normalization\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "255\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast Name Name keyword Call Attribute dtype Name Name\n", + "Label = ['sample', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x sample\n", + "output sample\n", + "i sample\n", + "name sample\n", + "input sample\n", + "kernel sample\n", + "batch sample\n", + "[PAD] size\n", + "shape size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "256\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute cells Name Index UnaryOp USub Num Index Num\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape state\n", + "output state\n", + "keras state\n", + "reshape state\n", + "recurrent state\n", + "kernel state\n", + "data state\n", + "[PAD] size\n", + "shape size\n", + "format size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "257\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice Num Assign Name input shape Subscript Name Index Num\n", + "Label = ['constants', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output constants\n", + "shape constants\n", + "x constants\n", + "i constants\n", + "kernel constants\n", + "input constants\n", + "self constants\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "258\n", + "[CLS] If Call Name Name Name If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute build Name BinOp List Name Add Name Expr Call Attribute build Name Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape call\n", + "append call\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "259\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "260\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Attribute weights Name Assign Name weights Subscript Name Slice Name For Tuple Name sw Name w Call Name Attribute weights Name Name Expr Call Attribute append Name Tuple Name Name Assign Name weights Subscript Name Slice Name\n", + "Label = ['num', 'param', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output num\n", + "x num\n", + "shape num\n", + "i num\n", + "self num\n", + "kernel num\n", + "input num\n", + "[PAD] param\n", + "shape param\n", + "kernel param\n", + "output param\n", + "size param\n", + "out param\n", + "format param\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "261\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Attribute losses Name AugAssign Name losses Add Name Return Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "262\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cell arg return sequences arg return state arg go backwards arg stateful arg unroll arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "263\n", + "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name constants shape Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name constants shape NameConstant\n", + "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape num\n", + "data num\n", + "output num\n", + "bias num\n", + "recurrent num\n", + "kernel num\n", + "keras num\n", + "[PAD] constants\n", + "shape constants\n", + "kernel constants\n", + "format constants\n", + "output constants\n", + "size constants\n", + "state constants\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "264\n", + "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "265\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Attribute is keras tensor Name Name NotEq Name Raise Call Name Str\n", + "Label = ['tensor', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x tensor\n", + "i tensor\n", + "shape tensor\n", + "name tensor\n", + "output tensor\n", + "input tensor\n", + "self tensor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "266\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name initial state Attribute states Name Assign Name initial state Call Attribute get initial state Name Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape stateful\n", + "data stateful\n", + "kernel stateful\n", + "bias stateful\n", + "recurrent stateful\n", + "output stateful\n", + "keras stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "267\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name updates List For Name i Call Name Call Name Name Expr Call Attribute append Name Tuple Subscript Attribute states Name Index Name Subscript Name Index Name Expr Call Attribute add update Name Name Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name stateful\n", + "shape stateful\n", + "data stateful\n", + "append stateful\n", + "kernel stateful\n", + "return stateful\n", + "bias stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "268\n", + "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "269\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute cell Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "270\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return BinOp Attribute losses Attribute cell Name Add Name\n", + "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape cell\n", + "call cell\n", + "output cell\n", + "add cell\n", + "kernel cell\n", + "bias cell\n", + "keras cell\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "271\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "272\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init call\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "273\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "274\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name BinOp Attribute units Name Mult Num keyword Str keyword Attribute recurrent initializer Name keyword Attribute recurrent regularizer Name keyword Attribute recurrent constraint Name\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", + "Pred =\n", + "add add\n", + "[PAD] weight\n", + "shape weight\n", + "kernel weight\n", + "format weight\n", + "size weight\n", + "output weight\n", + "weight weight\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "275\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name\n", + "Label = ['kernel', 'z', '[PAD]', '[PAD]']\n", + "Pred =\n", + "kernel kernel\n", + "[PAD] z\n", + "shape z\n", + "kernel z\n", + "size z\n", + "format z\n", + "output z\n", + "i z\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "276\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "277\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", + "Label = ['recurrent', 'kernel', 'h', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "bias recurrent\n", + "recurrent recurrent\n", + "[PAD] kernel\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] h\n", + "shape h\n", + "kernel h\n", + "size h\n", + "format h\n", + "output h\n", + "i h\n", + "[PAD] [PAD]\n", + "\n", + "278\n", + "[CLS] BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant\n", + "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data recurrent\n", + "shape recurrent\n", + "append recurrent\n", + "output recurrent\n", + "bias recurrent\n", + "recurrent recurrent\n", + "[PAD] dropout\n", + "shape dropout\n", + "format dropout\n", + "kernel dropout\n", + "size dropout\n", + "output dropout\n", + "length dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "279\n", + "[CLS] If Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num AugAssign Name inputs Mult Subscript Name Index Num\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dropout\n", + "data dropout\n", + "append dropout\n", + "bias dropout\n", + "output dropout\n", + "recurrent dropout\n", + "call dropout\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "280\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n", + "Label = ['x', 'r', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] r\n", + "shape r\n", + "kernel r\n", + "output r\n", + "size r\n", + "format r\n", + "out r\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "281\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['matrix', 'inner', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x matrix\n", + "output matrix\n", + "i matrix\n", + "kernel matrix\n", + "shape matrix\n", + "input matrix\n", + "batch matrix\n", + "[PAD] inner\n", + "shape inner\n", + "kernel inner\n", + "output inner\n", + "size inner\n", + "out inner\n", + "format inner\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "282\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dot\n", + "kernel dot\n", + "append dot\n", + "add dot\n", + "recurrent dot\n", + "bias dot\n", + "call dot\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "283\n", + "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape recurrent\n", + "kernel recurrent\n", + "keras recurrent\n", + "bias recurrent\n", + "recurrent recurrent\n", + "[PAD] kernel\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "284\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg config If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Num Assign Subscript Name Index Str Num Return Call Name keyword Name Name\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self cls\n", + "x cls\n", + "shape cls\n", + "args cls\n", + "data cls\n", + "kernel cls\n", + "y cls\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "285\n", + "[CLS] ExtSlice Slice Slice BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Num BinOp Attribute units Name Num\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name units\n", + "data units\n", + "shape units\n", + "output units\n", + "kernel units\n", + "bias units\n", + "recurrent units\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "286\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n", + "Label = ['recurrent', 'kernel', 'i', '[PAD]']\n", + "Pred =\n", + "kernel recurrent\n", + "bias recurrent\n", + "recurrent recurrent\n", + "[PAD] kernel\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] i\n", + "shape i\n", + "kernel i\n", + "size i\n", + "format i\n", + "output i\n", + "i i\n", + "[PAD] [PAD]\n", + "\n", + "287\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output o\n", + "x o\n", + "i o\n", + "shape o\n", + "kernel o\n", + "input o\n", + "name o\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "288\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", + "Label = ['recurrent', 'activation', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items recurrent\n", + "append recurrent\n", + "init recurrent\n", + "name recurrent\n", + "sqrt recurrent\n", + "add recurrent\n", + "kernel recurrent\n", + "[PAD] activation\n", + "shape activation\n", + "size activation\n", + "kernel activation\n", + "format activation\n", + "output activation\n", + "i activation\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "289\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name z Call Attribute bias add Name Name Attribute bias Name\n", + "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", + "Pred =\n", + "bias use\n", + "kernel use\n", + "shape use\n", + "data use\n", + "recurrent use\n", + "add use\n", + "output use\n", + "[PAD] bias\n", + "shape bias\n", + "kernel bias\n", + "format bias\n", + "size bias\n", + "output bias\n", + "i bias\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "290\n", + "[CLS] Subscript Name ExtSlice Slice Slice BinOp Num Mult Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Attribute units Name\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name units\n", + "data units\n", + "shape units\n", + "output units\n", + "kernel units\n", + "bias units\n", + "recurrent units\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "291\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "292\n", + "[CLS] If Compare Name Gt Num Return ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name comprehension Name Call Name Name\n", + "Label = ['in', 'train', 'phase', '[PAD]']\n", + "Pred =\n", + "shape in\n", + "call in\n", + "append in\n", + "add in\n", + "bias in\n", + "kernel in\n", + "recurrent in\n", + "[PAD] train\n", + "shape train\n", + "format train\n", + "kernel train\n", + "size train\n", + "output train\n", + "i train\n", + "[PAD] phase\n", + "shape phase\n", + "format phase\n", + "kernel phase\n", + "size phase\n", + "output phase\n", + "i phase\n", + "[PAD] [PAD]\n", + "\n", + "293\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sqrt\n", + "items sqrt\n", + "init sqrt\n", + "kernel sqrt\n", + "sqrt sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "294\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant Assign Name output mask Call Attribute any Name Call Attribute not equal Name Name Attribute mask value Name keyword UnaryOp USub Num Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "295\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute any Name Call Attribute not equal Name Name Attribute mask value Name keyword UnaryOp USub Num\n", + "Label = ['output', 'mask', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] mask\n", + "shape mask\n", + "output mask\n", + "kernel mask\n", + "size mask\n", + "format mask\n", + "out mask\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "296\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Name Is NameConstant Subscript Name Index Name Name comprehension Tuple Name axis Name shape Call Name Attribute noise shape Name\n", + "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output noise\n", + "shape noise\n", + "x noise\n", + "i noise\n", + "name noise\n", + "input noise\n", + "kernel noise\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "297\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name Assign Name noise shape Tuple Subscript Name Index Num Num Subscript Name Index Num Return Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "298\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg rate arg data format arg kwargs NameConstant Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute data format Name Call Attribute normalize data format Name Name Assign Attribute input spec Name Call Name keyword Num Attribute legacy spatialdropoutNd support Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "299\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Num Num\n", + "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output noise\n", + "shape noise\n", + "x noise\n", + "kernel noise\n", + "self noise\n", + "i noise\n", + "input noise\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "300\n", + "[CLS] If Compare Name Lt Num If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str AugAssign Name known Mult Name\n", + "Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x unknown\n", + "output unknown\n", + "shape unknown\n", + "i unknown\n", + "self unknown\n", + "kernel unknown\n", + "input unknown\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "301\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "302\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "303\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute repeat Name Name Attribute n Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "304\n", + "[CLS] If Call Name Name Name Return ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name x elem Name Return Call Attribute int shape Name Name\n", + "Label = ['int', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "append int\n", + "shape int\n", + "add int\n", + "bias int\n", + "call int\n", + "data int\n", + "output int\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "305\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant Assign Name arguments Attribute arguments Name If Call Name Attribute function Name Str Assign Subscript Name Index Str Name Return Call Attribute function Name Name keyword Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "306\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask NameConstant If Call Name Attribute mask Name Return Call Attribute mask Name Name Name Return Attribute mask Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "307\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute LambdaType Name Assign Name function Call Name Attribute function Name Assign Name function type Str Assign Name function Attribute name Attribute function Name Assign Name function type Str\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape function\n", + "output function\n", + "data function\n", + "call function\n", + "input function\n", + "kernel function\n", + "add function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "308\n", + "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name keyword Num keyword Dict UnaryOp USub Num Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape input\n", + "output input\n", + "kernel input\n", + "bias input\n", + "recurrent input\n", + "data input\n", + "input input\n", + "[PAD] spec\n", + "shape spec\n", + "kernel spec\n", + "size spec\n", + "format spec\n", + "output spec\n", + "i spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "309\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Assert BoolOp And Name Compare Call Name Name GtE Num Assert Subscript Name Index UnaryOp USub Num Assign Name output shape Call Name Name Assign Subscript Name Index UnaryOp Num Attribute units Name Return Call Name Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "310\n", + "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items items\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "311\n", + "[CLS] If BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant Assign Name alt Call Name\n", + "Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call Function\n", + "init Function\n", + "shape Function\n", + "data Function\n", + "output Function\n", + "append Function\n", + "format Function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "312\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Name Attribute Function Attribute cntk py Name Return Call Attribute eval Name If BoolOp Or Call Name Name Attribute Constant Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name Return Attribute value Name Raise Call Name BinOp Str Mod Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "313\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Attribute shape Name If Compare Subscript Name Index BinOp Name Add Name Is NameConstant Expr Call Attribute append Name Subscript Attribute shape Name Index Name Expr Call Attribute append Name Subscript Name Index BinOp Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "314\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name a Attribute dynamic axes Name Assign Name shape BinOp Call Name Name Add Name\n", + "Label = ['dynamic', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dynamic\n", + "x dynamic\n", + "shape dynamic\n", + "i dynamic\n", + "name dynamic\n", + "kernel dynamic\n", + "input dynamic\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "315\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output seed\n", + "x seed\n", + "i seed\n", + "shape seed\n", + "kernel seed\n", + "self seed\n", + "initial seed\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "316\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "init normal\n", + "append normal\n", + "add normal\n", + "kernel normal\n", + "bias normal\n", + "format normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "317\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute float32 Name Assign Name dtype Call Name Name\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dtype\n", + "x dtype\n", + "shape dtype\n", + "i dtype\n", + "name dtype\n", + "kernel dtype\n", + "input dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "318\n", + "[CLS] Return Call Name keyword Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword Name\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape zeros\n", + "call zeros\n", + "bias zeros\n", + "add zeros\n", + "data zeros\n", + "recurrent zeros\n", + "output zeros\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "319\n", + "[CLS] While Compare Name Lt BinOp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Num Assign Name x Call Attribute swapaxes Name Name Name BinOp Name Add Num AugAssign Name i Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "320\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Name BinOp Name Add Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "321\n", + "[CLS] While Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Name BinOp Name Sub Num AugAssign Name i Num\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "output y\n", + "i y\n", + "shape y\n", + "kernel y\n", + "input y\n", + "self y\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "322\n", + "[CLS] IfExp Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num BinOp Call Name Attribute shape Name Sub Num Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "323\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Eq Attribute FreeDimension Name Attribute InferredDimension Name Name comprehension Name Name\n", + "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x new\n", + "output new\n", + "shape new\n", + "i new\n", + "kernel new\n", + "input new\n", + "batch new\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "324\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name Add Name\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output n\n", + "name n\n", + "x n\n", + "shape n\n", + "i n\n", + "input n\n", + "y n\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "325\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name keyword Tuple keyword Name keyword BinOp Name Add Num\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x result\n", + "output result\n", + "shape result\n", + "i result\n", + "kernel result\n", + "batch result\n", + "input result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "326\n", + "[CLS] BoolOp And Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Call Name Name Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "327\n", + "[CLS] BinOp BinOp Num Sub Name Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Name\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append log\n", + "items log\n", + "shape log\n", + "init log\n", + "kernel log\n", + "output log\n", + "sqrt log\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "328\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name BinOp BinOp Name Mult Name Add BinOp Name BinOp Num Sub Name\n", + "Label = ['assign', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape assign\n", + "append assign\n", + "output assign\n", + "init assign\n", + "name assign\n", + "items assign\n", + "recurrent assign\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "329\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute constant Name Num keyword Attribute shape Name keyword Str\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output g\n", + "x g\n", + "shape g\n", + "i g\n", + "kernel g\n", + "self g\n", + "input g\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "330\n", + "[CLS] Call Name ListComp IfExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute InferredDimension Name Name comprehension Name Name\n", + "Label = ['FreeDimension', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data FreeDimension\n", + "shape FreeDimension\n", + "name FreeDimension\n", + "append FreeDimension\n", + "output FreeDimension\n", + "kernel FreeDimension\n", + "bias FreeDimension\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "331\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute slice Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output current\n", + "x current\n", + "shape current\n", + "i current\n", + "kernel current\n", + "input current\n", + "batch current\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "332\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call slice\n", + "init slice\n", + "shape slice\n", + "name slice\n", + "append slice\n", + "get slice\n", + "output slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "333\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute slice Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['mask', 'slice', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output mask\n", + "x mask\n", + "shape mask\n", + "i mask\n", + "kernel