diff --git a/graph_cls/Untitled.ipynb b/graph_cls/Untitled.ipynb
deleted file mode 100644
index e17332b..0000000
--- a/graph_cls/Untitled.ipynb
+++ /dev/null
@@ -1,498 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {},
- "outputs": [],
- "source": [
- "import networkx as nx\n",
- "import numpy as np\n",
- "import scipy as sc\n",
- "import os\n",
- "import re\n",
- "import csv\n",
- "from scipy import sparse, io"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 103,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "def read_graphfile(datadir, dataname, max_nodes=None):\n",
- " ''' Read data from https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets\n",
- " graph index starts with 1 in file\n",
- " Returns:\n",
- " List of networkx objects with graph and node labels\n",
- " '''\n",
- " prefix = os.path.join(datadir, dataname, dataname)\n",
- " filename_graph_indic = prefix + '_graph_indicator.txt'\n",
- " # index of graphs that a given node belongs to\n",
- " graph_indic={}\n",
- " with open(filename_graph_indic) as f:\n",
- " i=1\n",
- " for line in f:\n",
- " line=line.strip(\"\\n\")\n",
- " graph_indic[i]=int(line)\n",
- " i+=1\n",
- "\n",
- " filename_nodes=prefix + '_node_labels.txt'\n",
- " node_labels=[]\n",
- " min_label_val = None\n",
- " try:\n",
- " with open(filename_nodes) as f:\n",
- " has_zero = False\n",
- " for line in f:\n",
- " line=line.strip(\"\\n\")\n",
- " l = int(line)\n",
- " node_labels+=[l]\n",
- " if min_label_val is None or min_label_val > l:\n",
- " min_label_val = l\n",
- " # assume that node labels are consecutive\n",
- " num_unique_node_labels = max(node_labels) - min_label_val + 1\n",
- " node_labels = [l - min_label_val for l in node_labels]\n",
- " except IOError:\n",
- " print('No node labels')\n",
- " \n",
- " filename_node_attrs=prefix + '_node_attributes.txt'\n",
- " node_attrs=[]\n",
- " try:\n",
- " with open(filename_node_attrs) as f:\n",
- " for line in f:\n",
- " line = line.strip(\"\\s\\n\")\n",
- " attrs = [float(attr) for attr in re.split(\"[,\\s]+\", line) if not attr == '']\n",
- " node_attrs.append(np.array(attrs))\n",
- " except IOError:\n",
- " print('No node attributes')\n",
- " \n",
- " label_has_zero = False\n",
- " filename_graphs=prefix + '_graph_labels.txt'\n",
- " graph_labels=[]\n",
- "\n",
- " label_vals = []\n",
- " with open(filename_graphs) as f:\n",
- " for line in f:\n",
- " line=line.strip(\"\\n\")\n",
- " val = int(line)\n",
- " if val not in label_vals:\n",
- " label_vals.append(val)\n",
- " graph_labels.append(val)\n",
- "\n",
- " label_map_to_int = {val:i for i, val in enumerate(label_vals)}\n",
- " graph_labels = np.array([label_map_to_int[l] for l in graph_labels])\n",
- " \n",
- " filename_adj=prefix + '_A.txt'\n",
- " adj_list={i:[] for i in range(1,len(graph_labels)+1)} \n",
- " index_graph={i:[] for i in range(1,len(graph_labels)+1)}\n",
- " num_edges = 0\n",
- " with open(filename_adj) as f:\n",
- " for line in f:\n",
- " line=line.strip(\"\\n\").split(\",\")\n",
- " e0,e1=(int(line[0].strip(\" \")),int(line[1].strip(\" \")))\n",
- " adj_list[graph_indic[e0]].append((e0,e1))\n",
- " index_graph[graph_indic[e0]]+=[e0,e1]\n",
- " num_edges += 1\n",
- " for k in index_graph.keys():\n",
- " index_graph[k]=[u-1 for u in set(index_graph[k])]\n",
- "\n",
- " graphs=[]\n",
- " for i in range(1,1+len(adj_list)):\n",
- " # indexed from 1 here\n",
- " G=nx.from_edgelist(adj_list[i])\n",
- " if max_nodes is not None and G.number_of_nodes() > max_nodes:\n",
- " continue\n",
- " \n",
- " # add features and labels\n",
- " G.graph['label'] = graph_labels[i-1]\n",
- " for u in G.nodes():\n",
- " if len(node_labels) > 0:\n",
- " #node_label_one_hot = [0] * num_unique_node_labels\n",
- " node_label = node_labels[u-1]\n",
- " #node_label_one_hot[node_label] = 1\n",
- " G.node[u]['label'] = node_label\n",
- " if len(node_attrs) > 0:\n",
- " G.node[u]['feat'] = node_attrs[u-1]\n",
- " if len(node_attrs) > 0:\n",
- " G.graph['feat_dim'] = node_attrs[0].shape[0]\n",
- "\n",
- " # relabeling\n",
- " mapping={}\n",
- " it=0\n",
- " if float(nx.__version__)<2.0:\n",
- " for n in G.nodes():\n",
- " mapping[n]=it\n",
- " it+=1\n",
- " else:\n",
- " for n in G.nodes:\n",
- " mapping[n]=it\n",
- " it+=1\n",
- " \n",
- " # indexed from 0\n",
- " graphs.append(nx.relabel_nodes(G, mapping))\n",
- " return graphs, num_unique_node_labels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 125,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No node attributes\n"
- ]
- }
- ],
- "source": [
- "datadir = \"\"\n",
- "dataname = \"Tox21_AHR\"\n",
- "Gs, nb_unique_node_labels = read_graphfile(datadir, dataname, max_nodes=None)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 126,
- "metadata": {},
- "outputs": [],
- "source": [
- "Gs = [G for G in Gs if len(G.nodes) < 64]\n",
- "Gs = [G for G in Gs if len(G.nodes) > 5]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 127,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "7807"
- ]
- },
- "execution_count": 127,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(Gs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 128,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "NodeDataView({0: {'label': 1}, 1: {'label': 0}, 2: {'label': 1}, 3: {'label': 2}, 4: {'label': 4}, 5: {'label': 1}, 6: {'label': 1}, 7: {'label': 1}, 8: {'label': 1}, 9: {'label': 1}, 10: {'label': 1}, 11: {'label': 1}, 12: {'label': 6}})"
- ]
- },
- "execution_count": 128,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Gs[392].nodes(data=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 129,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([], dtype=int64),)"
- ]
- },
- "execution_count": 129,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.where(np.asarray([len(G.nodes) for G in Gs]) == 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 130,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "49"
- ]
- },
- "execution_count": 130,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nb_unique_node_labels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 131,
- "metadata": {},
- "outputs": [],
- "source": [
- "count_0, count_1 = 0,0\n",
- "for G in Gs:\n",
- " if G.graph['label'] == 0:\n",
- " count_0 += 1\n",
- " else:\n",
- " count_1 += 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 132,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(6865, 942)"
- ]
- },
- "execution_count": 132,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "count_0,count_1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 133,
- "metadata": {},
- "outputs": [],
- "source": [
- "idx = range(len(Gs))\n",
- "shuffled = np.random.permutation(idx)\n",
- "pivot = int(len(Gs)*0.2)\n",
- "test_idx = shuffled[:pivot]\n",
- "train_idx = shuffled[pivot:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 134,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Labels\n",
- "train_labels, test_labels = [],[]\n",
- "for idx,G in enumerate(Gs):\n",
- " l = G.graph['label']\n",
- " if idx in train_idx:\n",
- " train_labels.append(l)\n",
- " else:\n",
- " test_labels.append(l)\n",
- " \n",
- "with open(os.path.join(datadir, dataname+'_label.txt'), 'w') as f:\n",
- " for l in train_labels:\n",
- " f.write(str(l)+'\\n')\n",
- "\n",
- "with open(os.path.join(datadir, dataname+'_label_val.txt'), 'w') as f:\n",
- " for l in test_labels:\n",
- " f.write(str(l)+'\\n')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 135,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TK\n",
- "train_tk, test_tk = [],[]\n",
- "for idx,G in enumerate(Gs):\n",
- " l = [n[1]['label'] for n in G.nodes(data=True)]\n",
- " if idx in train_idx:\n",
- " train_tk.append(l)\n",
- " else:\n",
- " test_tk.append(l)\n",
- "\n",
- "with open(os.path.join(datadir, dataname+'_tk.txt'), 'w') as f:\n",
- " w = csv.writer(f, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
- " for tk in train_tk:\n",
- " w.writerow(tk)\n",
- " \n",
- "\n",
- "with open(os.path.join(datadir, dataname+'_tk_val.txt'), 'w') as f:\n",
- " w = csv.writer(f, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
- " for tk in test_tk:\n",
- " w.writerow(tk)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 136,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "NodeDataView({0: {'label': 1}, 1: {'label': 0}, 2: {'label': 1}, 3: {'label': 2}, 4: {'label': 4}, 5: {'label': 1}, 6: {'label': 1}, 7: {'label': 1}, 8: {'label': 1}, 9: {'label': 1}, 10: {'label': 1}, 11: {'label': 1}, 12: {'label': 6}})"
- ]
- },
- "execution_count": 136,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Gs[392].nodes(data=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 137,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ADJ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 145,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 139,
- "metadata": {},
- "outputs": [],
- "source": [
- "max_len = 64"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_count, test_count = 0,0\n",
- "for idx,G in enumerate(Gs):\n",
- " if idx in train_idx:\n",
- " fname = str(train_count)+'_'+dataname+\"_adj\"+suffix+\".mtx\"\n",
- " else:\n",
- " fname = str(test_count)+'_'+dataname+\"_adj\"+suffix+\".mtx\"\n",
- " \n",
- " G_u = G.to_undirected()\n",
- " adj = nx.adj_matrix(G_u).todense()\n",
- " final = np.zeros((max_len,max_len), dtype=int)\n",
- " final[1:adj.shape[0]+1, 1:adj.shape[1]+1] = adj\n",
- " final += np.eye(max_len, dtype=int)\n",
- " final[:,0] = np.ones(max_len)\n",
- " final[0,:] = np.ones(max_len)\n",
- "\n",
- " m = sparse.csr_matrix(final)\n",
- " sparsedir = os.path.join(datadir, 'adj')\n",
- " if not os.path.exists(sparsedir):\n",
- " os.makedirs(sparsedir)\n",
- " io.mmwrite(os.path.join(sparsedir, fname), m)\n",
- " \n",
- " if idx in train_idx:\n",
- " train_count+= 1\n",
- " else:\n",
- " test_count += 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_count,len(train_idx), test_count,len(test_idx) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "# VOCAB"
- ]
- },
- {
- "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/large-corpus/Merge Vocab.ipynb b/large-corpus/Merge Vocab.ipynb
index bc194a8..17c9866 100644
--- a/large-corpus/Merge Vocab.ipynb
+++ b/large-corpus/Merge Vocab.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -33,24 +33,33 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
- "files = ['pytorch_mlm_vocab-code.txt', 'sklearn_mlm_vocab-code.txt', 'keras_mlm_vocab-code.txt']\n",
+ "corpus_members = ['pytorch', 'sklearn', 'keras', 'ansible', 'youtube-dl', 'flask', 'httpie', 'requests', 'django', 'bert']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "files = [f+'_mlm_vocab-code.txt' for f in corpus_members]\n",
"out_file = 'global_vocab.csv'"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "3823\n"
+ "9769\n"
]
}
],
diff --git a/mid-corpus/Merge Vocab.ipynb b/mid-corpus/Merge Vocab.ipynb
new file mode 100644
index 0000000..bc194a8
--- /dev/null
+++ b/mid-corpus/Merge Vocab.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import csv\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def merge_vocab(files, out):\n",
+ " global_vocab = []\n",
+ " for f in files:\n",
+ " with open(f, 'r') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+ " for row in reader:\n",
+ " if row[0] not in global_vocab:\n",
+ " global_vocab.append(row[0])\n",
+ " print(len(set(global_vocab)))\n",
+ " with open(out, 'w') as csvfile:\n",
+ " writer = csv.writer(csvfile, delimiter=' ',\n",
+ " quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
+ " for v in global_vocab:\n",
+ " writer.writerow([v])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "files = ['pytorch_mlm_vocab-code.txt', 'sklearn_mlm_vocab-code.txt', 'keras_mlm_vocab-code.txt']\n",
+ "out_file = 'global_vocab.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3823\n"
+ ]
+ }
+ ],
+ "source": [
+ "merge_vocab(files, out_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_files = ['pytorch_cls_vocab-label.txt', 'sklearn_cls_vocab-label.txt', 'keras_cls_vocab-label.txt']\n",
+ "label_out = 'label_vocab.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2490\n"
+ ]
+ }
+ ],
+ "source": [
+ "merge_vocab(label_files, label_out)"
+ ]
+ },
+ {
+ "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": {
+ "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/large-corpus/Untitled.ipynb b/mid-corpus/Untitled.ipynb
similarity index 100%
rename from large-corpus/Untitled.ipynb
rename to mid-corpus/Untitled.ipynb
diff --git a/node_cls/Untitled.ipynb b/node_cls/Untitled.ipynb
new file mode 100644
index 0000000..b5fdada
--- /dev/null
+++ b/node_cls/Untitled.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import scipy.sparse as sp\n",
+ "from scipy import io\n",
+ "import networkx as nx\n",
+ "import os\n",
+ "import csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def normalize(mx):\n",
+ " \"\"\"Row-normalize sparse matrix\"\"\"\n",
+ " rowsum = np.array(mx.sum(1))\n",
+ " r_inv = np.power(rowsum, -1).flatten()\n",
+ " r_inv[np.isinf(r_inv)] = 0.\n",
+ " r_mat_inv = sp.diags(r_inv)\n",
+ " mx = r_mat_inv.dot(mx)\n",
+ " return mx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_map = {\n",
+ " \"Case_Based\":0,\n",
+ " \"Genetic_Algorithms\":1,\n",
+ " \"Neural_Networks\":2,\n",
+ " \"Probabilistic_Methods\":3,\n",
+ " \"Reinforcement_Learning\":4,\n",
+ " \"Rule_Learning\": 5,\n",
+ " \"Theory\":6\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_data(path=\"cora/\", dataset=\"cora\"):\n",
+ " \"\"\"Load citation network dataset (cora only for now)\"\"\"\n",
+ " print('Loading {} dataset...'.format(dataset))\n",
+ "\n",
+ " idx_features_labels = np.genfromtxt(\"{}{}.content\".format(path, dataset),\n",
+ " dtype=np.dtype(str))\n",
+ " \n",
+ " features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\n",
+ " labels = idx_features_labels[:, -1]\n",
+ "\n",
+ " # build graph\n",
+ " idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\n",
+ " idx_map = {j: i for i, j in enumerate(idx)}\n",
+ " edges_unordered = np.genfromtxt(\"{}{}.cites\".format(path, dataset),\n",
+ " dtype=np.int32)\n",
+ " edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),\n",
+ " dtype=np.int32).reshape(edges_unordered.shape)\n",
+ " adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),\n",
+ " shape=(labels.shape[0], labels.shape[0]),\n",
+ " dtype=np.float32)\n",
+ "\n",
+ " # build symmetric adjacency matrix\n",
+ " adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\n",
+ "\n",
+ " features = normalize(features)\n",
+ " #adj = normalize(adj + sp.eye(adj.shape[0]))\n",
+ " labels_int = [label_map[f] for f in idx_features_labels[:, -1]]\n",
+ " \n",
+ " idx_train = range(1000)\n",
+ " idx_val = None\n",
+ " idx_test = range(1000, 1500)\n",
+ "\n",
+ " return adj, features, labels_int, idx_train, idx_val, idx_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loading cora dataset...\n"
+ ]
+ }
+ ],
+ "source": [
+ "adj, features, labels, idx_train, idx_val, idx_test = load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "G = nx.from_scipy_sparse_matrix(adj)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_len = 128"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataname = \"CORA\"\n",
+ "datadir = \"cora\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_count, test_count = 0,0\n",
+ "train_tk, test_tk = [],[]\n",
+ "node_vocab = []\n",
+ "\n",
+ "for idx in range(1500):\n",
+ " G_sub = nx.ego_graph(G, idx, radius=3)\n",
+ " node_ids = [labels[n] for n in G_sub.nodes]\n",
+ " if len(node_ids) > max_len-1:\n",
+ " continue\n",
+ " \n",
+ " # ADJ\n",
+ " if idx in idx_train:\n",
+ " fname = str(train_count)+'_'+dataname+\"_adj.mtx\"\n",
+ " else:\n",
+ " fname = str(test_count)+'_'+dataname+\"_adj_val.mtx\"\n",
+ " \n",
+ " G_u = G_sub.to_undirected()\n",
+ " adj = nx.adj_matrix(G_u).todense()\n",
+ " final = np.zeros((max_len,max_len), dtype=int)\n",
+ " final[1:adj.shape[0]+1, 1:adj.shape[1]+1] = adj\n",
+ " final += np.eye(max_len, dtype=int)\n",
+ " final[:,0] = np.ones(max_len)\n",
+ " final[0,:] = np.ones(max_len)\n",
+ "\n",
+ " m = sp.csr_matrix(final)\n",
+ " sparsedir = os.path.join(datadir, 'adj')\n",
+ " if not os.path.exists(sparsedir):\n",
+ " os.makedirs(sparsedir)\n",