mask\n", + "input mask\n", + "batch mask\n", + "[PAD] slice\n", + "shape slice\n", + "kernel slice\n", + "output slice\n", + "size slice\n", + "out slice\n", + "format slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "334\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call slice\n", + "init slice\n", + "shape slice\n", + "name slice\n", + "append slice\n", + "get slice\n", + "output slice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "335\n", + "[CLS] If Compare Call Name Name Eq Num If Call Name Name Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute to batch Name Name Expr Call Attribute append Name Call Attribute user function Name Call Name Name Expr Call Attribute append Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "336\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Attribute dynamic axes Name comprehension Name Name\n", + "Label = ['place', 'holders', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output place\n", + "x place\n", + "i place\n", + "shape place\n", + "kernel place\n", + "self place\n", + "input place\n", + "[PAD] holders\n", + "shape holders\n", + "kernel holders\n", + "output holders\n", + "size holders\n", + "format holders\n", + "out holders\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "337\n", + "[CLS] If BoolOp And Compare Call Name Name Eq Num Compare Call Name Name Num If Call Name Name Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute unpack batch Name Name Expr Call Attribute append Name Call Attribute user function Name Call Name Name keyword Subscript Attribute shape Name Index Num Expr Call Attribute append Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "338\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Subscript Attribute shape Name Index Num\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items user\n", + "append user\n", + "shape user\n", + "get user\n", + "init user\n", + "add user\n", + "reshape user\n", + "[PAD] function\n", + "shape function\n", + "format function\n", + "kernel function\n", + "size function\n", + "length function\n", + "output function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "339\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute reduce sum Name Call Attribute square Name Name keyword Subscript Name Index Num\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items sqrt\n", + "append sqrt\n", + "sqrt sqrt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "340\n", + "[CLS] BinOp Name Mult BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Sub Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "341\n", + "[CLS] BoolOp And Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Name NotEq Num\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape type\n", + "append type\n", + "items type\n", + "add type\n", + "call type\n", + "bias type\n", + "data type\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "342\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg depthwise kernel arg pointwise kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "343\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Tuple Num Num Num keyword List NameConstant\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape convolution\n", + "call convolution\n", + "bias convolution\n", + "data convolution\n", + "recurrent convolution\n", + "kernel convolution\n", + "output convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "344\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", + "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output depthwise\n", + "x depthwise\n", + "shape depthwise\n", + "i depthwise\n", + "kernel depthwise\n", + "batch depthwise\n", + "input depthwise\n", + "[PAD] kernel\n", + "shape kernel\n", + "kernel kernel\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "345\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Subscript Name Index Num keyword List NameConstant Name Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape convolution\n", + "call convolution\n", + "bias convolution\n", + "data convolution\n", + "recurrent convolution\n", + "output convolution\n", + "kernel convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "346\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute MAX POOLING Name Name Name keyword List Name\n", + "Label = ['pooling', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call pooling\n", + "shape pooling\n", + "kernel pooling\n", + "bias pooling\n", + "add pooling\n", + "recurrent pooling\n", + "data pooling\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "347\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Name Eq UnaryOp USub Num UnaryOp Num BinOp Name Sub Num\n", + "Label = ['axis', 'without', 'batch', '[PAD]']\n", + "Pred =\n", + "output axis\n", + "x axis\n", + "shape axis\n", + "i axis\n", + "kernel axis\n", + "name axis\n", + "input axis\n", + "[PAD] without\n", + "shape without\n", + "kernel without\n", + "output without\n", + "size without\n", + "out without\n", + "format without\n", + "[PAD] batch\n", + "shape batch\n", + "kernel batch\n", + "output batch\n", + "size batch\n", + "out batch\n", + "format batch\n", + "[PAD] [PAD]\n", + "\n", + "348\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Div Call Attribute reduce sum Name Name keyword UnaryOp USub Num\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "349\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Attribute shape Name\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "350\n", + "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Expr Call Attribute append Name Name Raise Call Name BinOp Str Mod Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "351\n", + "[CLS] BoolOp And Compare Name NotEq Name Compare Name Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Attribute FreeDimension Name\n", + "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape InferredDimension\n", + "data InferredDimension\n", + "bias InferredDimension\n", + "output InferredDimension\n", + "kernel InferredDimension\n", + "recurrent InferredDimension\n", + "keras InferredDimension\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "352\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute constant Name keyword Num keyword Name keyword Name\n", + "Label = ['splice', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape splice\n", + "append splice\n", + "call splice\n", + "add splice\n", + "bias splice\n", + "recurrent splice\n", + "kernel splice\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "353\n", + "[CLS] Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Num Sub IfExp Compare Name Gt Num Num Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "354\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If BoolOp Or Call Name Name Attribute Parameter Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Return Attribute value Name Return Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "355\n", + "[CLS] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name If Call Name Name Tuple Name Name Assign Name value Call Attribute full Name Attribute shape Name Name keyword Call Name Assign Attribute value Name Name Raise Name\n", + "Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call Parameter\n", + "init Parameter\n", + "format Parameter\n", + "shape Parameter\n", + "get Parameter\n", + "outputs Parameter\n", + "append Parameter\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "356\n", + "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute full Name Attribute shape Name Name keyword Call Name\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output value\n", + "x value\n", + "shape value\n", + "i value\n", + "kernel value\n", + "input value\n", + "batch value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "357\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x Call Name Name\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append user\n", + "items user\n", + "add user\n", + "init user\n", + "bias user\n", + "kernel user\n", + "sqrt user\n", + "[PAD] function\n", + "shape function\n", + "kernel function\n", + "format function\n", + "size function\n", + "output function\n", + "weight function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "358\n", + "[CLS] If Compare Call Name Name Gt Num Return Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name Return NameConstant\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "keras dynamic\n", + "output dynamic\n", + "kernel dynamic\n", + "data dynamic\n", + "bias dynamic\n", + "recurrent dynamic\n", + "[PAD] axes\n", + "shape axes\n", + "format axes\n", + "size axes\n", + "kernel axes\n", + "output axes\n", + "i axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "359\n", + "[CLS] Return Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "keras dynamic\n", + "kernel dynamic\n", + "output dynamic\n", + "data dynamic\n", + "recurrent dynamic\n", + "bias dynamic\n", + "[PAD] axes\n", + "shape axes\n", + "format axes\n", + "size axes\n", + "kernel axes\n", + "output axes\n", + "i axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "360\n", + "[CLS] Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Call Attribute default dynamic axis Attribute Axis Name\n", + "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape dynamic\n", + "keras dynamic\n", + "output dynamic\n", + "kernel dynamic\n", + "data dynamic\n", + "recurrent dynamic\n", + "bias dynamic\n", + "[PAD] axes\n", + "shape axes\n", + "size axes\n", + "format axes\n", + "kernel axes\n", + "output axes\n", + "i axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "361\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute as shape Call Attribute data Name BinOp Tuple Name Add Attribute target shape Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output result\n", + "x result\n", + "shape result\n", + "i result\n", + "kernel result\n", + "input result\n", + "batch result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "362\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Call Attribute as shape Name BinOp Tuple Name Add Attribute from shape Name\n", + "Label = ['Value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items Value\n", + "append Value\n", + "init Value\n", + "get Value\n", + "call Value\n", + "format Value\n", + "sum Value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "363\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Subscript Attribute inputs Name Index Num Slice Num Attribute dtype Subscript Attribute inputs Name Index Num List Name\n", + "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append output\n", + "add output\n", + "shape output\n", + "bias output\n", + "reshape output\n", + "init output\n", + "output output\n", + "[PAD] variable\n", + "shape variable\n", + "format variable\n", + "kernel variable\n", + "output variable\n", + "size variable\n", + "length variable\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "364\n", + "[CLS] Return BoolOp And Compare Name IsNot NameConstant Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Call Attribute upper Name\n", + "Label = ['device', 'type', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data device\n", + "append device\n", + "name device\n", + "output device\n", + "shape device\n", + "kernel device\n", + "ndim device\n", + "[PAD] type\n", + "shape type\n", + "kernel type\n", + "format type\n", + "size type\n", + "output type\n", + "i type\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "365\n", + "[CLS] FunctionDef arguments Expr Str Global If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute list devices Call Name Return ListComp Attribute name Name comprehension Name x Name Compare Attribute device type Name Eq Str\n", + "Label = ['LOCAL', 'DEVICES', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x LOCAL\n", + "output LOCAL\n", + "i LOCAL\n", + "shape LOCAL\n", + "name LOCAL\n", + "kernel LOCAL\n", + "self LOCAL\n", + "[PAD] DEVICES\n", + "shape DEVICES\n", + "kernel DEVICES\n", + "output DEVICES\n", + "size DEVICES\n", + "out DEVICES\n", + "format DEVICES\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "366\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute list devices Call Name\n", + "Label = ['LOCAL', 'DEVICES', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output LOCAL\n", + "x LOCAL\n", + "i LOCAL\n", + "shape LOCAL\n", + "input LOCAL\n", + "self LOCAL\n", + "kernel LOCAL\n", + "[PAD] DEVICES\n", + "shape DEVICES\n", + "output DEVICES\n", + "kernel DEVICES\n", + "size DEVICES\n", + "format DEVICES\n", + "out DEVICES\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "367\n", + "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute keras shape Name Attribute shape Name If Call Name Name Str Assign Attribute keras shape Name Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "call ndarray\n", + "output ndarray\n", + "keras ndarray\n", + "data ndarray\n", + "add ndarray\n", + "kernel ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "368\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str Return Call Attribute zeros like Name Name keyword Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "369\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Call Attribute random normal initializer Name Name Name keyword Name keyword Name Name\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output value\n", + "x value\n", + "i value\n", + "kernel value\n", + "name value\n", + "shape value\n", + "input value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "370\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Name List UnaryOp USub Num Subscript Name Index UnaryOp Num\n", + "Label = ['xt', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output xt\n", + "x xt\n", + "shape xt\n", + "i xt\n", + "kernel xt\n", + "input xt\n", + "batch xt\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "371\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name keyword Name List Subscript Name Index UnaryOp USub Num UnaryOp Num\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append reshape\n", + "items reshape\n", + "add reshape\n", + "init reshape\n", + "bias reshape\n", + "reshape reshape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "372\n", + "[CLS] Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "i a\n", + "output a\n", + "shape a\n", + "self a\n", + "new a\n", + "state a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "373\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute multiply Name Name Name Subscript Name Index Num\n", + "Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append reduce\n", + "items reduce\n", + "add reduce\n", + "shape reduce\n", + "data reduce\n", + "bias reduce\n", + "init reduce\n", + "[PAD] sum\n", + "shape sum\n", + "format sum\n", + "size sum\n", + "kernel sum\n", + "output sum\n", + "length sum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "374\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute not equal Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "375\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name var Call Attribute moments Attribute nn Name Name Name NameConstant NameConstant NameConstant\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x mean\n", + "i mean\n", + "output mean\n", + "shape mean\n", + "y mean\n", + "kernel mean\n", + "new mean\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "376\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name If Compare Call Name Name Gt Num Assign Name beta Call Attribute reshape Name Name UnaryOp USub Num\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output beta\n", + "x beta\n", + "i beta\n", + "shape beta\n", + "kernel beta\n", + "name beta\n", + "input beta\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "377\n", + "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute fused batch norm Attribute nn Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword NameConstant\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x y\n", + "i y\n", + "output y\n", + "shape y\n", + "self y\n", + "kernel y\n", + "y y\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "378\n", + "[CLS] If Compare Name Eq Str Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name cols Tuple Num Num Assign Tuple Name rows Name cols Tuple Num Num\n", + "Label = ['rows', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x rows\n", + "i rows\n", + "output rows\n", + "shape rows\n", + "self rows\n", + "kernel rows\n", + "y rows\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "379\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Mult Call Attribute constant Name Call Attribute array Name List Name Name keyword Str\n", + "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x new\n", + "output new\n", + "i new\n", + "shape new\n", + "y new\n", + "kernel new\n", + "config new\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "380\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Name keyword Tuple Num Num\n", + "Label = ['set', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "items set\n", + "init set\n", + "shape set\n", + "add set\n", + "get set\n", + "data set\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "381\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num Assign Name output Call Name Name Name keyword Num Assign Name output Call Name Name Name keyword Num Return Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "382\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n Expr Str Assert Compare Call Name Name Eq Num Assign Name x Call Attribute expand dims Name Name Num Assign Name pattern Call Attribute stack Name List Num Name Num Return Call Attribute tile Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "383\n", + "[CLS] If Compare Name NotEq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x result\n", + "output result\n", + "shape result\n", + "i result\n", + "kernel result\n", + "input result\n", + "self result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "384\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Expr Str Return Call Attribute expand dims Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "385\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis Expr Str Return Call Attribute squeeze Name Name List Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "386\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg start arg size Expr Str Return Call Attribute slice Name Name Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "387\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute as numpy dtype Call Attribute as dtype Name Attribute dtype Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "388\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute as numpy dtype Call Attribute as dtype Name Attribute dtype Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items append\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "389\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute concatenate Name Tuple Call Attribute expand dims Name Attribute row Name Num Call Attribute expand dims Name Attribute col Name Num Num\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output indices\n", + "x indices\n", + "i indices\n", + "shape indices\n", + "kernel indices\n", + "input indices\n", + "batch indices\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "390\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute run Name keyword Name keyword Name keyword Attribute session kwargs Name\n", + "Label = ['updated', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output updated\n", + "x updated\n", + "i updated\n", + "shape updated\n", + "kernel updated\n", + "input updated\n", + "config updated\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "391\n", + "[CLS] Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call split\n", + "init split\n", + "append split\n", + "items split\n", + "format split\n", + "get split\n", + "add split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "392\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If UnaryOp Not BoolOp Or Call Name Attribute run Attribute Session Name Name NameConstant Call Name Attribute init Name Name NameConstant Assign Name msg BinOp Str Mod Name Raise Call Name Name\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x key\n", + "i key\n", + "name key\n", + "output key\n", + "shape key\n", + "axis key\n", + "new key\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "393\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute stack Name List Num Subscript Call Attribute shape Name Name Index Num\n", + "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape tile\n", + "append tile\n", + "call tile\n", + "output tile\n", + "data tile\n", + "reshape tile\n", + "recurrent tile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "394\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Call Attribute stack Name List Num Subscript Call Attribute shape Name Name Index Num\n", + "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape tile\n", + "append tile\n", + "call tile\n", + "output tile\n", + "data tile\n", + "reshape tile\n", + "recurrent tile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "395\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Gt Num Name Call Attribute ones like Name Name\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape where\n", + "call where\n", + "append where\n", + "data where\n", + "output where\n", + "recurrent where\n", + "add where\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "396\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute relu Attribute nn Name BinOp UnaryOp USub Name Add Name\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x negative\n", + "output negative\n", + "i negative\n", + "kernel negative\n", + "shape negative\n", + "input negative\n", + "batch negative\n", + "[PAD] part\n", + "shape part\n", + "kernel part\n", + "output part\n", + "size part\n", + "out part\n", + "format part\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "397\n", + "[CLS] BinOp Name Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute greater Name Name Name Call Name\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items cast\n", + "append cast\n", + "sqrt cast\n", + "add cast\n", + "init cast\n", + "kernel cast\n", + "name cast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "398\n", + "[CLS] If Compare Name Eq Num Return Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Name Gt Num Name BinOp Name Mult Name\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape where\n", + "append where\n", + "kernel where\n", + "output where\n", + "init where\n", + "recurrent where\n", + "bias where\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "399\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute softplus Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "400\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute softsign Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "401\n", + "[CLS] If Compare Call Name Name GtE Num Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Call Attribute shape Name Name Slice UnaryOp USub Num Return Name\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape reshape\n", + "append reshape\n", + "output reshape\n", + "add reshape\n", + "call reshape\n", + "bias reshape\n", + "kernel reshape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "402\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute log Name BinOp Name Div BinOp Num Sub Name\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "403\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute sigmoid Attribute nn Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "404\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg level arg noise shape arg seed NameConstant NameConstant Expr Str Assign Name retain prob BinOp Num Sub Name If Compare Name Is NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute dropout Attribute nn Name BinOp Name Mult Num Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "405\n", + "[CLS] If BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str Assign Name x Call Attribute cast Name Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init split\n", + "call split\n", + "items split\n", + "append split\n", + "format split\n", + "get split\n", + "add split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "406\n", + "[CLS] BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call split\n", + "init split\n", + "append split\n", + "items split\n", + "format split\n", + "get split\n", + "add split\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "407\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x padding\n", + "output padding\n", + "name padding\n", + "shape padding\n", + "i padding\n", + "config padding\n", + "[PAD] padding\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "408\n", + "[CLS] If Compare Name Eq Str If Compare Name NotEq Str Raise Call Name Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num Assign Name x Call Name Name Tuple Name Num Assign Name padding Str\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x left\n", + "output left\n", + "shape left\n", + "i left\n", + "self left\n", + "kernel left\n", + "name left\n", + "[PAD] pad\n", + "shape pad\n", + "output pad\n", + "kernel pad\n", + "size pad\n", + "format pad\n", + "out pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "409\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output left\n", + "x left\n", + "shape left\n", + "i left\n", + "kernel left\n", + "input left\n", + "config left\n", + "[PAD] pad\n", + "shape pad\n", + "kernel pad\n", + "output pad\n", + "size pad\n", + "format pad\n", + "out pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "410\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call convolution\n", + "init convolution\n", + "append convolution\n", + "format convolution\n", + "add convolution\n", + "kernel convolution\n", + "bias convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "411\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call convolution\n", + "init convolution\n", + "append convolution\n", + "format convolution\n", + "add convolution\n", + "kernel convolution\n", + "bias convolution\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "412\n", + "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute stack Name Name\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x output\n", + "output output\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "413\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name NotEq Tuple Num Num Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name force transpose NameConstant\n", + "Label = ['force', 'transpose', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output force\n", + "x force\n", + "shape force\n", + "i force\n", + "self force\n", + "kernel force\n", + "input force\n", + "[PAD] transpose\n", + "shape transpose\n", + "kernel transpose\n", + "output transpose\n", + "format transpose\n", + "size transpose\n", + "out transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "414\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "415\n", + "[CLS] BinOp Tuple Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Index Num Add Call Name Subscript Name Slice Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "416\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute atrous conv2d transpose Attribute nn Name Name Name Name Subscript Name Index Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "417\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Num Num Add BinOp Name Mult Num\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output strides\n", + "x strides\n", + "shape strides\n", + "i strides\n", + "kernel strides\n", + "self strides\n", + "config strides\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "418\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "419\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Tuple Num Add Name Tuple Num Assign Name strides BinOp Tuple Num Num Name\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output strides\n", + "x strides\n", + "shape strides\n", + "i strides\n", + "self strides\n", + "kernel strides\n", + "input strides\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "420\n", + "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "421\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg pool mode Tuple Num Num Str NameConstant Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "422\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg pool mode Tuple Num Num Num Str NameConstant Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "423\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute avg pool3d Attribute nn Name Name Name Name keyword Name keyword Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "424\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "425\n", + "[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Attribute nn Name Name Name keyword Str AugAssign Name x Add Call Name Name BinOp Tuple Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "426\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape random\n", + "append random\n", + "add random\n", + "call random\n", + "kernel random\n", + "bias random\n", + "init random\n", + "[PAD] uniform\n", + "shape uniform\n", + "kernel uniform\n", + "format uniform\n", + "size uniform\n", + "output uniform\n", + "i uniform\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "427\n", + "[CLS] FunctionDef arguments arg arg [MASK] [MASK] [MASK] [MASK] Return Compare Call Attribute expand dims Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n", + "Label = ['current', 'input', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self current\n", + "x current\n", + "args current\n", + "shape current\n", + "y current\n", + "data current\n", + "kernel current\n", + "[PAD] input\n", + "shape input\n", + "size input\n", + "kernel input\n", + "format input\n", + "output input\n", + "out input\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "428\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute squeeze Name Name keyword UnaryOp USub Num\n", + "Label = ['to', 'int32', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items to\n", + "append to\n", + "init to\n", + "add to\n", + "get to\n", + "sqrt to\n", + "is to\n", + "[PAD] int32\n", + "shape int32\n", + "format int32\n", + "size int32\n", + "kernel int32\n", + "output int32\n", + "length int32\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "429\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute ctc loss Name keyword Name keyword Name keyword Name Num\n", + "Label = ['expand', 'dims', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append expand\n", + "add expand\n", + "init expand\n", + "shape expand\n", + "bias expand\n", + "kernel expand\n", + "output expand\n", + "[PAD] dims\n", + "shape dims\n", + "format dims\n", + "kernel dims\n", + "size dims\n", + "output dims\n", + "weight dims\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "430\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute sparse to dense Name Attribute indices Name Attribute dense shape Name Attribute values Name keyword UnaryOp USub Num comprehension Name st Name\n", + "Label = ['decoded', 'dense', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output decoded\n", + "x decoded\n", + "shape decoded\n", + "i decoded\n", + "kernel decoded\n", + "self decoded\n", + "input decoded\n", + "[PAD] dense\n", + "shape dense\n", + "kernel dense\n", + "output dense\n", + "size dense\n", + "format dense\n", + "out dense\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "431\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Global Expr Call Attribute append Name Name Expr Yield Expr Call Attribute pop Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self name\n", + "x name\n", + "args name\n", + "y name\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "432\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name shape Call Name ListComp NameConstant comprehension Name Call Name Name\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output ndim\n", + "shape ndim\n", + "x ndim\n", + "i ndim\n", + "kernel ndim\n", + "input ndim\n", + "self ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "433\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Return Call Attribute ones like Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "434\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Num keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "call normal\n", + "init normal\n", + "append normal\n", + "add normal\n", + "format normal\n", + "bias normal\n", + "kernel normal\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "435\n", + "[CLS] If Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Str Str Call Name Name\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "i a\n", + "name a\n", + "output a\n", + "shape a\n", + "self a\n", + "new a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "436\n", + "[CLS] BoolOp Or Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute dtype Name Eq Str\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data dtype\n", + "shape dtype\n", + "kernel dtype\n", + "output dtype\n", + "bias dtype\n", + "name dtype\n", + "append dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "437\n", + "[CLS] IfExp Name BinOp Tuple Num Mult Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Num\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "append keras\n", + "output keras\n", + "bias keras\n", + "recurrent keras\n", + "reshape keras\n", + "data keras\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "438\n", + "[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Num For Name a Subscript Name Slice UnaryOp USub Num Expr Call Attribute pop Name Name If UnaryOp Not Name Assign Name keras shape list Tuple Num\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x a\n", + "i a\n", + "shape a\n", + "output a\n", + "name a\n", + "kernel a\n", + "self a\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "439\n", + "[CLS] If BoolOp And Compare Name IsNot NameConstant Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['max', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x max\n", + "output max\n", + "i max\n", + "shape max\n", + "kernel max\n", + "self max\n", + "input max\n", + "[PAD] value\n", + "shape value\n", + "kernel value\n", + "output value\n", + "size value\n", + "format value\n", + "out value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "440\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name gamma Call Name Name\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x gamma\n", + "output gamma\n", + "shape gamma\n", + "i gamma\n", + "kernel gamma\n", + "input gamma\n", + "self gamma\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "441\n", + "[CLS] If Compare Name Is NameConstant If Compare Name NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name beta Call Name Name\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output beta\n", + "x beta\n", + "shape beta\n", + "i beta\n", + "kernel beta\n", + "input beta\n", + "name beta\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "442\n", + "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Attribute ndim Name Gt Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data ndim\n", + "shape ndim\n", + "append ndim\n", + "name ndim\n", + "output ndim\n", + "items ndim\n", + "kernel ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "443\n", + "[CLS] BinOp BinOp List Str Mult BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Num Add List Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name ndim\n", + "shape ndim\n", + "data ndim\n", + "append ndim\n", + "output ndim\n", + "sqrt ndim\n", + "dtype ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "444\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name axis 2 Num Raise Call Name Str Name\n", + "Label = ['axis', '1', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x axis\n", + "output axis\n", + "i axis\n", + "shape axis\n", + "self axis\n", + "input axis\n", + "kernel axis\n", + "[PAD] 1\n", + "shape 1\n", + "kernel 1\n", + "output 1\n", + "size 1\n", + "out 1\n", + "format 1\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "445\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Attribute keras shape Name Expr Call Attribute insert Name Num Name Assign Attribute keras shape Name Call Name Name\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output shape\n", + "x shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "446\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name j Call Name Attribute keras shape Name Name If Compare Name Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Name Mult Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "447\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Attribute keras shape Name Slice UnaryOp USub Num Add Tuple NameConstant\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output output\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "448\n", + "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute keras shape Name Index Num Assign Name output shape BinOp Subscript Attribute keras shape Name Slice UnaryOp USub Name Add BinOp Tuple NameConstant Mult Name Assign Name output shape BinOp Tuple NameConstant Attribute ndim Name\n", + "Label = ['n', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output n\n", + "x n\n", + "shape n\n", + "i n\n", + "kernel n\n", + "input n\n", + "batch n\n", + "[PAD] size\n", + "shape size\n", + "output size\n", + "kernel size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "449\n", + "[CLS] If Compare Name Lt Num If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute type Name Eq Num Assign Name axis Num Assign Name axis BinOp BinOp Name Mod Attribute ndim Attribute type Name Add Num\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name ndim\n", + "[PAD] ndim\n", + "append ndim\n", + "data ndim\n", + "shape ndim\n", + "init ndim\n", + "items ndim\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "450\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mod Attribute ndim Attribute type Name Add Num\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axis\n", + "x axis\n", + "name axis\n", + "shape axis\n", + "i axis\n", + "input axis\n", + "config axis\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "451\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute set subtensor Name Subscript Name ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Slice Name\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output result\n", + "x result\n", + "shape result\n", + "i result\n", + "kernel result\n", + "input result\n", + "batch result\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "452\n", + "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "453\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num NameConstant\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "454\n", + "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute keras shape Name Index Num Name Name\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "keras keras\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "455\n", + "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute keras shape Name Index Num Name Name Name\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape keras\n", + "keras keras\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "456\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If UnaryOp Not Call Name Name Str Raise Call Name Str Return Call Attribute get value Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "457\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute asarray Name Name keyword Attribute dtype Name\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "items set\n", + "name set\n", + "add set\n", + "init set\n", + "sqrt set\n", + "is set\n", + "[PAD] value\n", + "shape value\n", + "kernel value\n", + "size value\n", + "format value\n", + "weight value\n", + "output value\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "458\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assert Call Name Name Tuple Name Name Return Call Attribute function Name Starred Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "459\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List Num Num Add Call Name Call Name Num Name\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output axes\n", + "x axes\n", + "shape axes\n", + "name axes\n", + "i axes\n", + "input axes\n", + "kernel axes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "460\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute stack Name Starred ListComp Subscript Name Index Name comprehension Name states at step Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "items append\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "461\n", + "[CLS] If Compare Call Name Name Gt Num Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Num Num\n", + "Label = ['unbroadcast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append unbroadcast\n", + "shape unbroadcast\n", + "add unbroadcast\n", + "call unbroadcast\n", + "data unbroadcast\n", + "output unbroadcast\n", + "bias unbroadcast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "462\n", + "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Num Num\n", + "Label = ['unbroadcast', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append unbroadcast\n", + "shape unbroadcast\n", + "add unbroadcast\n", + "call unbroadcast\n", + "data unbroadcast\n", + "output unbroadcast\n", + "bias unbroadcast\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "463\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg alt arg training NameConstant Expr Str Return Call Name Name Name keyword Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self x\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "464\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func If UnaryOp Not Call Name Name Name Raise Call Name Str\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self module\n", + "x module\n", + "args module\n", + "y module\n", + "shape module\n", + "data module\n", + "output module\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "465\n", + "[CLS] If Compare Name NotEq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute relu Attribute nnet Name BinOp UnaryOp USub Name Add Name Assign Name negative part Call Attribute relu Attribute nnet Name UnaryOp Name\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x negative\n", + "output negative\n", + "i negative\n", + "kernel negative\n", + "shape negative\n", + "input negative\n", + "self negative\n", + "[PAD] part\n", + "shape part\n", + "kernel part\n", + "output part\n", + "size part\n", + "out part\n", + "format part\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "466\n", + "[CLS] If Compare Name NotEq Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call Name Assign Name x Call Attribute relu Attribute nnet Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "467\n", + "[CLS] If BoolOp And Compare Name NotEq UnaryOp USub Num Compare Name NotIn Name Raise Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Call Name Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "468\n", + "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Attribute format Str Name Str Call Attribute format Str Call Name Call Name Name\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "format format\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "469\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name Call Attribute maximum Name Name Call Name\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output norm\n", + "x norm\n", + "i norm\n", + "name norm\n", + "input norm\n", + "kernel norm\n", + "num norm\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "470\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", + "Label = ['image', 'shape', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output image\n", + "x image\n", + "i image\n", + "shape image\n", + "input image\n", + "kernel image\n", + "config image\n", + "[PAD] shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "471\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "472\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "473\n", + "[CLS] ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "474\n", + "[CLS] Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "475\n", + "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "476\n", + "[CLS] If Compare BinOp Subscript Name Index Num Mod Num Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "x conv\n", + "shape conv\n", + "i conv\n", + "kernel conv\n", + "self conv\n", + "input conv\n", + "[PAD] out\n", + "shape out\n", + "kernel out\n", + "output out\n", + "size out\n", + "out out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "477\n", + "[CLS] If Compare Name Eq Str If UnaryOp Not Name Raise Call Name Str Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num Assign Name x Call Name Name Tuple Name Num Assign Name padding Str\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x left\n", + "output left\n", + "shape left\n", + "i left\n", + "self left\n", + "kernel left\n", + "name left\n", + "[PAD] pad\n", + "shape pad\n", + "output pad\n", + "kernel pad\n", + "size pad\n", + "format pad\n", + "out pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "478\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name spatial start dim Num\n", + "Label = ['spatial', 'start', 'dim', '[PAD]']\n", + "Pred =\n", + "x spatial\n", + "output spatial\n", + "shape spatial\n", + "i spatial\n", + "self spatial\n", + "kernel spatial\n", + "input spatial\n", + "[PAD] start\n", + "shape start\n", + "kernel start\n", + "output start\n", + "size start\n", + "out start\n", + "format start\n", + "[PAD] dim\n", + "shape dim\n", + "kernel dim\n", + "output dim\n", + "size dim\n", + "out dim\n", + "format dim\n", + "[PAD] [PAD]\n", + "\n", + "479\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d Attribute nnet Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Subscript Name Index Num\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output conv\n", + "x conv\n", + "i conv\n", + "shape conv\n", + "kernel conv\n", + "self conv\n", + "input conv\n", + "[PAD] out\n", + "shape out\n", + "kernel out\n", + "output out\n", + "size out\n", + "format out\n", + "out out\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "480\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str\n", + "Label = ['pool', '2d', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape pool\n", + "call pool\n", + "add pool\n", + "bias pool\n", + "output pool\n", + "kernel pool\n", + "recurrent pool\n", + "[PAD] 2d\n", + "shape 2d\n", + "format 2d\n", + "kernel 2d\n", + "output 2d\n", + "size 2d\n", + "i 2d\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "481\n", + "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "482\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", + "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output w\n", + "x w\n", + "shape w\n", + "i w\n", + "kernel w\n", + "self w\n", + "input w\n", + "[PAD] pad\n", + "shape pad\n", + "kernel pad\n", + "output pad\n", + "size pad\n", + "format pad\n", + "out pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "483\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", + "Label = ['h', 'pad', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output h\n", + "x h\n", + "shape h\n", + "i h\n", + "kernel h\n", + "self h\n", + "input h\n", + "[PAD] pad\n", + "shape pad\n", + "kernel pad\n", + "output pad\n", + "size pad\n", + "format pad\n", + "out pad\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "484\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", + "Label = ['expected', 'height', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output expected\n", + "shape expected\n", + "x expected\n", + "i expected\n", + "kernel expected\n", + "config expected\n", + "input expected\n", + "[PAD] height\n", + "shape height\n", + "kernel height\n", + "output height\n", + "size height\n", + "format height\n", + "out height\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "485\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Subscript Name Index Num Num Num Num AugAssign Name x Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "486\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "487\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "488\n", + "[CLS] If Compare Call Name Name Eq Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Subscript Name Index Num Num AugAssign Name x Call Name Name Tuple Num Subscript Name Index Num Subscript Name Index Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "489\n", + "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Subscript Name Index Num\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "490\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Num BinOp BinOp Call Attribute max Name Call Attribute concatenate Name List Name List UnaryOp USub Num Num Num\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append maximum\n", + "items maximum\n", + "init maximum\n", + "kernel maximum\n", + "output maximum\n", + "shape maximum\n", + "sqrt maximum\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "491\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Slice Name BinOp Subscript Name Slice Name Add Name\n", + "Label = ['set', 'subtensor', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append set\n", + "items set\n", + "shape set\n", + "add set\n", + "init set\n", + "kernel set\n", + "input set\n", + "[PAD] subtensor\n", + "shape subtensor\n", + "kernel subtensor\n", + "size subtensor\n", + "format subtensor\n", + "output subtensor\n", + "length subtensor\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "492\n", + "[CLS] Assign Tuple List Name [MASK] [MASK] [MASK] [MASK] Name log f probs Name b active Name log b probs Name Call Attribute scan Name Name keyword List Name Subscript Name ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num keyword List Call Attribute int32 Name Num Name Call Attribute int32 Name Num Name\n", + "Label = ['f', 'active', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x f\n", + "i f\n", + "output f\n", + "self f\n", + "shape f\n", + "kernel f\n", + "y f\n", + "[PAD] active\n", + "shape active\n", + "kernel active\n", + "output active\n", + "size active\n", + "out active\n", + "format active\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "493\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Call Attribute arange Name Subscript Attribute shape Name Index Num Str Num\n", + "Label = ['idxs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output idxs\n", + "x idxs\n", + "shape idxs\n", + "i idxs\n", + "kernel idxs\n", + "input idxs\n", + "self idxs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "494\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name elems Subscript Name Slice Num\n", + "Label = ['initializer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output initializer\n", + "x initializer\n", + "shape initializer\n", + "i initializer\n", + "kernel initializer\n", + "self initializer\n", + "input initializer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "495\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Slice Tuple Num UnaryOp USub Num Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "496\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name BinOp Name Mult Name BinOp BinOp Name Name Add Subscript Name Index Num\n", + "Label = ['slice', 'col', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output slice\n", + "x slice\n", + "shape slice\n", + "i slice\n", + "kernel slice\n", + "y slice\n", + "input slice\n", + "[PAD] col\n", + "shape col\n", + "kernel col\n", + "output col\n", + "size col\n", + "format col\n", + "out col\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "497\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg data format arg file format arg scale arg kwargs NameConstant NameConstant NameConstant If Compare Name Is NameConstant Assign Name data format Call Attribute image data format Name Return Call Attribute save img Name Name Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self path\n", + "x path\n", + "args path\n", + "shape path\n", + "kernel path\n", + "data path\n", + "y path\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "498\n", + "[CLS] Module Expr Str ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias Assign Name [MASK] [MASK] [MASK] [MASK] Attribute pad sequences Name Assign Name make sampling table Attribute make sampling table Name Assign Name skipgrams Attribute skipgrams Name Assign Name remove long seq Attribute remove long seq Name ClassDef Attribute TimeseriesGenerator Name Attribute Sequence Name Expr Str Pass\n", + "Label = ['pad', 'sequences', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output pad\n", + "x pad\n", + "i pad\n", + "shape pad\n", + "kernel pad\n", + "self pad\n", + "input pad\n", + "[PAD] sequences\n", + "shape sequences\n", + "kernel sequences\n", + "output sequences\n", + "size sequences\n", + "out sequences\n", + "format sequences\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "499\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "name warn\n", + "items warn\n", + "warn warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "500\n", + "[CLS] Call Name keyword Name keyword List keyword List keyword List keyword Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute outputs Name keyword ListComp NameConstant comprehension Name Attribute inputs Name keyword ListComp NameConstant comprehension Name Attribute outputs Name keyword ListComp Attribute keras shape Name comprehension Name x Attribute inputs Name keyword ListComp Attribute keras shape Name comprehension Name x Attribute outputs Name\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inputs\n", + "append inputs\n", + "name inputs\n", + "data inputs\n", + "self inputs\n", + "bias inputs\n", + "output inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "501\n", + "[CLS] Call Name GeneratorExp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name UnaryOp Not Call Name Name Tuple Name Name\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "502\n", + "[CLS] ListComp BoolOp And Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name layer Attribute layers Name\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape stateful\n", + "name stateful\n", + "data stateful\n", + "kernel stateful\n", + "output stateful\n", + "recurrent stateful\n", + "bias stateful\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "503\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Name Str List If Compare Attribute input spec Name Is NameConstant Expr Call Attribute append Name NameConstant If UnaryOp Not Call Name Attribute input spec Name Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Attribute input spec Name AugAssign Name specs Attribute input spec Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "i layer\n", + "name layer\n", + "output layer\n", + "shape layer\n", + "new layer\n", + "state layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "504\n", + "[CLS] If BoolOp And Call Name Name Str Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute keras shape Name Expr Call Attribute append Name Name Assign Name output shapes NameConstant\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output shape\n", + "x shape\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "505\n", + "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Name kept nodes Num Assign Name kept nodes Num\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape class\n", + "call class\n", + "output class\n", + "data class\n", + "add class\n", + "kernel class\n", + "keras class\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "506\n", + "[CLS] If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "507\n", + "[CLS] While Name For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name layer Subscript Name Index Subscript Name Index Str If Compare Name In Name For Name node data Call Attribute pop Name Name Expr Call Name Name Name\n", + "Label = ['layer', 'data', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "i layer\n", + "output layer\n", + "shape layer\n", + "name layer\n", + "self layer\n", + "kernel layer\n", + "[PAD] data\n", + "shape data\n", + "kernel data\n", + "name data\n", + "output data\n", + "out data\n", + "size data\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "508\n", + "[CLS] If BoolOp And Compare Str NotIn Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Str In Name Assign Name f Subscript Name Index Str\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "data attrs\n", + "shape attrs\n", + "output attrs\n", + "bias attrs\n", + "kernel attrs\n", + "recurrent attrs\n", + "call attrs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "509\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] [MASK] [MASK] [MASK] Name Name Attribute layers Name keyword Name keyword Name\n", + "Label = ['load', 'weights', 'from', 'hdf5']\n", + "Pred =\n", + "append load\n", + "add load\n", + "shape load\n", + "bias load\n", + "init load\n", + "kernel load\n", + "output load\n", + "[PAD] weights\n", + "shape weights\n", + "kernel weights\n", + "format weights\n", + "output weights\n", + "size weights\n", + "state weights\n", + "[PAD] from\n", + "shape from\n", + "kernel from\n", + "format from\n", + "output from\n", + "size from\n", + "state from\n", + "[PAD] hdf5\n", + "shape hdf5\n", + "kernel hdf5\n", + "format hdf5\n", + "output hdf5\n", + "size hdf5\n", + "state hdf5\n", + "\n", + "510\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Lambda arguments arg x Subscript Name Index Name\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append sort\n", + "items sort\n", + "add sort\n", + "bias sort\n", + "kernel sort\n", + "init sort\n", + "call sort\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "511\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape count\n", + "name count\n", + "output count\n", + "items count\n", + "call count\n", + "states count\n", + "outputs count\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "512\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape count\n", + "name count\n", + "output count\n", + "call count\n", + "items count\n", + "states count\n", + "keras count\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "513\n", + "[CLS] If BoolOp And Name Name Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant If BoolOp Name Name Call Name Subscript Name Index Num Str Call Name Subscript Name Index Num Str Expr Call Name BinOp Str Mod Tuple Subscript Attribute shape Subscript Name Index Num Index Num Subscript Attribute shape Subscript Name Index Num Index Num\n", + "Label = ['do', 'validation', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output do\n", + "shape do\n", + "x do\n", + "i do\n", + "kernel do\n", + "self do\n", + "input do\n", + "[PAD] validation\n", + "shape validation\n", + "output validation\n", + "kernel validation\n", + "format validation\n", + "size validation\n", + "out validation\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "514\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name count mode Str\n", + "Label = ['count', 'mode', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output count\n", + "x count\n", + "i count\n", + "self count\n", + "shape count\n", + "input count\n", + "kernel count\n", + "[PAD] mode\n", + "shape mode\n", + "kernel mode\n", + "output mode\n", + "size mode\n", + "out mode\n", + "format mode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "515\n", + "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num Assign Name val outs Call Name Name For Tuple Name l Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x val\n", + "output val\n", + "i val\n", + "shape val\n", + "name val\n", + "input val\n", + "kernel val\n", + "[PAD] outs\n", + "shape outs\n", + "kernel outs\n", + "output outs\n", + "size outs\n", + "out outs\n", + "name outs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "516\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "i l\n", + "output l\n", + "name l\n", + "shape l\n", + "y l\n", + "new l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "517\n", + "[CLS] If Compare Name Eq BinOp Call Name Name Sub Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num Assign Name val outs Call Name Name For Tuple Name l Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x val\n", + "output val\n", + "i val\n", + "name val\n", + "shape val\n", + "input val\n", + "kernel val\n", + "[PAD] outs\n", + "shape outs\n", + "kernel outs\n", + "output outs\n", + "name outs\n", + "size outs\n", + "out outs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "518\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "i l\n", + "output l\n", + "name l\n", + "y l\n", + "shape l\n", + "new l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "519\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name Assign Name progbar Call Name keyword Name\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output progbar\n", + "x progbar\n", + "i progbar\n", + "shape progbar\n", + "kernel progbar\n", + "self progbar\n", + "input progbar\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "520\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Attribute feed inputs Name If BoolOp And Call Name Subscript Name Index Name UnaryOp Not Call Attribute is sparse Name Subscript Attribute feed inputs Name Index Name Expr Call Attribute append Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "521\n", + "[CLS] BoolOp And Call Name Subscript Name Index Name UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute feed inputs Name Index Name\n", + "Label = ['is', 'sparse', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append is\n", + "add is\n", + "shape is\n", + "data is\n", + "call is\n", + "init is\n", + "output is\n", + "[PAD] sparse\n", + "shape sparse\n", + "format sparse\n", + "kernel sparse\n", + "output sparse\n", + "size sparse\n", + "length sparse\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "522\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name batch out Call Name Name Expr Call Attribute append Subscript Name Index Name Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "523\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Call Attribute toarray Subscript Name Index Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "524\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Name comprehension Tuple Name i Name name Call Name Attribute metrics names Name Compare Call Name Name In Attribute stateful metric names Name\n", + "Label = ['stateful', 'metric', 'indices', '[PAD]']\n", + "Pred =\n", + "output stateful\n", + "x stateful\n", + "i stateful\n", + "name stateful\n", + "shape stateful\n", + "input stateful\n", + "config stateful\n", + "[PAD] metric\n", + "shape metric\n", + "output metric\n", + "kernel metric\n", + "size metric\n", + "format metric\n", + "out metric\n", + "[PAD] indices\n", + "shape indices\n", + "output indices\n", + "kernel indices\n", + "size indices\n", + "format indices\n", + "out indices\n", + "[PAD] [PAD]\n", + "\n", + "525\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name batch out Call Name Name If Compare Name In Name Assign Subscript Name Index Name Call Name Name AugAssign Subscript Name Index Name Add Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "526\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name If Compare Name NotIn Name AugAssign Subscript Name Index Name Div Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "527\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name keyword Name keyword BinOp Attribute name Name Add Str\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "528\n", + "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute outputs Name NameConstant Assign Attribute inputs Name NameConstant If Attribute outputs Name Assign Attribute outbound nodes Subscript Attribute layers Name Index UnaryOp USub Num List Assign Attribute outputs Name List Attribute output Subscript Attribute layers Name Index UnaryOp Num Expr Call Attribute build Name\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape layers\n", + "data layers\n", + "bias layers\n", + "output layers\n", + "recurrent layers\n", + "keras layers\n", + "call layers\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "529\n", + "[CLS] Call Name keyword Name keyword Name keyword BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "530\n", + "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute init graph network Name Attribute inputs Name Attribute outputs Name keyword Attribute name Name Assign Attribute built Name NameConstant\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inputs\n", + "data inputs\n", + "bias inputs\n", + "kernel inputs\n", + "append inputs\n", + "name inputs\n", + "recurrent inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "531\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Attribute name Name Call Attribute deepcopy Name Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output config\n", + "x config\n", + "name config\n", + "shape config\n", + "i config\n", + "config config\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "532\n", + "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute dumps Name Dict Str Str Str Str Str Dict Str Str Attribute name Attribute class Attribute optimizer Name Call Attribute get config Attribute optimizer Name Attribute loss Name Attribute metrics Name Attribute sample weight mode Name Attribute loss weights Name keyword Name Str\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init encode\n", + "call encode\n", + "append encode\n", + "items encode\n", + "get encode\n", + "name encode\n", + "format encode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "533\n", + "[CLS] While Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Add Str Call Name Name AugAssign Name idx Num\n", + "Label = ['unique', 'name', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output unique\n", + "x unique\n", + "i unique\n", + "shape unique\n", + "name unique\n", + "input unique\n", + "self unique\n", + "[PAD] name\n", + "shape name\n", + "output name\n", + "kernel name\n", + "size name\n", + "out name\n", + "format name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "534\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Name weights Attribute weights Name If Name Expr Call Attribute append Name Name\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x layer\n", + "i layer\n", + "output layer\n", + "shape layer\n", + "name layer\n", + "kernel layer\n", + "self layer\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "535\n", + "[CLS] If Compare Call Name Name NotEq Call Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "536\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "537\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "538\n", + "[CLS] Try Expr Call Name Name Name Name If Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append close\n", + "items close\n", + "add close\n", + "init close\n", + "name close\n", + "bias close\n", + "sqrt close\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "539\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name d Dict Assign Name f Call Name Name Expr Call Name Name Name Return Name\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "x model\n", + "args model\n", + "y model\n", + "kernel model\n", + "data model\n", + "input model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "540\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name f Call Name Name keyword Str Return Call Name Name\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self state\n", + "x state\n", + "args state\n", + "y state\n", + "name state\n", + "shape state\n", + "kernel state\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "541\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Name comprehension Name x Name Compare Call Name Name Gt Name\n", + "Label = ['bad', 'attributes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x bad\n", + "output bad\n", + "i bad\n", + "shape bad\n", + "kernel bad\n", + "input bad\n", + "name bad\n", + "[PAD] attributes\n", + "shape attributes\n", + "kernel attributes\n", + "output attributes\n", + "size attributes\n", + "format attributes\n", + "out attributes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "542\n", + "[CLS] Expr Call Name Name Str ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str comprehension Name layer Name\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "init encode\n", + "call encode\n", + "append encode\n", + "items encode\n", + "format encode\n", + "name encode\n", + "get encode\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "543\n", + "[CLS] Assign Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Str Call Attribute encode Call Attribute backend Name Str\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape attrs\n", + "output attrs\n", + "keras attrs\n", + "data attrs\n", + "kernel attrs\n", + "add attrs\n", + "recurrent attrs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "544\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Tuple Name w Name val Call Name Call Name Name Name If BoolOp And Call Name Name Str Attribute name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Add Call Name Name Expr Call Attribute append Name Call Attribute encode Name Str\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "545\n", + "[CLS] Compare Subscript Name Slice Num NotEq Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "keras kernel\n", + "output kernel\n", + "kernel kernel\n", + "[PAD] size\n", + "shape size\n", + "format size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "546\n", + "[CLS] Compare Subscript Name Slice Num Eq Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Num\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape kernel\n", + "keras kernel\n", + "output kernel\n", + "kernel kernel\n", + "[PAD] size\n", + "shape size\n", + "format size\n", + "kernel size\n", + "output size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "547\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append transpose\n", + "shape transpose\n", + "add transpose\n", + "bias transpose\n", + "data transpose\n", + "output transpose\n", + "init transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "548\n", + "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append transpose\n", + "shape transpose\n", + "add transpose\n", + "bias transpose\n", + "output transpose\n", + "data transpose\n", + "init transpose\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "549\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num keyword UnaryOp USub Num\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append concatenate\n", + "add concatenate\n", + "bias concatenate\n", + "shape concatenate\n", + "call concatenate\n", + "init concatenate\n", + "reshape concatenate\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "550\n", + "[CLS] If Compare Name Eq Tuple Num BinOp Name Mult Name Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple BinOp Name Name Assign Name source Str Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x source\n", + "output source\n", + "shape source\n", + "i source\n", + "self source\n", + "name source\n", + "kernel source\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "551\n", + "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Attribute reset after Name Assign Name target Str Assign Name target Str\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output target\n", + "x target\n", + "i target\n", + "shape target\n", + "self target\n", + "kernel target\n", + "input