+ " io.mmwrite(os.path.join(sparsedir, fname), m)\n",
+ " \n",
+ " if idx in idx_train:\n",
+ " train_count+= 1\n",
+ " else:\n",
+ " test_count += 1\n",
+ " \n",
+ " for w in node_ids:\n",
+ " if w not in node_vocab:\n",
+ " node_vocab.append(w)\n",
+ " node_ids.insert(0,'[CLS]')\n",
+ " if idx in idx_train:\n",
+ " train_tk.append(node_ids)\n",
+ " else:\n",
+ " test_tk.append(node_ids)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(541, 322)"
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_count, test_count"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(os.path.join(datadir, dataname+'-vocab.txt'), 'w') as f: \n",
+ " for i in range(np.max(node_vocab)+1):\n",
+ " f.write(str(i)+'\\n')\n",
+ " f.write(\"[CLS]\"+'\\n')\n",
+ " f.write(\"[MASK]\"+'\\n')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(os.path.join(datadir, dataname+'_tk.txt'), 'w') as f:\n",
+ " w = csv.writer(f, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
+ " for tk in train_tk:\n",
+ " w.writerow(tk)\n",
+ " w.writerow([])\n",
+ " \n",
+ "\n",
+ "with open(os.path.join(datadir, dataname+'_tk_val.txt'), 'w') as f:\n",
+ " w = csv.writer(f, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
+ " for tk in test_tk:\n",
+ " w.writerow(tk)\n",
+ " w.writerow([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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 - Graph.ipynb b/notebook/Inspect Predictions - Graph.ipynb
index 33b7c33..abe0666 100644
--- a/notebook/Inspect Predictions - Graph.ipynb
+++ b/notebook/Inspect Predictions - Graph.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
@@ -32,7 +32,7 @@
},
{
"cell_type": "code",
- "execution_count": 162,
+ "execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
@@ -42,16 +42,16 @@
},
{
"cell_type": "code",
- "execution_count": 163,
+ "execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
- "prefix='ENZYMES'"
+ "prefix='MSRC_21'"
]
},
{
"cell_type": "code",
- "execution_count": 164,
+ "execution_count": 104,
"metadata": {},
"outputs": [
{
@@ -81,278 +81,743 @@
" | 3 | \n",
" 4 | \n",
" 5 | \n",
+ " 6 | \n",
+ " 7 | \n",
+ " 8 | \n",
+ " 9 | \n",
+ " ... | \n",
+ " 12 | \n",
+ " 13 | \n",
+ " 14 | \n",
+ " 15 | \n",
+ " 16 | \n",
+ " 17 | \n",
+ " 18 | \n",
+ " 19 | \n",
+ " 20 | \n",
+ " 21 | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
- " 0.080322 | \n",
- " 0.406207 | \n",
- " 0.377125 | \n",
- " 0.101128 | \n",
- " 0.000117 | \n",
- " 0.035101 | \n",
+ " 0.027983 | \n",
+ " 0.079788 | \n",
+ " 0.036235 | \n",
+ " 0.023015 | \n",
+ " 0.065610 | \n",
+ " 0.043837 | \n",
+ " 0.052609 | \n",
+ " 0.055141 | \n",
+ " 0.029711 | \n",
+ " 0.049585 | \n",
+ " ... | \n",
+ " 0.035592 | \n",
+ " 0.045377 | \n",
+ " 0.052197 | \n",
+ " 0.042461 | \n",
+ " 0.029877 | \n",
+ " 0.049065 | \n",
+ " 0.059313 | \n",
+ " 0.057875 | \n",
+ " 0.023566 | \n",
+ " 0.021527 | \n",
"
\n",
" \n",
" | 1 | \n",
- " 0.789287 | \n",
- " 0.068335 | \n",
- " 0.047145 | \n",
- " 0.094098 | \n",
- " 0.000980 | \n",
- " 0.000155 | \n",
+ " 0.082299 | \n",
+ " 0.040789 | \n",
+ " 0.077948 | \n",
+ " 0.062490 | \n",
+ " 0.031518 | \n",
+ " 0.034720 | \n",
+ " 0.037025 | \n",
+ " 0.035525 | \n",
+ " 0.095922 | \n",
+ " 0.033952 | \n",
+ " ... | \n",
+ " 0.041695 | \n",
+ " 0.033751 | \n",
+ " 0.027364 | \n",
+ " 0.068650 | \n",
+ " 0.033005 | \n",
+ " 0.041409 | \n",
+ " 0.051750 | \n",
+ " 0.051339 | \n",
+ " 0.029467 | \n",
+ " 0.026703 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 0.762841 | \n",
- " 0.024799 | \n",
- " 0.137096 | \n",
- " 0.073537 | \n",
- " 0.001202 | \n",
- " 0.000525 | \n",
+ " 0.032876 | \n",
+ " 0.055980 | \n",
+ " 0.046175 | \n",
+ " 0.029865 | \n",
+ " 0.054967 | \n",
+ " 0.066957 | \n",
+ " 0.064695 | \n",
+ " 0.055646 | \n",
+ " 0.039173 | \n",
+ " 0.055304 | \n",
+ " ... | \n",
+ " 0.038458 | \n",
+ " 0.044789 | \n",
+ " 0.065258 | \n",
+ " 0.046748 | \n",
+ " 0.023493 | \n",
+ " 0.048465 | \n",
+ " 0.043368 | \n",
+ " 0.054927 | \n",
+ " 0.021698 | \n",
+ " 0.020615 | \n",
"
\n",
" \n",
" | 3 | \n",
- " 0.009087 | \n",
- " 0.000259 | \n",
- " 0.024026 | \n",
- " 0.875603 | \n",
- " 0.011644 | \n",
- " 0.079382 | \n",
+ " 0.031383 | \n",
+ " 0.054110 | \n",
+ " 0.034033 | \n",
+ " 0.028100 | \n",
+ " 0.069831 | \n",
+ " 0.047403 | \n",
+ " 0.057749 | \n",
+ " 0.082137 | \n",
+ " 0.033190 | \n",
+ " 0.044436 | \n",
+ " ... | \n",
+ " 0.042111 | \n",
+ " 0.044177 | \n",
+ " 0.047025 | \n",
+ " 0.043393 | \n",
+ " 0.029789 | \n",
+ " 0.048610 | \n",
+ " 0.058187 | \n",
+ " 0.053505 | \n",
+ " 0.024899 | \n",
+ " 0.028315 | \n",
"
\n",
" \n",
" | 4 | \n",
- " 0.701870 | \n",
- " 0.000069 | \n",
- " 0.026003 | \n",
- " 0.156832 | \n",
- " 0.012586 | \n",
- " 0.102640 | \n",
+ " 0.029956 | \n",
+ " 0.060813 | \n",
+ " 0.046997 | \n",
+ " 0.026556 | \n",
+ " 0.085200 | \n",
+ " 0.047002 | \n",
+ " 0.056402 | \n",
+ " 0.049594 | \n",
+ " 0.028837 | \n",
+ " 0.052252 | \n",
+ " ... | \n",
+ " 0.036659 | \n",
+ " 0.040185 | \n",
+ " 0.045348 | \n",
+ " 0.045102 | \n",
+ " 0.028286 | \n",
+ " 0.056712 | \n",
+ " 0.056932 | \n",
+ " 0.054910 | \n",
+ " 0.023694 | \n",
+ " 0.021604 | \n",
"
\n",
" \n",
" | 5 | \n",
- " 0.016722 | \n",
- " 0.001152 | \n",
- " 0.075507 | \n",
- " 0.088209 | \n",
- " 0.008319 | \n",
- " 0.810092 | \n",
+ " 0.032505 | \n",
+ " 0.050670 | \n",
+ " 0.060178 | \n",
+ " 0.029576 | \n",
+ " 0.043828 | \n",
+ " 0.036130 | \n",
+ " 0.045985 | \n",
+ " 0.041704 | \n",
+ " 0.034196 | \n",
+ " 0.041549 | \n",
+ " ... | \n",
+ " 0.028658 | \n",
+ " 0.035186 | \n",
+ " 0.044895 | \n",
+ " 0.036150 | \n",
+ " 0.035234 | \n",
+ " 0.041594 | \n",
+ " 0.049718 | \n",
+ " 0.059555 | \n",
+ " 0.031673 | \n",
+ " 0.029378 | \n",
"
\n",
" \n",
" | 6 | \n",
- " 0.652011 | \n",
- " 0.014694 | \n",
- " 0.254270 | \n",
- " 0.076600 | \n",
- " 0.001153 | \n",
- " 0.001272 | \n",
+ " 0.103993 | \n",
+ " 0.040298 | \n",
+ " 0.066262 | \n",
+ " 0.071247 | \n",
+ " 0.028470 | \n",
+ " 0.039297 | \n",
+ " 0.036226 | \n",
+ " 0.035376 | \n",
+ " 0.104617 | \n",
+ " 0.035680 | \n",
+ " ... | \n",
+ " 0.041949 | \n",
+ " 0.031776 | \n",
+ " 0.035615 | \n",
+ " 0.066399 | \n",
+ " 0.032411 | \n",
+ " 0.034684 | \n",
+ " 0.034398 | \n",
+ " 0.046455 | \n",
+ " 0.032355 | \n",
+ " 0.028351 | \n",
"
\n",
" \n",
" | 7 | \n",
- " 0.503152 | \n",
- " 0.190103 | \n",
- " 0.146537 | \n",
- " 0.140299 | \n",
- " 0.019612 | \n",
- " 0.000297 | \n",
+ " 0.032741 | \n",
+ " 0.075311 | \n",
+ " 0.043249 | \n",
+ " 0.029438 | \n",
+ " 0.045879 | \n",
+ " 0.047930 | \n",
+ " 0.057232 | \n",
+ " 0.051124 | \n",
+ " 0.038082 | \n",
+ " 0.067939 | \n",
+ " ... | \n",
+ " 0.040822 | \n",
+ " 0.046295 | \n",
+ " 0.065309 | \n",
+ " 0.059787 | \n",
+ " 0.023003 | \n",
+ " 0.051813 | \n",
+ " 0.047852 | \n",
+ " 0.060252 | \n",
+ " 0.021338 | \n",
+ " 0.018432 | \n",
"
\n",
" \n",
" | 8 | \n",
- " 0.902592 | \n",
- " 0.000175 | \n",
- " 0.027184 | \n",
- " 0.066996 | \n",
- " 0.001453 | \n",
- " 0.001600 | \n",
+ " 0.054265 | \n",
+ " 0.047404 | \n",
+ " 0.050633 | \n",
+ " 0.048070 | \n",
+ " 0.033149 | \n",
+ " 0.046264 | \n",
+ " 0.051029 | \n",
+ " 0.042253 | \n",
+ " 0.072159 | \n",
+ " 0.044815 | \n",
+ " ... | \n",
+ " 0.035738 | \n",
+ " 0.084483 | \n",
+ " 0.038417 | \n",
+ " 0.061949 | \n",
+ " 0.041061 | \n",
+ " 0.040599 | \n",
+ " 0.048070 | \n",
+ " 0.044206 | \n",
+ " 0.024852 | \n",
+ " 0.027938 | \n",
"
\n",
" \n",
" | 9 | \n",
- " 0.107776 | \n",
- " 0.036636 | \n",
- " 0.003279 | \n",
- " 0.797520 | \n",
- " 0.000214 | \n",
- " 0.054574 | \n",
+ " 0.026146 | \n",
+ " 0.054937 | \n",
+ " 0.048305 | \n",
+ " 0.025034 | \n",
+ " 0.053544 | \n",
+ " 0.036021 | \n",
+ " 0.047155 | \n",
+ " 0.045463 | \n",
+ " 0.026715 | \n",
+ " 0.041865 | \n",
+ " ... | \n",
+ " 0.029586 | \n",
+ " 0.038165 | \n",
+ " 0.045176 | \n",
+ " 0.034079 | \n",
+ " 0.036344 | \n",
+ " 0.044400 | \n",
+ " 0.056563 | \n",
+ " 0.058041 | \n",
+ " 0.030263 | \n",
+ " 0.028583 | \n",
"
\n",
" \n",
" | 10 | \n",
- " 0.946610 | \n",
- " 0.000071 | \n",
- " 0.014849 | \n",
- " 0.027311 | \n",
- " 0.007029 | \n",
- " 0.004130 | \n",
+ " 0.044803 | \n",
+ " 0.043459 | \n",
+ " 0.058550 | \n",
+ " 0.037652 | \n",
+ " 0.048391 | \n",
+ " 0.053492 | \n",
+ " 0.055440 | \n",
+ " 0.044599 | \n",
+ " 0.060610 | \n",
+ " 0.041145 | \n",
+ " ... | \n",
+ " 0.030184 | \n",
+ " 0.036030 | \n",
+ " 0.048406 | \n",
+ " 0.039524 | \n",
+ " 0.027613 | \n",
+ " 0.036440 | \n",
+ " 0.040421 | \n",
+ " 0.063431 | \n",
+ " 0.025538 | \n",
+ " 0.026594 | \n",
"
\n",
" \n",
" | 11 | \n",
- " 0.493794 | \n",
- " 0.010965 | \n",
- " 0.027937 | \n",
- " 0.185381 | \n",
- " 0.000163 | \n",
- " 0.281761 | \n",
+ " 0.039564 | \n",
+ " 0.051063 | \n",
+ " 0.054792 | \n",
+ " 0.036674 | \n",
+ " 0.033903 | \n",
+ " 0.045849 | \n",
+ " 0.049141 | \n",
+ " 0.040761 | \n",
+ " 0.034873 | \n",
+ " 0.051248 | \n",
+ " ... | \n",
+ " 0.034034 | \n",
+ " 0.097834 | \n",
+ " 0.042714 | \n",
+ " 0.052912 | \n",
+ " 0.044169 | \n",
+ " 0.042934 | \n",
+ " 0.052739 | \n",
+ " 0.045535 | \n",
+ " 0.027757 | \n",
+ " 0.029915 | \n",
"
\n",
" \n",
" | 12 | \n",
- " 0.863658 | \n",
- " 0.107837 | \n",
- " 0.022543 | \n",
- " 0.002440 | \n",
- " 0.003282 | \n",
- " 0.000239 | \n",
+ " 0.025149 | \n",
+ " 0.068353 | \n",
+ " 0.036118 | \n",
+ " 0.022852 | \n",
+ " 0.066839 | \n",
+ " 0.042206 | \n",
+ " 0.055307 | \n",
+ " 0.051360 | \n",
+ " 0.029594 | \n",
+ " 0.047536 | \n",
+ " ... | \n",
+ " 0.032840 | \n",
+ " 0.045380 | \n",
+ " 0.052624 | \n",
+ " 0.039152 | \n",
+ " 0.029014 | \n",
+ " 0.047138 | \n",
+ " 0.058443 | \n",
+ " 0.063205 | \n",
+ " 0.022645 | \n",
+ " 0.022546 | \n",
"
\n",
" \n",
" | 13 | \n",
- " 0.029164 | \n",
- " 0.388486 | \n",
- " 0.003546 | \n",
- " 0.142325 | \n",
- " 0.436363 | \n",
- " 0.000116 | \n",
+ " 0.034673 | \n",
+ " 0.085115 | \n",
+ " 0.041795 | \n",
+ " 0.028061 | \n",
+ " 0.050224 | \n",
+ " 0.049211 | \n",
+ " 0.054105 | \n",
+ " 0.054421 | \n",
+ " 0.036506 | \n",
+ " 0.058971 | \n",
+ " ... | \n",
+ " 0.042580 | \n",
+ " 0.047073 | \n",
+ " 0.060709 | \n",
+ " 0.058086 | \n",
+ " 0.024581 | \n",
+ " 0.053250 | \n",
+ " 0.049764 | \n",
+ " 0.057859 | \n",
+ " 0.021567 | \n",
+ " 0.017666 | \n",
"
\n",
" \n",
" | 14 | \n",
- " 0.862394 | \n",
- " 0.027993 | \n",
- " 0.011049 | \n",
- " 0.077691 | \n",
- " 0.020628 | \n",
- " 0.000244 | \n",
+ " 0.042686 | \n",
+ " 0.046866 | \n",
+ " 0.064636 | \n",
+ " 0.038779 | \n",
+ " 0.044685 | \n",
+ " 0.033205 | \n",
+ " 0.039628 | \n",
+ " 0.042859 | \n",
+ " 0.039978 | \n",
+ " 0.036608 | \n",
+ " ... | \n",
+ " 0.037727 | \n",
+ " 0.037264 | \n",
+ " 0.031588 | \n",
+ " 0.048745 | \n",
+ " 0.043206 | \n",
+ " 0.051650 | \n",
+ " 0.075562 | \n",
+ " 0.053607 | \n",
+ " 0.034320 | \n",
+ " 0.029350 | \n",
"
\n",
" \n",
" | 15 | \n",
- " 0.862394 | \n",
- " 0.027993 | \n",
- " 0.011049 | \n",
- " 0.077691 | \n",
- " 0.020628 | \n",
- " 0.000244 | \n",
+ " 0.078728 | \n",
+ " 0.037026 | \n",
+ " 0.098867 | \n",
+ " 0.061680 | \n",
+ " 0.027539 | \n",
+ " 0.034491 | \n",
+ " 0.037569 | \n",
+ " 0.033485 | \n",
+ " 0.084809 | \n",
+ " 0.034476 | \n",
+ " ... | \n",
+ " 0.038219 | \n",
+ " 0.031806 | \n",
+ " 0.028372 | \n",
+ " 0.060416 | \n",
+ " 0.035160 | \n",
+ " 0.040257 | \n",
+ " 0.046530 | \n",
+ " 0.050388 | \n",
+ " 0.034917 | \n",
+ " 0.029220 | \n",
"
\n",
" \n",
" | 16 | \n",
- " 0.843085 | \n",
- " 0.091937 | \n",
- " 0.059917 | \n",
- " 0.001220 | \n",
- " 0.003040 | \n",
- " 0.000801 | \n",
+ " 0.091983 | \n",
+ " 0.051195 | \n",
+ " 0.057827 | \n",
+ " 0.059279 | \n",
+ " 0.037479 | \n",
+ " 0.038869 | \n",
+ " 0.038894 | \n",
+ " 0.041632 | \n",
+ " 0.087360 | \n",
+ " 0.037998 | \n",
+ " ... | \n",
+ " 0.042463 | \n",
+ " 0.034077 | \n",
+ " 0.036834 | \n",
+ " 0.069890 | \n",
+ " 0.030210 | \n",
+ " 0.039705 | \n",
+ " 0.045320 | \n",
+ " 0.051975 | \n",
+ " 0.025327 | \n",
+ " 0.022671 | \n",
"
\n",
" \n",
" | 17 | \n",
- " 0.811183 | \n",
- " 0.167679 | \n",
- " 0.015733 | \n",
- " 0.003890 | \n",
- " 0.001309 | \n",
- " 0.000206 | \n",
+ " 0.087725 | \n",
+ " 0.037342 | \n",
+ " 0.061506 | \n",
+ " 0.071084 | \n",
+ " 0.028710 | \n",
+ " 0.038580 | \n",
+ " 0.037890 | \n",
+ " 0.035150 | \n",
+ " 0.126100 | \n",
+ " 0.034042 | \n",
+ " ... | \n",
+ " 0.041944 | \n",
+ " 0.034975 | \n",
+ " 0.030699 | \n",
+ " 0.066693 | \n",
+ " 0.033787 | \n",
+ " 0.036047 | \n",
+ " 0.038344 | \n",
+ " 0.044913 | \n",
+ " 0.032446 | \n",
+ " 0.030885 | \n",
"
\n",
" \n",
" | 18 | \n",
- " 0.000140 | \n",
- " 0.901083 | \n",
- " 0.034030 | \n",
- " 0.023189 | \n",
- " 0.022851 | \n",
- " 0.018707 | \n",
+ " 0.032999 | \n",
+ " 0.057029 | \n",
+ " 0.032893 | \n",
+ " 0.031609 | \n",
+ " 0.072348 | \n",
+ " 0.041264 | \n",
+ " 0.046101 | \n",
+ " 0.052090 | \n",
+ " 0.034681 | \n",
+ " 0.042465 | \n",
+ " ... | \n",
+ " 0.045231 | \n",
+ " 0.049808 | \n",
+ " 0.037461 | \n",
+ " 0.046802 | \n",
+ " 0.039396 | \n",
+ " 0.052338 | \n",
+ " 0.076085 | \n",
+ " 0.051606 | \n",
+ " 0.029022 | \n",
+ " 0.029630 | \n",
"
\n",
" \n",
" | 19 | \n",
- " 0.855388 | \n",
- " 0.021839 | \n",
- " 0.004056 | \n",
- " 0.011667 | \n",
- " 0.106996 | \n",
- " 0.000054 | \n",
+ " 0.032931 | \n",
+ " 0.041823 | \n",
+ " 0.033188 | \n",
+ " 0.033331 | \n",
+ " 0.115316 | \n",
+ " 0.046938 | \n",
+ " 0.050329 | \n",
+ " 0.046147 | \n",
+ " 0.032505 | \n",
+ " 0.042334 | \n",
+ " ... | \n",
+ " 0.040709 | \n",
+ " 0.041920 | \n",
+ " 0.035222 | \n",
+ " 0.034031 | \n",
+ " 0.041053 | \n",
+ " 0.048224 | \n",
+ " 0.056014 | \n",
+ " 0.044282 | \n",
+ " 0.033669 | \n",
+ " 0.040630 | \n",
"
\n",
" \n",
" | 20 | \n",
- " 0.000404 | \n",
- " 0.943844 | \n",
- " 0.006562 | \n",
- " 0.047808 | \n",
- " 0.000533 | \n",
- " 0.000849 | \n",
+ " 0.030760 | \n",
+ " 0.054202 | \n",
+ " 0.044486 | \n",
+ " 0.035570 | \n",
+ " 0.041990 | \n",
+ " 0.042787 | \n",
+ " 0.057056 | \n",
+ " 0.045820 | \n",
+ " 0.039764 | \n",
+ " 0.093046 | \n",
+ " ... | \n",
+ " 0.034534 | \n",
+ " 0.042414 | \n",
+ " 0.061096 | \n",
+ " 0.058227 | \n",
+ " 0.026941 | \n",
+ " 0.047547 | \n",
+ " 0.048564 | \n",
+ " 0.058848 | \n",
+ " 0.026303 | \n",
+ " 0.027081 | \n",
"
\n",
" \n",
" | 21 | \n",
- " 0.010487 | \n",
- " 0.174528 | \n",
- " 0.544113 | \n",
- " 0.001307 | \n",
- " 0.000843 | \n",
- " 0.268721 | \n",
+ " 0.033354 | \n",
+ " 0.059032 | \n",
+ " 0.036468 | \n",
+ " 0.030955 | \n",
+ " 0.064287 | \n",
+ " 0.039781 | \n",
+ " 0.045661 | \n",
+ " 0.050616 | \n",
+ " 0.032897 | \n",
+ " 0.043318 | \n",
+ " ... | \n",
+ " 0.042850 | \n",
+ " 0.054727 | \n",
+ " 0.036977 | \n",
+ " 0.049809 | \n",
+ " 0.040564 | \n",
+ " 0.052956 | \n",
+ " 0.079728 | \n",
+ " 0.051484 | \n",
+ " 0.027723 | \n",
+ " 0.027752 | \n",
"
\n",
" \n",
" | 22 | \n",
- " 0.000149 | \n",
- " 0.930636 | \n",
- " 0.060365 | \n",
- " 0.006790 | \n",
- " 0.000683 | \n",
- " 0.001377 | \n",
+ " 0.043943 | \n",
+ " 0.050321 | \n",
+ " 0.044892 | \n",
+ " 0.037023 | \n",
+ " 0.041556 | \n",
+ " 0.047991 | \n",
+ " 0.037874 | \n",
+ " 0.042047 | \n",
+ " 0.035697 | \n",
+ " 0.039862 | \n",
+ " ... | \n",
+ " 0.150360 | \n",
+ " 0.033615 | \n",
+ " 0.038781 | \n",
+ " 0.048114 | \n",
+ " 0.035387 | \n",
+ " 0.052964 | \n",
+ " 0.036683 | \n",
+ " 0.042427 | \n",
+ " 0.037527 | \n",
+ " 0.031521 | \n",
"
\n",
" \n",
" | 23 | \n",
- " 0.000320 | \n",
- " 0.517026 | \n",
- " 0.044142 | \n",
- " 0.193372 | \n",
- " 0.216142 | \n",
- " 0.028998 | \n",
+ " 0.086575 | \n",
+ " 0.034601 | \n",
+ " 0.058675 | \n",
+ " 0.073111 | \n",
+ " 0.029412 | \n",
+ " 0.038210 | \n",
+ " 0.037658 | \n",
+ " 0.035155 | \n",
+ " 0.128458 | \n",
+ " 0.032865 | \n",
+ " ... | \n",
+ " 0.042086 | \n",
+ " 0.035956 | \n",
+ " 0.029215 | \n",
+ " 0.062356 | \n",
+ " 0.037203 | \n",
+ " 0.035600 | \n",
+ " 0.038399 | \n",
+ " 0.042228 | \n",
+ " 0.035287 | \n",
+ " 0.035151 | \n",
"
\n",
" \n",
" | 24 | \n",
- " 0.045499 | \n",
- " 0.580400 | \n",
- " 0.002498 | \n",
- " 0.346710 | \n",
- " 0.003845 | \n",
- " 0.021047 | \n",
+ " 0.033616 | \n",
+ " 0.077075 | \n",
+ " 0.042619 | \n",
+ " 0.028655 | \n",
+ " 0.046721 | \n",
+ " 0.049217 | \n",
+ " 0.056031 | \n",
+ " 0.052203 | \n",
+ " 0.038103 | \n",
+ " 0.061711 | \n",
+ " ... | \n",
+ " 0.041874 | \n",
+ " 0.047211 | \n",