target\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "552\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "553\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "554\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Call Attribute format Str Attribute name Name Add Call Attribute format Str Attribute shape Subscript Name Index Name Attribute shape Subscript Name Index Name\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append warn\n", + "shape warn\n", + "items warn\n", + "name warn\n", + "init warn\n", + "output warn\n", + "reshape warn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "555\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Subscript Name Index Name Call Attribute format Str Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "556\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps per epoch arg epochs arg verbose arg callbacks arg validation data arg validation steps arg class weight arg max queue size arg workers arg use multiprocessing arg shuffle arg initial epoch NameConstant Num Num NameConstant NameConstant NameConstant NameConstant Num Num NameConstant NameConstant Num\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "x model\n", + "shape model\n", + "args model\n", + "kernel model\n", + "output model\n", + "data model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "557\n", + "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Subscript Call Name Call Attribute values Name Index Num Index Num Assign Name batch size Subscript Attribute shape Name Index Num\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output batch\n", + "shape batch\n", + "x batch\n", + "i batch\n", + "kernel batch\n", + "self batch\n", + "input batch\n", + "[PAD] size\n", + "shape size\n", + "output size\n", + "kernel size\n", + "size size\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "558\n", + "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x l\n", + "i l\n", + "output l\n", + "name l\n", + "shape l\n", + "y l\n", + "new l\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "559\n" + ] + }, { - "data": { - "text/plain": [ - "'clone'" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[CLS] ListComp Name comprehension Tuple Name [MASK] [MASK] [MASK] [MASK] Name name Call Name Attribute metrics names Name Compare Call Name Name In Attribute stateful metric names Name\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x i\n", + "i i\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "560\n", + "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n", + "Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x steps\n", + "output steps\n", + "shape steps\n", + "i steps\n", + "self steps\n", + "input steps\n", + "kernel steps\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "561\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute average Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "562\n", + "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps arg max queue size arg workers arg use multiprocessing arg verbose NameConstant Num Num NameConstant Num\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self model\n", + "x model\n", + "shape model\n", + "args model\n", + "kernel model\n", + "output model\n", + "data model\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "563\n", + "[CLS] If Compare Call Name Name Eq Num If Compare Name Num Return Subscript Subscript Name Index Num Index Num Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append concatenate\n", + "shape concatenate\n", + "add concatenate\n", + "bias concatenate\n", + "reshape concatenate\n", + "init concatenate\n", + "kernel concatenate\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "564\n", + "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get Name Str\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output dtype\n", + "x dtype\n", + "shape dtype\n", + "i dtype\n", + "self dtype\n", + "kernel dtype\n", + "input dtype\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "565\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name trainable Call Name Name Str NameConstant If Name Return Attribute trainable weights Name Return List Name\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "566\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name keyword Name\n", + "Label = ['variable', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append variable\n", + "items variable\n", + "add variable\n", + "init variable\n", + "bias variable\n", + "kernel variable\n", + "call variable\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "567\n", + "[CLS] If UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name Assign Name input spec Call Name Attribute input spec Name Assign Name input spec Attribute input spec Name\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append input\n", + "shape input\n", + "call input\n", + "output input\n", + "add input\n", + "init input\n", + "bias input\n", + "[PAD] spec\n", + "shape spec\n", + "format spec\n", + "size spec\n", + "kernel spec\n", + "output spec\n", + "length spec\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "568\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "569\n", + "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute ndim Name Str Call Name Call Attribute ndim Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "570\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name Str Call Name Call Attribute ndim Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "571\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute dtype Name Str Call Name Call Attribute dtype Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "572\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute dtype Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "573\n", + "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "574\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "575\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name Str Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "576\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "577\n", + "[CLS] If UnaryOp Not Call Name Name If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str If Compare Str NotIn Name Assign Subscript Name Index Str Name\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape call\n", + "call call\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "578\n", + "[CLS] If Call Name Name Name If Call Name GeneratorExp Compare Name IsNot NameConstant comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Name Raise Call Name BinOp BinOp BinOp Str Attribute name Name Str Call Name Name\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x m\n", + "i m\n", + "name m\n", + "output m\n", + "shape m\n", + "new m\n", + "self m\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "579\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return Call Attribute get node attribute at index Name Name Str Str\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self self\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "580\n", + "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num Raise Call Name BinOp BinOp Str Add Attribute name Name Str If UnaryOp Not Attribute inbound nodes Name Raise Call Name BinOp BinOp Str Attribute name Name Str\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape inbound\n", + "call inbound\n", + "output inbound\n", + "data inbound\n", + "keras inbound\n", + "add inbound\n", + "recurrent inbound\n", + "[PAD] nodes\n", + "shape nodes\n", + "format nodes\n", + "kernel nodes\n", + "size nodes\n", + "output nodes\n", + "i nodes\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "581\n", + "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Name inputs hash NameConstant\n", + "Label = ['inputs', 'hash', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output inputs\n", + "x inputs\n", + "shape inputs\n", + "i inputs\n", + "self inputs\n", + "kernel inputs\n", + "input inputs\n", + "[PAD] hash\n", + "shape hash\n", + "output hash\n", + "kernel hash\n", + "format hash\n", + "size hash\n", + "out hash\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "582\n", + "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "583\n", + "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name intermediate Call Attribute sub Name Str Str Name Assign Name insecure Call Attribute lower Call Attribute sub Name Str Str Name If Compare Subscript Name Index Num NotEq Str Return Name Return BinOp Str Add Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "self name\n", + "x name\n", + "name name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "584\n", + "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Try Expr Call Attribute append Name Call Attribute int shape Name Name ExceptHandler Name Expr Call Attribute append Name NameConstant\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x x\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "585\n", + "[CLS] BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape output\n", + "name output\n", + "output output\n", + "[PAD] names\n", + "shape names\n", + "name names\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "format names\n", + "kernel names\n", + "size names\n", + "output names\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "586\n", + "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute outputs Name Str Call Name Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "call outputs\n", + "output outputs\n", + "data outputs\n", + "keras outputs\n", + "input outputs\n", + "outputs outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "587\n", + "[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "items outputs\n", + "output outputs\n", + "name outputs\n", + "states outputs\n", + "call outputs\n", + "outputs outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "588\n", + "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "output outputs\n", + "name outputs\n", + "items outputs\n", + "call outputs\n", + "states outputs\n", + "outputs outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "589\n", + "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "output outputs\n", + "name outputs\n", + "items outputs\n", + "call outputs\n", + "states outputs\n", + "outputs outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "590\n", + "[CLS] Raise Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "items outputs\n", + "output outputs\n", + "append outputs\n", + "call outputs\n", + "input outputs\n", + "data outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "591\n", + "[CLS] Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape outputs\n", + "items outputs\n", + "output outputs\n", + "name outputs\n", + "states outputs\n", + "call outputs\n", + "outputs outputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "592\n", + "[CLS] If Compare Name Eq Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Expr Call Attribute append Name Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "593\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "items append\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "594\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute metrics names Name BinOp Subscript Attribute output names Name Index Name Add Str\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "595\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Attribute binary accuracy Name If Compare Name Tuple Str Str Assign Name metric fn Attribute binary crossentropy Name\n", + "Label = ['metric', 'fn', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output metric\n", + "x metric\n", + "i metric\n", + "shape metric\n", + "kernel metric\n", + "self metric\n", + "input metric\n", + "[PAD] fn\n", + "shape fn\n", + "kernel fn\n", + "output fn\n", + "size fn\n", + "format fn\n", + "out fn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "596\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Attribute categorical accuracy Name If Compare Name Tuple Str Str Assign Name metric fn Attribute categorical crossentropy Name\n", + "Label = ['metric', 'fn', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output metric\n", + "x metric\n", + "i metric\n", + "shape metric\n", + "self metric\n", + "kernel metric\n", + "input metric\n", + "[PAD] fn\n", + "shape fn\n", + "kernel fn\n", + "output fn\n", + "size fn\n", + "format fn\n", + "out fn\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "597\n", + "[CLS] If Compare Name In Tuple Str Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple Str Str Assign Name suffix Str\n", + "Label = ['suffix', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x suffix\n", + "output suffix\n", + "shape suffix\n", + "i suffix\n", + "self suffix\n", + "kernel suffix\n", + "input suffix\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "598\n", + "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Name metric result Call Name Name Name keyword Name keyword Subscript Name Index Name\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape name\n", + "append name\n", + "input name\n", + "output name\n", + "data name\n", + "add name\n", + "init name\n", + "[PAD] scope\n", + "shape scope\n", + "format scope\n", + "size scope\n", + "kernel scope\n", + "output scope\n", + "length scope\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "599\n", + "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name BinOp List Attribute total loss Name Add Attribute metrics tensors Name keyword Name keyword Str keyword Attribute function kwargs Name\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape function\n", + "call function\n", + "add function\n", + "append function\n", + "bias function\n", + "kernel function\n", + "output function\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "600\n", + "[CLS] If Compare Name Is NameConstant If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute call Name Call Name Attribute inputs Name keyword Name Assign Name outputs Call Attribute call Name Call Name Attribute inputs Name\n", + "Label = ['expects', 'training', 'arg', '[PAD]']\n", + "Pred =\n", + "shape expects\n", + "data expects\n", + "bias expects\n", + "kernel expects\n", + "recurrent expects\n", + "append expects\n", + "output expects\n", + "[PAD] training\n", + "shape training\n", + "format training\n", + "kernel training\n", + "output training\n", + "size training\n", + "state training\n", + "[PAD] arg\n", + "shape arg\n", + "format arg\n", + "kernel arg\n", + "output arg\n", + "size arg\n", + "state arg\n", + "[PAD] [PAD]\n", + "\n", + "601\n", + "[CLS] Call Name GeneratorExp BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute is tensor Name Name comprehension Name v Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "append ndarray\n", + "add ndarray\n", + "data ndarray\n", + "input ndarray\n", + "call ndarray\n", + "output ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "602\n", + "[CLS] If Call Name Name Name Raise Call Name Str If BoolOp And UnaryOp Not Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Call Attribute is tensor Name Name Raise Call Name BinOp Str Add Call Name Name\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape ndarray\n", + "call ndarray\n", + "append ndarray\n", + "add ndarray\n", + "output ndarray\n", + "data ndarray\n", + "input ndarray\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "603\n", + "[CLS] If Compare Name IsNot NameConstant AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name keyword NameConstant\n", + "Label = ['all', 'inputs', '[PAD]', '[PAD]']\n", + "Pred =\n", + "x all\n", + "output all\n", + "i all\n", + "shape all\n", + "name all\n", + "y all\n", + "input all\n", + "[PAD] inputs\n", + "shape inputs\n", + "kernel inputs\n", + "output inputs\n", + "out inputs\n", + "size inputs\n", + "format inputs\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "604\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Attribute optimizer Name keyword Attribute loss Name keyword Attribute metrics Name keyword Attribute loss weights Name keyword Name\n", + "Label = ['compile', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append compile\n", + "add compile\n", + "bias compile\n", + "init compile\n", + "kernel compile\n", + "call compile\n", + "shape compile\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "605\n", + "[CLS] BoolOp And Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Compare Call Name Name In List Num Num\n", + "Label = ['image', 'data', 'format', '[PAD]']\n", + "Pred =\n", + "shape image\n", + "append image\n", + "items image\n", + "data image\n", + "call image\n", + "add image\n", + "output image\n", + "[PAD] data\n", + "shape data\n", + "format data\n", + "kernel data\n", + "size data\n", + "output data\n", + "length data\n", + "[PAD] format\n", + "shape format\n", + "format format\n", + "[PAD] [PAD]\n", + "\n", + "606\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Tuple Subscript Name Index Num Num Add Subscript Name Slice Num\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "607\n", + "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Name Slice UnaryOp USub Num Add Tuple Num\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "append append\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "608\n", + "[CLS] If BoolOp Or UnaryOp Not Call Name Name Str Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name NameConstant Is NameConstant Expr Call Attribute append Name NameConstant Expr Call Attribute append Name Name\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape name\n", + "append name\n", + "data name\n", + "output name\n", + "init name\n", + "input name\n", + "recurrent name\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "609\n", + "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name BinOp Call Name Subscript Attribute shape Subscript Name Index Num Index Num Mult BinOp Num Sub Name\n", + "Label = ['split', 'at', '[PAD]', '[PAD]']\n", + "Pred =\n", + "output split\n", + "x split\n", + "shape split\n", + "i split\n", + "kernel split\n", + "input split\n", + "config split\n", + "[PAD] at\n", + "shape at\n", + "kernel at\n", + "output at\n", + "format at\n", + "size at\n", + "out at\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "610\n", + "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Str Call Name Name Str\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape shape\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n", + "611\n", + "[CLS] If Call Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name ins BinOp BinOp BinOp Name Add Name Name List Num Assign Name ins BinOp BinOp Name Name Name\n", + "Label = ['uses', 'dynamic', 'learning', 'phase']\n", + "Pred =\n", + "append uses\n", + "items uses\n", + "shape uses\n", + "kernel uses\n", + "add uses\n", + "call uses\n", + "output uses\n", + "[PAD] dynamic\n", + "shape dynamic\n", + "kernel dynamic\n", + "size dynamic\n", + "format dynamic\n", + "i dynamic\n", + "output dynamic\n", + "[PAD] learning\n", + "shape learning\n", + "kernel learning\n", + "size learning\n", + "format learning\n", + "i learning\n", + "output learning\n", + "[PAD] phase\n", + "shape phase\n", + "kernel phase\n", + "size phase\n", + "format phase\n", + "i phase\n", + "output phase\n", + "\n", + "612\n", + "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", + "Label = ['evaluate', 'generator', '[PAD]', '[PAD]']\n", + "Pred =\n", + "shape evaluate\n", + "append evaluate\n", + "call evaluate\n", + "add evaluate\n", + "kernel evaluate\n", + "bias evaluate\n", + "init evaluate\n", + "[PAD] generator\n", + "shape generator\n", + "kernel generator\n", + "format generator\n", + "size generator\n", + "output generator\n", + "i generator\n", + "[PAD] [PAD]\n", + "[PAD] [PAD]\n", + "\n" + ] } ], "source": [ - "vocab_label_df.loc[0][0]" + "n=7\n", + "pred_str = []; score = [0]*4; score_full_name=0; score_no_pad = 0; rank =[0]*4\n", + "for idx in range(613):\n", + " print(idx)\n", + " print(snippet.loc[idx][0])\n", + " print(\"Label =\", labels_str[idx])\n", + " msk_idx = snippet.loc[idx][0].split(\" \").index('[MASK]')\n", + " preds_ = []\n", + " print(\"Pred =\")\n", + " r = preds_all[idx]\n", + " \n", + " label_len = 0; full_token_score = 0\n", + " for j in range(4):\n", + " l = labels_str[idx][j]\n", + " if l != \"[PAD]\":\n", + " label_len += 1\n", + " for i in range(n):\n", + " p = vocab_label_df.loc[r[msk_idx+j][i]][0]\n", + " print(p,l)\n", + " if p == l:\n", + " score[j] += 1\n", + " rank[j] += (i+1)\n", + " if l != \"[PAD]\":\n", + " full_token_score += 1\n", + " break\n", + " elif i==n-1:\n", + " rank[j] += n\n", + " if full_token_score == label_len:\n", + " score_full_name += 1\n", + " score_no_pad += full_token_score / label_len\n", + " \n", + " print()" ] }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'[CLS] FunctionDef arguments arg self Expr Str For Name item GeneratorExp Subscript Name Index Name comprehension Name i Call Name Call Name Name Expr Yield Name'" + "[0.2068266306184522,\n", + " 0.3870967741935484,\n", + " 0.8192771084337349,\n", + " 0.9714285714285714]" ] }, - "execution_count": 184, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "snippet = pd.read_csv(path+'cls_funcdefsplit_magret_tk_val.txt', header=None)\n", - "snippet.loc[10][0]" + "[612 / _ for _ in rank]" ] }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 172, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CLS] FunctionDef arguments arg x Expr Str Assign Name alpha Num Assign Name scale Num Return BinOp Name Mult Call Attribute elu Name Name Name\n", - "Label = selu\n", - "Pred =\n", - " 0. elu\n", - " 1. relu\n", - " 2. count_params\n", - " 3. hard_sigmoid\n", - " 4. set_learning_phase\n", - " 5. get_value\n", - " 6. call\n", - " 7. update_add\n", - " 8. pow\n", - " 9. raise_duplicate_arg_error\n", - "\n", - "[CLS] FunctionDef arguments arg self arg epoch arg logs NameConstant Assign Attribute seen Name Num Assign Attribute totals Name Dict\n", - "Label = on_epoch_begin\n", - "Pred =\n", - " 0. on_train_begin\n", - " 1. on_epoch_end\n", - " 2. on_train_end\n", - " 3. __init__\n", - " 4. filter_sk_params\n", - " 5. compute_mask\n", - " 6. clip\n", - " 7. compute_output_shape\n", - " 8. infer_outputs\n", - " 9. on_batch_end\n", - "\n", - "[CLS] FunctionDef arguments arg self arg max value arg axis Num Num Assign Attribute max value Name Name Assign Attribute axis Name Name\n", - "Label = __init__\n", - "Pred =\n", - "---- 0. __init__\n", - " 1. trainable\n", - " 2. hard_sigmoid\n", - " 3. concatenate\n", - " 4. argmin\n", - " 5. set_value\n", - " 6. max\n", - " 7. l1_l2\n", - " 8. update\n", - " 9. _canonical_to_params\n", - "\n", - "[CLS] FunctionDef arguments arg self arg minval arg maxval arg seed UnaryOp USub Num Num NameConstant Assign Attribute minval Name Name Assign Attribute maxval Name Name Assign Attribute seed Name Name\n", - "Label = __init__\n", - "Pred =\n", - " 0. random_uniform\n", - " 1. call\n", - "---- 2. __init__\n", - " 3. on_train_begin\n", - " 4. clear_session\n", - " 5. prod\n", - " 6. dropped_inputs\n", - " 7. _canonical_to_params\n", - " 8. evaluate_generator\n", - " 9. _preprocess_conv2d_kernel\n", - "\n", - "[CLS] FunctionDef arguments arg self arg shape arg dtype NameConstant Return Call Attribute random uniform Name Name Attribute minval Name Attribute maxval Name keyword Name keyword Attribute seed Name\n", - "Label = __call__\n", - "Pred =\n", - " 0. random_uniform\n", - " 1. call\n", - "---- 2. __call__\n", - " 3. trainable\n", - " 4. infer_outputs\n", - " 5. from_config\n", - " 6. evaluate_generator\n", - " 7. dropped_inputs\n", - " 8. in_top_k\n", - " 9. _get_noise_shape\n", - "\n", - "[CLS] FunctionDef arguments arg self arg mean arg stddev arg seed Num Num NameConstant Assign Attribute mean Name Name Assign Attribute stddev Name Name Assign Attribute seed Name Name\n", - "Label = __init__\n", - "Pred =\n", - "---- 0. __init__\n", - " 1. hard_sigmoid\n", - " 2. batch_set_value\n", - " 3. function\n", - " 4. trainable\n", - " 5. get_config\n", - " 6. mean\n", - " 7. l1_l2\n", - " 8. state_updates\n", - " 9. add\n", - "\n", - "[CLS] FunctionDef arguments arg self arg shape arg dtype NameConstant Return Call Attribute truncated normal Name Name Attribute mean Name Attribute stddev Name keyword Name keyword Attribute seed Name\n", - "Label = __call__\n", - "Pred =\n", - " 0. truncated_normal\n", - "---- 1. __call__\n", - " 2. call\n", - " 3. trainable\n", - " 4. noised\n", - " 5. _get_noise_shape\n", - " 6. get_config\n", - " 7. mean_absolute_percentage_error\n", - " 8. trainable_weights\n", - " 9. zeros\n", - "\n", - "[CLS] FunctionDef arguments arg identifier If Call Name Name Name Return Call Name Name If Call Name Name Attribute string types Name Assign Name config Dict Str Str Call Name Name Dict Return Call Name Name If Call Name Name Return Name Raise Call Name BinOp Str Add Call Name Name\n", - "Label = get\n", - "Pred =\n", - "---- 0. get\n", - " 1. get_config\n", - " 2. get_losses_for\n", - " 3. model_from_yaml\n", - " 4. serialize_keras_object\n", - " 5. handle_value\n", - " 6. is_keras_tensor\n", - " 7. function\n", - " 8. reverse\n", - " 9. output\n", - "\n", - "[CLS] FunctionDef arguments arg y true arg y pred Return Call Attribute mean Name Call Attribute maximum Name BinOp Num Sub BinOp Name Mult Name Num keyword UnaryOp USub Num\n", - "Label = hinge\n", - "Pred =\n", - " 0. squared_hinge\n", - " 1. mean_absolute_error\n", - " 2. categorical_hinge\n", - " 3. poisson\n", - " 4. logcosh\n", - " 5. bias_initializer\n", - " 6. cosine_proximity\n", - " 7. mean_squared_error\n", - " 8. moving_average_update\n", - " 9. get_variable_shape\n", - "\n", - "[CLS] FunctionDef arguments arg args arg kwargs Assign Name converted List If Compare Str In Name If Compare Str Name Assign Name length Call Attribute pop Name Str Assign Name length NameConstant Assign Name input shape Tuple Name Call Attribute pop Name Str Assign Subscript Name Index Str Name Expr Call Attribute append Name Tuple Str Str Return Tuple Name Name Name\n", - "Label = conv1d_args_preprocessor\n", - "Pred =\n", - " 0. batchnorm_args_preprocessor\n", - " 1. get_config\n", - " 2. AtrousConvolution2D\n", - " 3. compute_output_shape\n", - " 4. embedding_kwargs_preprocessor\n", - " 5. _get_noise_shape\n", - " 6. validate_file\n", - " 7. ask_to_proceed_with_overwrite\n", - " 8. AtrousConvolution1D\n", - " 9. identity\n", - "\n", - "[CLS] FunctionDef arguments arg self Expr Str For Name item GeneratorExp Subscript Name Index Name comprehension Name i Call Name Call Name Name Expr Yield Name\n", - "Label = __iter__\n", - "Pred =\n", - " 0. name_scope\n", - " 1. set_model\n", - " 2. get\n", - " 3. _collect_input_shape\n", - " 4. model\n", - " 5. get_weights\n", - " 6. get_json_type\n", - " 7. reset_states\n", - " 8. model_from_json\n", - " 9. losses\n", - "\n", - "[CLS] FunctionDef arguments arg self arg sequence arg use multiprocessing arg shuffle NameConstant NameConstant Expr Call Attribute init Call Name Name Name Name Name Assign Attribute shuffle Name Name\n", - "Label = __init__\n", - "Pred =\n", - "---- 0. __init__\n", - " 1. function\n", - " 2. batch_set_value\n", - " 3. hard_sigmoid\n", - " 4. on_train_begin\n", - " 5. trainable\n", - " 6. _init_subclassed_network\n", - " 7. add\n", - " 8. mean\n", - " 9. raise_duplicate_arg_error\n", - "\n", - "[CLS] FunctionDef arguments arg self arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name\n", - "Label = _get_executor_init\n", - "Pred =\n", - "---- 0. _get_executor_init\n", - " 1. recurrent_conv\n", - " 2. __init__\n", - " 3. _preprocess_conv2d_input\n", - " 4. any\n", - " 5. get\n", - " 6. __call__\n", - " 7. add\n", - " 8. _pooling_function\n", - " 9. function\n", - "\n", - "[CLS] FunctionDef arguments arg self Expr Str Expr Call Attribute send sequence Name With withitem Call Name Call Attribute executor fn Name Name Name executor While NameConstant If Call Attribute is set Attribute stop signal Name Return Expr Call Attribute put Attribute queue Name Call Attribute apply async Name Name Tuple Attribute uid Name keyword NameConstant\n", - "Label = _run\n", - "Pred =\n", - " 0. call\n", - " 1. _wait_queue\n", - " 2. build\n", - " 3. get\n", - " 4. state_size\n", - " 5. init_pool_generator\n", - " 6. __call__\n", - " 7. next_sample\n", - " 8. ask_to_proceed_with_overwrite\n", - " 9. get_params\n", - "\n", - "[CLS] FunctionDef arguments arg self arg args Assign Attribute custom objects Name Name Assign Attribute backup Name NameConstant\n", - "Label = __init__\n", - "Pred =\n", - "---- 0. __init__\n", - " 1. __enter__\n", - " 2. __call__\n", - " 3. __exit__\n", - " 4. trainable\n", - " 5. _get_executor_init\n", - " 6. hard_sigmoid\n", - " 7. get_config\n", - " 8. mean\n", - " 9. _pooling_function\n", - "\n", - "[CLS] FunctionDef arguments arg args arg kwargs Return Call Attribute VGG19 Name Starred Name keyword Name Name\n", - "Label = VGG19\n", - "Pred =\n", - " 0. DenseNet169\n", - " 1. DenseNet121\n", - " 2. preprocess_input\n", - " 3. decode_predictions\n", - " 4. NASNetLarge\n", - " 5. InceptionV3\n", - " 6. bias_initializer\n", - " 7. ResNet50\n", - " 8. set_value\n", - " 9. get_word_index\n", - "\n", - "[CLS] FunctionDef arguments arg args arg kwargs Return Call Attribute DenseNet201 Name Starred Name keyword Name Name\n", - "Label = DenseNet201\n", - "Pred =\n", - " 0. decode_predictions\n", - " 1. DenseNet169\n", - " 2. VGG16\n", - " 3. preprocess_input\n", - " 4. MobileNetV2\n", - " 5. Xception\n", - " 6. DenseNet121\n", - " 7. h5wrapper\n", - " 8. bias_initializer\n", - " 9. ResNet50\n", - "\n", - "[CLS] FunctionDef arguments arg self arg input shape Expr Call Attribute build Call Name Name Name Name If Compare Call Name Name NotEq Num Raise Call Name Str\n", - "Label = build\n", - "Pred =\n", - " 0. __init__\n", - " 1. _get_noise_shape\n", - " 2. name_scope\n", - " 3. reset_states\n", - " 4. _merge_function\n", - " 5. ndim\n", - " 6. truncated_normal\n", - " 7. add_unprocessed_node\n", - " 8. gradients\n", - " 9. set_learning_phase\n", - "\n", - "[CLS] FunctionDef arguments arg self arg inputs Assign Name output Subscript Name Index Num For Name i Call Name Num Call Name Name Assign Name output Call Attribute maximum Name Name Subscript Name Index Name Return Name\n", - "Label = _merge_function\n", - "Pred =\n", - "---- 0. _merge_function\n", - " 1. call\n", - " 2. _get_noise_shape\n", - " 3. infer_outputs\n", - " 4. __init__\n", - " 5. get_losses_for\n", - " 6. _pooling_function\n", - " 7. add\n", - " 8. trainable\n", - " 9. size\n", - "\n", - "[CLS] FunctionDef arguments arg inputs arg kwargs Expr Str Return Call Call Name keyword Name Name\n", - "Label = average\n", - "Pred =\n", - " 0. maximum\n", - " 1. subtract\n", - " 2. minimum\n", - " 3. add\n", - " 4. lecun_normal\n", - " 5. has_seq_axis\n", - " 6. greater\n", - " 7. get_config\n", - " 8. predict_proba\n", - " 9. identity\n", - "\n", - "[CLS] FunctionDef arguments arg self If Call Name Attribute layer Name Str Return Attribute losses Attribute layer Name Return List Name\n", - "Label = losses\n", - "Pred =\n", - "---- 0. losses\n", - " 1. updates\n", - " 2. get_losses_for\n", - " 3. __enter__\n", - " 4. activity_regularizer\n", - " 5. trainable_weights\n", - " 6. identity\n", - " 7. _get_available_devices\n", - " 8. is_tensor\n", - " 9. _is_explicit_shape\n", - "\n", - "[CLS] FunctionDef arguments arg self arg value Assign Attribute trainable Name Name Assign Attribute trainable Attribute forward layer Name Name Assign Attribute trainable Attribute backward layer Name Name Attribute setter Name\n", - "Label = trainable\n", - "Pred =\n", - " 0. trainable_weights\n", - "---- 1. trainable\n", - " 2. non_trainable_weights\n", - " 3. get_config\n", - " 4. infer_outputs\n", - " 5. _check_trainable_weights_consistency\n", - " 6. __init__\n", - " 7. call\n", - " 8. _get_noise_shape\n", - " 9. gather\n", - "\n", - "[CLS] FunctionDef arguments arg self If Call Name Attribute forward layer Name Str Return BinOp Attribute losses Attribute forward layer Name Add Attribute losses Attribute backward layer Name Return List Name\n", - "Label = losses\n", - "Pred =\n", - "---- 0. losses\n", - " 1. updates\n", - " 2. get_losses_for\n", - " 3. activity_regularizer\n", - " 4. trainable_weights\n", - " 5. __enter__\n", - " 6. non_trainable_weights\n", - " 7. _updated_config\n", - " 8. get_weights\n", - " 9. output\n", - "\n", - "[CLS] FunctionDef arguments arg self Assign Name constraints Dict If Call Name Attribute forward layer Name Str Expr Call Attribute update Name Attribute constraints Attribute forward layer Name Expr Call Attribute update Name Attribute constraints Attribute backward layer Name Return Name Name\n", - "Label = constraints\n", - "Pred =\n", - " 0. __setstate__\n", - " 1. filter_sk_params\n", - " 2. get_config\n", - " 3. get_params\n", - " 4. pickle_model\n", - " 5. trainable_weights\n", - " 6. reset_states\n", - " 7. int_shape\n", - " 8. serialize_keras_object\n", - " 9. on_batch_end\n", - "\n", - "[CLS] FunctionDef arguments arg self arg data format arg kwargs NameConstant Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute data format Name Call Attribute normalize data format Name Name Assign Attribute input spec Name Call Name keyword Num Attribute legacy global pooling support Name\n", - "Label = __init__\n", - "Pred =\n", - "---- 0. __init__\n", - " 1. batch_set_value\n", - " 2. hard_sigmoid\n", - " 3. function\n", - " 4. get_config\n", - " 5. _init_subclassed_network\n", - " 6. mean\n", - " 7. state_updates\n", - " 8. on_train_begin\n", - " 9. trainable\n", - "\n", - "[CLS] FunctionDef arguments arg self arg inputs Return Call Attribute spatial 3d padding Name Name keyword Attribute padding Name keyword Attribute data format Name\n", - "Label = call\n", - "Pred =\n", - "---- 0. call\n", - " 1. __call__\n", - " 2. _get_noise_shape\n", - " 3. _merge_function\n", - " 4. infer_outputs\n", - " 5. get_losses_for\n", - " 6. count_params\n", - " 7. std\n", - " 8. compute_mask\n", - " 9. recurrent_conv\n", - "\n", - "[CLS] FunctionDef arguments arg self arg inputs arg mask NameConstant If UnaryOp Not Attribute mask zero Name Return NameConstant Assign Name output mask Call Attribute not equal Name Name Num Return Name\n", - "Label = compute_mask\n", - "Pred =\n", - "---- 0. compute_mask\n", - " 1. infer_outputs\n", - " 2. on_epoch_end\n", - " 3. call\n", - " 4. get_updates_for\n", - " 5. non_trainable_weights\n", - " 6. trainable_weights\n", - " 7. handle_value\n", - " 8. uses_learning_phase\n", - " 9. ctc_cost\n", - "\n", - "[CLS] FunctionDef arguments arg cls arg config If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Num Assign Subscript Name Index Str Num Return Call Name keyword Name Name\n", - "Label = from_config\n", - "Pred =\n", - "---- 0. from_config\n", - " 1. AtrousConvolution2D\n", - " 2. model_from_config\n", - " 3. AtrousConvolution1D\n", - " 4. concatenate\n", - " 5. _is_current_explicit_device\n", - " 6. mean_squared_error\n", - " 7. Xception\n", - " 8. get_config\n", - " 9. _convert_string_dtype\n", - "\n", - "[CLS] FunctionDef arguments arg ones arg rate arg training arg count NameConstant Num FunctionDef arguments Return Call Attribute dropout Name Name Name If Compare Name Gt Num Return ListComp Call Attribute in train phase Name Name Name keyword Name comprehension Name Call Name Name Return Call Attribute in train phase Name Name Name keyword Name\n", - "Label = _generate_dropout_mask\n", - "Pred =\n", - " 0. call\n", - " 1. count_params\n", - " 2. in_test_phase\n", - " 3. on_train_end\n", - " 4. get_losses_for\n", - " 5. on_batch_end\n", - " 6. _merge_function\n", - " 7. output\n", - " 8. l2_normalize\n", - " 9. _pooling_function\n", - "\n", - "[CLS] FunctionDef arguments arg self arg inputs If Compare Attribute data format Name Eq Str Assign Name permutation List Num Expr Call Attribute extend Name ListComp Name comprehension Name i Call Name Num Call Attribute ndim Name Name Expr Call Attribute append Name Num Assign Name inputs Call Attribute permute dimensions Name Name Name Return Call Attribute batch flatten Name Name\n", - "Label = call\n", - "Pred =\n", - "---- 0. call\n", - " 1. infer_outputs\n", - " 2. _get_noise_shape\n", - " 3. _merge_function\n", - " 4. compute_mask\n", - " 5. _pooling_function\n", - " 6. get_losses_for\n", - " 7. size\n", - " 8. get_weights\n", - " 9. trainable_weights\n", - "\n", - "[CLS] FunctionDef arguments arg self Assign Name config Dict Str Attribute n Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = get_config\n", - "Pred =\n", - "---- 0. get_config\n", - " 1. on_epoch_end\n", - " 2. get\n", - " 3. trainable_weights\n", - " 4. model_from_yaml\n", - " 5. __call__\n", - " 6. _updated_config\n", - " 7. identity\n", - " 8. states\n", - " 9. make_batches\n", - "\n", - "[CLS] FunctionDef arguments arg floatx Expr Str Global If Compare Name NotIn Set Str Str Str Raise Call Name BinOp Str Add Call Name Name Assign Name FLOATX Call Name Name\n", - "Label = set_floatx\n", - "Pred =\n", - " 0. set_learning_phase\n", - " 1. set_image_data_format\n", - " 2. _preprocess_padding\n", - " 3. is_keras_tensor\n", - " 4. any\n", - " 5. normalize_data_format\n", - " 6. all\n", - " 7. _make_node_key\n", - " 8. get_config\n", - " 9. _merge_function\n", - "\n", - "[CLS] FunctionDef arguments arg x If Call Name Name Attribute Function Attribute cntk py Name Return Call Attribute eval Name If BoolOp Or Call Name Name Attribute Constant Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name Return Attribute value Name Raise Call Name BinOp Str Mod Call Name Name\n", - "Label = eval\n", - "Pred =\n", - " 0. get_value\n", - "---- 1. eval\n", - " 2. is_tensor\n", - " 3. count_params\n", - " 4. set_value\n", - " 5. elu\n", - " 6. predict_on_batch\n", - " 7. noised\n", - " 8. batch_set_value\n", - " 9. batch_get_value\n", - "\n", - "[CLS] FunctionDef arguments arg size arg dtype arg name NameConstant NameConstant If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute eye Name Name Name Name\n", - "Label = eye\n", - "Pred =\n", - "---- 0. eye\n", - " 1. zeros\n", - " 2. preprocess_input\n", - " 3. step\n", - " 4. decode_predictions\n", - " 5. to_list\n", - " 6. is_tensor\n", - " 7. int_or_none\n", - " 8. deserialize\n", - " 9. __getattr__\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis arg keepdims NameConstant NameConstant Assign Name m Call Name Name Name keyword NameConstant Assign Name devs squared Call Attribute square Name BinOp Name Sub Name Return Call Name Name keyword Name keyword Name\n", - "Label = var\n", - "Pred =\n", - " 0. logsumexp\n", - " 1. std\n", - " 2. min\n", - " 3. max\n", - " 4. all\n", - " 5. mean_squared_error\n", - " 6. __call__\n", - " 7. l2_normalize\n", - " 8. sum\n", - " 9. prod\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis UnaryOp USub Num Assign Name axis List Name Assign Name axis Call Name Name Name Assign Name output Call Attribute argmax Attribute ops Name Name keyword Subscript Name Index Num Return Call Name Name Name\n", - "Label = argmax\n", - "Pred =\n", - " 0. concatenate\n", - " 1. argmin\n", - "---- 2. argmax\n", - " 3. cumsum\n", - " 4. l2_normalize\n", - " 5. squeeze\n", - " 6. __init__\n", - " 7. elu\n", - " 8. predict_classes\n", - " 9. softmax\n", - "\n", - "[CLS] FunctionDef arguments arg target arg output arg from logits NameConstant If Name Assign Name output Call Attribute sigmoid Name Name Assign Name output Call Attribute clip Name Name Call Name BinOp Num Sub Call Name Assign Name output BinOp BinOp UnaryOp USub Name Mult Call Attribute log Name Name BinOp BinOp Num Name Call Attribute log Name BinOp Num Name Return Name\n", - "Label = binary_crossentropy\n", - "Pred =\n", - "---- 0. binary_crossentropy\n", - " 1. hard_sigmoid\n", - " 2. kullback_leibler_divergence\n", - " 3. call\n", - " 4. logcosh\n", - " 5. any\n", - " 6. poisson\n", - " 7. compute_mask\n", - " 8. handle_value\n", - " 9. _logcosh\n", - "\n", - "[CLS] FunctionDef arguments arg x arg increment Assign Name result BinOp Name Add Name Return Call Attribute assign Name Name Name\n", - "Label = update_add\n", - "Pred =\n", - "---- 0. update_add\n", - " 1. to_dense\n", - " 2. elu\n", - " 3. multiply\n", - " 4. ctc_cost\n", - " 5. batch_get_value\n", - " 6. in_test_phase\n", - " 7. _preprocess_border_mode\n", - " 8. moving_average_update\n", - " 9. transpose\n", - "\n", - "[CLS] FunctionDef arguments arg x arg shape If BoolOp And Call Name Name Str Compare Call Name GtE Num Assign Name const a Call Attribute unpack batch Name Name Assign Name const a Call Attribute reshape Name Name Name Return Call Attribute to batch Name Name Return Call Attribute user function Name Call Name Name Subscript Name Slice Num\n", - "Label = _reshape_batch\n", - "Pred =\n", - " 0. batch_flatten\n", - " 1. get_weights\n", - " 2. compute_output_shape\n", - " 3. _regular_normalize_batch_in_training\n", - " 4. int_shape\n", - " 5. gather\n", - " 6. trainable_weights\n", - " 7. sqrt\n", - " 8. repeat\n", - " 9. get_json_type\n", - "\n", - "[CLS] FunctionDef arguments arg self arg arguments arg device arg outputs to retain NameConstant NameConstant Return Tuple NameConstant Call Attribute Value Attribute cntk py Name Call Attribute data Name\n", - "Label = forward\n", - "Pred =\n", - "---- 0. forward\n", - " 1. call\n", - " 2. __call__\n", - " 3. compute_output_shape\n", - " 4. get\n", - " 5. backward\n", - " 6. on_train_end\n", - " 7. _get_noise_shape\n", - " 8. _uses_dynamic_learning_phase\n", - " 9. any\n", - "\n", - "[CLS] FunctionDef arguments arg self arg argument arg device arg outputs to retain NameConstant NameConstant If Call Attribute when Name Name Expr Call Attribute execute Name Name Return Tuple NameConstant Name\n", - "Label = forward\n", - "Pred =\n", - "---- 0. forward\n", - " 1. compute_output_shape\n", - " 2. call\n", - " 3. __call__\n", - " 4. _preprocess_conv2d_input\n", - " 5. _get_noise_shape\n", - " 6. get_monitor_value\n", - " 7. mean_absolute_percentage_error\n", - " 8. stop_gradient\n", - " 9. constant\n", - "\n", - "[CLS] FunctionDef arguments arg prefix Str Expr Str Global Assign Name graph Call Attribute get default graph Name If Compare Name NotIn Name Assign Subscript Name Index Name Call Name Name AugAssign Subscript Subscript Name Index Name Index Name Add Num Return Subscript Subscript Name Index Name Index Name\n", - "Label = get_uid\n", - "Pred =\n", - "---- 0. get_uid\n", - " 1. learning_phase\n", - " 2. set_learning_phase\n", - " 3. _get_current_tf_device\n", - " 4. ctc_create_skip_idxs\n", - " 5. normalize_padding\n", - " 6. _contain_seqence_axis\n", - " 7. ensure_value_to_cell\n", - " 8. add_unprocessed_node\n", - " 9. elu\n", - "\n", - "[CLS] FunctionDef arguments Expr Str Global If Compare Name Is NameConstant Assign Name LOCAL DEVICES Call Attribute list devices Call Name Return ListComp Attribute name Name comprehension Name x Name Compare Attribute device type Name Eq Str\n", - "Label = _get_available_gpus\n", - "Pred =\n", - " 0. _is_current_explicit_device\n", - " 1. count_params\n", - " 2. l2_normalize\n", - " 3. _get_available_devices\n", - " 4. is_placeholder\n", - " 5. gradients\n", - " 6. _preprocess_conv3d_input\n", - " 7. dropout\n", - " 8. flatten\n", - " 9. truncated_normal\n", - "\n", - "[CLS] FunctionDef arguments arg reference arg indices Expr Str Return Call Attribute embedding lookup Attribute nn Name Name Name\n", - "Label = gather\n", - "Pred =\n", - "---- 0. gather\n", - " 1. one_hot\n", - " 2. softplus\n", - " 3. trainable_weights\n", - " 4. is_sparse\n", - " 5. _normalize_device_name\n", - " 6. sigmoid\n", - " 7. non_trainable_weights\n", - " 8. elu\n", - " 9. _check_trainable_weights_consistency\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis UnaryOp USub Num Expr Str Return Call Attribute argmax Name Name Name\n", - "Label = argmax\n", - "Pred =\n", - "---- 0. argmax\n", - " 1. cumsum\n", - " 2. argmin\n", - " 3. elu\n", - " 4. squeeze\n", - " 5. predict_classes\n", - " 6. unpack_singleton\n", - " 7. flatten\n", - " 8. concatenate\n", - " 9. categorical_accuracy\n", - "\n", - "[CLS] FunctionDef arguments arg x arg n Expr Str If Call Name Name Name Assign Name n List Name Return Call Attribute tile Name Name Name\n", - "Label = tile\n", - "Pred =\n", - " 0. reverse\n", - " 1. stop_gradient\n", - " 2. add\n", - " 3. to_list\n", - " 4. dl_progress\n", - " 5. object_list_uid\n", - " 6. to_dense\n", - " 7. transform_kernels\n", - " 8. clone_model\n", - " 9. ctc_cost\n", - "\n", - "[CLS] FunctionDef arguments arg inputs arg outputs arg updates arg kwargs NameConstant Expr Str If Name For Name key Name If UnaryOp Not BoolOp Or Call Name Attribute run Attribute Session Name Name NameConstant Call Name Attribute init Name Name NameConstant Assign Name msg BinOp Str Mod Name Raise Call Name Name Return Call Name Name Name keyword Name keyword Name\n", - "Label = function\n", - "Pred =\n", - " 0. __init__\n", - "---- 1. function\n", - " 2. _init_subclassed_network\n", - " 3. state_updates\n", - " 4. add\n", - " 5. raise_duplicate_arg_error\n", - " 6. reverse\n", - " 7. mean\n", - " 8. batch_set_value\n", - " 9. set_value\n", - "\n", - "[CLS] FunctionDef arguments arg x arg level arg noise shape arg seed NameConstant NameConstant Expr Str Assign Name retain prob BinOp Num Sub Name If Compare Name Is NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute dropout Attribute nn Name BinOp Name Mult Num Name Name keyword Name\n", - "Label = dropout\n", - "Pred =\n", - "---- 0. dropout\n", - " 1. random_uniform\n", - " 2. _get_noise_shape\n", - " 3. l2_normalize\n", - " 4. truncated_normal\n", - " 5. random_normal\n", - " 6. foldr\n", - " 7. random_binomial\n", - " 8. classification_error\n", - " 9. from_config\n", - "\n", - "[CLS] FunctionDef arguments arg shape arg mean arg stddev arg dtype arg seed Num Num NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name If Compare Name NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute random normal Name Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = random_normal\n", - "Pred =\n", - "---- 0. random_normal\n", - " 1. random_binomial\n", - " 2. truncated_normal\n", - " 3. random_uniform\n", - " 4. noised\n", - " 5. constant\n", - " 6. zeros\n", - " 7. ones\n", - " 8. random_normal_variable\n", - " 9. bias_initializer\n", - "\n", - "[CLS] FunctionDef arguments arg shape arg minval arg maxval arg dtype arg seed Num Num NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name If Compare Name NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute random uniform Name Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = random_uniform\n", - "Pred =\n", - "---- 0. random_uniform\n", - " 1. random_normal\n", - " 2. call\n", - " 3. from_config\n", - " 4. random_binomial\n", - " 5. evaluate_generator\n", - " 6. clip\n", - " 7. _pooling_function\n", - " 8. truncated_normal\n", - " 9. clear_session\n", - "\n", - "[CLS] FunctionDef arguments arg fn arg elems arg initializer arg name NameConstant NameConstant Expr Str Return Call Attribute foldr Name Name Name keyword Name keyword Name\n", - "Label = foldr\n", - "Pred =\n", - "---- 0. foldr\n", - " 1. map_fn\n", - " 2. foldl\n", - " 3. prod\n", - " 4. _preprocess_conv2d_kernel\n", - " 5. _assert_has_capability\n", - " 6. dropout\n", - " 7. print_tensor\n", - " 8. min\n", - " 9. std\n", - "\n", - "[CLS] FunctionDef arguments arg shape arg dtype arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute ones Name Name Name Name\n", - "Label = ones\n", - "Pred =\n", - "---- 0. ones\n", - " 1. ones_like\n", - " 2. map_fn\n", - " 3. eye\n", - " 4. set_of_lengths\n", - " 5. zeros\n", - " 6. eval\n", - " 7. reshape\n", - " 8. forward\n", - " 9. less_equal\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis Num Expr Str Return Call Attribute cumprod Attribute extra ops Name Name keyword Name\n", - "Label = cumprod\n", - "Pred =\n", - " 0. cumsum\n", - "---- 1. cumprod\n", - " 2. stack\n", - " 