+ " 0.069920 | \n",
+ " 0.057508 | \n",
+ " 0.023108 | \n",
+ " 0.052065 | \n",
+ " 0.046508 | \n",
+ " 0.059036 | \n",
+ " 0.021542 | \n",
+ " 0.018080 | \n",
"
\n",
" \n",
" | 25 | \n",
- " 0.006702 | \n",
- " 0.022606 | \n",
- " 0.171615 | \n",
- " 0.000125 | \n",
- " 0.798483 | \n",
- " 0.000468 | \n",
+ " 0.030743 | \n",
+ " 0.062387 | \n",
+ " 0.043996 | \n",
+ " 0.032692 | \n",
+ " 0.043117 | \n",
+ " 0.044674 | \n",
+ " 0.057717 | \n",
+ " 0.047802 | \n",
+ " 0.038690 | \n",
+ " 0.086031 | \n",
+ " ... | \n",
+ " 0.036194 | \n",
+ " 0.043663 | \n",
+ " 0.064207 | \n",
+ " 0.059341 | \n",
+ " 0.024537 | \n",
+ " 0.049248 | \n",
+ " 0.048372 | \n",
+ " 0.060466 | \n",
+ " 0.023488 | \n",
+ " 0.022653 | \n",
"
\n",
" \n",
" | 26 | \n",
- " 0.000488 | \n",
- " 0.951579 | \n",
- " 0.005742 | \n",
- " 0.000836 | \n",
- " 0.041309 | \n",
- " 0.000046 | \n",
+ " 0.078209 | \n",
+ " 0.049287 | \n",
+ " 0.065102 | \n",
+ " 0.057921 | \n",
+ " 0.032000 | \n",
+ " 0.040219 | \n",
+ " 0.046224 | \n",
+ " 0.038248 | \n",
+ " 0.100858 | \n",
+ " 0.040724 | \n",
+ " ... | \n",
+ " 0.043450 | \n",
+ " 0.036104 | \n",
+ " 0.038153 | \n",
+ " 0.072896 | \n",
+ " 0.026318 | \n",
+ " 0.039224 | \n",
+ " 0.040962 | \n",
+ " 0.052158 | \n",
+ " 0.025452 | \n",
+ " 0.022144 | \n",
"
\n",
" \n",
" | 27 | \n",
- " 0.419345 | \n",
- " 0.062639 | \n",
- " 0.001323 | \n",
- " 0.506182 | \n",
- " 0.010420 | \n",
- " 0.000090 | \n",
+ " 0.034559 | \n",
+ " 0.065191 | \n",
+ " 0.040685 | \n",
+ " 0.029936 | \n",
+ " 0.048363 | \n",
+ " 0.046057 | \n",
+ " 0.051913 | \n",
+ " 0.047731 | \n",
+ " 0.033854 | \n",
+ " 0.051908 | \n",
+ " ... | \n",
+ " 0.036911 | \n",
+ " 0.083034 | \n",
+ " 0.046336 | \n",
+ " 0.055994 | \n",
+ " 0.035326 | \n",
+ " 0.047796 | \n",
+ " 0.062853 | \n",
+ " 0.052634 | \n",
+ " 0.021429 | \n",
+ " 0.022588 | \n",
"
\n",
" \n",
" | 28 | \n",
- " 0.026586 | \n",
- " 0.755681 | \n",
- " 0.003308 | \n",
- " 0.034438 | \n",
- " 0.179953 | \n",
- " 0.000034 | \n",
+ " 0.086283 | \n",
+ " 0.045861 | \n",
+ " 0.062427 | \n",
+ " 0.058375 | \n",
+ " 0.028427 | \n",
+ " 0.044481 | \n",
+ " 0.042494 | \n",
+ " 0.037384 | \n",
+ " 0.071423 | \n",
+ " 0.042134 | \n",
+ " ... | \n",
+ " 0.035771 | \n",
+ " 0.064745 | \n",
+ " 0.040726 | \n",
+ " 0.066867 | \n",
+ " 0.038131 | \n",
+ " 0.034964 | \n",
+ " 0.039009 | \n",
+ " 0.045857 | \n",
+ " 0.026465 | \n",
+ " 0.026685 | \n",
"
\n",
" \n",
" | 29 | \n",
- " 0.039306 | \n",
- " 0.790481 | \n",
- " 0.000972 | \n",
- " 0.162480 | \n",
- " 0.006046 | \n",
- " 0.000714 | \n",
+ " 0.062459 | \n",
+ " 0.041017 | \n",
+ " 0.058636 | \n",
+ " 0.057057 | \n",
+ " 0.041305 | \n",
+ " 0.047517 | \n",
+ " 0.050627 | \n",
+ " 0.041722 | \n",
+ " 0.076179 | \n",
+ " 0.040864 | \n",
+ " ... | \n",
+ " 0.041665 | \n",
+ " 0.037326 | \n",
+ " 0.046682 | \n",
+ " 0.068976 | \n",
+ " 0.026715 | \n",
+ " 0.036728 | \n",
+ " 0.038237 | \n",
+ " 0.064387 | \n",
+ " 0.026799 | \n",
+ " 0.025560 | \n",
"
\n",
" \n",
" | ... | \n",
@@ -362,371 +827,962 @@
" ... | \n",
" ... | \n",
" ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 82 | \n",
+ " 0.065614 | \n",
+ " 0.039598 | \n",
+ " 0.111301 | \n",
+ " 0.051659 | \n",
+ " 0.029684 | \n",
+ " 0.034585 | \n",
+ " 0.040770 | \n",
+ " 0.034798 | \n",
+ " 0.067167 | \n",
+ " 0.036953 | \n",
+ " ... | \n",
+ " 0.037534 | \n",
+ " 0.034122 | \n",
+ " 0.029891 | \n",
+ " 0.054243 | \n",
+ " 0.034711 | \n",
+ " 0.043401 | \n",
+ " 0.050102 | \n",
+ " 0.052792 | \n",
+ " 0.034134 | \n",
+ " 0.028355 | \n",
+ "
\n",
+ " \n",
+ " | 83 | \n",
+ " 0.035028 | \n",
+ " 0.071044 | \n",
+ " 0.042615 | \n",
+ " 0.029416 | \n",
+ " 0.056253 | \n",
+ " 0.044821 | \n",
+ " 0.069191 | \n",
+ " 0.051838 | \n",
+ " 0.037086 | \n",
+ " 0.058182 | \n",
+ " ... | \n",
+ " 0.043910 | \n",
+ " 0.049231 | \n",
+ " 0.060864 | \n",
+ " 0.051451 | \n",
+ " 0.025514 | \n",
+ " 0.049892 | \n",
+ " 0.049833 | \n",
+ " 0.052714 | \n",
+ " 0.023082 | \n",
+ " 0.019822 | \n",
+ "
\n",
+ " \n",
+ " | 84 | \n",
+ " 0.101112 | \n",
+ " 0.048375 | \n",
+ " 0.064964 | \n",
+ " 0.062281 | \n",
+ " 0.031821 | \n",
+ " 0.042080 | \n",
+ " 0.039278 | \n",
+ " 0.040072 | \n",
+ " 0.083869 | \n",
+ " 0.040515 | \n",
+ " ... | \n",
+ " 0.042343 | \n",
+ " 0.032872 | \n",
+ " 0.044111 | \n",
+ " 0.064455 | \n",
+ " 0.030700 | \n",
+ " 0.036691 | \n",
+ " 0.033516 | \n",
+ " 0.048578 | \n",
+ " 0.029632 | \n",
+ " 0.024524 | \n",
+ "
\n",
+ " \n",
+ " | 85 | \n",
+ " 0.030393 | \n",
+ " 0.052510 | \n",
+ " 0.037849 | \n",
+ " 0.029890 | \n",
+ " 0.086273 | \n",
+ " 0.044839 | \n",
+ " 0.050140 | \n",
+ " 0.050668 | \n",
+ " 0.030873 | \n",
+ " 0.043122 | \n",
+ " ... | \n",
+ " 0.040347 | \n",
+ " 0.044339 | \n",
+ " 0.037591 | \n",
+ " 0.040867 | \n",
+ " 0.036208 | \n",
+ " 0.052100 | \n",
+ " 0.068486 | \n",
+ " 0.052356 | \n",
+ " 0.028037 | \n",
+ " 0.028341 | \n",
"
\n",
" \n",
" | 86 | \n",
- " 0.000623 | \n",
- " 0.008444 | \n",
- " 0.345578 | \n",
- " 0.001594 | \n",
- " 0.641204 | \n",
- " 0.002557 | \n",
+ " 0.034393 | \n",
+ " 0.074622 | \n",
+ " 0.042625 | \n",
+ " 0.028708 | \n",
+ " 0.053640 | \n",
+ " 0.046086 | \n",
+ " 0.066082 | \n",
+ " 0.052191 | \n",
+ " 0.037034 | \n",
+ " 0.059698 | \n",
+ " ... | \n",
+ " 0.043335 | \n",
+ " 0.048588 | \n",
+ " 0.062705 | \n",
+ " 0.053943 | \n",
+ " 0.024106 | \n",
+ " 0.050823 | \n",
+ " 0.049352 | \n",
+ " 0.055087 | \n",
+ " 0.021811 | \n",
+ " 0.018500 | \n",
"
\n",
" \n",
" | 87 | \n",
- " 0.018849 | \n",
- " 0.475657 | \n",
- " 0.000473 | \n",
- " 0.416433 | \n",
- " 0.088511 | \n",
- " 0.000076 | \n",
+ " 0.034628 | \n",
+ " 0.048158 | \n",
+ " 0.034503 | \n",
+ " 0.029887 | \n",
+ " 0.063577 | \n",
+ " 0.045613 | \n",
+ " 0.057075 | \n",
+ " 0.096326 | \n",
+ " 0.034519 | \n",
+ " 0.042707 | \n",
+ " ... | \n",
+ " 0.043000 | \n",
+ " 0.042002 | \n",
+ " 0.046209 | \n",
+ " 0.042759 | \n",
+ " 0.032315 | \n",
+ " 0.046696 | \n",
+ " 0.053674 | \n",
+ " 0.049011 | \n",
+ " 0.028683 | \n",
+ " 0.035491 | \n",
"
\n",
" \n",
" | 88 | \n",
- " 0.028410 | \n",
- " 0.285428 | \n",
- " 0.000136 | \n",
- " 0.646256 | \n",
- " 0.039668 | \n",
- " 0.000101 | \n",
+ " 0.029707 | \n",
+ " 0.052203 | \n",
+ " 0.044853 | \n",
+ " 0.027425 | \n",
+ " 0.052746 | \n",
+ " 0.038242 | \n",
+ " 0.049532 | \n",
+ " 0.043025 | \n",
+ " 0.034755 | \n",
+ " 0.041804 | \n",
+ " ... | \n",
+ " 0.028580 | \n",
+ " 0.038398 | \n",
+ " 0.049179 | \n",
+ " 0.032092 | \n",
+ " 0.034688 | \n",
+ " 0.037681 | \n",
+ " 0.045880 | \n",
+ " 0.060169 | \n",
+ " 0.030638 | \n",
+ " 0.032345 | \n",
"
\n",
" \n",
" | 89 | \n",
- " 0.001069 | \n",
- " 0.003075 | \n",
- " 0.004691 | \n",
- " 0.000604 | \n",
- " 0.988923 | \n",
- " 0.001638 | \n",
+ " 0.032829 | \n",
+ " 0.060884 | \n",
+ " 0.036295 | \n",
+ " 0.030453 | \n",
+ " 0.066238 | \n",
+ " 0.039350 | \n",
+ " 0.045534 | \n",
+ " 0.051757 | \n",
+ " 0.033199 | \n",
+ " 0.043487 | \n",
+ " ... | \n",
+ " 0.044384 | \n",
+ " 0.048178 | \n",
+ " 0.037859 | \n",
+ " 0.050210 | \n",
+ " 0.038253 | \n",
+ " 0.054807 | \n",
+ " 0.079265 | \n",
+ " 0.052785 | \n",
+ " 0.028003 | \n",
+ " 0.026768 | \n",
"
\n",
" \n",
" | 90 | \n",
- " 0.001752 | \n",
- " 0.001348 | \n",
- " 0.001095 | \n",
- " 0.001028 | \n",
- " 0.994771 | \n",
- " 0.000006 | \n",
+ " 0.039032 | \n",
+ " 0.044988 | \n",
+ " 0.047372 | \n",
+ " 0.039070 | \n",
+ " 0.039515 | \n",
+ " 0.053132 | \n",
+ " 0.053471 | \n",
+ " 0.041611 | \n",
+ " 0.036943 | \n",
+ " 0.048158 | \n",
+ " ... | \n",
+ " 0.033388 | \n",
+ " 0.102279 | \n",
+ " 0.044595 | \n",
+ " 0.045020 | \n",
+ " 0.044324 | \n",
+ " 0.038106 | \n",
+ " 0.045737 | \n",
+ " 0.046186 | \n",
+ " 0.028856 | \n",
+ " 0.034689 | \n",
"
\n",
" \n",
" | 91 | \n",
- " 0.907584 | \n",
- " 0.002283 | \n",
- " 0.025072 | \n",
- " 0.003035 | \n",
- " 0.056052 | \n",
- " 0.005975 | \n",
+ " 0.035166 | \n",
+ " 0.051704 | \n",
+ " 0.040098 | \n",
+ " 0.030816 | \n",
+ " 0.108649 | \n",
+ " 0.053707 | \n",
+ " 0.060677 | \n",
+ " 0.045415 | \n",
+ " 0.034643 | \n",
+ " 0.058854 | \n",
+ " ... | \n",
+ " 0.038591 | \n",
+ " 0.038493 | \n",
+ " 0.050301 | \n",
+ " 0.040746 | \n",
+ " 0.029858 | \n",
+ " 0.049727 | \n",
+ " 0.039092 | \n",
+ " 0.045415 | \n",
+ " 0.028905 | \n",
+ " 0.032281 | \n",
"
\n",
" \n",
" | 92 | \n",
- " 0.582291 | \n",
- " 0.002645 | \n",
- " 0.349497 | \n",
- " 0.022665 | \n",
- " 0.002794 | \n",
- " 0.040108 | \n",
+ " 0.040830 | \n",
+ " 0.045328 | \n",
+ " 0.083087 | \n",
+ " 0.039127 | \n",
+ " 0.033020 | \n",
+ " 0.036804 | \n",
+ " 0.041127 | \n",
+ " 0.042001 | \n",
+ " 0.031746 | \n",
+ " 0.043694 | \n",
+ " ... | \n",
+ " 0.039158 | \n",
+ " 0.036597 | \n",
+ " 0.040224 | \n",
+ " 0.047358 | \n",
+ " 0.042660 | \n",
+ " 0.053042 | \n",
+ " 0.055306 | \n",
+ " 0.048353 | \n",
+ " 0.042441 | \n",
+ " 0.031642 | \n",
"
\n",
" \n",
" | 93 | \n",
- " 0.020871 | \n",
- " 0.000205 | \n",
- " 0.078531 | \n",
- " 0.005291 | \n",
- " 0.003539 | \n",
- " 0.891563 | \n",
+ " 0.041650 | \n",
+ " 0.057578 | \n",
+ " 0.045023 | \n",
+ " 0.034186 | \n",
+ " 0.042197 | \n",
+ " 0.048577 | \n",
+ " 0.040336 | \n",
+ " 0.043883 | \n",
+ " 0.034974 | \n",
+ " 0.042952 | \n",
+ " ... | \n",
+ " 0.141277 | \n",
+ " 0.034930 | \n",
+ " 0.042204 | \n",
+ " 0.051819 | \n",
+ " 0.031393 | \n",
+ " 0.055175 | \n",
+ " 0.038236 | \n",
+ " 0.045235 | \n",
+ " 0.032450 | \n",
+ " 0.026259 | \n",
"
\n",
" \n",
" | 94 | \n",
- " 0.144768 | \n",
- " 0.000388 | \n",
- " 0.031331 | \n",
- " 0.755301 | \n",
- " 0.015876 | \n",
- " 0.052336 | \n",
+ " 0.032713 | \n",
+ " 0.061353 | \n",
+ " 0.042793 | \n",
+ " 0.027991 | \n",
+ " 0.056604 | \n",
+ " 0.060919 | \n",
+ " 0.061583 | \n",
+ " 0.057007 | \n",
+ " 0.037884 | \n",
+ " 0.055115 | \n",
+ " ... | \n",
+ " 0.039434 | \n",
+ " 0.046046 | \n",
+ " 0.060298 | \n",
+ " 0.051175 | \n",
+ " 0.024780 | \n",
+ " 0.052373 | \n",
+ " 0.051754 | \n",
+ " 0.053424 | \n",
+ " 0.020771 | \n",
+ " 0.019766 | \n",
"
\n",
" \n",
" | 95 | \n",
- " 0.055254 | \n",
- " 0.005295 | \n",
- " 0.026642 | \n",
- " 0.022017 | \n",
- " 0.714126 | \n",
- " 0.176665 | \n",
+ " 0.045771 | \n",
+ " 0.049979 | \n",
+ " 0.041616 | \n",
+ " 0.033799 | \n",
+ " 0.042908 | \n",
+ " 0.042321 | \n",
+ " 0.056762 | \n",
+ " 0.104992 | \n",
+ " 0.042933 | \n",
+ " 0.047098 | \n",
+ " ... | \n",
+ " 0.043015 | \n",
+ " 0.038799 | \n",
+ " 0.050409 | \n",
+ " 0.051644 | \n",
+ " 0.031264 | \n",
+ " 0.044841 | \n",
+ " 0.042476 | \n",
+ " 0.044926 | \n",
+ " 0.032908 | \n",
+ " 0.039834 | \n",
"
\n",
" \n",
" | 96 | \n",
- " 0.001022 | \n",
- " 0.000145 | \n",
- " 0.066657 | \n",
- " 0.000202 | \n",
- " 0.005606 | \n",
- " 0.926368 | \n",
+ " 0.040931 | \n",
+ " 0.047643 | \n",
+ " 0.040107 | \n",
+ " 0.031950 | \n",
+ " 0.046874 | \n",
+ " 0.045935 | \n",
+ " 0.060147 | \n",
+ " 0.108192 | \n",
+ " 0.038718 | \n",
+ " 0.047083 | \n",
+ " ... | \n",
+ " 0.042215 | \n",
+ " 0.039123 | \n",
+ " 0.053071 | \n",
+ " 0.045750 | \n",
+ " 0.030564 | \n",
+ " 0.043848 | \n",
+ " 0.040309 | \n",
+ " 0.045465 | \n",
+ " 0.032688 | \n",
+ " 0.040211 | \n",
"
\n",
" \n",
" | 97 | \n",
- " 0.017080 | \n",
- " 0.055413 | \n",
- " 0.075454 | \n",
- " 0.013985 | \n",
- " 0.000121 | \n",
- " 0.837947 | \n",
+ " 0.034741 | \n",
+ " 0.052510 | \n",
+ " 0.039006 | \n",
+ " 0.036425 | \n",
+ " 0.061919 | \n",
+ " 0.043182 | \n",
+ " 0.046908 | \n",
+ " 0.049995 | \n",
+ " 0.042774 | \n",
+ " 0.037348 | \n",
+ " ... | \n",
+ " 0.045532 | \n",
+ " 0.045355 | \n",
+ " 0.033203 | \n",
+ " 0.044898 | \n",
+ " 0.037339 | \n",
+ " 0.087399 | \n",
+ " 0.061417 | \n",
+ " 0.044706 | \n",
+ " 0.036172 | \n",
+ " 0.032557 | \n",
"
\n",
" \n",
" | 98 | \n",
- " 0.531418 | \n",
- " 0.113480 | \n",
- " 0.292914 | \n",
- " 0.005153 | \n",
- " 0.000225 | \n",
- " 0.056809 | \n",
+ " 0.070671 | \n",
+ " 0.035206 | \n",
+ " 0.065472 | \n",
+ " 0.066309 | \n",
+ " 0.030341 | \n",
+ " 0.036256 | \n",
+ " 0.042044 | \n",
+ " 0.033793 | \n",
+ " 0.098926 | \n",
+ " 0.046726 | \n",
+ " ... | \n",
+ " 0.032634 | \n",
+ " 0.031806 | \n",
+ " 0.034654 | \n",
+ " 0.058815 | \n",
+ " 0.034700 | \n",
+ " 0.034561 | \n",
+ " 0.041438 | \n",
+ " 0.053717 | \n",
+ " 0.035184 | \n",
+ " 0.036769 | \n",
"
\n",
" \n",
" | 99 | \n",
- " 0.060437 | \n",
- " 0.044330 | \n",
- " 0.089644 | \n",
- " 0.004676 | \n",
- " 0.000929 | \n",
- " 0.799984 | \n",
+ " 0.072850 | \n",
+ " 0.049221 | \n",
+ " 0.067587 | \n",
+ " 0.054379 | \n",
+ " 0.032313 | \n",
+ " 0.039659 | \n",
+ " 0.043492 | \n",
+ " 0.037515 | \n",
+ " 0.100107 | \n",
+ " 0.038627 | \n",
+ " ... | \n",
+ " 0.036176 | \n",
+ " 0.033324 | \n",
+ " 0.038019 | \n",
+ " 0.064474 | \n",
+ " 0.027746 | \n",
+ " 0.037270 | \n",
+ " 0.041472 | \n",
+ " 0.059757 | \n",
+ " 0.026183 | \n",
+ " 0.023426 | \n",
"
\n",
" \n",
" | 100 | \n",
- " 0.135961 | \n",
- " 0.022681 | \n",
- " 0.230616 | \n",
- " 0.541610 | \n",
- " 0.000142 | \n",
- " 0.068991 | \n",
+ " 0.025679 | \n",
+ " 0.046917 | \n",
+ " 0.043180 | \n",
+ " 0.025024 | \n",
+ " 0.085426 | \n",
+ " 0.044842 | \n",
+ " 0.053735 | \n",
+ " 0.045834 | \n",
+ " 0.025718 | \n",
+ " 0.042990 | \n",
+ " ... | \n",
+ " 0.029402 | \n",
+ " 0.037000 | \n",
+ " 0.045183 | \n",
+ " 0.028420 | \n",
+ " 0.033811 | \n",
+ " 0.042795 | \n",
+ " 0.048573 | \n",
+ " 0.055434 | \n",
+ " 0.028894 | \n",
+ " 0.030096 | \n",
"
\n",
" \n",
" | 101 | \n",
- " 0.117584 | \n",
- " 0.483514 | \n",
- " 0.042713 | \n",
- " 0.343828 | \n",
- " 0.011076 | \n",
- " 0.001286 | \n",
+ " 0.034231 | \n",
+ " 0.049624 | \n",
+ " 0.045663 | \n",
+ " 0.034626 | \n",
+ " 0.047241 | \n",
+ " 0.056521 | \n",
+ " 0.060523 | \n",
+ " 0.045989 | \n",
+ " 0.034630 | \n",
+ " 0.051727 | \n",
+ " ... | \n",
+ " 0.034507 | \n",
+ " 0.082339 | \n",
+ " 0.056270 | \n",
+ " 0.049734 | \n",
+ " 0.032287 | \n",
+ " 0.039418 | \n",
+ " 0.043215 | \n",
+ " 0.056819 | \n",
+ " 0.023095 | \n",
+ " 0.025953 | \n",
"
\n",
" \n",
" | 102 | \n",
- " 0.000066 | \n",
- " 0.077169 | \n",
- " 0.160841 | \n",
- " 0.008179 | \n",
- " 0.619626 | \n",
- " 0.134119 | \n",
+ " 0.033792 | \n",
+ " 0.040988 | \n",
+ " 0.047591 | \n",
+ " 0.034842 | \n",
+ " 0.059777 | \n",
+ " 0.075034 | \n",
+ " 0.064774 | \n",
+ " 0.054025 | \n",
+ " 0.039948 | \n",
+ " 0.048264 | \n",
+ " ... | \n",
+ " 0.038036 | \n",
+ " 0.042633 | \n",
+ " 0.060574 | \n",
+ " 0.036926 | \n",
+ " 0.026981 | \n",
+ " 0.042624 | \n",
+ " 0.038169 | \n",
+ " 0.052943 | \n",
+ " 0.026706 | \n",
+ " 0.027815 | \n",
"
\n",
" \n",
" | 103 | \n",
- " 0.000418 | \n",
- " 0.000241 | \n",
- " 0.010694 | \n",
- " 0.000410 | \n",
- " 0.000046 | \n",
- " 0.988191 | \n",
+ " 0.044039 | \n",
+ " 0.050073 | \n",
+ " 0.044893 | \n",
+ " 0.037138 | \n",
+ " 0.041541 | \n",
+ " 0.047957 | \n",
+ " 0.037785 | \n",
+ " 0.041982 | \n",
+ " 0.035756 | \n",
+ " 0.039755 | \n",
+ " ... | \n",
+ " 0.150544 | \n",
+ " 0.033580 | \n",
+ " 0.038651 | \n",
+ " 0.047953 | \n",
+ " 0.035551 | \n",
+ " 0.052896 | \n",
+ " 0.036629 | \n",
+ " 0.042309 | \n",
+ " 0.037734 | \n",
+ " 0.031740 | \n",
"
\n",
" \n",
" | 104 | \n",
- " 0.034173 | \n",
- " 0.399004 | \n",
- " 0.174507 | \n",
- " 0.255038 | \n",
- " 0.088751 | \n",
- " 0.048528 | \n",
+ " 0.039508 | \n",
+ " 0.048220 | \n",
+ " 0.070464 | \n",
+ " 0.037606 | \n",
+ " 0.031462 | \n",
+ " 0.042207 | \n",
+ " 0.045672 | \n",
+ " 0.040224 | \n",
+ " 0.031544 | \n",
+ " 0.049167 | \n",
+ " ... | \n",
+ " 0.033919 | \n",
+ " 0.075613 | \n",
+ " 0.040119 | \n",
+ " 0.051125 | \n",
+ " 0.045410 | \n",
+ " 0.046406 | \n",
+ " 0.055250 | \n",
+ " 0.046127 | \n",
+ " 0.031071 | \n",
+ " 0.029454 | \n",
"
\n",
" \n",
" | 105 | \n",
- " 0.000126 | \n",
- " 0.008806 | \n",
- " 0.072347 | \n",
- " 0.003892 | \n",
- " 0.001063 | \n",
- " 0.913765 | \n",
+ " 0.033318 | \n",
+ " 0.056001 | \n",
+ " 0.038777 | \n",
+ " 0.028807 | \n",
+ " 0.108424 | \n",
+ " 0.052708 | \n",
+ " 0.060891 | \n",
+ " 0.047204 | \n",
+ " 0.033393 | \n",
+ " 0.058685 | \n",
+ " ... | \n",
+ " 0.038305 | \n",
+ " 0.039929 | \n",
+ " 0.049885 | \n",
+ " 0.042224 | \n",
+ " 0.028364 | \n",
+ " 0.051583 | \n",
+ " 0.042900 | \n",
+ " 0.047559 | \n",
+ " 0.025985 | \n",
+ " 0.028312 | \n",
"
\n",
" \n",
" | 106 | \n",
- " 0.000118 | \n",
- " 0.784845 | \n",
- " 0.014905 | \n",
- " 0.032352 | \n",
- " 0.002857 | \n",
- " 0.164923 | \n",
+ " 0.034721 | \n",
+ " 0.055520 | \n",
+ " 0.051867 | \n",
+ " 0.031946 | \n",
+ " 0.046088 | \n",
+ " 0.039720 | \n",
+ " 0.045655 | \n",
+ " 0.045936 | \n",
+ " 0.029345 | \n",
+ " 0.044850 | \n",
+ " ... | \n",
+ " 0.036477 | \n",
+ " 0.068055 | \n",
+ " 0.036590 | \n",
+ " 0.051115 | \n",
+ " 0.043800 | \n",
+ " 0.051092 | \n",
+ " 0.075092 | \n",
+ " 0.050028 | \n",
+ " 0.027076 | \n",
+ " 0.026337 | \n",
"
\n",
" \n",
" | 107 | \n",
- " 0.001325 | \n",
- " 0.005722 | \n",
- " 0.620649 | \n",
- " 0.001493 | \n",
- " 0.362734 | \n",
- " 0.008077 | \n",
+ " 0.032552 | \n",
+ " 0.054551 | \n",
+ " 0.039057 | \n",
+ " 0.030704 | \n",
+ " 0.067524 | \n",
+ " 0.035670 | \n",
+ " 0.043789 | \n",
+ " 0.048266 | \n",
+ " 0.035439 | \n",
+ " 0.038089 | \n",
+ " ... | \n",
+ " 0.037341 | \n",
+ " 0.043735 | \n",
+ " 0.034683 | \n",
+ " 0.042035 | \n",
+ " 0.041449 | \n",
+ " 0.048337 | \n",
+ " 0.078866 | \n",
+ " 0.055057 | \n",
+ " 0.029704 | \n",
+ " 0.030256 | \n",
"
\n",
" \n",
" | 108 | \n",
- " 0.051256 | \n",
- " 0.000053 | \n",
- " 0.040290 | \n",
- " 0.344267 | \n",
- " 0.010992 | \n",
- " 0.553142 | \n",
+ " 0.033187 | \n",
+ " 0.039050 | \n",
+ " 0.044955 | \n",
+ " 0.036343 | \n",
+ " 0.057956 | \n",
+ " 0.066457 | \n",
+ " 0.058844 | \n",
+ " 0.049751 | \n",
+ " 0.039147 | \n",
+ " 0.047794 | \n",
+ " ... | \n",
+ " 0.037655 | \n",
+ " 0.042061 | \n",
+ " 0.075929 | \n",
+ " 0.032373 | \n",
+ " 0.027754 | \n",
+ " 0.039334 | \n",
+ " 0.033061 | \n",
+ " 0.055804 | \n",
+ " 0.031685 | \n",
+ " 0.031679 | \n",
"
\n",
" \n",
" | 109 | \n",
- " 0.012218 | \n",
- " 0.059597 | \n",
- " 0.012133 | \n",
- " 0.084107 | \n",
- " 0.000024 | \n",
- " 0.831921 | \n",
+ " 0.033913 | \n",
+ " 0.073319 | \n",
+ " 0.045752 | \n",
+ " 0.030965 | \n",
+ " 0.046855 | \n",
+ " 0.050233 | \n",
+ " 0.056725 | \n",
+ " 0.051077 | \n",
+ " 0.042377 | \n",
+ " 0.053276 | \n",
+ " ... | \n",
+ " 0.043072 | \n",
+ " 0.044549 | \n",
+ " 0.054345 | \n",
+ " 0.056473 | \n",
+ " 0.023588 | \n",
+ " 0.076329 | \n",
+ " 0.045670 | \n",
+ " 0.054391 | \n",
+ " 0.024653 | \n",
+ " 0.019419 | \n",
"
\n",
" \n",
" | 110 | \n",
- " 0.021150 | \n",
- " 0.290898 | \n",
- " 0.068613 | \n",
- " 0.405920 | \n",
- " 0.000064 | \n",
- " 0.213356 | \n",
+ " 0.037041 | \n",
+ " 0.067774 | \n",
+ " 0.044258 | \n",
+ " 0.029269 | \n",
+ " 0.046685 | \n",
+ " 0.054345 | \n",
+ " 0.056277 | \n",
+ " 0.054281 | \n",
+ " 0.040839 | \n",
+ " 0.059444 | \n",
+ " ... | \n",
+ " 0.042391 | \n",
+ " 0.046726 | \n",
+ " 0.059803 | \n",
+ " 0.060979 | \n",
+ " 0.026854 | \n",
+ " 0.054391 | \n",
+ " 0.052803 | \n",
+ " 0.049506 | \n",
+ " 0.022400 | \n",
+ " 0.020520 | \n",
"
\n",
" \n",
" | 111 | \n",
- " 0.013645 | \n",
- " 0.160267 | \n",
- " 0.033778 | \n",
- " 0.468831 | \n",
- " 0.000045 | \n",
- " 0.323434 | \n",
- "
\n",
- " \n",
- " | 112 | \n",
- " 0.000130 | \n",
- " 0.006521 | \n",
- " 0.030344 | \n",
- " 0.008270 | \n",
- " 0.235646 | \n",
- " 0.719089 | \n",
- "
\n",
- " \n",
- " | 113 | \n",
- " 0.011857 | \n",
- " 0.000790 | \n",
- " 0.041395 | \n",
- " 0.209530 | \n",
- " 0.000407 | \n",
- " 0.736020 | \n",
- "
\n",
- " \n",
- " | 114 | \n",
- " 0.002886 | \n",
- " 0.044982 | \n",
- " 0.800494 | \n",
- " 0.003210 | \n",
- " 0.104091 | \n",
- " 0.044337 | \n",
- "
\n",
- " \n",
- " | 115 | \n",
- " 0.000651 | \n",
- " 0.000124 | \n",
- " 0.047553 | \n",
- " 0.001987 | \n",
- " 0.008576 | \n",
- " 0.941108 | \n",
+ " 0.034605 | \n",
+ " 0.073372 | \n",
+ " 0.042616 | \n",
+ " 0.028921 | \n",
+ " 0.054673 | \n",
+ " 0.045587 | \n",
+ " 0.067342 | \n",
+ " 0.052097 | \n",
+ " 0.037022 | \n",
+ " 0.059170 | \n",
+ " ... | \n",
+ " 0.043520 | \n",
+ " 0.048837 | \n",
+ " 0.062061 | \n",
+ " 0.053001 | \n",
+ " 0.024568 | \n",
+ " 0.050495 | \n",
+ " 0.049570 | \n",
+ " 0.054210 | \n",
+ " 0.022218 | \n",
+ " 0.018925 | \n",
"
\n",
" \n",
"\n",
- "116 rows × 6 columns
\n",
+ "112 rows × 22 columns
\n",
""
],
"text/plain": [
- " 0 1 2 3 4 5\n",
- "0 0.080322 0.406207 0.377125 0.101128 0.000117 0.035101\n",
- "1 0.789287 0.068335 0.047145 0.094098 0.000980 0.000155\n",
- "2 0.762841 0.024799 0.137096 0.073537 0.001202 0.000525\n",
- "3 0.009087 0.000259 0.024026 0.875603 0.011644 0.079382\n",
- "4 0.701870 0.000069 0.026003 0.156832 0.012586 0.102640\n",
- "5 0.016722 0.001152 0.075507 0.088209 0.008319 0.810092\n",
- "6 0.652011 0.014694 0.254270 0.076600 0.001153 0.001272\n",
- "7 0.503152 0.190103 0.146537 0.140299 0.019612 0.000297\n",
- "8 0.902592 0.000175 0.027184 0.066996 0.001453 0.001600\n",
- "9 0.107776 0.036636 0.003279 0.797520 0.000214 0.054574\n",
- "10 0.946610 0.000071 0.014849 0.027311 0.007029 0.004130\n",
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- "22 0.000149 0.930636 0.060365 0.006790 0.000683 0.001377\n",
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- "24 0.045499 0.580400 0.002498 0.346710 0.003845 0.021047\n",
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- "27 0.419345 0.062639 0.001323 0.506182 0.010420 0.000090\n",
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- ".. ... ... ... ... ... ...\n",
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+ " 0 1 2 3 4 5 6 \\\n",
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+ "7 0.032741 0.075311 0.043249 0.029438 0.045879 0.047930 0.057232 \n",
+ "8 0.054265 0.047404 0.050633 0.048070 0.033149 0.046264 0.051029 \n",
+ "9 0.026146 0.054937 0.048305 0.025034 0.053544 0.036021 0.047155 \n",
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+ "\n",
+ " 7 8 9 ... 12 13 14 \\\n",
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"\n",
- "[116 rows x 6 columns]"
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+ "26 0.072896 0.026318 0.039224 0.040962 0.052158 0.025452 0.022144 \n",
+ "27 0.055994 0.035326 0.047796 0.062853 0.052634 0.021429 0.022588 \n",
+ "28 0.066867 0.038131 0.034964 0.039009 0.045857 0.026465 0.026685 \n",
+ "29 0.068976 0.026715 0.036728 0.038237 0.064387 0.026799 0.025560 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "82 0.054243 0.034711 0.043401 0.050102 0.052792 0.034134 0.028355 \n",
+ "83 0.051451 0.025514 0.049892 0.049833 0.052714 0.023082 0.019822 \n",
+ "84 0.064455 0.030700 0.036691 0.033516 0.048578 0.029632 0.024524 \n",
+ "85 0.040867 0.036208 0.052100 0.068486 0.052356 0.028037 0.028341 \n",
+ "86 0.053943 0.024106 0.050823 0.049352 0.055087 0.021811 0.018500 \n",
+ "87 0.042759 0.032315 0.046696 0.053674 0.049011 0.028683 0.035491 \n",
+ "88 0.032092 0.034688 0.037681 0.045880 0.060169 0.030638 0.032345 \n",
+ "89 0.050210 0.038253 0.054807 0.079265 0.052785 0.028003 0.026768 \n",
+ "90 0.045020 0.044324 0.038106 0.045737 0.046186 0.028856 0.034689 \n",
+ "91 0.040746 0.029858 0.049727 0.039092 0.045415 0.028905 0.032281 \n",
+ "92 0.047358 0.042660 0.053042 0.055306 0.048353 0.042441 0.031642 \n",
+ "93 0.051819 0.031393 0.055175 0.038236 0.045235 0.032450 0.026259 \n",
+ "94 0.051175 0.024780 0.052373 0.051754 0.053424 0.020771 0.019766 \n",
+ "95 0.051644 0.031264 0.044841 0.042476 0.044926 0.032908 0.039834 \n",
+ "96 0.045750 0.030564 0.043848 0.040309 0.045465 0.032688 0.040211 \n",
+ "97 0.044898 0.037339 0.087399 0.061417 0.044706 0.036172 0.032557 \n",
+ "98 0.058815 0.034700 0.034561 0.041438 0.053717 0.035184 0.036769 \n",
+ "99 0.064474 0.027746 0.037270 0.041472 0.059757 0.026183 0.023426 \n",
+ "100 0.028420 0.033811 0.042795 0.048573 0.055434 0.028894 0.030096 \n",
+ "101 0.049734 0.032287 0.039418 0.043215 0.056819 0.023095 0.025953 \n",
+ "102 0.036926 0.026981 0.042624 0.038169 0.052943 0.026706 0.027815 \n",
+ "103 0.047953 0.035551 0.052896 0.036629 0.042309 0.037734 0.031740 \n",
+ "104 0.051125 0.045410 0.046406 0.055250 0.046127 0.031071 0.029454 \n",
+ "105 0.042224 0.028364 0.051583 0.042900 0.047559 0.025985 0.028312 \n",
+ "106 0.051115 0.043800 0.051092 0.075092 0.050028 0.027076 0.026337 \n",
+ "107 0.042035 0.041449 0.048337 0.078866 0.055057 0.029704 0.030256 \n",
+ "108 0.032373 0.027754 0.039334 0.033061 0.055804 0.031685 0.031679 \n",
+ "109 0.056473 0.023588 0.076329 0.045670 0.054391 0.024653 0.019419 \n",
+ "110 0.060979 0.026854 0.054391 0.052803 0.049506 0.022400 0.020520 \n",
+ "111 0.053001 0.024568 0.050495 0.049570 0.054210 0.022218 0.018925 \n",
+ "\n",
+ "[112 rows x 22 columns]"
]
},
- "execution_count": 164,
+ "execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "results_df = pd.read_csv(path+'cls_output-'+prefix+'/test_results.tsv', header=None, sep='\\t')\n",
+ "results_df = pd.read_csv(path+'cls_output-no-lm-'+prefix+'/test_results.tsv', header=None, sep='\\t')\n",
"results_df"
]
},
{
"cell_type": "code",
- "execution_count": 165,
+ "execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(116, 6)"
+ "(112, 22)"
]
},
- "execution_count": 165,
+ "execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
@@ -737,7 +1793,7 @@
},
{
"cell_type": "code",
- "execution_count": 166,
+ "execution_count": 106,
"metadata": {},
"outputs": [
{
@@ -789,21 +1845,101 @@
" 5 | \n",
" 5 | \n",
" \n",
+ " \n",
+ " | 6 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 8 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 9 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 10 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 11 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 13 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 14 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 16 | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " 17 | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " 18 | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " 19 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " [CLS] | \n",
+ "
\n",
+ " \n",
+ " | 21 | \n",
+ " [MASK] | \n",
+ "
\n",
" \n",
"\n",
""
],
"text/plain": [
- " 0\n",
- "0 0\n",
- "1 1\n",
- "2 2\n",
- "3 3\n",
- "4 4\n",
- "5 5"
+ " 0\n",
+ "0 0\n",
+ "1 1\n",
+ "2 2\n",
+ "3 3\n",
+ "4 4\n",
+ "5 5\n",
+ "6 6\n",
+ "7 7\n",
+ "8 8\n",
+ "9 9\n",
+ "10 10\n",
+ "11 11\n",
+ "12 12\n",
+ "13 13\n",
+ "14 14\n",
+ "15 15\n",
+ "16 16\n",
+ "17 17\n",
+ "18 18\n",
+ "19 19\n",
+ "20 [CLS]\n",
+ "21 [MASK]"
]
},
- "execution_count": 166,
+ "execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
@@ -815,7 +1951,7 @@
},
{
"cell_type": "code",
- "execution_count": 167,
+ "execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
@@ -828,131 +1964,127 @@
},
{
"cell_type": "code",
- "execution_count": 168,
+ "execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[[1, 2, 3, 0, 5, 4],\n",
- " [0, 3, 1, 2, 4, 5],\n",
- " [0, 2, 3, 1, 4, 5],\n",
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- " [0, 3, 5, 2, 4, 1],\n",
- " [5, 3, 2, 0, 4, 1],\n",
- " [0, 2, 3, 1, 5, 4],\n",
- " [0, 1, 2, 3, 4, 5],\n",
- " [0, 3, 2, 5, 4, 1],\n",
- " [3, 0, 5, 1, 2, 4],\n",
- " [0, 3, 2, 4, 5, 1],\n",
- " [0, 5, 3, 2, 1, 4],\n",
- " [0, 1, 2, 4, 3, 5],\n",
- " [4, 1, 3, 0, 2, 5],\n",
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- " [1, 3, 0, 5, 4, 2],\n",
- " [4, 2, 1, 0, 5, 3],\n",
- " [1, 4, 2, 3, 0, 5],\n",
- " [3, 0, 1, 4, 2, 5],\n",
- " [1, 4, 3, 0, 2, 5],\n",
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- " [3, 0, 1, 2, 5, 4],\n",
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- " [1, 5, 2, 3, 0, 4],\n",
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- " [2, 4, 3, 0, 5, 1],\n",
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- " [2, 5, 1, 4, 3, 0],\n",
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- " [5, 2, 3, 0, 1, 4],\n",
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- " [4, 1, 0, 3, 2, 5],\n",
- " [2, 3, 0, 4, 5, 1],\n",
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- " [2, 3, 0, 4, 5, 1],\n",
- " [3, 4, 2, 5, 0, 1],\n",
- " [3, 2, 5, 4, 0, 1],\n",
- " [2, 3, 4, 0, 1, 5],\n",
- " [4, 1, 2, 3, 0, 5],\n",
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- " [5, 2, 3, 4, 0, 1],\n",
- " [5, 3, 1, 2, 0, 4],\n",
- " [3, 1, 5, 0, 4, 2],\n",
- " [1, 3, 2, 0, 5, 4],\n",
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- " [0, 4, 3, 2, 1, 5],\n",
- " [3, 1, 0, 4, 5, 2],\n",
- " [5, 1, 3, 2, 0, 4],\n",
- " [3, 1, 4, 0, 5, 2],\n",
- " [3, 5, 4, 2, 0, 1],\n",
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- " [3, 4, 5, 0, 2, 1],\n",
- " [3, 4, 5, 2, 0, 1],\n",
- " [3, 1, 0, 4, 5, 2],\n",
- " [3, 0, 1, 5, 4, 2],\n",
- " [1, 3, 0, 4, 5, 2],\n",
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- " [4, 2, 0, 3, 5, 1],\n",
- " [4, 2, 0, 3, 5, 1],\n",
- " [3, 1, 0, 4, 5, 2],\n",
- " [4, 2, 0, 3, 5, 1],\n",
- " [4, 2, 0, 3, 5, 1],\n",
- " [4, 3, 1, 0, 2, 5],\n",
- " [4, 1, 0, 3, 2, 5],\n",
- " [4, 2, 1, 5, 3, 0],\n",
- " [1, 3, 4, 0, 2, 5],\n",
- " [3, 1, 4, 0, 2, 5],\n",
- " [4, 2, 1, 5, 0, 3],\n",
- " [4, 0, 1, 2, 3, 5],\n",
- " [0, 4, 2, 5, 3, 1],\n",
- " [0, 2, 5, 3, 4, 1],\n",
- " [5, 2, 0, 3, 4, 1],\n",
- " [3, 0, 5, 2, 4, 1],\n",
- " [4, 5, 0, 2, 3, 1],\n",
- " [5, 2, 4, 0, 3, 1],\n",
- " [5, 2, 1, 0, 3, 4],\n",
- " [0, 2, 1, 5, 3, 4],\n",
- " [5, 2, 0, 1, 3, 4],\n",
- " [3, 2, 0, 5, 1, 4],\n",
- " [1, 3, 0, 2, 4, 5],\n",
- " [4, 2, 5, 1, 3, 0],\n",
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- " [5, 2, 1, 3, 4, 0],\n",
- " [1, 5, 3, 2, 4, 0],\n",
- " [2, 4, 5, 1, 3, 0],\n",
- " [5, 3, 0, 2, 4, 1],\n",
- " [5, 3, 1, 0, 2, 4],\n",
- " [3, 1, 5, 2, 0, 4],\n",
- " [3, 5, 1, 2, 0, 4],\n",
- " [5, 4, 2, 3, 1, 0],\n",
- " [5, 3, 2, 0, 1, 4],\n",
- " [2, 4, 1, 5, 3, 0],\n",
- " [5, 2, 4, 3, 0, 1]]"
+ "[[1, 4, 11, 18, 10, 19, 7, 6, 14, 9],\n",
+ " [8, 0, 2, 15, 3, 18, 19, 12, 17, 1],\n",
+ " [5, 14, 6, 1, 7, 9, 4, 19, 17, 11],\n",
+ " [7, 4, 18, 6, 11, 1, 19, 17, 5, 14],\n",
+ " [4, 11, 1, 18, 17, 6, 19, 9, 7, 5],\n",
+ " [10, 11, 2, 19, 1, 18, 6, 14, 4, 7],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 1, 5, 6],\n",
+ " [1, 9, 14, 19, 15, 6, 17, 7, 5, 18],\n",
+ " [13, 8, 15, 0, 6, 2, 3, 18, 1, 5],\n",
+ " [10, 11, 19, 18, 1, 4, 2, 6, 7, 14],\n",
+ " [10, 19, 8, 2, 11, 6, 5, 14, 4, 0],\n",
+ " [13, 2, 15, 18, 9, 1, 6, 11, 5, 19],\n",
+ " [10, 1, 11, 4, 19, 18, 6, 14, 7, 9],\n",
+ " [1, 14, 9, 15, 19, 7, 6, 17, 4, 18],\n",
+ " [18, 10, 2, 11, 19, 17, 15, 1, 4, 16],\n",
+ " [2, 8, 0, 3, 15, 19, 18, 17, 10, 12],\n",
+ " [0, 8, 15, 3, 2, 19, 1, 18, 12, 7],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 5, 18, 6],\n",
+ " [18, 4, 11, 1, 17, 7, 19, 13, 15, 6],\n",
+ " [4, 11, 18, 6, 17, 5, 10, 7, 19, 9],\n",
+ " [9, 14, 19, 15, 6, 1, 18, 17, 7, 2],\n",
+ " [18, 4, 1, 11, 13, 17, 19, 7, 15, 6],\n",
+ " [12, 17, 1, 15, 5, 2, 0, 19, 7, 4],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 18, 5, 6],\n",
+ " [1, 14, 9, 19, 15, 6, 7, 17, 5, 13],\n",
+ " [9, 14, 1, 19, 15, 6, 17, 18, 7, 5],\n",
+ " [8, 0, 15, 2, 3, 19, 1, 6, 12, 18],\n",
+ " [13, 1, 18, 15, 19, 6, 9, 11, 4, 17],\n",
+ " [0, 8, 15, 13, 2, 3, 1, 19, 5, 6],\n",
+ " [8, 15, 19, 0, 2, 3, 6, 5, 14, 7],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 5, 18, 16],\n",
+ " [8, 0, 3, 2, 15, 19, 12, 5, 20, 16],\n",
+ " [10, 11, 17, 4, 6, 19, 1, 7, 18, 2],\n",
+ " [1, 14, 9, 15, 7, 19, 6, 5, 17, 4],\n",
+ " [9, 14, 19, 15, 1, 6, 18, 17, 7, 2],\n",
+ " [10, 2, 13, 11, 18, 19, 1, 9, 6, 16],\n",
+ " [8, 0, 15, 2, 3, 19, 1, 12, 6, 5],\n",
+ " [1, 13, 15, 9, 14, 6, 19, 5, 18, 7],\n",
+ " [7, 4, 6, 18, 1, 11, 19, 14, 17, 15],\n",
+ " [9, 14, 19, 15, 6, 1, 18, 17, 7, 2],\n",
+ " [10, 11, 2, 18, 19, 1, 4, 17, 6, 7],\n",
+ " [10, 11, 19, 2, 4, 18, 1, 6, 14, 7],\n",
+ " [18, 17, 4, 11, 1, 7, 13, 19, 12, 15],\n",
+ " [4, 11, 18, 10, 19, 7, 6, 5, 17, 1],\n",
+ " [8, 0, 2, 3, 15, 19, 10, 1, 6, 5],\n",
+ " [18, 11, 4, 1, 10, 19, 17, 7, 6, 13],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 5, 18, 17],\n",
+ " [18, 1, 15, 19, 4, 17, 7, 6, 11, 13],\n",
+ " [1, 6, 14, 9, 4, 19, 15, 7, 17, 18],\n",
+ " [18, 4, 1, 11, 7, 17, 13, 19, 15, 12],\n",
+ " [11, 19, 10, 1, 4, 6, 18, 14, 7, 17],\n",
+ " [1, 14, 6, 9, 19, 15, 7, 17, 4, 18],\n",
+ " [10, 11, 2, 19, 18, 4, 16, 21, 6, 8],\n",
+ " [8, 0, 3, 15, 2, 19, 12, 18, 5, 20],\n",
+ " [12, 1, 17, 15, 5, 2, 19, 7, 9, 4],\n",
+ " [8, 0, 15, 3, 2, 5, 18, 19, 12, 17],\n",
+ " [2, 8, 0, 15, 19, 3, 18, 1, 17, 6],\n",
+ " [1, 18, 11, 4, 19, 10, 17, 7, 6, 14],\n",
+ " [12, 1, 15, 17, 19, 14, 9, 5, 7, 6],\n",