3. has_seq_axis\n", - " 4. print_tensor\n", - " 5. backward\n", - " 6. random_binomial\n", - " 7. clear_session\n", - " 8. pow\n", - " 9. batch_get_value\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis arg keepdims NameConstant NameConstant Expr Str Assign Name dtype NameConstant If BoolOp Or Compare Str In Attribute dtype Name Compare Attribute dtype Name Eq Str Assign Name dtype Call Name Return Call Attribute mean Name Name keyword Name keyword Name keyword Name\n", - "Label = mean\n", - "Pred =\n", - " 0. prod\n", - "---- 1. mean\n", - " 2. constant\n", - " 3. min\n", - " 4. max\n", - " 5. std\n", - " 6. set_value\n", - " 7. _prepare_name\n", - " 8. any\n", - " 9. map_fn\n", - "\n", - "[CLS] FunctionDef arguments arg x arg axis Num Return Call Attribute stack Name Name keyword Name\n", - "Label = stack\n", - "Pred =\n", - "---- 0. stack\n", - " 1. cumsum\n", - " 2. cumprod\n", - " 3. softmax\n", - " 4. std\n", - " 5. has_seq_axis\n", - " 6. argmin\n", - " 7. backward\n", - " 8. compute_output_shape\n", - " 9. Xception\n", - "\n", - "[CLS] FunctionDef arguments arg x arg alpha Num Expr Str Expr Call Name Attribute nnet Name Str Return Call Attribute elu Attribute nnet Name Name Name\n", - "Label = elu\n", - "Pred =\n", - "---- 0. elu\n", - " 1. pow\n", - " 2. count_params\n", - " 3. get_value\n", - " 4. is_tensor\n", - " 5. to_list\n", - " 6. eval\n", - " 7. update_add\n", - " 8. batch_set_value\n", - " 9. tanh\n", - "\n", - "[CLS] FunctionDef arguments arg image shape arg data format FunctionDef arguments arg value Try Return Call Name Name ExceptHandler Name Return NameConstant If Compare Name Eq Str If Name Assign Name image shape Call Name Name Str keyword Tuple Num Num If Compare Name IsNot NameConstant Assign Name image shape Call Name GeneratorExp Call Name Name comprehension Name v Name Return Name\n", - "Label = _preprocess_conv2d_image_shape\n", - "Pred =\n", - " 0. gradients\n", - " 1. int_shape\n", - " 2. range_less_than\n", - " 3. _pooling_function\n", - " 4. clip\n", - " 5. _preprocess_conv2d_input\n", - " 6. reshape\n", - " 7. _postprocess_conv2d_output\n", - " 8. _preprocess_conv3d_input\n", - " 9. int_or_none\n", - "\n", - "[CLS] FunctionDef arguments arg value Try Return Call Name Name ExceptHandler Name Return NameConstant\n", - "Label = int_or_none\n", - "Pred =\n", - "---- 0. int_or_none\n", - " 1. DenseNet121\n", - " 2. range_less_than\n", - " 3. convert_nested_time_distributed\n", - " 4. logsumexp\n", - " 5. state_updates\n", - " 6. _collect_input_shape\n", - " 7. _assert_has_capability\n", - " 8. flatten\n", - " 9. MobileNet\n", - "\n", - "[CLS] FunctionDef arguments arg self arg generator arg steps arg max queue size arg workers arg use multiprocessing arg verbose NameConstant Num Num NameConstant Num Expr Str Return Call Attribute predict generator Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name Attribute legacy generator methods support Name\n", - "Label = predict_generator\n", - "Pred =\n", - " 0. evaluate_generator\n", - " 1. call\n", - " 2. get\n", - " 3. _wait_queue\n", - " 4. get_losses_for\n", - " 5. _to_snake_case\n", - " 6. sparse_top_k_categorical_accuracy\n", - " 7. random_binomial\n", - " 8. save\n", - " 9. he_normal\n", - "\n", - "[CLS] FunctionDef arguments arg seq Expr Str While NameConstant For Name item Name Expr Yield Name\n", - "Label = iter_sequence_infinite\n", - "Pred =\n", - " 0. name_scope\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1. _prepare_name\n", - " 2. squeeze\n", - " 3. sum\n", - " 4. l2_normalize\n", - " 5. argmin\n", - " 6. permute_dimensions\n", - " 7. less_equal\n", - " 8. is_all_none\n", - " 9. save\n", - "\n" - ] + "data": { + "text/plain": [ + "[0.42810457516339867,\n", + " 0.7745098039215687,\n", + " 0.9722222222222222,\n", + " 0.9983660130718954]" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "pred_str = []; score = 0; rank =[]\n", - "for idx, r in enumerate(preds):\n", - " print(snippet.loc[idx][0])\n", - " print(\"Label =\", labels_str[idx])\n", - " preds_ = []\n", - " print(\"Pred =\")\n", - " for i in range(n):\n", - " p = vocab_label_df.loc[r[i]][0] \n", - " if p==labels_str[idx]:\n", - " score +=1\n", - " rank.append(i)\n", - " print(\"---- {}. {}\".format(i,p))\n", - " else:\n", - " print(\" {}. {}\".format(i,p))\n", - " preds_.append(p)\n", - " pred_str.append(preds_)\n", - " print()" + "[s / 612 for s in score]" ] }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6666666666666666" + "0.42960239651416127" ] }, - "execution_count": 186, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "score/60" + "score_no_pad/612" ] }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.3" + "0.39052287581699346" ] }, - "execution_count": 187, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.mean(rank)" + "score_full_name / 612" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, diff --git a/notebook/Inspect Predictions.ipynb b/notebook/Inspect Predictions.ipynb deleted file mode 100644 index e97ebe4..0000000 --- a/notebook/Inspect Predictions.ipynb +++ /dev/null @@ -1,5307 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import csv" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "classifier.py\t\t __pycache__\r\n", - "cls_magret\t\t README.md\r\n", - "create_pretraining_data.py requirements-3_5.txt\r\n", - "extract_features.py\t requirements-3_7.txt\r\n", - "funcname_magret\t\t run_classifier.sh\r\n", - "__init__.py\t\t run_create_data.sh\r\n", - "modeling.py\t\t run_mlm.sh\r\n", - "modeling.pyc\t\t run_prepare_data.sh\r\n", - "modeling_test.py\t run_prepare_single_data.sh\r\n", - "multimask\t\t run_pretraining.py\r\n", - "notebook\t\t sparse\r\n", - "optimization.py\t\t split_magret\r\n", - "optimization.pyc\t tokenization.py\r\n", - "optimization_test.py\t tokenization.pyc\r\n", - "prepare_pretraining_data.py tokenization_test.py\r\n", - "py35\t\t\t utils\r\n", - "py37\t\t\t varname\r\n" - ] - } - ], - "source": [ - "!ls .." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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- "output_type": "execute_result" - } - ], - "source": [ - "results_df = pd.read_csv('../funcname_magret/pretraining_output/eval_results_masked_lm.txt')\n", - "results_df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1156, 1)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vocab_df = pd.read_csv('../multimask/vocab-code.txt', header=None)\n", - "vocab_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1146, 1)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vocab_df2 = pd.read_csv('../../bert-cmp/bert/vocab-code.txt', header=None)\n", - "vocab_df2.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'[cls]',\n", - " 'accuracy',\n", - " 'batches',\n", - " 'categorical',\n", - " 'cw',\n", - " 'existing',\n", - " 'lengths',\n", - " 'modes',\n", - " 'ref',\n", - " 'score',\n", - " 'suffix'}" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(vocab_df[0]) - set(vocab_df2[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1156" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(vocab_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "accuracy = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "per_token_acc = {}; per_token_count = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(results_df)):\n", - " snippet = [results_df[str(_)][i] for _ in range(64)]\n", - " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", - " masked_tk = snippet[msk_idx]\n", - " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", - " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", - " if per_token_acc.get(label, None) == None:\n", - " per_token_acc[label] = 0\n", - " per_token_count[label] = 0\n", - " per_token_acc[label] += int(prediction == label)\n", - " per_token_count[label] += 1\n", - " accuracy += int(prediction == label)\n", - " #print(\"Predicted --\", prediction)\n", - " #print(\"Label --\", label)\n", - " #print()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "total_per_token_accuracy = {}\n", - "per_token_freq = {}\n", - "for k,v in per_token_acc.items():\n", - " if per_token_count[k] > 0:\n", - " total_per_token_accuracy[k] = v / per_token_count[k]\n", - " per_token_freq[k] = per_token_count[k] / len(results_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('sparse', 1.0),\n", - " ('usub', 1.0),\n", - " ('crossentropy', 1.0),\n", - " ('expand', 1.0),\n", - " ('parameter', 1.0),\n", - " ('join', 1.0),\n", - " ('initial', 1.0),\n", - " ('abstractconv2d', 1.0),\n", - " ('set', 1.0),\n", - " ('randint', 1.0),\n", - " ('row', 1.0),\n", - " ('stateful', 1.0),\n", - " ('outs', 1.0),\n", - " 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(30,10))\n", - "labels, values = zip(*c.most_common(100))\n", - "\n", - "indexes = np.arange(len(labels))\n", - "width = 1\n", - "\n", - "freqs = [per_token_freq[l] for l in labels]\n", - "\n", - "mean_freq = np.mean(list(per_token_freq.values()))\n", - "mean_acc = (accuracy / len(results_df))\n", - "\n", - "plt.bar(indexes, values, width, label='Accuracy')\n", - "plt.bar(indexes, freqs, width, label='Frequency')\n", - "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('MAGRET (100k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "#plt.savefig('MAGRET-100k_epochs_top100.png')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[2,\n", - " 398,\n", - " 53,\n", - " 1142,\n", - " 95,\n", - " 25,\n", - " 53,\n", - " 1142,\n", - " 298,\n", - " 25,\n", - " 38,\n", - " 25,\n", - " 961,\n", - " 4,\n", - " 655,\n", - " 7,\n", - " 98,\n", - " 319,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred = list(results_df.loc[10][3:])\n", - "pred" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "pred_str = [vocab_df.loc[i][0] for i in pred]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['[CLS]',\n", - " 'return',\n", - " 'call',\n", - " 'attribute',\n", - " 'mean',\n", - " 'name',\n", - " 'call',\n", - " 'attribute',\n", - " 'square',\n", - " 'name',\n", - " 'binop',\n", - " 'name',\n", - " 'sub',\n", - " '[MASK]',\n", - " 'keyword',\n", - " 'unaryop',\n", - " 'usub',\n", - " 'num',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " 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"d.most_common(100)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20,10))\n", - "labels, values = zip(*d.most_common(100))\n", - "\n", - "indexes = np.arange(len(labels))\n", - "width = 1\n", - "\n", - "accuracies = [c[tok] for tok in labels]\n", - "\n", - "plt.bar(indexes, accuracies, width, label='Accuracy')\n", - "plt.bar(indexes, values, width, label='Frequency')\n", - "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('MAGRET (100k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "plt.savefig('MAGRET-freq-100k_epochs_top100.png')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "confusion = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(results_df)):\n", - " snippet = [results_df[str(_)][i] for _ in range(64)]\n", - " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", - " masked_tk = snippet[msk_idx]\n", - " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", - " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", - " if confusion.get(label, None) == None:\n", - " confusion[label] = []\n", - " if prediction != label:\n", - " confusion[label].append(prediction)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "confusion_counter = {c: Counter(confusion[c]) for c in confusion}" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1': Counter({'2': 2, 'binop': 3}),\n", - " '2': Counter({'binop': 1}),\n", - " 'a': Counter({'h': 1, 'y': 2}),\n", - " 'abs': Counter({'exp': 1, 'max': 2, 'mean': 1, 'name': 3}),\n", - " 'abstract': Counter({'nn': 1}),\n", - " 'abstractconv2d': Counter(),\n", - " 'accuracy': Counter({'img': 1, 'in': 3}),\n", - " 'activation': Counter({'bias': 2, 'items': 3, 'kernel': 1, 'name': 1}),\n", - " 'active': Counter({'tuple': 5}),\n", - " 'activity': Counter({'build': 1, 'kernel': 1}),\n", - " 'add': Counter({'call': 1,\n", - " 'mod': 2,\n", - " 'mult': 5,\n", - " 'name': 3,\n", - " 'num': 2,\n", - " 'str': 1,\n", - " 'sub': 7}),\n", - " 'additional': Counter({'nameconstant': 4}),\n", - " 'adj': Counter({'name': 5}),\n", - " 'after': Counter({'shape': 2}),\n", - " 'alias': Counter({'str': 1}),\n", - " 'all': Counter({'y': 2}),\n", - " 'alpha': Counter({'arg': 3}),\n", - " 'alt': Counter({'axis': 3}),\n", - " 'and': Counter({'assign': 1, 'attribute': 4, 'name': 8, 'or': 16}),\n", - " 'any': Counter(),\n", - " 'append': Counter({'assign': 1,\n", - " 'comprehension': 1,\n", - " 'if': 1,\n", - " 'init': 2,\n", - " 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-- matrix\n", - "Preds -- i (2) log (1) name (1)\n", - "\n", - "Label -- metric\n", - "Preds -- fn (1)\n", - "\n", - "Label -- argmin\n", - "Preds -- ops (2)\n", - "\n", - "Label -- batch\n", - "Preds -- shape (6) call (2) name (2) state (1) binop (1)\n", - "\n", - "Label -- greater\n", - "Preds -- clip (5) prod (3) monitor (1) cast (1) get (1)\n", - "\n", - "Label -- axes\n", - "Preds -- name (1)\n", - "\n", - "Label -- regularizer\n", - "Preds -- initializer (2) build (1)\n", - "\n", - "Label -- prob\n", - "Preds -- num (2)\n", - "\n", - "Label -- 2\n", - "Preds -- binop (1)\n", - "\n", - "Label -- convolution\n", - "Preds -- x (4)\n", - "\n", - "Label -- converted\n", - "Preds -- args (1)\n", - "\n", - "Label -- len\n", - "Preds -- padding (2)\n", - "\n", - "Label -- any\n", - "Preds -- \n", - "\n", - "Label -- filter\n", - "Preds -- attribute (6) subscript (2) w (1)\n", - "\n", - "Label -- rho\n", - "Preds -- epsilon (4) maximum (4) lr (3) 2 (2) value (1)\n", - "\n", - "Label -- to\n", - "Preds -- unaryop (5)\n", - "\n", - "Label -- sort\n", - "Preds -- items (2)\n", - "\n", - "Label -- expected\n", - "Preds -- \n", - "\n", - "Label -- mode\n", - "Preds -- inputs (4) num (1)\n", - "\n", - "Label -- bar\n", - "Preds -- binop (1)\n", - "\n", - "Label -- logs\n", - "Preds -- self (2) name (1) str (1)\n", - "\n", - "Label -- variables\n", - "Preds -- name (5) function (2) value (1) call (1)\n", - "\n", - "Label -- cudnn\n", - "Preds -- outputs (2)\n", - "\n", - "Label -- rate\n", - "Preds -- padding (3) inputs (2) args (2) length (1)\n", - "\n", - "Label -- hdf5\n", - "Preds -- config (2)\n", - "\n", - "Label -- inbound\n", - "Preds -- name (3) index (2) layer (2) binop (1)\n", - "\n", - "Label -- lambda\n", - "Preds -- attribute (2) name (2) functiondef (1)\n", - "\n", - "Label -- relu\n", - "Preds -- cast (3) num (2) nn (1)\n", - "\n", - "Label -- objects\n", - "Preds -- logs (1)\n", - "\n", - "Label -- uint8\n", - "Preds -- data (1)\n", - "\n", - "Label -- ins\n", - "Preds -- name (1) shape (1)\n", - "\n", - "Label -- rank\n", - "Preds -- input (3)\n", - "\n", - "Label -- min\n", - "Preds -- max (8) ndim (2)\n", - "\n", - "Label -- enqueuer\n", - "Preds -- str (3) phase (1)\n", - "\n", - "Label -- fit\n", - "Preds -- init (1)\n", - "\n", - "Label -- train\n", - "Preds -- keyword (7) metrics (4) functiondef (3)\n", - "\n", - "Label -- continue\n", - "Preds -- return (1)\n", - "\n", - "Label -- metrics\n", - "Preds -- stateful (3) name (1)\n", - "\n", - "Label -- adj\n", - "Preds -- name (5)\n", - "\n", - "Label -- cast\n", - "Preds -- tile (1) num (1)\n", - "\n", - "Label -- lock\n", - "Preds -- model (1)\n", - "\n", - "Label -- bias\n", - "Preds -- kernel (5) initializer (3) functiondef (1)\n", - "\n", - "Label -- abstractconv2d\n", - "Preds -- \n", - "\n", - "Label -- default\n", - "Preds -- name (1)\n", - "\n", - "Label -- keras\n", - "Preds -- test (4) output (3) name (2) w (2) init (1)\n", - "\n", - "Label -- total\n", - "Preds -- functiondef (4) mult (2) num (1) list (1) str (1)\n", - "\n", - "Label -- eq\n", - "Preds -- in (8) attribute (6) gt (4) call (1)\n", - "\n", - "Label -- ishape\n", - "Preds -- n (1)\n", - "\n", - "Label -- shifted\n", - "Preds -- variance (4)\n", - "\n", - "Label -- c\n", - "Preds -- nameconstant (2) f (1)\n", - "\n", - "Label -- val\n", - "Preds -- name (6) dtype (5) tuple (3) binop (3) comprehension (1)\n", - "\n", - "Label -- epsilon\n", - "Preds -- sqrt (8) mean (1)\n", - "\n", - "Label -- norm\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- num\n", - "Preds -- name (185) str (28) nameconstant (20) tuple (13) mult (13)\n", - "\n", - "Label -- alpha\n", - "Preds -- arg (3)\n", - "\n", - "Label -- root\n", - "Preds -- keys (3)\n", - "\n", - "Label -- names\n", - "Preds -- cls (1)\n", - "\n", - "Label -- source\n", - "Preds -- shape (1) dim (1)\n", - "\n", - "Label -- and\n", - "Preds -- or (16) name (8) attribute (4) assign (1)\n", - "\n", - "Label -- set\n", - "Preds -- \n", - "\n", - "Label -- yaml\n", - "Preds -- config (2) self (1)\n", - "\n", - "Label -- output\n", - "Preds -- shape (10) name (9) call (6) attribute (5) keras (4)\n", - "\n", - "Label -- py\n", - "Preds -- function (4) format (2) eval (1) shape (1) call (1)\n", - "\n", - "Label -- permute\n", - "Preds -- call (1)\n", - "\n", - "Label -- global\n", - "Preds -- name (5)\n", - "\n", - "Label -- equal\n", - "Preds -- \n", - "\n", - "Label -- separable\n", - "Preds -- keyword (3) x (1) support (1)\n", - "\n", - "Label -- by\n", - "Preds -- add (1)\n", - "\n", - "Label -- numpy\n", - "Preds -- \n", - "\n", - "Label -- randint\n", - "Preds -- \n", - "\n", - "Label -- optimizer\n", - "Preds -- nameconstant (3) functiondef (1) callbacks (1) name (1) model (1)\n", - "\n", - "Label -- row\n", - "Preds -- \n", - "\n", - "Label -- add\n", - "Preds -- sub (7) mult (5) name (3) num (2) mod (2)\n", - "\n", - "Label -- r\n", - "Preds -- f (4) kernel (3) h (1) name (1) negative (1)\n", - "\n", - "Label -- conv1d\n", - "Preds -- deconv (2)\n", - "\n", - "Label -- desired\n", - "Preds -- axis (1)\n", - "\n", - "Label -- verbose\n", - "Preds -- size (3) lr (2) kwargs (1) dtype (1)\n", - "\n", - "Label -- gte\n", - "Preds -- gt (4) eq (4)\n", - "\n", - "Label -- prev\n", - "Preds -- name (1) list (1)\n", - "\n", - "Label -- keyword\n", - "Preds -- call (38) name (29) return (17) subscript (11) str (10)\n", - "\n", - "Label -- support\n", - "Preds -- assign (3) name (2)\n", - "\n", - "Label -- constraint\n", - "Preds -- regularizer (2)\n", - "\n", - "Label -- embeddings\n", - "Preds -- format (6) init (3) class (1)\n", - "\n", - "Label -- transpose\n", - "Preds -- x (3) shape (2) format (1)\n", - "\n", - "Label -- clip\n", - "Preds -- sum (2) swapaxes (1)\n", - "\n", - "Label -- uniform\n", - "Preds -- call (3)\n", - "\n", - "Label -- type\n", - "Preds -- class (4) dimshuffle (1)\n", - "\n", - "Label -- format\n", - "Preds -- summary (4) if (3) name (3) ndarray (1)\n", - "\n", - "Label -- best\n", - "Preds -- call (7) expr (3) monitor (2)\n", - "\n", - "Label -- x\n", - "Preds -- dtype (8) self (7) name (5) expr (5) cell (4)\n", - "\n", - "Label -- l1\n", - "Preds -- return (2) square (2) call (1)\n", - "\n", - "Label -- active\n", - "Preds -- tuple (5)\n", - "\n", - "Label -- not\n", - "Preds -- greater (6) usub (3)\n", - "\n", - "Label -- list\n", - "Preds -- call (33) name (27) str (10) keyword (8) tuple (6)\n", - "\n", - "Label -- attribute\n", - "Preds -- name (129) assign (61) call (26) str (19) keyword (14)\n", - "\n", - "Label -- all\n", - "Preds -- y (2)\n", - "\n", - "Label -- states\n", - "Preds -- cell (8) output (3) a (2) mask (1) i (1)\n", - "\n", - "Label -- regularization\n", - "Preds -- return (4) call (2) div (1)\n", - "\n", - "Label -- call\n", - "Preds -- binop (44) attribute (41) tuple (34) if (32) name (27)\n", - "\n", - "Label -- compute\n", - "Preds -- int (1)\n", - "\n", - "Label -- compare\n", - "Preds -- call (20) name (10) binop (8) data (5) randint (4)\n", - "\n", - "Label -- freedimension\n", - "Preds -- \n", - "\n", - "Label -- splits\n", - "Preds -- weight (5)\n", - "\n", - "Label -- sandbox\n", - "Preds -- normalization (3)\n", - "\n", - "Label -- function\n", - "Preds -- tensor (4) name (4) in (2) shape (2) join (2)\n", - "\n", - "Label -- feature\n", - "Preds -- constraint (2)\n", - "\n", - "Label -- backend\n", - "Preds -- square (1)\n", - "\n", - "Label -- copy\n", - "Preds -- cell (1) activation (1)\n", - "\n", - "Label -- starred\n", - "Preds -- keys (3) str (3) add (1) num (1)\n", - "\n", - "Label -- threshold\n", - "Preds -- nn (3)\n", - "\n", - "Label -- idx\n", - "Preds -- info (1) str (1)\n", - "\n", - "Label -- masking\n", - "Preds -- \n", - "\n", - "Label -- volume\n", - "Preds -- output (2)\n", - "\n", - "Label -- group\n", - "Preds -- keys (1)\n", - "\n", - "Label -- decay\n", - "Preds -- value (1)\n", - "\n", - "Label -- stateful\n", - "Preds -- \n", - "\n", - "Label -- depth\n", - "Preds -- name (2) x (2)\n", - "\n", - "Label -- proba\n", - "Preds -- clip (1)\n", - "\n", - "Label -- masks\n", - "Preds -- output (5) name (2)\n", - "\n", - "Label -- v\n", - "Preds -- add (6) batch (2) x (2)\n", - "\n", - "Label -- outs\n", - "Preds -- \n", - "\n", - "Label -- weights\n", - "Preds -- if (5) name (4) num (3) trainable (3) x (3)\n", - "\n", - "Label -- totals\n", - "Preds -- \n", - "\n", - "Label -- edge\n", - "Preds -- layer (1)\n", - "\n", - "Label -- unique\n", - "Preds -- name (5) axes (2)\n", - "\n", - "Label -- prefix\n", - "Preds -- binop (4)\n", - "\n", - "Label -- assign\n", - "Preds -- attribute (43) call (32) return (14) for (13) keyword (10)\n", - "\n", - "Label -- dataset\n", - "Preds -- dtype (4) variable (3) zeros (2)\n", - "\n", - "Label -- keys\n", - "Preds -- append (5)\n", - "\n", - "Label -- s\n", - "Preds -- sum (2)\n", - "\n", - "Label -- key\n", - "Preds -- x (2) attrs (1)\n", - "\n", - "Label -- standardize\n", - "Preds -- name (2)\n", - "\n", - "Label -- 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(9) gt (4) call (4) eq (4) name (2)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label -- scope\n", - "Preds -- for (3) binop (3) variable (2) append (1)\n", - "\n", - "Label -- extra\n", - "Preds -- expand (2) set (1)\n", - "\n", - "Label -- index\n", - "Preds -- slice (12) name (8) add (6) multiprocessing (2) tuple (1)\n", - "\n", - "Label -- label\n", - "Preds -- data (4) call (1)\n", - "\n", - "Label -- session\n", - "Preds -- run (3) freedimension (1)\n", - "\n", - "Label -- abs\n", - "Preds -- name (3) max (2) exp (1) mean (1)\n", - "\n", - "Label -- dimshuffle\n", - "Preds -- transpose (3) format (3)\n", - "\n", - "Label -- max\n", - "Preds -- axis (3) nn (2) transpose (2) num (1) mean (1)\n", - "\n", - "Label -- keepdims\n", - "Preds -- axis (1)\n", - "\n", - "Label -- history\n", - "Preds -- shape (6) name (1)\n", - "\n", - "Label -- exists\n", - "Preds -- append (10) join (3)\n", - "\n", - "Label -- constant\n", - "Preds -- name (5) 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- "\n", - "Label -- uses\n", - "Preds -- call (2)\n", - "\n", - "Label -- attrs\n", - "Preds -- \n", - "\n", - "Label -- alt\n", - "Preds -- axis (3)\n", - "\n", - "Label -- lte\n", - "Preds -- \n", - "\n", - "Label -- additional\n", - "Preds -- nameconstant (4)\n", - "\n", - "Label -- unpickle\n", - "Preds -- inputs (1)\n", - "\n", - "Label -- placeholders\n", - "Preds -- subscript (1)\n", - "\n", - "Label -- gain\n", - "Preds -- stddev (4)\n", - "\n", - "Label -- toarray\n", - "Preds -- values (5)\n", - "\n", - "Label -- trainable\n", - "Preds -- forward (2) stateful (2) weights (1) functiondef (1) set (1)\n", - "\n", - "Label -- select\n", - "Preds -- call (7) keyword (1)\n", - "\n", - "Label -- recurrent\n", - "Preds -- bias (4) kernel (3) nameconstant (2) input (2) num (2)\n", - "\n", - "Label -- generator\n", - "Preds -- normal (4) support (1)\n", - "\n", - "Label -- for\n", - "Preds -- call (14) assign (12) if (7) index (6) node (2)\n", - "\n", - "Label -- activation\n", - "Preds -- items (3) bias (2) name (1) kernel (1)\n", - "\n", - "Label -- normalize\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- nn\n", - "Preds -- \n", - "\n", - "Label -- boolop\n", - "Preds -- nameconstant (2) gt (1)\n", - "\n", - "Label -- cudnngru\n", - "Preds -- constraint (3)\n", - "\n", - "Label -- beta\n", - "Preds -- \n", - "\n", - "Label -- make\n", - "Preds -- name (1)\n", - "\n", - "Label -- char\n", - "Preds -- split (1)\n", - "\n", - "Label -- training\n", - "Preds -- attribute (4) y (3)\n", - "\n", - "Label -- classification\n", - "Preds -- name (3)\n", - "\n", - "Label -- kernels\n", - "Preds -- lt (5) args (1)\n", - "\n", - "Label -- tf\n", - "Preds -- name (1)\n", - "\n", - "Label -- t\n", - "Preds -- reduce (4) v (3) binop (3) normalization (2) int (1)\n", - "\n", - "Label -- noteq\n", - "Preds -- eq (15) gt (10) lt (5) name (2)\n", - "\n", - "Label -- with\n", - "Preds -- attribute (5) while (2) for (2) str (1)\n", - "\n", - "Label -- learning\n", - "Preds -- call (1)\n", - "\n", - "Label -- rng\n", - "Preds -- random (2)\n", - "\n", - "Label -- rmtree\n", - "Preds -- join (1) if (1)\n", - "\n", - "Label -- cell\n", - "Preds -- losses (3) state (2) self (1) init (1) backwards (1)\n", - "\n", - "Label -- hsplit\n", - "Preds -- \n", - "\n", - "Label -- fpath\n", - "Preds -- attribute (1)\n", - "\n", - "Label -- params\n", - "Preds -- nameconstant (1)\n", - "\n", - "Label -- arg\n", - "Preds -- name (19) max (6) attribute (6) assign (5) node (4)\n", - "\n", - "Label -- part\n", - "Preds -- \n", - "\n", - "Label -- placeholder\n", - "Preds -- name (2) constant (2) call (1)\n", - "\n", - "Label -- a\n", - "Preds -- y (2) h (1)\n", - "\n", - "Label -- layers\n", - "Preds -- layer (8) states (7) spec (4) kernel (1) x (1)\n", - "\n", - "Label -- sample\n", - "Preds -- name (4) feed (1)\n", - "\n", - "Label -- signature\n", - "Preds -- line (3) name (2)\n", - "\n", - "Label -- dropout\n", - "Preds -- mask (2) recurrent (1)\n", - "\n", - "Label -- requestexception\n", - "Preds -- path (2)\n", - "\n", - "Label -- outbound\n", - "Preds -- for (8)\n", - "\n", - "Label -- device\n", - "Preds -- params (4) self (2) freedimension (1)\n", - "\n", - "Label -- sum\n", - "Preds -- concatenate (2) items (2) name (2) append (2) call (1)\n", - "\n", - "Label -- div\n", - "Preds -- mult (4) mod (2) p (1) add (1) assign (1)\n", - "\n", - "Label -- md5\n", - "Preds -- str (2)\n", - "\n", - "Label -- count\n", - "Preds -- weights (4) name (1)\n", - "\n", - "Label -- grad\n", - "Preds -- x (5)\n", - "\n", - "Label -- ones\n", - "Preds -- zeros (10) items (2) shape (1)\n", - "\n", - "Label -- combine\n", - "Preds -- name (4)\n", - "\n", - "Label -- step\n", - "Preds -- state (1)\n", - "\n", - "Label -- nonzero\n", - "Preds -- num (3)\n", - "\n", - "Label -- custom\n", - "Preds -- \n", - "\n", - "Label -- permutation\n", - "Preds -- axis (1)\n", - "\n", - "Label -- y\n", - "Preds -- x (11) output (2) shape (2) name (1) num (1)\n", - "\n", - "Label -- common\n", - "Preds -- num (1)\n", - "\n", - "Label -- xs\n", - "Preds -- \n", - "\n", - "Label -- d\n", - "Preds -- new (1) expr (1)\n", - "\n", - "Label -- n\n", - "Preds -- i (1)\n", - "\n", - "Label -- arrays\n", - "Preds -- if (1)\n", - "\n", - "Label -- slice\n", - "Preds -- index (49) num (2) mod (1) expr (1)\n", - "\n", - "Label -- bool\n", - "Preds -- dtype (12) epsilon (1)\n", - "\n", - "Label -- try\n", - "Preds -- if (4) name (1)\n", - "\n", - "Label -- gpus\n", - "Preds -- name (1)\n", - "\n", - "Label -- gen\n", - "Preds -- val (3) keyword (3) subscript (2)\n", - "\n", - "Label -- mult\n", - "Preds -- add (11) sub (4) num (3) mod (2)\n", - "\n", - "Label -- dict\n", - "Preds -- attribute (5) tuple (2) model (2) expr (1) list (1)\n", - "\n", - "Label -- types\n", - "Preds -- \n", - "\n", - "Label -- l2\n", - "Preds -- call (1)\n", - "\n", - "Label -- clipnorm\n", - "Preds -- shape (5)\n", - "\n", - "Label -- gaussiannoise\n", - "Preds -- masking (1)\n", - "\n", - "Label -- warn\n", - "Preds -- keyword (2) pop (2) path (1)\n", - "\n", - "Label -- load\n", - "Preds -- layer (1) weights (1)\n", - "\n", - "Label -- broadcast\n", - "Preds -- relu (1)\n", - "\n", - "Label -- axis\n", - "Preds -- name (6) states (5) join (1) num (1)\n", - "\n", - "Label -- hot\n", - "Preds -- dtype (5) uniform (3)\n", - "\n", - "Label -- makedirs\n", - "Preds -- file (4) name (1) path (1) append (1)\n", - "\n", - "Label -- nbytes\n", - "Preds -- where (1) pool (1)\n", - "\n", - "Label -- expr\n", - "Preds -- name (26) call (11) attribute (8) assign (6) raise (6)\n", - "\n", - "Label -- sequences\n", - "Preds -- inputs (1)\n", - "\n", - "Label -- factor\n", - "Preds -- num (1)\n", - "\n", - "Label -- methods\n", - "Preds -- support (5)\n", - "\n", - "Label -- maximum\n", - "Preds -- \n", - "\n", - "Label -- result\n", - "Preds -- normalization (6) output (2) mean (1) num (1)\n", - "\n", - "Label -- losses\n", - "Preds -- updates (5) for (4) cell (3) inputs (3) call (2)\n", - "\n", - "Label -- fill\n", - "Preds -- tile (3)\n", - "\n", - "Label -- node\n", - "Preds -- break (4) name (3) append (1)\n", - "\n", - "Label -- variable\n", - "Preds -- name (1)\n", - "\n", - "Label -- element\n", - "Preds -- mean (5) call (3) equal (1) name (1) reduce (1)\n", - "\n", - "Label -- classes\n", - "Preds -- shape (4) train (2)\n", - "\n", - "Label -- elems\n", - "Preds -- index (3)\n", - "\n", - "Label -- unknown\n", - "Preds -- tensor (4) steps (2)\n", - "\n", - "Label -- supports\n", - "Preds -- \n", - "\n", - "Label -- loss\n", - "Preds -- losses (5) withitem (5) mult (2) name (2) nameconstant (1)\n", - "\n", - "Label -- broadcastable\n", - "Preds -- ndim (1)\n", - "\n", - "Label -- neg\n", - "Preds -- relu (1) alpha (1)\n", - "\n", - "Label -- array\n", - "Preds -- prod (2) zeros (1)\n", - "\n", - "Label -- stack\n", - "Preds -- \n", - "\n", - "Label -- prime\n", - "Preds -- call (1) binop (1)\n", - "\n", - "Label -- floordiv\n", - "Preds -- mult (5) add (2) num (1)\n", - "\n", - "Label -- rows\n", - "Preds -- cols (4) padding (1)\n", - "\n", - "Label -- period\n", - "Preds -- nameconstant (2)\n", - "\n", - "Label -- like\n", - "Preds -- name (8) keyword (4) index (3) call (3) loss (1)\n", - "\n", - "Label -- flag\n", - "Preds -- nodes (2)\n", - "\n", - "Label -- lr\n", - "Preds -- 2 (6) decay (5) value (4) nameconstant (1) binop (1)\n", - "\n", - "Label -- asarray\n", - "Preds -- warn (3) zeros (2)\n", - "\n", - "Label -- softmax\n", - "Preds -- log (2)\n", - "\n", - "Label -- withitem\n", - "Preds -- attribute (3) tuple (1) for (1)\n", - "\n", - "Label -- tiled\n", - "Preds -- size (1)\n", - "\n", - "Label -- float64\n", - "Preds -- shape (3)\n", - "\n", - "Label -- headers\n", - "Preds -- \n", - "\n", - "Label -- cls\n", - "Preds -- \n", - "\n", - "Label -- out\n", - "Preds -- states (2) name (1) x (1)\n", - "\n", - "Label -- nameconstant\n", - "Preds -- name (52) num (16) keyword (8) call (6) random (5)\n", - "\n", - "Label -- tensors\n", - "Preds -- output (3) name (2)\n", - "\n", - "Label -- from\n", - "Preds -- target (5) group (2)\n", - "\n", - "Label -- reduce\n", - "Preds -- if (4) call (1) num (1)\n", - "\n", - "Label -- importfrom\n", - "Preds -- functiondef (2) expr (1)\n", - "\n", - "Label -- targets\n", - "Preds -- num (3)\n", - "\n", - "Label -- exp\n", - "Preds -- num (1)\n", - "\n", - "Label -- noise\n", - "Preds -- output (5) shape (1)\n", - "\n", - "Label -- top\n", - "Preds -- split (1)\n", - "\n", - "Label -- build\n", - "Preds -- keyword (4) layer (3) append (2)\n", - "\n", - "Label -- zero\n", - "Preds -- attribute (2)\n", - "\n", - "Label -- pow\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- callback\n", - "Preds -- attribute (9) assign (3) boolop (1)\n", - "\n", - "Label -- bilinear\n", - "Preds -- call (4)\n", - "\n", - "Label -- go\n", - "Preds -- \n", - "\n", - "Label -- symbols\n", - "Preds -- keyword (3) if (1)\n", - "\n", - "Label -- original\n", - "Preds -- name (3)\n", - "\n", - "Label -- metadata\n", - "Preds -- class (1)\n", - "\n", - "Label -- items\n", - "Preds -- zeros (2)\n", - "\n", - "Label -- dim1\n", - "Preds -- kernel (1)\n", - "\n", - "Label -- hasher\n", - "Preds -- stdout (1)\n", - "\n", - "Label -- delete\n", - "Preds -- subscript (1)\n", - "\n", - "Label -- dim3\n", - "Preds -- width (4) length (1)\n", - "\n", - "Label -- at\n", - "Preds -- name (2) inbound (1)\n", - "\n", - "Label -- words\n", - "Preds -- split (2)\n", - "\n", - "Label -- built\n", - "Preds -- units (2) name (1)\n", - "\n", - "Label -- isfile\n", - "Preds -- join (2) warn (2) append (1)\n", - "\n", - "Label -- backwards\n", - "Preds -- \n", - "\n", - "Label -- concatenate\n", - "Preds -- tile (1)\n", - "\n", - "Label -- string\n", - "Preds -- config (2)\n", - "\n", - "Label -- normalization\n", - "Preds -- \n", - "\n", - "Label -- implementation\n", - "Preds -- stateful (2) backwards (1)\n", - "\n", - "Label -- i\n", - "Preds -- listcomp (5) c (3) out (1) l (1) bias (1)\n", - "\n", - "Label -- exceptions\n", - "Preds -- path (1)\n", - "\n", - "Label -- dimensions\n", - "Preds -- state (1)\n", - "\n", - "Label -- eval\n", - "Preds -- floatx (1)\n", - "\n", - "Label -- multiprocessing\n", - "Preds -- arg (1)\n", - "\n", - "Label -- cumprod\n", - "Preds -- equal (4)\n", - "\n", - "Label -- less\n", - "Preds -- \n", - "\n", - "Label -- new\n", - "Preds -- t (4) nameconstant (4) name (3) for (3) keyword (3)\n", - "\n", - "Label -- deconv\n", - "Preds -- \n", - "\n", - "Label -- ifexp\n", - "Preds -- call (5) name (4)\n", - "\n", - "Label -- generic\n", - "Preds -- states (2) model (1)\n", - "\n", - "Label -- eta\n", - "Preds -- data (3) floordiv (2)\n", - "\n", - "Label -- value\n", - "Preds -- name (7) variables (4) losses (2) str (2) return (1)\n", - "\n", - "Label -- convert\n", - "Preds -- padding (1)\n", - "\n", - "Label -- if\n", - "Preds -- call (17) for (10) output (6) device (5) in (4)\n", - "\n", - "Label -- length\n", - "Preds -- states (6) spec (6) shape (4) size (2)\n", - "\n", - "Label -- extslice\n", - "Preds -- subscript (8) call (3)\n", - "\n", - "Label -- state\n", - "Preds -- output (6) constant (5) cell (3) name (3) kernel (2)\n", - "\n", - "Label -- closure\n", - "Preds -- nameconstant (9)\n", - "\n", - "Label -- save\n", - "Preds -- arg (1)\n", - "\n", - "Label -- update\n", - "Preds -- attribute (5) v (1) keys (1) name (1)\n", - "\n", - "Label -- nesterov\n", - "Preds -- epsilon (2)\n", - "\n", - "Label -- argmax\n", - "Preds -- shape (2)\n", - "\n", - "Label -- unaryop\n", - "Preds -- num (6) index (4) name (3) call (2) attribute (1)\n", - "\n", - "Label -- generatorexp\n", - "Preds -- listcomp (14) name (2) x (2)\n", - "\n", - "Label -- child\n", - "Preds -- batch (4) shape (1)\n", - "\n", - "Label -- gt\n", - "Preds -- eq (8) noteq (7) notin (5) lt (3) name (3)\n", - "\n", - "Label -- conv\n", - "Preds -- sum (3)\n", - "\n", - "Label -- multiply\n", - "Preds -- reshape (1) transpose (1)\n", - "\n", - "Label -- explicitly\n", - "Preds -- name (2)\n", - "\n", - "Label -- functiondef\n", - "Preds -- attribute (3) if (1) for (1) assign (1) num (1)\n", - "\n", - "Label -- sub\n", - "Preds -- add (21) mult (4) num (3) rank (1)\n", - "\n", - "Label -- feed\n", - "Preds -- tensor (2) or (1) append (1)\n", - "\n", - "Label -- f\n", - "Preds -- length (2)\n", - "\n" - ] - } - ], - "source": [ - "for key, c in confusion_counter.items():\n", - " print(\"Label -- \", key)\n", - " print('Preds -- ',' '.join([\"{} ({})\".format(c0, c1) for c0,c1 in c.most_common(5)]))\n", - " print()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "token_names = [\"Module\",\"Interactive\",\"Expression\",\"Suite\",\"FunctionDef\",\"AsyncFunctionDef\",\"ClassDef\",\"Return\",\"Delete\",\"Assign\",\"AugAssign\",\"For\",\"AsyncFor\",\"While\",\"If\",\"With\",\"AsyncWith\",\"Raise\",\"Try\",\"Assert\",\"Import\",\"ImportFrom\",\"Global\",\"Nonlocal\",\"Expr\",\"Pass\",\"Break\",\"Continue\",\"BoolOp\",\"BinOp\",\"UnaryOp\",\"Lambda\",\"IfExp\",\"Dict\",\"Set\",\"ListComp\",\"SetComp\",\"DictComp\",\"GeneratorExp\",\"Await\",\"Yield\",\"YieldFrom\",\"Compare\",\"Call\",\"Num\",\"Str\",\"FormattedValue\",\"JoinedStr\",\"Bytes\",\"NameConstant\",\"Ellipsis\",\"Constant\",\"Attribute\",\"Subscript\",\"Starred\",\"Name\",\"List\",\"Tuple\",\"Load\",\"Store\",\"Del\",\"AugLoad\",\"AugStore\",\"Param\",\"Slice\",\"ExtSlice\",\"Index\",\"And\",\"Or\",\"Add\",\"Sub\",\"Mult\",\"MatMult\",\"Div\",\"Mod\",\"Pow\",\"LShift\",\"RShift\",\"BitOr\",\"BitXor\",\"BitAnd\",\"FloorDiv\",\"Invert\",\"Not\",\"UAdd\",\"USub\",\"Eq\",\"NotEq\",\"Lt\",\"LtE\",\"Gt\",\"GtE\",\"Is\",\"IsNot\",\"In\",\"NotIn\",\"excepthandler\",\"ExceptHandler\",\"arguments\",\"arg\",\"keyword\",\"alias\",\"withitem\",\"comprehension\"]\n", - "token_names = [t.lower() for t in token_names]" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "def is_ast_token(t):\n", - " return t in token_names" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "mistaken = {}; tot_right = 0; tot_wrong = 0\n", - "for i in range(len(results_df)):\n", - " snippet = [results_df[str(_)][i] for _ in range(64)]\n", - " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", - " masked_tk = snippet[msk_idx]\n", - " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", - " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", - " if mistaken.get(label, None) == None:\n", - " mistaken[label] = {'correct':0, 'wrong':0}\n", - " right = is_ast_token(prediction) == is_ast_token(label)\n", - " if prediction != label:\n", - " if right:\n", - " mistaken[label]['correct'] += 1\n", - " tot_right += 1\n", - " else:\n", - " mistaken[label]['wrong'] += 1\n", - " tot_wrong += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7645970937912814, 0.23540290620871862)" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tot_right / (tot_right + tot_wrong), tot_wrong / (tot_right + tot_wrong)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9529000000000001" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "0.8 + 0.7645 * 0.2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "MOD_SYMBOLS = [\"Module\", \"Interactive\", \"Expression\", \"Suite\"]\n", - "STMT_SYMBOLS = [\"FunctionDef\", \"AsyncFunctionDef\", \"ClassDef\", \"Return\", \"Delete\", \\\n", - " \"Assign\", \"AugAssign\", \"For\", \"AsyncFor\", \"While\", \"If\", \"With\", \"AsyncWith\", \\\n", - " \"Raise\", \"Try\", \"Assert\", \"Import\", \"ImportFrom\", \"Global\", \"Nonlocal\", \\\n", - " \"Expr\", \"Pass\", \"Break\", \"Continue\"]\n", - "EXPR_SYMBOLS = [\"BoolOp\", \"BinOp\", \"UnaryOp\", \"Lambda\", \"IfExp\", \"Dict\", \"Set\", \"ListComp\", \\\n", - " \"SetComp\", \"DictComp\", \"GeneratorExp\", \"Await\", \"Yield\", \"YieldFrom\", \\\n", - " \"Compare\", \"Call\", \"Num\", \"Str\", \"FormattedValue\", \"JoinedStr\", \"Bytes\", \\\n", - " \"NameConstant\", \"Ellipsis\", \"Constant\", \"Attribute\", \"Subscript\", \\\n", - " \"Starred\", \"Name\", \"List\", \"Tuple\"]\n", - "EXPR_CONTENT_SYMBOLS = [\"Load\", \"Store\", \"Del\", \"AugLoad\", \"AugStore\", \"Param\"]\n", - "SLICE_SYMBOLS = [\"Slice\", \"ExtSlice\", \"Index\"]\n", - "BOOLOP_SYMBOLS = [\"And\", \"Or\"]\n", - "OPERATOR_SYMBOLS = [\"Add\", \"Sub\", \"Mult\", \"MatMult\", \"Div\", \"Mod\", \"Pow\", \"LShift\", \"RShift\", \\\n", - " \"BitOr\", \"BitXor\", \"BitAnd\", \"FloorDiv\"]\n", - "UNARYOP_SYMBOLS = [\"Invert\", \"Not\", \"UAdd\", \"USub\"]\n", - "CMPOP_SYMBOLS = [\"Eq\", \"NotEq\", \"Lt\", \"LtE\", \"Gt\", \"GtE\", \"Is\", \"IsNot\", \"In\", \"NotIn\"]\n", - "COMPREHENSION_SYMBOLS = [\"comprehension\"]\n", - "EXCEPT_SYMBOLS = [\"excepthandler\", \"ExceptHandler\"]\n", - "ARG_SYMBOLS = [\"arguments\", \"arg\", \"keyword\"]\n", - "IMPORT_SYMBOLS = [\"alias\", \"withitem\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "def to_lower(l):\n", - " return [l_.lower() for l_ in l]" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "MOD_SYMBOLS = to_lower(MOD_SYMBOLS)\n", - "STMT_SYMBOLS = to_lower(STMT_SYMBOLS)\n", - "EXPR_SYMBOLS = to_lower(EXPR_SYMBOLS)\n", - "EXPR_CONTENT_SYMBOLS = to_lower(EXPR_CONTENT_SYMBOLS)\n", - "SLICE_SYMBOLS = to_lower(SLICE_SYMBOLS)\n", - "BOOLOP_SYMBOLS = to_lower(BOOLOP_SYMBOLS)\n", - "OPERATOR_SYMBOLS = to_lower(OPERATOR_SYMBOLS)\n", - "UNARYOP_SYMBOLS = to_lower(UNARYOP_SYMBOLS)\n", - "CMPOP_SYMBOLS = to_lower(CMPOP_SYMBOLS)\n", - "COMPREHENSION_SYMBOLS = to_lower(COMPREHENSION_SYMBOLS)\n", - "EXCEPT_SYMBOLS = to_lower(EXCEPT_SYMBOLS)\n", - "ARG_SYMBOLS = to_lower(ARG_SYMBOLS)\n", - "IMPORT_SYMBOLS = to_lower(IMPORT_SYMBOLS)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "def get_token_class_id(t):\n", - " if t in MOD_SYMBOLS: return 0\n", - " if t in STMT_SYMBOLS: return 1\n", - " if t in EXPR_SYMBOLS: return 2\n", - " if t in EXPR_CONTENT_SYMBOLS: return 3\n", - " if t in SLICE_SYMBOLS: return 4\n", - " if t in BOOLOP_SYMBOLS: return 5\n", - " if t in OPERATOR_SYMBOLS: return 6\n", - " if t in UNARYOP_SYMBOLS: return 7\n", - " if t in CMPOP_SYMBOLS: return 8\n", - " if t in ARG_SYMBOLS: return 9\n", - " if t in EXCEPT_SYMBOLS: return 10\n", - " if t in COMPREHENSION_SYMBOLS: return 11\n", - " if t in IMPORT_SYMBOLS: return 12\n", - " else: return 13" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "def is_same_class(t0, t1):\n", - " return get_token_class_id(t0) == get_token_class_id(t1)" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [], - "source": [ - "mistaken = {}; tot_right = 0; tot_wrong = 0\n", - "for i in range(len(results_df)):\n", - " snippet = [results_df[str(_)][i] for _ in range(64)]\n", - " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", - " masked_tk = snippet[msk_idx]\n", - " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", - " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", - " if mistaken.get(label, None) == None:\n", - " mistaken[label] = {'correct':0, 'wrong':0}\n", - " right = is_same_class(prediction, label)\n", - " #if prediction != label:\n", - " if right:\n", - " mistaken[label]['correct'] += 1\n", - " tot_right += 1\n", - " else:\n", - " mistaken[label]['wrong'] += 1\n", - " tot_wrong += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9109836065573771, 0.08901639344262295)" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tot_right / (tot_right + tot_wrong), tot_wrong / (tot_right + tot_wrong)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "classes = [\"MOD\", \"STMT\", \"EXPR\", \"EXPR_CONT\", \"SLICE\", \"BOOLOP\", \"OPERATOR\", \"UNARY\", \"CMPOP\", \"COMPR\", \"EXCEPT\", \"ARG\", \"IMPORT\", \"VAR\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "confusion_mat = np.zeros((14,14))\n", - "class_freqs = {str(i):0 for i in range(14)};\n", - "for i in range(len(results_df)):\n", - " snippet = [results_df[str(_)][i] for _ in range(64)]\n", - " msk_idx = results_df.iloc[i]['masked_lm_positions']\n", - " masked_tk = snippet[msk_idx]\n", - " prediction = vocab_df.loc[results_df.iloc[i]['masked_lm_predictions']][0]\n", - " label = vocab_df.loc[results_df.iloc[i]['label_ids']][0]\n", - " t0 = get_token_class_id(prediction)\n", - " t1 = get_token_class_id(label)\n", - " confusion_mat[t0][t1] += 1\n", - " class_freqs[str(t1)] += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", - " [0.0000e+00, 2.1230e+03, 4.0600e+02, 0.0000e+00, 2.0000e+00,\n", - " 1.0000e+00, 1.0000e+00, 0.0000e+00, 3.0000e+00, 4.6000e+01,\n", - " 2.0000e+00, 3.0000e+00, 1.0000e+00, 1.4000e+02],\n", - " [0.0000e+00, 2.9600e+02, 2.2159e+04, 0.0000e+00, 2.2000e+01,\n", - " 1.4000e+01, 1.4000e+01, 0.0000e+00, 2.2000e+01, 1.5000e+02,\n", - " 1.0000e+00, 5.0000e+00, 5.0000e+00, 6.3400e+02],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", - " [0.0000e+00, 8.0000e+00, 6.4000e+01, 0.0000e+00, 1.0370e+03,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.3000e+01],\n", - " [0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.3000e+02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00],\n", - " [0.0000e+00, 4.0000e+00, 8.8000e+01, 0.0000e+00, 7.0000e+00,\n", - " 0.0000e+00, 9.4400e+02, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.1000e+01],\n", - " [0.0000e+00, 0.0000e+00, 1.1000e+01, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.0000e+00, 2.2000e+02, 0.0000e+00, 2.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00],\n", - " [0.0000e+00, 1.1000e+01, 4.9000e+01, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 4.5500e+02, 2.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.4000e+01],\n", - " [0.0000e+00, 2.6000e+01, 1.9300e+02, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 6.0000e+00, 0.0000e+00, 0.0000e+00, 1.3710e+03,\n", - " 1.0000e+00, 0.0000e+00, 0.0000e+00, 8.5000e+01],\n", - " [0.0000e+00, 0.0000e+00, 2.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 2.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", - " [0.0000e+00, 1.0000e+00, 5.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.2900e+02, 0.0000e+00, 3.0000e+00],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 2.4000e+01, 5.0000e+00],\n", - " [0.0000e+00, 8.3000e+01, 7.2000e+02, 2.0000e+00, 3.0000e+00,\n", - " 0.0000e+00, 3.0000e+00, 6.0000e+00, 5.0000e+00, 4.1000e+01,\n", - " 2.0000e+00, 0.0000e+00, 0.0000e+00, 4.7470e+03]])" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_mat" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'0': 0.0,\n", - " '1': 0.06975409836065574,\n", - " '10': 0.0002185792349726776,\n", - " '11': 0.0037431693989071037,\n", - " '12': 0.000819672131147541,\n", - " '13': 0.15475409836065573,\n", - " '2': 0.6474590163934426,\n", - " '3': 8.19672131147541e-05,\n", - " '4': 0.02926229508196721,\n", - " '5': 0.003961748633879781,\n", - " '6': 0.026475409836065573,\n", - " '7': 0.006174863387978142,\n", - " '8': 0.013251366120218579,\n", - " '9': 0.044043715846994534}" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "freqs = {k: v/len(results_df) for k,v in class_freqs.items()}\n", - "freqs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0000e+00, 2.5530e+03, 2.3697e+04, 3.0000e+00, 1.0710e+03,\n", - " 1.4500e+02, 9.6900e+02, 2.2600e+02, 4.8500e+02, 1.6120e+03,\n", - " 8.0000e+00, 1.3700e+02, 3.0000e+01, 5.6640e+03])" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(confusion_mat, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,15))\n", - "n = np.sum(confusion_mat, axis=0)\n", - "n[0] = 1\n", - "plt.imshow(confusion_mat / n)\n", - "plt.xticks(range(14), classes, rotation=90)\n", - "plt.yticks(range(14), classes)\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MOD 0.0 0 0.0\n", - "STMT 0.832 2553 0.07\n", - "EXPR 0.935 23697 0.647\n", - "EXPR_CONT 0.333 3 0.0\n", - "SLICE 0.968 1071 0.029\n", - "BOOLOP 0.897 145 0.004\n", - "OPERATOR 0.974 969 0.026\n", - "UNARY 0.973 226 0.006\n", - "CMPOP 0.938 485 0.013\n", - "COMPR 0.85 1612 0.044\n", - "EXCEPT 0.25 8 0.0\n", - "ARG 0.942 137 0.004\n", - "IMPORT 0.8 30 0.001\n", - "VAR 0.838 5664 0.155\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n = np.sum(confusion_mat, axis=0)\n", - "n[0] = 1\n", - "normed = confusion_mat / n\n", - "for i in range(14):\n", - " plt.bar(classes[i], np.around(normed[i][i],3), color='b', alpha=0.5)\n", - " plt.bar(classes[i], np.around(freqs[str(i)],3), color='orange')\n", - " print(classes[i], np.around(normed[i][i],3), class_freqs[str(i)], np.around(freqs[str(i)],3))\n", - "plt.xticks(range(14), [str(class_freqs[str(i)])+\" - \"+c + \" (\" + str(np.around(normed[i][i],3)) + \")\" for i,c in enumerate(classes)], rotation=90);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.00000000e+00, 2.14534683e+03, 2.21755757e+04, 2.23606798e+00,\n", - " 1.03726323e+03, 1.30755497e+02, 9.44128699e+02, 2.20081803e+02,\n", - " 4.55568875e+02, 1.38056003e+03, 3.74165739e+00, 1.29131716e+02,\n", - " 2.45356883e+01, 4.79203840e+03])" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n = np.linalg.norm(confusion_mat, axis=0)\n", - "n[0] = 1\n", - "n" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "def scale(X, x_min, x_max):\n", - " nom = (X-X.min(axis=0))*(x_max-x_min)\n", - " denom = X.max(axis=0) - X.min(axis=0)\n", - " denom[denom==0] = 1\n", - " return x_min + nom/denom " - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "X_scaled = scale(confusion_mat, -1, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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