+ " [12, 17, 5, 1, 15, 0, 2, 4, 7, 19],\n",
+ " [10, 11, 19, 4, 2, 1, 6, 18, 14, 7],\n",
+ " [7, 6, 14, 1, 19, 5, 9, 4, 15, 17],\n",
+ " [12, 1, 17, 15, 5, 2, 19, 7, 0, 9],\n",
+ " [13, 15, 1, 9, 18, 6, 19, 5, 14, 2],\n",
+ " [8, 0, 3, 2, 15, 19, 12, 20, 5, 18],\n",
+ " [9, 14, 19, 15, 6, 1, 18, 17, 7, 2],\n",
+ " [8, 0, 3, 2, 15, 19, 12, 20, 5, 18],\n",
+ " [1, 14, 9, 15, 7, 6, 19, 5, 17, 4],\n",
+ " [5, 6, 4, 14, 11, 10, 19, 7, 2, 9],\n",
+ " [1, 17, 14, 15, 6, 19, 9, 7, 5, 4],\n",
+ " [0, 8, 3, 2, 15, 19, 12, 20, 5, 16],\n",
+ " [1, 14, 15, 9, 6, 17, 7, 5, 18, 19],\n",
+ " [5, 14, 11, 6, 4, 10, 19, 7, 9, 2],\n",
+ " [17, 2, 1, 15, 6, 19, 7, 18, 5, 11],\n",
+ " [4, 6, 11, 10, 9, 5, 14, 1, 19, 17],\n",
+ " [6, 7, 5, 14, 4, 19, 11, 10, 9, 2],\n",
+ " [10, 4, 11, 2, 19, 6, 5, 8, 0, 9],\n",
+ " [13, 15, 1, 8, 2, 19, 0, 9, 6, 18],\n",
+ " [17, 1, 6, 15, 5, 7, 19, 2, 4, 8],\n",
+ " [9, 1, 14, 19, 15, 6, 17, 7, 18, 5],\n",
+ " [10, 11, 19, 4, 1, 14, 6, 18, 7, 2],\n",
+ " [1, 17, 14, 15, 9, 19, 6, 7, 5, 18],\n",
+ " [2, 8, 0, 15, 19, 3, 18, 10, 17, 11],\n",
+ " [1, 6, 14, 9, 4, 19, 7, 15, 17, 18],\n",
+ " [0, 8, 2, 15, 3, 19, 1, 14, 12, 5],\n",
+ " [4, 18, 11, 1, 19, 17, 7, 6, 10, 5],\n",
+ " [1, 6, 14, 9, 19, 15, 4, 7, 17, 18],\n",
+ " [7, 4, 6, 18, 11, 19, 1, 17, 14, 5],\n",
+ " [10, 11, 19, 4, 1, 6, 14, 18, 2, 7],\n",
+ " [18, 4, 1, 11, 17, 19, 7, 15, 13, 6],\n",
+ " [13, 6, 5, 9, 11, 2, 19, 18, 10, 15],\n",
+ " [4, 6, 9, 5, 1, 14, 17, 11, 7, 19],\n",
+ " [2, 10, 11, 18, 17, 19, 15, 1, 9, 16],\n",
+ " [12, 1, 17, 15, 5, 19, 2, 7, 9, 14],\n",
+ " [6, 1, 5, 14, 7, 4, 9, 19, 17, 18],\n",
+ " [7, 6, 15, 14, 1, 9, 0, 19, 17, 12],\n",
+ " [7, 6, 14, 1, 9, 4, 5, 15, 19, 17],\n",
+ " [17, 4, 18, 1, 11, 7, 6, 12, 13, 15],\n",
+ " [8, 0, 3, 2, 15, 19, 9, 10, 6, 18],\n",
+ " [8, 0, 2, 15, 19, 3, 1, 6, 18, 10],\n",
+ " [10, 4, 11, 19, 6, 18, 1, 7, 14, 5],\n",
+ " [13, 6, 19, 5, 14, 9, 11, 15, 1, 4],\n",
+ " [5, 6, 14, 4, 11, 7, 19, 10, 9, 2],\n",
+ " [12, 17, 1, 5, 15, 2, 0, 19, 7, 4],\n",
+ " [13, 2, 10, 18, 11, 15, 9, 1, 17, 19],\n",
+ " [4, 6, 9, 1, 5, 17, 14, 11, 19, 7],\n",
+ " [18, 13, 11, 1, 2, 15, 17, 19, 10, 4],\n",
+ " [18, 11, 4, 10, 19, 1, 17, 7, 6, 13],\n",
+ " [14, 5, 11, 10, 6, 4, 19, 7, 9, 2],\n",
+ " [17, 1, 6, 15, 19, 14, 9, 7, 5, 4],\n",
+ " [1, 15, 14, 9, 6, 17, 5, 7, 18, 19],\n",
+ " [1, 6, 14, 9, 4, 19, 15, 7, 17, 18]]"
]
},
- "execution_count": 168,
+ "execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
@@ -963,16 +2095,16 @@
},
{
"cell_type": "code",
- "execution_count": 169,
+ "execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(116, 1)"
+ "(112, 1)"
]
},
- "execution_count": 169,
+ "execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
@@ -984,20 +2116,20 @@
},
{
"cell_type": "code",
- "execution_count": 170,
+ "execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"labels= []; labels_str =[]\n",
"\n",
"for idx, row in label_df.iterrows():\n",
- " labels.append(vocab_label_df.index[vocab_label_df[0]==row[0]][0])\n",
+ " labels.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n",
" labels_str.append(row[0])"
]
},
{
"cell_type": "code",
- "execution_count": 171,
+ "execution_count": 111,
"metadata": {
"scrolled": true
},
@@ -1005,125 +2137,121 @@
{
"data": {
"text/plain": [
- "[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",
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- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 1,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
+ "[1,\n",
" 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 2,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
- " 3,\n",
+ " 5,\n",
+ " 7,\n",
+ " 11,\n",
+ " 10,\n",
" 3,\n",
+ " 9,\n",
+ " 8,\n",
+ " 10,\n",
+ " 5,\n",
+ " 13,\n",
+ " 18,\n",
+ " 1,\n",
+ " 18,\n",
+ " 2,\n",
" 3,\n",
" 3,\n",
+ " 11,\n",
+ " 4,\n",
+ " 9,\n",
+ " 18,\n",
+ " 12,\n",
+ " 8,\n",
+ " 14,\n",
+ " 9,\n",
+ " 6,\n",
+ " 13,\n",
+ " 0,\n",
+ " 15,\n",
+ " 8,\n",
" 3,\n",
+ " 17,\n",
+ " 1,\n",
+ " 9,\n",
+ " 16,\n",
+ " 8,\n",
+ " 13,\n",
+ " 7,\n",
+ " 9,\n",
+ " 10,\n",
+ " 10,\n",
+ " 17,\n",
+ " 11,\n",
+ " 0,\n",
+ " 18,\n",
+ " 5,\n",
+ " 18,\n",
+ " 6,\n",
+ " 1,\n",
+ " 11,\n",
+ " 6,\n",
+ " 10,\n",
+ " 15,\n",
+ " 12,\n",
+ " 5,\n",
+ " 2,\n",
+ " 19,\n",
+ " 12,\n",
+ " 12,\n",
+ " 10,\n",
+ " 7,\n",
+ " 12,\n",
+ " 15,\n",
" 3,\n",
+ " 9,\n",
" 3,\n",
+ " 1,\n",
+ " 5,\n",
+ " 17,\n",
" 3,\n",
+ " 5,\n",
+ " 14,\n",
+ " 17,\n",
" 4,\n",
+ " 7,\n",
" 4,\n",
+ " 13,\n",
+ " 17,\n",
+ " 9,\n",
+ " 10,\n",
+ " 17,\n",
+ " 2,\n",
+ " 6,\n",
+ " 0,\n",
+ " 11,\n",
+ " 6,\n",
+ " 7,\n",
+ " 10,\n",
+ " 18,\n",
+ " 19,\n",
" 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 4,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
+ " 15,\n",
+ " 12,\n",
" 5,\n",
+ " 7,\n",
+ " 7,\n",
+ " 17,\n",
+ " 19,\n",
+ " 19,\n",
+ " 11,\n",
+ " 15,\n",
" 5,\n",
+ " 12,\n",
+ " 16,\n",
+ " 4,\n",
+ " 13,\n",
+ " 18,\n",
+ " 14,\n",
+ " 17,\n",
" 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5,\n",
- " 5]"
+ " 6]"
]
},
- "execution_count": 171,
+ "execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
@@ -1134,12 +2262,12 @@
},
{
"cell_type": "code",
- "execution_count": 172,
+ "execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -1166,476 +2294,460 @@
},
{
"cell_type": "code",
- "execution_count": 173,
+ "execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Label = 0\n",
+ "Label = 1\n",
"Pred =\n",
- " 0. 1\n",
+ "---- 0. 1\n",
"\n",
- "Label = 0\n",
+ "Label = 2\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 8\n",
"\n",
- "Label = 0\n",
+ "Label = 5\n",
"Pred =\n",
- "---- 0. 0\n",
+ "---- 0. 5\n",
"\n",
- "Label = 0\n",
+ "Label = 7\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 7\n",
"\n",
- "Label = 0\n",
+ "Label = 11\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 4\n",
"\n",
- "Label = 0\n",
+ "Label = 10\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 10\n",
"\n",
- "Label = 0\n",
+ "Label = 3\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 8\n",
"\n",
- "Label = 0\n",
+ "Label = 9\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 1\n",
"\n",
- "Label = 0\n",
+ "Label = 8\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 13\n",
"\n",
- "Label = 0\n",
+ "Label = 10\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 10\n",
"\n",
- "Label = 0\n",
+ "Label = 5\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 10\n",
"\n",
- "Label = 0\n",
+ "Label = 13\n",
"Pred =\n",
- "---- 0. 0\n",
+ "---- 0. 13\n",
"\n",
- "Label = 0\n",
+ "Label = 18\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 10\n",
"\n",
- "Label = 0\n",
+ "Label = 1\n",
"Pred =\n",
- " 0. 4\n",
+ "---- 0. 1\n",
"\n",
- "Label = 0\n",
+ "Label = 18\n",
"Pred =\n",
- "---- 0. 0\n",
+ "---- 0. 18\n",
"\n",
- "Label = 0\n",
+ "Label = 2\n",
"Pred =\n",
- "---- 0. 0\n",
+ "---- 0. 2\n",
"\n",
- "Label = 0\n",
+ "Label = 3\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 0\n",
"\n",
- "Label = 0\n",
+ "Label = 3\n",
"Pred =\n",
- "---- 0. 0\n",
+ " 0. 8\n",
"\n",
- "Label = 0\n",
+ "Label = 11\n",
"Pred =\n",
- " 0. 1\n",
+ " 0. 18\n",
"\n",
- "Label = 0\n",
+ "Label = 4\n",
"Pred =\n",
- "---- 0. 0\n",
+ "---- 0. 4\n",
"\n",
- "Label = 1\n",
+ "Label = 9\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 9\n",
"\n",
- "Label = 1\n",
+ "Label = 18\n",
"Pred =\n",
- " 0. 2\n",
+ "---- 0. 18\n",
"\n",
- "Label = 1\n",
+ "Label = 12\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 12\n",
"\n",
- "Label = 1\n",
+ "Label = 8\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 14\n",
"Pred =\n",
- "---- 0. 1\n",
+ " 0. 1\n",
"\n",
- "Label = 1\n",
+ "Label = 9\n",
"Pred =\n",
- " 0. 4\n",
+ "---- 0. 9\n",
"\n",
- "Label = 1\n",
+ "Label = 6\n",
"Pred =\n",
- "---- 0. 1\n",
+ " 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 13\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 13\n",
"\n",
- "Label = 1\n",
+ "Label = 0\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 0\n",
"\n",
- "Label = 1\n",
+ "Label = 15\n",
"Pred =\n",
- "---- 0. 1\n",
+ " 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 8\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 3\n",
"Pred =\n",
- " 0. 4\n",
+ " 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 17\n",
"Pred =\n",
- "---- 0. 1\n",
+ " 0. 10\n",
"\n",
"Label = 1\n",
"Pred =\n",
"---- 0. 1\n",
"\n",
- "Label = 1\n",
+ "Label = 9\n",
"Pred =\n",
- " 0. 0\n",
+ "---- 0. 9\n",
"\n",
- "Label = 1\n",
+ "Label = 16\n",
"Pred =\n",
- " 0. 3\n",
+ " 0. 10\n",
"\n",
- "Label = 1\n",
+ "Label = 8\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 8\n",
"\n",
- "Label = 1\n",
+ "Label = 13\n",
"Pred =\n",
- "---- 0. 1\n",
+ " 0. 1\n",
"\n",
- "Label = 1\n",
+ "Label = 7\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 7\n",
"\n",
- "Label = 1\n",
+ "Label = 9\n",
"Pred =\n",
- "---- 0. 1\n",
+ "---- 0. 9\n",
"\n",
- "Label = 1\n",
+ "Label = 10\n",
"Pred =\n",
- " 0. 2\n",
+ "---- 0. 10\n",
"\n",
- "Label = 1\n",
+ "Label = 10\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 10\n",
"\n",
- "Label = 2\n",
+ "Label = 17\n",
"Pred =\n",
- "---- 0. 2\n",
+ " 0. 18\n",
"\n",
- "Label = 2\n",
+ "Label = 11\n",
"Pred =\n",
" 0. 4\n",
"\n",
- "Label = 2\n",
- "Pred =\n",
- " 0. 1\n",
- "\n",
- "Label = 2\n",
+ "Label = 0\n",
"Pred =\n",
- " 0. 4\n",
+ " 0. 8\n",
"\n",
- "Label = 2\n",
+ "Label = 18\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 18\n",
"\n",
- "Label = 2\n",
+ "Label = 5\n",
"Pred =\n",
- "---- 0. 2\n",
+ " 0. 8\n",
"\n",
- "Label = 2\n",
+ "Label = 18\n",
"Pred =\n",
- " 0. 4\n",
+ "---- 0. 18\n",
"\n",
- "Label = 2\n",
+ "Label = 6\n",
"Pred =\n",
- "---- 0. 2\n",
+ " 0. 1\n",
"\n",
- "Label = 2\n",
+ "Label = 1\n",
"Pred =\n",
- " 0. 3\n",
+ " 0. 18\n",
"\n",
- "Label = 2\n",
+ "Label = 11\n",
"Pred =\n",
- "---- 0. 2\n",
+ "---- 0. 11\n",
"\n",
- "Label = 2\n",
+ "Label = 6\n",
"Pred =\n",
- "---- 0. 2\n",
+ " 0. 1\n",
"\n",
- "Label = 2\n",
+ "Label = 10\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 10\n",
"\n",
- "Label = 2\n",
+ "Label = 15\n",
"Pred =\n",
- " 0. 3\n",
+ " 0. 8\n",
"\n",
- "Label = 2\n",
+ "Label = 12\n",
"Pred =\n",
- "---- 0. 2\n",
+ "---- 0. 12\n",
"\n",
- "Label = 2\n",
+ "Label = 5\n",
"Pred =\n",
- " 0. 4\n",
+ " 0. 8\n",
"\n",
"Label = 2\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 2\n",
"\n",
- "Label = 2\n",
+ "Label = 19\n",
"Pred =\n",
" 0. 1\n",
"\n",
- "Label = 2\n",
- "Pred =\n",
- " 0. 5\n",
- "\n",
- "Label = 2\n",
+ "Label = 12\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 12\n",
"\n",
- "Label = 3\n",
+ "Label = 12\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 12\n",
"\n",
- "Label = 3\n",
+ "Label = 10\n",
"Pred =\n",
- "---- 0. 3\n",
+ "---- 0. 10\n",
"\n",
- "Label = 3\n",
+ "Label = 7\n",
"Pred =\n",
- " 0. 1\n",
+ "---- 0. 7\n",
"\n",
- "Label = 3\n",
+ "Label = 12\n",
"Pred =\n",
- "---- 0. 3\n",
+ "---- 0. 12\n",
"\n",
- "Label = 3\n",
+ "Label = 15\n",
"Pred =\n",
- " 0. 0\n",
+ " 0. 13\n",
"\n",
"Label = 3\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 8\n",
"\n",
- "Label = 3\n",
+ "Label = 9\n",
"Pred =\n",
- " 0. 5\n",
+ "---- 0. 9\n",
"\n",
"Label = 3\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 8\n",
"\n",
- "Label = 3\n",
+ "Label = 1\n",
"Pred =\n",
- "---- 0. 3\n",
+ "---- 0. 1\n",
"\n",
- "Label = 3\n",
+ "Label = 5\n",
"Pred =\n",
- "---- 0. 3\n",
+ "---- 0. 5\n",
"\n",
- "Label = 3\n",
+ "Label = 17\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 1\n",
"\n",
"Label = 3\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 0\n",
"\n",
- "Label = 3\n",
+ "Label = 5\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 1\n",
"\n",
- "Label = 3\n",
+ "Label = 14\n",
"Pred =\n",
- "---- 0. 3\n",
+ " 0. 5\n",
"\n",
- "Label = 3\n",
+ "Label = 17\n",
"Pred =\n",
- " 0. 1\n",
+ "---- 0. 17\n",
"\n",
"Label = 4\n",
"Pred =\n",
"---- 0. 4\n",
"\n",
- "Label = 4\n",
+ "Label = 7\n",
"Pred =\n",
- "---- 0. 4\n",
+ " 0. 6\n",
"\n",
"Label = 4\n",
"Pred =\n",
- " 0. 0\n",
+ " 0. 10\n",
"\n",
- "Label = 4\n",
+ "Label = 13\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 13\n",
"\n",
- "Label = 4\n",
+ "Label = 17\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 17\n",
"\n",
- "Label = 4\n",
+ "Label = 9\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 9\n",
"\n",
- "Label = 4\n",
+ "Label = 10\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 10\n",
"\n",
- "Label = 4\n",
+ "Label = 17\n",
"Pred =\n",
- "---- 0. 4\n",
+ " 0. 1\n",
"\n",
- "Label = 4\n",
+ "Label = 2\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 2\n",
"\n",
- "Label = 4\n",
+ "Label = 6\n",
"Pred =\n",
- "---- 0. 4\n",
+ " 0. 1\n",
"\n",
- "Label = 4\n",
+ "Label = 0\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 0\n",
"\n",
- "Label = 4\n",
+ "Label = 11\n",
+ "Pred =\n",
+ " 0. 4\n",
+ "\n",
+ "Label = 6\n",
"Pred =\n",
" 0. 1\n",
"\n",
- "Label = 4\n",
+ "Label = 7\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 7\n",
"\n",
- "Label = 4\n",
+ "Label = 10\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 10\n",
"\n",
- "Label = 4\n",
+ "Label = 18\n",
"Pred =\n",
- "---- 0. 4\n",
+ "---- 0. 18\n",
"\n",
- "Label = 4\n",
+ "Label = 19\n",
"Pred =\n",
- " 0. 0\n",
+ " 0. 13\n",
"\n",
- "Label = 5\n",
+ "Label = 4\n",
"Pred =\n",
- " 0. 0\n",
+ "---- 0. 4\n",
"\n",
- "Label = 5\n",
+ "Label = 15\n",
"Pred =\n",
- "---- 0. 5\n",
+ " 0. 2\n",
"\n",
- "Label = 5\n",
+ "Label = 12\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 12\n",
"\n",
"Label = 5\n",
"Pred =\n",
- " 0. 4\n",
+ " 0. 6\n",
"\n",
- "Label = 5\n",
+ "Label = 7\n",
"Pred =\n",
- "---- 0. 5\n",
+ "---- 0. 7\n",
"\n",
- "Label = 5\n",
+ "Label = 7\n",
"Pred =\n",
- "---- 0. 5\n",
+ "---- 0. 7\n",
"\n",
- "Label = 5\n",
+ "Label = 17\n",
"Pred =\n",
- " 0. 0\n",
+ "---- 0. 17\n",
"\n",
- "Label = 5\n",
+ "Label = 19\n",
"Pred =\n",
- "---- 0. 5\n",
+ " 0. 8\n",
"\n",
- "Label = 5\n",
+ "Label = 19\n",
"Pred =\n",
- " 0. 3\n",
+ " 0. 8\n",
"\n",
- "Label = 5\n",
+ "Label = 11\n",
"Pred =\n",
- " 0. 1\n",
+ " 0. 10\n",
"\n",
- "Label = 5\n",
+ "Label = 15\n",
"Pred =\n",
- " 0. 4\n",
+ " 0. 13\n",
"\n",
"Label = 5\n",
"Pred =\n",
"---- 0. 5\n",
"\n",
- "Label = 5\n",
+ "Label = 12\n",
"Pred =\n",
- " 0. 1\n",
+ "---- 0. 12\n",
"\n",
- "Label = 5\n",
+ "Label = 16\n",
"Pred =\n",
- "---- 0. 5\n",
+ " 0. 13\n",
"\n",
- "Label = 5\n",
+ "Label = 4\n",
"Pred =\n",
- " 0. 1\n",
+ "---- 0. 4\n",
"\n",
- "Label = 5\n",
+ "Label = 13\n",
"Pred =\n",
- " 0. 2\n",
+ " 0. 18\n",
"\n",
- "Label = 5\n",
+ "Label = 18\n",
"Pred =\n",
- "---- 0. 5\n",
+ "---- 0. 18\n",
"\n",
- "Label = 5\n",
+ "Label = 14\n",
"Pred =\n",
- "---- 0. 5\n",
+ "---- 0. 14\n",
"\n",
- "Label = 5\n",
+ "Label = 17\n",
"Pred =\n",
- " 0. 3\n",
+ "---- 0. 17\n",
"\n",
"Label = 5\n",
"Pred =\n",
- " 0. 3\n",
+ " 0. 1\n",
"\n",
- "Label = 5\n",
+ "Label = 6\n",
"Pred =\n",
- "---- 0. 5\n",
- "\n",
- "Label = 5\n",
- "Pred =\n",
- "---- 0. 5\n",
- "\n",
- "Label = 5\n",
- "Pred =\n",
- " 0. 2\n",
- "\n",
- "Label = 5\n",
- "Pred =\n",
- "---- 0. 5\n",
+ " 0. 1\n",
"\n"
]
}
@@ -1666,16 +2778,16 @@
},
{
"cell_type": "code",
- "execution_count": 174,
+ "execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.5603448275862069"
+ "0.5446428571428571"
]
},
- "execution_count": 174,
+ "execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
@@ -1691,234 +2803,6 @@
"outputs": [],
"source": []
},
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [],
- "source": [
- "from scipy import io"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [],
- "source": [
- "m = io.mmread('../sparse/adj/0_sparse_fname2_split_magret_adj.mtx').toarray()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
- "plt.imshow(m)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.count_nonzero(m[56,:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "eye = np.eye(64)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [],
- "source": [
- "shuffle_idx = np.random.permutation(20)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "G = nx.from_numpy_matrix(m)\n",
- "shuffled_adj = nx.adjacency_matrix(G, nodelist=shuffle_idx).todense()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
- "plt.imshow(shuffled_adj)\n",
- "#plt.xticks(range(20), idx);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [],
- "source": [
- "eye[:len(shuffled_adj), :len(shuffled_adj)] = shuffled_adj"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
- "plt.imshow(eye)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(4800, 768)"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "enc_df = pd.read_csv(path+'cls_output-embed/encoder_results.tsv', header=None, sep='\\t')\n",
- "enc_df.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 104,
- "metadata": {},
- "outputs": [],
- "source": [
- "embeddings = enc_df.values.reshape((75,64,768))"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -1926,913 +2810,6 @@
"outputs": [],
"source": []
},
- {
- "cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "('[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",
- " 31)"
- ]
- },
- "execution_count": 74,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "snippet.loc[5][0], len(snippet.loc[5][0].split(' '))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "('[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",
- " 29)"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "snippet.loc[11][0], len(snippet.loc[11][0].split(' '))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sim = np.dot(embeddings[:,5], embeddings[:,11].T)\n",
- "plt.figure(figsize=(10,10))\n",
- "plt.imshow(sim)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 105,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(20,15))\n",
- "plt.imshow(embeddings[:,0], aspect=10)\n",
- "plt.yticks(range(75), labels_str);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 106,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(75, 64, 768)"
- ]
- },
- "execution_count": 106,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "embeddings.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 107,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.metrics.pairwise import cosine_similarity"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 110,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(75, 75)"
- ]
- },
- "execution_count": 110,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cos = cosine_similarity(embeddings[:,0])\n",
- "cos.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 114,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(20,15))\n",
- "plt.imshow(cos)\n",
- "plt.yticks(range(75), labels_str);\n",
- "plt.xticks(range(75), labels_str, rotation=90);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 148,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "deserialize\n",
- "--- s: from_config p: deserialize\n",
- "--- s: model_from_json p: from_config\n",
- "--- s: get_word_index p: get_config\n",
- "--- s: from_config p: in_test_phase\n",
- "--- s: glorot_normal p: model_from_config\n",
- "--- s: _has_nchw_support p: _postprocess_conv3d_output\n",
- "--- s: dot p: _prepare_name\n",
- "\n",
- "set_model\n",
- "--- s: add p: __init__\n",
- "--- s: update_sub p: set_params\n",
- "--- s: __init__ p: on_epoch_end\n",
- "--- s: trainable_weights p: on_train_end\n",
- "--- s: call p: on_train_begin\n",
- "--- s: update p: predict\n",
- "--- s: updates p: __enter__\n",
- "\n",
- "get_monitor_value\n",
- "--- s: __init__ p: on_train_begin\n",
- "--- s: noised p: on_epoch_end\n",
- "--- s: from_config p: __init__\n",
- "--- s: save_img p: on_train_end\n",
- "--- s: image_dim_ordering p: get_config\n",
- "--- s: call p: on_batch_end\n",
- "--- s: __init__ p: validate_file\n",
- "\n",
- "is_indexed_slices\n",
- "--- s: sigmoid p: deserialize\n",
- "--- s: model_from_json p: from_config\n",
- "--- s: __init__ p: stop_gradient\n",
- "--- s: __init__ p: _prepare_name\n",
- "--- s: __init__ p: get_config\n",
- "--- s: _to_tensor p: is_sparse\n",
- "--- s: _has_nchw_support p: in_test_phase\n",
- "\n",
- "from_config\n",
- "--- s: from_config p: from_config\n",
- "--- s: deserialize p: get_config\n",
- "--- s: model_from_json p: update_add\n",
- "--- s: image_dim_ordering p: count_params\n",
- "--- s: dot p: call\n",
- "--- s: _node_key p: _pooling_function\n",
- "--- s: glorot_normal p: lecun_normal\n",
- "\n",
- "__init__\n",
- "--- s: __call__ p: __init__\n",
- "--- s: get_config p: __call__\n",
- "--- s: __init__ p: get_config\n",
- "--- s: __init__ p: call\n",
- "--- s: predict_classes p: random_uniform\n",
- "--- s: noised p: get_losses_for\n",
- "--- s: arange p: on_train_begin\n",
- "\n",
- "__call__\n",
- "--- s: get_config p: __call__\n",
- "--- s: noised p: call\n",
- "--- s: __init__ p: __init__\n",
- "--- s: _to_tensor p: random_normal\n",
- "--- s: arange p: random_uniform\n",
- "--- s: __init__ p: compute_mask\n",
- "--- s: __init__ p: backward\n",
- "\n",
- "get_config\n",
- "--- s: __call__ p: get_config\n",
- "--- s: noised p: __init__\n",
- "--- s: __init__ p: __call__\n",
- "--- s: arange p: trainable_weights\n",
- "--- s: get_config p: from_config\n",
- "--- s: add p: losses\n",
- "--- s: __init__ p: compute_output_shape\n",
- "\n",
- "glorot_normal\n",
- "--- s: deserialize p: glorot_uniform\n",
- "--- s: dot p: lecun_normal\n",
- "--- s: from_config p: he_uniform\n",
- "--- s: arange p: lecun_uniform\n",
- "--- s: bias_initializer p: he_normal\n",
- "--- s: __init__ p: gradients\n",
- "--- s: preprocess_input p: cumprod\n",
- "\n",
- "call\n",
- "--- s: call p: call\n",
- "--- s: call p: __call__\n",
- "--- s: call p: _merge_function\n",
- "--- s: infer_outputs p: non_trainable_weights\n",
- "--- s: _merge_function p: compute_mask\n",
- "--- s: compute_mask p: _pooling_function\n",
- "--- s: __init__ p: bias_initializer\n",
- "\n",
- "update\n",
- "--- s: decode_predictions p: __init__\n",
- "--- s: preprocess_input p: get_config\n",
- "--- s: bias_initializer p: get\n",
- "--- s: __init__ p: __call__\n",
- "--- s: __init__ p: call\n",
- "--- s: _merge_function p: preprocess_input\n",
- "--- s: __init__ p: get_losses_for\n",
- "\n",
- "__init__\n",
- "--- s: min p: __init__\n",
- "--- s: __init__ p: __call__\n",
- "--- s: __init__ p: forward\n",
- "--- s: add p: get_losses_for\n",
- "--- s: eval p: on_train_begin\n",
- "--- s: __init__ p: normalize\n",
- "--- s: set_model p: batch_set_value\n",
- "\n",
- "add\n",
- "--- s: update_sub p: __init__\n",
- "--- s: set_model p: get\n",
- "--- s: identity p: on_train_begin\n",
- "--- s: ctc_cost p: on_batch_begin\n",
- "--- s: __init__ p: on_epoch_end\n",
- "--- s: __call__ p: get_losses_for\n",
- "--- s: _to_tensor p: __iter__\n",
- "\n",
- "get_word_index\n",
- "--- s: deserialize p: get_word_index\n",
- "--- s: model_from_json p: print_tensor\n",
- "--- s: _has_nchw_support p: deserialize\n",
- "--- s: glorot_normal p: on_train_end\n",
- "--- s: from_config p: call\n",
- "--- s: __init__ p: relu\n",
- "--- s: is_indexed_slices p: set_params\n",
- "\n",
- "decode_predictions\n",
- "--- s: preprocess_input p: decode_predictions\n",
- "--- s: update p: preprocess_input\n",
- "--- s: add p: call\n",
- "--- s: multiply p: _contain_seqence_axis\n",
- "--- s: subtract p: random_uniform_variable\n",
- "--- s: bias_initializer p: average\n",
- "--- s: __init__ p: prod\n",
- "\n",
- "preprocess_input\n",
- "--- s: decode_predictions p: preprocess_input\n",
- "--- s: update p: decode_predictions\n",
- "--- s: add p: transform\n",
- "--- s: multiply p: MobileNetV2\n",
- "--- s: subtract p: permute_dimensions\n",
- "--- s: bias_initializer p: _preprocess_conv3d_input\n",
- "--- s: __init__ p: Xception\n",
- "\n",
- "__init__\n",
- "--- s: __init__ p: __init__\n",
- "--- s: __init__ p: on_train_begin\n",
- "--- s: __init__ p: is_all_none\n",
- "--- s: __init__ p: function\n",
- "--- s: decode_predictions p: get_losses_for\n",
- "--- s: preprocess_input p: get_config\n",
- "--- s: save_img p: on_train_end\n",
- "\n",
- "_merge_function\n",
- "--- s: dot p: _merge_function\n",
- "--- s: multiply p: compute_output_shape\n",
- "--- s: subtract p: call\n",
- "--- s: add p: binary_crossentropy\n",
- "--- s: bias_initializer p: infer_outputs\n",
- "--- s: call p: step\n",
- "--- s: call p: __init__\n",
- "\n",
- "_merge_function\n",
- "--- s: call p: call\n",
- "--- s: call p: __call__\n",
- "--- s: softmax p: _merge_function\n",
- "--- s: compute_mask p: compute_mask\n",
- "--- s: call p: _pooling_function\n",
- "--- s: call p: infer_outputs\n",
- "--- s: call p: get_losses_for\n",
- "\n",
- "add\n",
- "--- s: subtract p: maximum\n",
- "--- s: multiply p: minimum\n",
- "--- s: dot p: average\n",
- "--- s: preprocess_input p: preprocess_input\n",
- "--- s: decode_predictions p: call\n",
- "--- s: bias_initializer p: function\n",
- "--- s: __init__ p: decode_predictions\n",
- "\n",
- "subtract\n",
- "--- s: subtract p: maximum\n",
- "--- s: multiply p: minimum\n",
- "--- s: dot p: average\n",
- "--- s: preprocess_input p: preprocess_input\n",
- "--- s: decode_predictions p: concatenate\n",
- "--- s: bias_initializer p: function\n",
- "--- s: __init__ p: call\n",
- "\n",
- "multiply\n",
- "--- s: subtract p: maximum\n",
- "--- s: multiply p: minimum\n",
- "--- s: dot p: average\n",
- "--- s: preprocess_input p: preprocess_input\n",
- "--- s: decode_predictions p: call\n",
- "--- s: bias_initializer p: function\n",
- "--- s: __init__ p: decode_predictions\n",
- "\n",
- "dot\n",
- "--- s: subtract p: call\n",
- "--- s: add p: function\n",
- "--- s: multiply p: __init__\n",
- "--- s: decode_predictions p: decode_predictions\n",
- "--- s: preprocess_input p: maximum\n",
- "--- s: arange p: reverse\n",
- "--- s: glorot_normal p: average\n",
- "\n",
- "call\n",
- "--- s: call p: call\n",
- "--- s: call p: __call__\n",
- "--- s: _merge_function p: _merge_function\n",
- "--- s: call p: _pooling_function\n",
- "--- s: compute_mask p: compute_mask\n",
- "--- s: call p: bias_initializer\n",
- "--- s: call p: print_tensor\n",
- "\n",
- "call\n",
- "--- s: _merge_function p: call\n",
- "--- s: call p: __call__\n",
- "--- s: softmax p: _merge_function\n",
- "--- s: compute_mask p: _pooling_function\n",
- "--- s: call p: compute_mask\n",
- "--- s: call p: get_losses_for\n",
- "--- s: call p: infer_outputs\n",
- "\n",
- "__init__\n",
- "--- s: __init__ p: __init__\n",
- "--- s: __init__ p: is_all_none\n",
- "--- s: __init__ p: on_train_begin\n",
- "--- s: __init__ p: get_losses_for\n",
- "--- s: save_img p: zeros_like\n",
- "--- s: preprocess_input p: binary_crossentropy\n",
- "--- s: decode_predictions p: function\n",
- "\n",
- "compute_output_shape\n",
- "--- s: compute_output_shape p: compute_output_shape\n",
- "--- s: infer_outputs p: _merge_function\n",
- "--- s: argmax p: __init__\n",
- "--- s: compute_mask p: _get_noise_shape\n",
- "--- s: batch_flatten p: slice\n",
- "--- s: __init__ p: range_less_than\n",
- "--- s: __init__ p: step\n",
- "\n",
- "trainable_weights\n",
- "--- s: updates p: non_trainable_weights\n",
- "--- s: _node_key p: trainable_weights\n",
- "--- s: from_config p: get_weights\n",
- "--- s: losses p: _check_trainable_weights_consistency\n",
- "--- s: set_model p: trainable\n",
- "--- s: call p: weights\n",
- "--- s: __init__ p: get_updates_for\n",
- "\n",
- "updates\n",
- "--- s: trainable_weights p: updates\n",
- "--- s: _node_key p: get_updates_for\n",
- "--- s: losses p: get_config\n",
- "--- s: __init__ p: losses\n",
- "--- s: from_config p: stateful\n",
- "--- s: __init__ p: state_updates\n",
- "--- s: get_config p: get_weights\n",
- "\n",
- "get_config\n",
- "--- s: get_config p: get_config\n",
- "--- s: get_config p: get\n",
- "--- s: _node_key p: trainable_weights\n",
- "--- s: model_from_json p: from_config\n",
- "--- s: from_config p: losses\n",
- "--- s: losses p: pow\n",
- "--- s: call p: backward\n",
- "\n",
- "__init__\n",
- "--- s: __init__ p: __init__\n",
- "--- s: __init__ p: compute_output_shape\n",
- "--- s: __init__ p: get_config\n",
- "--- s: __init__ p: binary_crossentropy\n",
- "--- s: get_config p: is_all_none\n",
- "--- s: save_img p: cell\n",
- "--- s: get_config p: get_losses_for\n",
- "\n",
- "__init__\n",
- "--- s: __init__ p: __init__\n",
- "--- s: save_img p: is_all_none\n",
- "--- s: __init__ p: get_losses_for\n",
- "--- s: __init__ p: binary_crossentropy\n",
- "--- s: __init__ p: compute_output_shape\n",
- "--- s: __init__ p: on_train_begin\n",
- "--- s: preprocess_input p: function\n",
- "\n",
- "step\n",
- "--- s: compute_mask p: step\n",
- "--- s: call p: get_losses_for\n",
- "--- s: call p: concatenate\n",
- "--- s: from_config p: _merge_function\n",
- "--- s: losses p: reverse\n",
- "--- s: add p: infer_outputs\n",
- "--- s: subtract p: _preprocess_conv2d_depthwise_kernel\n",
- "\n",
- "get_config\n",
- "--- s: get_config p: get_config\n",
- "--- s: get_config p: compute_output_shape\n",
- "--- s: __init__ p: trainable_weights\n",
- "--- s: call p: get\n",
- "--- s: compute_output_shape p: from_config\n",
- "--- s: _node_key p: l2_normalize\n",
- "--- s: infer_outputs p: _pooling_function\n",
- "\n",
- "call\n",
- "--- s: call p: call\n",
- "--- s: call p: _pooling_function\n",
- "--- s: infer_outputs p: __call__\n",
- "--- s: call p: compute_mask\n",
- "--- s: _merge_function p: _merge_function\n",
- "--- s: get_config p: bias_initializer\n",
- "--- s: call p: function\n",
- "\n",
- "get_config\n",
- "--- s: get_config p: get_config\n",
- "--- s: get_config p: trainable_weights\n",
- "--- s: __init__ p: get\n",
- "--- s: __init__ p: __init__\n",
- "--- s: _node_key p: from_config\n",
- "--- s: get_config p: l2_normalize\n",
- "--- s: noised p: pow\n",
- "\n",
- "compute_mask\n",
- "--- s: call p: call\n",
- "--- s: call p: compute_mask\n",
- "--- s: step p: __call__\n",
- "--- s: infer_outputs p: _merge_function\n",
- "--- s: call p: infer_outputs\n",
- "--- s: _merge_function p: states\n",
- "--- s: __init__ p: int_or_none\n",
- "\n",
- "from_config\n",
- "--- s: from_config p: from_config\n",
- "--- s: deserialize p: get_config\n",
- "--- s: step p: deserialize\n",
- "--- s: _node_key p: update_add\n",
- "--- s: model_from_json p: call\n",
- "--- s: trainable_weights p: non_trainable_weights\n",
- "--- s: get_config p: stack\n",
- "\n",
- "losses\n",
- "--- s: call p: get_losses_for\n",
- "--- s: call p: losses\n",
- "--- s: step p: get_config\n",
- "--- s: compute_mask p: cell\n",
- "--- s: updates p: in_top_k\n",
- "--- s: call p: __call__\n",
- "--- s: get_config p: build\n",
- "\n",
- "call\n",
- "--- s: call p: call\n",
- "--- s: compute_mask p: __call__\n",
- "--- s: step p: compute_mask\n",
- "--- s: losses p: _pooling_function\n",
- "--- s: call p: _merge_function\n",
- "--- s: __init__ p: get_losses_for\n",
- "--- s: infer_outputs p: function\n",
- "\n",
- "call\n",
- "--- s: call p: call\n",
- "--- s: compute_mask p: compute_mask\n",
- "--- s: step p: __call__\n",
- "--- s: losses p: _merge_function\n",
- "--- s: call p: get_losses_for\n",
- "--- s: __init__ p: _pooling_function\n",
- "--- s: infer_outputs p: function\n",
- "\n",
- "bias_initializer\n",
- "--- s: decode_predictions p: bias_initializer\n",
- "--- s: preprocess_input p: call\n",
- "--- s: update p: non_trainable_weights\n",
- "--- s: add p: _preprocess_conv2d_kernel\n",
- "--- s: multiply p: _preprocess_conv2d_input\n",
- "--- s: subtract p: prod\n",
- "--- s: dot p: average\n",
- "\n",
- "noised\n",
- "--- s: __init__ p: __call__\n",
- "--- s: __call__ p: noised\n",
- "--- s: gather p: l2_normalize\n",
- "--- s: get_config p: random_normal\n",
- "--- s: bias_initializer p: gradients\n",
- "--- s: arange p: dropout\n",
- "--- s: __init__ p: _get_noise_shape\n",
- "\n",
- "__init__\n",
- "--- s: noised p: __init__\n",
- "--- s: __init__ p: AtrousConvolution2D\n",
- "--- s: __call__ p: get_config\n",
- "--- s: call p: is_all_none\n",
- "--- s: call p: on_train_begin\n",
- "--- s: __init__ p: random_normal\n",
- "--- s: __init__ p: embedding_kwargs_preprocessor\n",
- "\n",
- "__init__\n",
- "--- s: __init__ p: __init__\n",
- "--- s: __init__ p: binary_crossentropy\n",
- "--- s: __init__ p: get_losses_for\n",
- "--- s: __init__ p: is_all_none\n",
- "--- s: compute_mask p: on_train_begin\n",
- "--- s: __init__ p: compute_mask\n",
- "--- s: infer_outputs p: function\n",
- "\n",
- "compute_output_shape\n",
- "--- s: compute_output_shape p: compute_output_shape\n",
- "--- s: argmax p: _merge_function\n",
- "--- s: infer_outputs p: __init__\n",
- "--- s: batch_flatten p: binary_crossentropy\n",
- "--- s: compute_mask p: argmin\n",
- "--- s: flatten p: get_config\n",
- "--- s: softmax p: build\n",
- "\n",
- "cast_to_floatx\n",
- "--- s: get_value p: batch_get_value\n",
- "--- s: eval p: update_add\n",
- "--- s: _to_tensor p: batch_set_value\n",
- "--- s: sigmoid p: reshape\n",
- "--- s: batch_flatten p: permute_dimensions\n",
- "--- s: ndim p: print_tensor\n",
- "--- s: eval p: is_tensor\n",
- "\n",
- "image_dim_ordering\n",
- "--- s: is_placeholder p: _preprocess_padding\n",
- "--- s: from_config p: batch_get_value\n",
- "--- s: get_value p: set_floatx\n",
- "--- s: is_indexed_slices p: he_uniform\n",
- "--- s: get_config p: _preprocess_border_mode\n",
- "--- s: dot p: lecun_normal\n",
- "--- s: get_config p: to_dense\n",
- "\n",
- "eval\n",
- "--- s: get_value p: is_tensor\n",
- "--- s: eval p: batch_get_value\n",
- "--- s: is_placeholder p: __call__\n",
- "--- s: cast_to_floatx p: get_value\n",
- "--- s: gather p: stop_gradient\n",
- "--- s: _to_tensor p: count_params\n",
- "--- s: batch_flatten p: batch_set_value\n",
- "\n",
- "ndim\n",
- "--- s: get_value p: count_params\n",
- "--- s: cast_to_floatx p: __call__\n",
- "--- s: eval p: update_add\n",
- "--- s: __init__ p: is_keras_tensor\n",
- "--- s: argmax p: reshape\n",
- "--- s: compute_output_shape p: int_shape\n",
- "--- s: softmax p: pow\n",
- "\n",
- "gather\n",
- "--- s: noised p: __init__\n",
- "--- s: eval p: _get_noise_shape\n",
- "--- s: __init__ p: ctc_create_skip_idxs\n",
- "--- s: predict_classes p: gather\n",
- "--- s: __call__ p: one_hot\n",
- "--- s: batch_flatten p: elu\n",
- "--- s: compute_output_shape p: compute_output_shape\n",
- "\n",
- "argmax\n",
- "--- s: softmax p: argmin\n",
- "--- s: compute_output_shape p: argmax\n",
- "--- s: batch_flatten p: _merge_function\n",
- "--- s: flatten p: concatenate\n",
- "--- s: compute_output_shape p: compute_output_shape\n",
- "--- s: compute_mask p: binary_crossentropy\n",
- "--- s: _merge_function p: cumprod\n",
- "\n",
- "softmax\n",
- "--- s: argmax p: argmin\n",
- "--- s: call p: argmax\n",
- "--- s: _merge_function p: cumprod\n",
- "--- s: flatten p: softmax\n",
- "--- s: predict_classes p: expand_dims\n",
- "--- s: compute_output_shape p: stack\n",
- "--- s: sum p: concatenate\n",
- "\n",
- "__init__\n",
- "--- s: ones p: __init__\n",
- "--- s: eval p: compute_output_shape\n",
- "--- s: __call__ p: get_losses_for\n",
- "--- s: ndim p: cell\n",
- "--- s: identity p: binary_crossentropy\n",
- "--- s: get_value p: get_config\n",
- "--- s: compute_output_shape p: build\n",
- "\n",
- "infer_outputs\n",
- "--- s: compute_output_shape p: call\n",
- "--- s: compute_mask p: infer_outputs\n",
- "--- s: compute_output_shape p: _merge_function\n",
- "--- s: call p: binary_crossentropy\n",
- "--- s: call p: compute_mask\n",
- "--- s: call p: compute_output_shape\n",
- "--- s: call p: moving_average_update\n",
- "\n",
- "_has_nchw_support\n",
- "--- s: model_from_json p: clear_session\n",
- "--- s: get_word_index p: get_uid\n",
- "--- s: deserialize p: argmin\n",
- "--- s: predict_classes p: validate_file\n",
- "--- s: is_indexed_slices p: squeeze\n",
- "--- s: glorot_normal p: on_train_end\n",
- "--- s: _node_key p: in_test_phase\n",
- "\n",
- "_to_tensor\n",
- "--- s: cast_to_floatx p: preprocess_input\n",
- "--- s: identity p: int_shape\n",
- "--- s: __call__ p: zeros_like\n",
- "--- s: ones p: ones_like\n",
- "--- s: eval p: is_tensor\n",
- "--- s: get_value p: transform\n",
- "--- s: is_placeholder p: not_equal\n",
- "\n",
- "eval\n",
- "--- s: get_value p: eval\n",
- "--- s: cast_to_floatx p: get_value\n",
- "--- s: eval p: int_shape\n",
- "--- s: is_placeholder p: batch_get_value\n",
- "--- s: _to_tensor p: is_tensor\n",
- "--- s: sigmoid p: minimum\n",
- "--- s: identity p: reset_states\n",
- "\n",
- "min\n",
- "--- s: sum p: prod\n",
- "--- s: sigmoid p: any\n",
- "--- s: __init__ p: logsumexp\n",
- "--- s: foldr p: all\n",
- "--- s: eval p: sum\n",
- "--- s: argmax p: max\n",
- "--- s: softmax p: std\n",
- "\n",
- "flatten\n",
- "--- s: batch_flatten p: argmin\n",
- "--- s: softmax p: compute_output_shape\n",
- "--- s: sigmoid p: flatten\n",
- "--- s: compute_output_shape p: is_keras_tensor\n",
- "--- s: argmax p: _normalize_device_name\n",
- "--- s: predict_classes p: argmax\n",
- "--- s: cast_to_floatx p: identity\n",
- "\n",
- "batch_flatten\n",
- "--- s: flatten p: batch_get_value\n",
- "--- s: foldr p: reverse\n",
- "--- s: sigmoid p: ones_like\n",
- "--- s: argmax p: stack\n",
- "--- s: compute_output_shape p: softsign\n",
- "--- s: cast_to_floatx p: identity\n",
- "--- s: predict_classes p: flatten\n",
- "\n",
- "get_value\n",
- "--- s: eval p: eval\n",
- "--- s: eval p: get_value\n",
- "--- s: is_placeholder p: update_add\n",
- "--- s: cast_to_floatx p: permute_dimensions\n",
- "--- s: identity p: reverse\n",
- "--- s: ndim p: cumprod\n",
- "--- s: _to_tensor p: get_variable_shape\n",
- "\n",
- "sigmoid\n",
- "--- s: min p: softsign\n",
- "--- s: cast_to_floatx p: is_tensor\n",
- "--- s: foldr p: update_add\n",
- "--- s: batch_flatten p: softplus\n",
- "--- s: flatten p: permute_dimensions\n",
- "--- s: eval p: tanh\n",
- "--- s: _to_tensor p: print_tensor\n",
- "\n",
- "is_placeholder\n",
- "--- s: get_value p: is_placeholder\n",
- "--- s: eval p: print_tensor\n",
- "--- s: eval p: int_shape\n",
- "--- s: _to_tensor p: in_test_phase\n",
- "--- s: identity p: batch_get_value\n",
- "--- s: image_dim_ordering p: softsign\n",
- "--- s: cast_to_floatx p: clear_session\n",
- "\n",
- "ones\n",
- "--- s: __init__ p: zeros\n",
- "--- s: _to_tensor p: eye\n",
- "--- s: __call__ p: zeros_like\n",
- "--- s: identity p: ones_like\n",
- "--- s: eval p: constant\n",
- "--- s: foldr p: prod\n",
- "--- s: ndim p: ones\n",
- "\n",
- "identity\n",
- "--- s: _to_tensor p: in_test_phase\n",
- "--- s: get_value p: ones_like\n",
- "--- s: update_sub p: int_shape\n",
- "--- s: sum p: identity\n",
- "--- s: is_placeholder p: zeros_like\n",
- "--- s: add p: update_add\n",
- "--- s: eval p: is_placeholder\n",
- "\n",
- "update_sub\n",
- "--- s: add p: update_add\n",
- "--- s: identity p: is_tensor\n",
- "--- s: _to_tensor p: update_sub\n",
- "--- s: set_model p: in_test_phase\n",
- "--- s: sigmoid p: in_top_k\n",
- "--- s: ctc_cost p: _postprocess_conv3d_output\n",
- "--- s: get_value p: flatten\n",
- "\n",
- "sum\n",
- "--- s: min p: prod\n",
- "--- s: identity p: std\n",
- "--- s: softmax p: logsumexp\n",
- "--- s: save_img p: sum\n",
- "--- s: _to_tensor p: max\n",
- "--- s: sigmoid p: min\n",
- "--- s: argmax p: any\n",
- "\n",
- "arange\n",
- "--- s: __call__ p: stop_gradient\n",
- "--- s: dot p: eye\n",
- "--- s: noised p: random_uniform\n",
- "--- s: _to_tensor p: random_normal\n",
- "--- s: glorot_normal p: gradients\n",
- "--- s: predict_classes p: random_uniform_variable\n",
- "--- s: get_config p: deserialize\n",
- "\n",
- "ctc_cost\n",
- "--- s: add p: is_tensor\n",
- "--- s: compute_mask p: mean_absolute_percentage_error\n",
- "--- s: call p: kullback_leibler_divergence\n",
- "--- s: call p: backward\n",
- "--- s: update_sub p: expand_dims\n",
- "--- s: step p: argmax\n",
- "--- s: _merge_function p: batch_flatten\n",
- "\n",
- "foldr\n",
- "--- s: batch_flatten p: foldl\n",
- "--- s: sigmoid p: map_fn\n",
- "--- s: min p: __init__\n",
- "--- s: __init__ p: elu\n",
- "--- s: flatten p: prod\n",
- "--- s: ones p: foldr\n",
- "--- s: argmax p: to_dense\n",
- "\n",
- "save_img\n",
- "--- s: __init__ p: __init__\n",
- "--- s: __init__ p: preprocess_input\n",
- "--- s: __init__ p: max\n",
- "--- s: __init__ p: clip\n",
- "--- s: _to_tensor p: zeros_like\n",
- "--- s: sum p: all\n",
- "--- s: __init__ p: identity\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "predict_classes\n",
- "--- s: softmax p: argmax\n",
- "--- s: flatten p: predict_proba\n",
- "--- s: batch_flatten p: compute_output_shape\n",
- "--- s: compute_output_shape p: argmin\n",
- "--- s: __call__ p: losses\n",
- "--- s: gather p: cell\n",
- "--- s: _to_tensor p: one_hot\n",
- "\n",
- "model_from_json\n",
- "--- s: deserialize p: get\n",
- "--- s: get_word_index p: deserialize\n",
- "--- s: from_config p: on_epoch_end\n",
- "--- s: _has_nchw_support p: model_from_yaml\n",
- "--- s: from_config p: model_from_config\n",
- "--- s: get_config p: from_config\n",
- "--- s: add p: forward\n",
- "\n",
- "_node_key\n",
- "--- s: updates p: _make_node_key\n",
- "--- s: trainable_weights p: from_config\n",
- "--- s: from_config p: is_keras_tensor\n",
- "--- s: __init__ p: raise_duplicate_arg_error\n",
- "--- s: get_config p: get_output_shape_at\n",
- "--- s: from_config p: get_uid\n",
- "--- s: __init__ p: count_params\n",
- "\n"
- ]
- }
- ],
- "source": [
- "score_sim = 0\n",
- "for i in range(75):\n",
- " print(label_df.loc[i][0])\n",
- " rnk = 7\n",
- " ranked = np.argsort(cos[i])[::-1][:rnk+1]\n",
- " r = preds[i]\n",
- " match = False\n",
- " \n",
- " for j in range(rnk):\n",
- " if (label_df.loc[ranked[0]][0]==label_df.loc[ranked[j+1]][0]) & (match == False):\n",
- " score_sim +=1\n",
- " match==True\n",
- " sim_pred = label_df.loc[ranked[j+1]][0]\n",
- " pred_pred = vocab_label_df.loc[r[j]][0]\n",
- " print(\"--- s: {} p: {}\".format(sim_pred, pred_pred))\n",
- " print()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 149,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.8533333333333334"
- ]
- },
- "execution_count": 149,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "score_sim/75"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
diff --git a/notebook/Inspect Predictions - MLM.ipynb b/notebook/Inspect Predictions - MLM.ipynb
index 2a89e16..cadaab3 100644
--- a/notebook/Inspect Predictions - MLM.ipynb
+++ b/notebook/Inspect Predictions - MLM.ipynb
@@ -24,11 +24,11 @@
"metadata": {},
"outputs": [],
"source": [
- "path = \"../large-corpus/\"\n",
- "prefix = \"sparse_tmp_\"\n",
+ "#path = \"../sparse/\"\n",
+ "#prefix = \"sparse_tmp_\"\n",
"\n",
- "#path = \"../../bert-cmp/bert/\"\n",
- "#prefix=\"\""
+ "path = \"../../bert-cmp/bert/\"\n",
+ "prefix=\"\""
]
},
{
@@ -83,16 +83,16 @@
" \n",
" \n",
" | 0 | \n",
- " 91 | \n",
- " 91 | \n",
- " 1 | \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 22 | \n",
" 2 | \n",
- " 91 | \n",
- " 62 | \n",
- " 8 | \n",
- " 422 | \n",
- " 93 | \n",
- " 8 | \n",
+ " 236 | \n",
+ " 24 | \n",
+ " 229 | \n",
+ " 37 | \n",
+ " 24 | \n",
+ " 241 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -107,64 +107,64 @@
"
\n",
" \n",
" | 1 | \n",
- " 60 | \n",
- " 60 | \n",
- " 10 | \n",
+ " 24 | \n",
+ " 426 | \n",
+ " 25 | \n",
" 2 | \n",
- " 20 | \n",
- " 22 | \n",
- " 86 | \n",
- " 20 | \n",
- " 22 | \n",
- " 198 | \n",
+ " 752 | \n",
+ " 24 | \n",
+ " 603 | \n",
+ " 564 | \n",
+ " 24 | \n",
+ " 199 | \n",
" ... | \n",
- " 0 | \n",
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- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
+ " 1142 | \n",
+ " 52 | \n",
+ " 1142 | \n",
+ " 769 | \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 654 | \n",
+ " 24 | \n",
+ " 3 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 8 | \n",
- " 8 | \n",
- " 11 | \n",
+ " 24 | \n",
+ " 97 | \n",
+ " 3 | \n",
" 2 | \n",
- " 25 | \n",
- " 80 | \n",
- " 7 | \n",
- " 8 | \n",
- " 65 | \n",
+ " 460 | \n",
+ " 6 | \n",
+ " 4 | \n",
+ " 318 | \n",
+ " 52 | \n",
" 24 | \n",
" ... | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
- " 0 | \n",
+ " 24 | \n",
+ " 236 | \n",
+ " 29 | \n",
+ " 1142 | \n",
+ " 135 | \n",
+ " 24 | \n",
+ " 256 | \n",
+ " 24 | \n",
+ " 37 | \n",
+ " 3 | \n",
"
\n",
" \n",
" | 3 | \n",
- " 20 | \n",
- " 20 | \n",
- " 8 | \n",
+ " 24 | \n",
+ " 47 | \n",
+ " 7 | \n",
" 2 | \n",
- " 11 | \n",
- " 22 | \n",
- " 101 | \n",
- " 309 | \n",
- " 8 | \n",
- " 20 | \n",
+ " 56 | \n",
+ " 57 | \n",
+ " 58 | \n",
+ " 106 | \n",
+ " 236 | \n",
+ " 24 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -179,16 +179,16 @@
"
\n",
" \n",
" | 4 | \n",
- " 8 | \n",
- " 8 | \n",
- " 7 | \n",
+ " 24 | \n",
+ " 57 | \n",
+ " 18 | \n",
" 2 | \n",
- " 25 | \n",
- " 20 | \n",
- " 22 | \n",
- " 511 | \n",
- " 8 | \n",
- " 20 | \n",
+ " 52 | \n",
+ " 24 | \n",
+ " 37 | \n",
+ " 52 | \n",
+ " 24 | \n",
+ " 52 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -203,16 +203,16 @@
"
\n",
" \n",
" | 5 | \n",
- " 296 | \n",
- " 489 | \n",
- " 4 | \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 22 | \n",
" 2 | \n",
- " 31 | \n",
- " 32 | \n",
- " 33 | \n",
- " 4 | \n",
- " 37 | \n",
- " 20 | \n",
+ " 5 | \n",
+ " 43 | \n",
+ " 24 | \n",
+ " 152 | \n",
+ " 318 | \n",
+ " 10 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -227,16 +227,16 @@
"
\n",
" \n",
" | 6 | \n",
- " 14 | \n",
- " 14 | \n",
- " 17 | \n",
+ " 24 | \n",
+ " 24 | \n",
+ " 32 | \n",
" 2 | \n",
- " 20 | \n",
- " 22 | \n",
- " 129 | \n",
- " 8 | \n",
- " 27 | \n",
- " 27 | \n",
+ " 37 | \n",
+ " 24 | \n",
+ " 80 | \n",
+ " 37 | \n",
+ " 318 | \n",
+ " 75 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -251,16 +251,16 @@
"
\n",
" \n",
" | 7 | \n",
- " 20 | \n",
- " 20 | \n",
- " 1 | \n",
+ " 24 | \n",
+ " 113 | \n",
+ " 3 | \n",
" 2 | \n",
+ " 112 | \n",
+ " 24 | \n",
" 4 | \n",
- " 22 | \n",
- " 612 | \n",
- " 613 | \n",
- " 8 | \n",
- " 8 | \n",
+ " 24 | \n",
+ " 619 | \n",
+ " 24 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -275,16 +275,16 @@
"
\n",
" \n",
" | 8 | \n",
- " 22 | \n",
- " 22 | \n",
- " 19 | \n",
- " 2 | \n",
+ " 24 | \n",
+ " 406 | \n",
" 6 | \n",
- " 22 | \n",
- " 606 | \n",
- " 271 | \n",
- " 8 | \n",
- " 37 | \n",
+ " 2 | \n",
+ " 56 | \n",
+ " 57 | \n",
+ " 58 | \n",
+ " 41 | \n",
+ " 58 | \n",
+ " 4 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -299,16 +299,16 @@
"
\n",
" \n",
" | 9 | \n",
- " 14 | \n",
- " 14 | \n",
- " 29 | \n",
+ " 24 | \n",
+ " 44 | \n",
+ " 4 | \n",
" 2 | \n",
- " 11 | \n",
- " 8 | \n",
- " 243 | \n",
- " 20 | \n",
- " 22 | \n",
- " 542 | \n",
+ " 5 | \n",
+ " 43 | \n",
+ " 24 | \n",
+ " 4 | \n",
+ " 24 | \n",
+ " 10 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
@@ -327,29 +327,29 @@
""
],
"text/plain": [
- " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 4 \\\n",
- "0 91 91 1 2 91 62 8 422 \n",
- "1 60 60 10 2 20 22 86 20 \n",
- "2 8 8 11 2 25 80 7 8 \n",
- "3 20 20 8 2 11 22 101 309 \n",
- "4 8 8 7 2 25 20 22 511 \n",
- "5 296 489 4 2 31 32 33 4 \n",
- "6 14 14 17 2 20 22 129 8 \n",
- "7 20 20 1 2 4 22 612 613 \n",
- "8 22 22 19 2 6 22 606 271 \n",
- "9 14 14 29 2 11 8 243 20 \n",
+ " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n",
+ "0 24 24 22 2 236 24 229 \n",
+ "1 24 426 25 2 752 24 603 \n",
+ "2 24 97 3 2 460 6 4 \n",
+ "3 24 47 7 2 56 57 58 \n",
+ "4 24 57 18 2 52 24 37 \n",
+ "5 24 24 22 2 5 43 24 \n",
+ "6 24 24 32 2 37 24 80 \n",
+ "7 24 113 3 2 112 24 4 \n",
+ "8 24 406 6 2 56 57 58 \n",
+ "9 24 44 4 2 5 43 24 \n",
"\n",
- " 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n",
- "0 93 8 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "1 22 198 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "2 65 24 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "3 8 20 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "4 8 20 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "5 37 20 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "6 27 27 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "7 8 8 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "8 8 37 ... 0 0 0 0 0 0 0 0 0 0 \n",
- "9 22 542 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n",
+ "0 37 24 241 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "1 564 24 199 ... 1142 52 1142 769 24 24 24 654 24 3 \n",
+ "2 318 52 24 ... 24 236 29 1142 135 24 256 24 37 3 \n",
+ "3 106 236 24 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "4 52 24 52 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "5 152 318 10 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "6 37 318 75 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "7 24 619 24 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "8 41 58 4 ... 0 0 0 0 0 0 0 0 0 0 \n",
+ "9 4 24 10 ... 0 0 0 0 0 0 0 0 0 0 \n",
"\n",
"[10 rows x 67 columns]"
]
@@ -360,43 +360,47 @@
}
],
"source": [
- "results_df = pd.read_csv(path+'pretraining_output/eval_results_masked_lm.txt')\n",
+ "results_df = pd.read_csv(path+'pretraining_output-100k-2/eval_results_masked_lm.txt')\n",
"results_df.head(10)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(3823, 1)"
+ "(1156, 1)"
]
},
- "execution_count": 5,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "vocab_df = pd.read_csv(path+'global_vocab.csv', header=None)\n",
+ "vocab_file = \"global_vocab.csv\"\n",
+ "vocab_file = \"sparse_tmp_vocab-code.txt\"\n",
+ "#vocab_file = \"vocab-code.txt\"\n",
+ "\n",
+ "vocab_df = pd.read_csv(path+vocab_file, header=None)\n",
"vocab_df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(1146, 1)"
+ "(1851, 1)"
]
},
- "execution_count": 6,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -408,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 21,
"metadata": {
"scrolled": true
},
@@ -416,1010 +420,10 @@
{
"data": {
"text/plain": [
- "{nan,\n",
- " 'xfit',\n",
- " 'categorical',\n",
- " 'squaredhinge',\n",
- " 'sim',\n",
- " 'denominator',\n",
- " 't0',\n",
- " 'sig',\n",
- " 'km2',\n",
- " 'coordinate',\n",
- " 'warning',\n",
- " 'logfile',\n",
- " 'socket',\n",
- " 'getatime',\n",
- " 'cd',\n",
- " 'yhat',\n",
- " 'pt',\n",
- " 'valids',\n",
- " 'transpose3d',\n",
- " 'multiclassclassifier',\n",
- " 'keep',\n",
- " 'kfunc',\n",
- " 'uniface',\n",
- " 'linearsvr',\n",
- " 'ser',\n",
- " 'cases',\n",
- " 'pool1d',\n",
- " 'estimator2',\n",
- " 'isomap',\n",
- " 'xred',\n",
- " 'categories',\n",
- " 'jobs',\n",
- " 'parametrize',\n",
- " 'plsca',\n",
- " 'izip',\n",
- " 'summed',\n",
- " '1row',\n",
- " 'pickler',\n",
- " 'sofar',\n",
- " 'concentration',\n",
- " 'tanhshrink',\n",
- " 'logisticregression',\n",
- " 'prewarm',\n",
- " 'ica',\n",
- " 'hasobject',\n",
- " 'eexist',\n",
- " 'impl',\n",
- " 'patch',\n",
- " 'nr',\n",
- " '10',\n",
- " 'direction',\n",
- " 'encoder',\n",
- " 'combining',\n",
- " 'ln',\n",
- " 'builtinfunctiontype',\n",
- " 'sv',\n",
- " 'scalar',\n",
- " 'resolution',\n",
- " 'clean',\n",
- " 'emit',\n",
- " 'yeo',\n",
- " 'standardmsg',\n",
- " 'pickled',\n",
- " 'extents',\n",
- " 'marginal',\n",
- " 'engine',\n",
- " 'nk',\n",
- " 'estimator0',\n",
- " 'qr',\n",
- " 'inplace',\n",
- " 'urlopen',\n",
- " 'myx',\n",
- " 'iterkeys',\n",
- " 'replicate',\n",
- " 'tell',\n",
- " 'refit',\n",
- " 'protocol',\n",
- " 'inert',\n",
- " 'percent10',\n",
- " 'xbz',\n",
- " 'mkdtemp',\n",
- " 'intc',\n",
- " 'sha256hash',\n",
- " 'iteration',\n",
- " 'fu',\n",
- " 'labels2',\n",
- " 'dst',\n",
- " 'based',\n",
- " 'blankline',\n",
- " 'compressobj',\n",
- " 'instancetype',\n",
- " 'fc',\n",
- " 'nearestneighbors',\n",
- " 'lw',\n",
- " 'sc',\n",
- " 'spacing',\n",
- " 'reducers',\n",
- " 'curloc',\n",
- " 'need',\n",
- " 'cancelled',\n",
- " 'islower',\n",
- " 'bysvd',\n",
- " 'wrong',\n",
- " 'rescaled',\n",
- " 'rkf',\n",
- " 'subimports',\n",
- " 'optima',\n",
- " 'classtype',\n",
- " 'executable',\n",
- " 'velocities',\n",
- " 'own',\n",
- " 'triplet',\n",
- " 'fa',\n",
- " 'hx',\n",
- " 'server',\n",
- " 'treeclassifier',\n",
- " 'isnone',\n",
- " 'analyzer',\n",
- " 'capsys',\n",
- " 'tb',\n",
- " 'omp',\n",
- " 'macro',\n",
- " 'plscanonical',\n",
- " 'linkage',\n",
- " 'halftensor',\n",
- " 'alphak',\n",
- " 'sel',\n",
- " 'clabel',\n",
- " 'rec',\n",
- " 'outqueue',\n",
- " 'validate',\n",
- " 'point',\n",
- " 'v2',\n",
- " 'linear',\n",
- " 'de',\n",
- " 'xi',\n",
- " 'assume',\n",
- " 'site',\n",
- " 'dest',\n",
- " 'amg',\n",
- " 'checked',\n",
- " 'iinfo',\n",
- " 'descr',\n",
- " 'compressed',\n",
- " 'inheritable',\n",
- " 'instances',\n",
- " 'inheriting',\n",
- " 'agc',\n",
- " 'wa',\n",
- " 'tree',\n",
- " 'tolil',\n",
- " 'dbscan',\n",
- " 'chain',\n",
- " 'addons',\n",
- " 'criteria',\n",
- " 'cancel',\n",
- " 'inject',\n",
- " 'solve',\n",
- " 'scores',\n",
- " 'concrete',\n",
- " 'ya',\n",
- " 'matvec',\n",
- " 'reindexed',\n",
- " 'deprecated',\n",
- " 'reconstructed',\n",
- " 'url',\n",
- " 'aglc2',\n",
- " 'estimator7',\n",
- " 'pad3d',\n",
- " 'leave',\n",
- " 'issubdtype',\n",
- " 'fork',\n",
- " 'nonlinearity',\n",
- " 'continuous',\n",
- " 'can',\n",
- " 'lkk',\n",
- " 'weights1',\n",
- " 'small32',\n",
- " 'np',\n",
- " 'sa',\n",
- " 'learn',\n",
- " 'proba2',\n",
- " '20newsgroups',\n",
- " 'measure',\n",
- " 'xmax',\n",
- " 'positives',\n",
- " 'ypred',\n",
- " 'ss',\n",
- " 'triangular',\n",
- " 'balanced',\n",
- " 'corrcoef',\n",
- " 'residues',\n",
- " 'x3',\n",
- " 'bynipals',\n",
- " 'pointer',\n",
- " 'tracker',\n",
- " 'hlines',\n",
- " 'values3',\n",
- " 'strerror',\n",
- " 'regions',\n",
- " 'rtol',\n",
- " 'system',\n",
- " 'pinvh',\n",
- " 'lr2',\n",
- " 'embedded',\n",
- " 'fdopen',\n",
- " 'existing',\n",
- " 'maj',\n",
- " 'factory',\n",
- " 'skeleton',\n",
- " 'points',\n",
- " 'contour',\n",
- " 't1p',\n",
- " 'failed',\n",
- " 'inttensor',\n",
- " 'buffered',\n",
- " 'uint16',\n",
- " 'ecut',\n",
- " 'w0',\n",
- " 'signals',\n",
- " 'xfail',\n",
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- " 'weights2',\n",
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- " 'softshrink',\n",
- " 'wrap',\n",
- " 'multilabel',\n",
- " 'bgmm2',\n",
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- " 'cached',\n",
- " 'ss4',\n",
- " 'str4',\n",
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- " 'boston',\n",
- " 'sgh',\n",
- " 'b110',\n",
- " 'v0',\n",
- " 'kpca',\n",
- " 'fraction',\n",
- " 'tsne',\n",
- " 'opcode',\n",
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- " 'i64',\n",
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- " 'microseconds',\n",
- " 'frac',\n",
- " 'sampled2',\n",
- " 'memmaped',\n",
- " 'adaptive',\n",
- " 'locs',\n",
- " 'sharedmem',\n",
- " 'unpickler',\n",
- " 'stream',\n",
- " 'helper',\n",
- " 'accept',\n",
- " 'recv',\n",
- " 'em',\n",
- " 'etimedout',\n",
- " 'spintercept2',\n",
- " 'repr',\n",
- " 'itemgetter',\n",
- " 'decompress',\n",
- " 'rcond',\n",
- " 'reader',\n",
- " 'ent',\n",
- " 'calibrated',\n",
- " '3classes',\n",
- " 'bgmm',\n",
- " 'memmappingpool',\n",
- " 'separator',\n",
- " 'marr',\n",
- " 'linearsvc',\n",
- " 'attrname',\n",
- " 'tk',\n",
- " 'firstlineno',\n",
- " 'diffusion',\n",
- " 'projection',\n",
- " 'nsec',\n",
- " '3264',\n",
- " 'src',\n",
- " 'amount',\n",
- " 'vertices',\n",
- " 'kill',\n",
- " 'bt1',\n",
- " 'reduced',\n",
- " 'whiten',\n",
- " 'y7',\n",
- " 'anorm',\n",
- " 'logsigmoid',\n",
- " 'timeout',\n",
- " 'seekable',\n",
- " 'kneighborsregressor',\n",
- " 'u4',\n",
- " 'unstructured',\n",
- " ...}"
+ "set()"
]
},
- "execution_count": 7,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -1430,1484 +434,1213 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "3823"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(vocab_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "accuracy = 0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "per_token_acc = {}; per_token_count = {}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "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": 16,
- "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": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "from collections import Counter\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " ('xi', 0.0),\n",
- " ('linearsvr', 0.0),\n",
- " ('successful', 0.0),\n",
- " ('eigvalsh', 0.0),\n",
- " ('poisson', 0.0),\n",
- " ('obj', 0.0),\n",
- " ('shift', 0.0),\n",
- " ('dbscan', 0.0),\n",
- " ('criteria', 0.0),\n",
- " ('narrow', 0.0),\n",
- " ('solve', 0.0),\n",
- " ('done', 0.0),\n",
- " ('reconstructed', 0.0),\n",
- " ('url', 0.0),\n",
- " ('wlock', 0.0),\n",
- " ('cpu', 0.0),\n",
- " ('interpolate', 0.0),\n",
- " ('can', 0.0),\n",
- " ('inputfeaturetype', 0.0),\n",
- " ('logprob', 0.0),\n",
- " ('seq', 0.0),\n",
- " ('measure', 0.0),\n",
- " ('yt', 0.0),\n",
- " ('corrcoef', 0.0),\n",
- " ('pointer', 0.0),\n",
- " ('as', 0.0),\n",
- " ('one', 0.0),\n",
- " ('int16', 0.0),\n",
- " ('rtol', 0.0),\n",
- " ('methodname', 0.0),\n",
- " ('pinvh', 0.0),\n",
- " ('embedded', 0.0),\n",
- " ('conv1d', 0.0),\n",
- " ('skeleton', 0.0),\n",
- " ('high', 0.0),\n",
- " ('nnet', 0.0),\n",
- " ('prelu', 0.0),\n",
- " ('network', 0.0)]"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "c = Counter(total_per_token_accuracy)\n",
- "c.most_common(1000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"data": {
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" | 2 | \n",
- " 0.000357 | \n",
- " 0.000431 | \n",
- " 0.000382 | \n",
- " 0.000431 | \n",
- " 0.000390 | \n",
- " 0.000319 | \n",
- " 0.000335 | \n",
- " 0.000440 | \n",
- " 0.000351 | \n",
- " 0.000313 | \n",
+ " 9.228524e-06 | \n",
+ " 3.372166e-05 | \n",
+ " 9.086550e-06 | \n",
+ " 6.295843e-05 | \n",
+ " 4.948483e-05 | \n",
+ " 7.433358e-05 | \n",
+ " 6.279901e-04 | \n",
+ " 8.630683e-06 | \n",
+ " 6.407512e-06 | \n",
+ " 8.860663e-05 | \n",
" ... | \n",
- " 0.000339 | \n",
- " 0.000456 | \n",
- " 0.000469 | \n",
- " 0.000364 | \n",
- " 0.000552 | \n",
- " 0.000466 | \n",
- " 0.000343 | \n",
- " 0.000358 | \n",
- " 0.000483 | \n",
- " 0.000465 | \n",
+ " 1.801482e-05 | \n",
+ " 1.693699e-05 | \n",
+ " 1.927819e-05 | \n",
+ " 1.354420e-04 | \n",
+ " 1.218900e-04 | \n",
+ " 1.574835e-05 | \n",
+ " 1.220086e-05 | \n",
+ " 1.585285e-05 | \n",
+ " 2.775633e-05 | \n",
+ " 2.249711e-05 | \n",
"
\n",
" \n",
" | 3 | \n",
- " 0.000421 | \n",
- " 0.000546 | \n",
- " 0.000441 | \n",
- " 0.000426 | \n",
- " 0.000496 | \n",
- " 0.000264 | \n",
- " 0.000278 | \n",
- " 0.000354 | \n",
- " 0.000326 | \n",
- " 0.000421 | \n",
+ " 1.625279e-03 | \n",
+ " 4.320768e-04 | \n",
+ " 7.980427e-04 | \n",
+ " 4.398703e-03 | \n",
+ " 7.204175e-04 | \n",
+ " 6.877603e-03 | \n",
+ " 2.316440e-03 | \n",
+ " 2.695289e-02 | \n",
+ " 1.449860e-03 | \n",
+ " 4.616880e-03 | \n",
" ... | \n",
- " 0.000227 | \n",
- " 0.000492 | \n",
- " 0.000341 | \n",
- " 0.000370 | \n",
- " 0.000574 | \n",
- " 0.000298 | \n",
- " 0.000363 | \n",
- " 0.000369 | \n",
- " 0.000392 | \n",
- " 0.000447 | \n",
+ " 4.829602e-04 | \n",
+ " 2.176314e-04 | \n",
+ " 6.519357e-04 | \n",
+ " 1.142669e-03 | \n",
+ " 5.279170e-03 | \n",
+ " 7.605951e-04 | \n",
+ " 2.147494e-04 | \n",
+ " 4.885770e-04 | \n",
+ " 1.646929e-03 | \n",
+ " 6.149090e-04 | \n",
"
\n",
" \n",
" | 4 | \n",
- " 0.000310 | \n",
- " 0.000455 | \n",
- " 0.000296 | \n",
- " 0.000462 | \n",
- " 0.000534 | \n",
- " 0.000281 | \n",
- " 0.000342 | \n",
- " 0.000409 | \n",
- " 0.000261 | \n",
- " 0.000323 | \n",
+ " 6.679990e-07 | \n",
+ " 1.118133e-07 | \n",
+ " 1.356601e-07 | \n",
+ " 3.105948e-07 | \n",
+ " 9.812925e-08 | \n",
+ " 2.302669e-06 | \n",
+ " 3.953598e-07 | \n",
+ " 7.584490e-08 | \n",
+ " 8.587980e-08 | \n",
+ " 2.130783e-07 | \n",
" ... | \n",
- " 0.000244 | \n",
- " 0.000466 | \n",
- " 0.000441 | \n",
- " 0.000425 | \n",
- " 0.000542 | \n",
- " 0.000402 | \n",
- " 0.000442 | \n",
- " 0.000384 | \n",
- " 0.000347 | \n",
- " 0.000664 | \n",
+ " 5.218141e-08 | \n",
+ " 1.580854e-07 | \n",
+ " 6.571570e-08 | \n",
+ " 1.611555e-07 | \n",
+ " 1.264892e-06 | \n",
+ " 5.802754e-08 | \n",
+ " 2.287778e-08 | \n",
+ " 6.092194e-08 | \n",
+ " 1.229654e-07 | \n",
+ " 1.394618e-07 | \n",
"
\n",
" \n",
" | 5 | \n",
- " 0.000318 | \n",
- " 0.000448 | \n",
- " 0.000290 | \n",
- " 0.000472 | \n",
- " 0.000533 | \n",
- " 0.000257 | \n",
- " 0.000319 | \n",
- " 0.000403 | \n",
- " 0.000274 | \n",
- " 0.000332 | \n",
+ " 2.491103e-08 | \n",
+ " 1.568859e-07 | \n",
+ " 6.609765e-08 | \n",
+ " 3.131541e-07 | \n",
+ " 3.274807e-07 | \n",
+ " 1.259794e-07 | \n",
+ " 2.869727e-08 | \n",
+ " 1.484190e-08 | \n",
+ " 1.604501e-08 | \n",
+ " 3.322065e-08 | \n",
" ... | \n",
- " 0.000234 | \n",
- " 0.000485 | \n",
- " 0.000468 | \n",
- " 0.000434 | \n",
- " 0.000532 | \n",
- " 0.000414 | \n",
- " 0.000427 | \n",
- " 0.000351 | \n",
- " 0.000377 | \n",
- " 0.000657 | \n",
+ " 2.739106e-08 | \n",
+ " 2.026524e-08 | \n",
+ " 2.147384e-08 | \n",
+ " 2.954245e-07 | \n",
+ " 2.366482e-07 | \n",
+ " 2.395059e-08 | \n",
+ " 1.943951e-08 | \n",
+ " 2.001486e-08 | \n",
+ " 4.482562e-08 | \n",
+ " 3.366723e-08 | \n",
"
\n",
" \n",
" | 6 | \n",
- " 0.000307 | \n",
- " 0.000469 | \n",
- " 0.000310 | \n",
- " 0.000387 | \n",
- " 0.000455 | \n",
- " 0.000272 | \n",
- " 0.000300 | \n",
- " 0.000425 | \n",
- " 0.000256 | \n",
- " 0.000382 | \n",
+ " 1.175133e-05 | \n",
+ " 6.302856e-06 | \n",
+ " 1.538858e-05 | \n",
+ " 1.975774e-06 | \n",
+ " 6.743026e-06 | \n",
+ " 8.401215e-06 | \n",
+ " 9.915345e-05 | \n",
+ " 3.187398e-06 | \n",
+ " 1.144623e-05 | \n",
+ " 3.738823e-06 | \n",
" ... | \n",
- " 0.000231 | \n",
- " 0.000504 | \n",
- " 0.000467 | \n",
- " 0.000423 | \n",
- " 0.000502 | \n",
- " 0.000372 | \n",
- " 0.000400 | \n",
- " 0.000304 | \n",
- " 0.000335 | \n",
- " 0.000635 | \n",
+ " 5.627634e-06 | \n",
+ " 1.611392e-05 | \n",
+ " 8.483434e-06 | \n",
+ " 8.323984e-06 | \n",
+ " 1.623187e-05 | \n",
+ " 5.388600e-06 | \n",
+ " 4.647758e-06 | \n",
+ " 6.885765e-06 | \n",
+ " 1.631825e-06 | \n",
+ " 2.885853e-06 | \n",
"
\n",
" \n",
" | 7 | \n",
- " 0.000331 | \n",
- " 0.000356 | \n",
- " 0.000347 | \n",
- " 0.000416 | \n",
- " 0.000542 | \n",
- " 0.000339 | \n",
- " 0.000294 | \n",
- " 0.000569 | \n",
- " 0.000331 | \n",
- " 0.000409 | \n",
+ " 1.037626e-07 | \n",
+ " 1.353383e-08 | \n",
+ " 7.288905e-09 | \n",
+ " 1.098443e-08 | \n",
+ " 4.959495e-08 | \n",
+ " 5.956720e-08 | \n",
+ " 1.022102e-07 | \n",
+ " 1.365160e-07 | \n",
+ " 3.209238e-08 | \n",
+ " 4.519928e-08 | \n",
" ... | \n",
- " 0.000312 | \n",
- " 0.000499 | \n",
- " 0.000434 | \n",
- " 0.000398 | \n",
- " 0.000591 | \n",
- " 0.000427 | \n",
- " 0.000272 | \n",
- " 0.000311 | \n",
- " 0.000340 | \n",
- " 0.000704 | \n",
+ " 1.253997e-08 | \n",
+ " 2.981331e-08 | \n",
+ " 1.712155e-08 | \n",
+ " 6.362873e-08 | \n",
+ " 3.363054e-08 | \n",
+ " 2.021101e-08 | \n",
+ " 1.268721e-08 | \n",
+ " 2.116206e-08 | \n",
+ " 4.744892e-08 | \n",
+ " 2.007199e-08 | \n",
"
\n",
" \n",
" | 8 | \n",
- " 0.000270 | \n",
- " 0.000397 | \n",
- " 0.000348 | \n",
- " 0.000472 | \n",
- " 0.000438 | \n",
- " 0.000314 | \n",
- " 0.000438 | \n",
- " 0.000399 | \n",
- " 0.000407 | \n",
- " 0.000308 | \n",
+ " 6.833109e-05 | \n",
+ " 3.645496e-05 | \n",
+ " 2.202850e-05 | \n",
+ " 1.781727e-05 | \n",
+ " 3.195088e-05 | \n",
+ " 1.572230e-04 | \n",
+ " 1.019967e-05 | \n",
+ " 1.786529e-05 | \n",
+ " 2.262647e-05 | \n",
+ " 3.677092e-05 | \n",
" ... | \n",
- " 0.000232 | \n",
- " 0.000470 | \n",
- " 0.000366 | \n",
- " 0.000372 | \n",
- " 0.000496 | \n",
- " 0.000479 | \n",
- " 0.000340 | \n",
- " 0.000398 | \n",
- " 0.000394 | \n",
- " 0.000606 | \n",
+ " 7.263287e-06 | \n",
+ " 3.531726e-05 | \n",
+ " 8.178367e-06 | \n",
+ " 2.379867e-05 | \n",
+ " 4.802139e-05 | \n",
+ " 6.168165e-06 | \n",
+ " 2.953384e-05 | \n",
+ " 6.080852e-06 | \n",
+ " 1.518107e-05 | \n",
+ " 4.603200e-05 | \n",
"
\n",
" \n",
" | 9 | \n",
- " 0.000565 | \n",
- " 0.000403 | \n",
- " 0.000453 | \n",
- " 0.000351 | \n",
- " 0.000524 | \n",
- " 0.000256 | \n",
- " 0.000438 | \n",
- " 0.000368 | \n",
- " 0.000306 | \n",
- " 0.000347 | \n",
+ " 1.659851e-05 | \n",
+ " 3.445375e-06 | \n",
+ " 7.167127e-05 | \n",
+ " 2.247873e-05 | \n",
+ " 5.783238e-06 | \n",
+ " 4.164083e-05 | \n",
+ " 8.055934e-05 | \n",
+ " 1.418358e-05 | \n",
+ " 1.055363e-04 | \n",
+ " 3.648655e-05 | \n",
" ... | \n",
- " 0.000273 | \n",
- " 0.000518 | \n",
- " 0.000302 | \n",
- " 0.000396 | \n",
- " 0.000354 | \n",
- " 0.000395 | \n",
- " 0.000404 | \n",
- " 0.000408 | \n",
- " 0.000450 | \n",
- " 0.000533 | \n",
+ " 6.429980e-06 | \n",
+ " 2.886483e-05 | \n",
+ " 8.141491e-06 | \n",
+ " 2.397202e-05 | \n",
+ " 1.312219e-05 | \n",
+ " 5.940739e-06 | \n",
+ " 3.801329e-06 | \n",
+ " 6.381523e-06 | \n",
+ " 7.229489e-06 | \n",
+ " 1.309865e-06 | \n",
"
\n",
" \n",
"\n",
- "10 rows × 2490 columns
\n",
+ "10 rows × 367 columns
\n",
""
],
"text/plain": [
- " 0 1 2 3 4 5 6 \\\n",
- "0 0.000301 0.000399 0.000289 0.000392 0.000464 0.000284 0.000504 \n",
- "1 0.000304 0.000392 0.000370 0.000378 0.000510 0.000277 0.000410 \n",
- "2 0.000357 0.000431 0.000382 0.000431 0.000390 0.000319 0.000335 \n",
- "3 0.000421 0.000546 0.000441 0.000426 0.000496 0.000264 0.000278 \n",
- "4 0.000310 0.000455 0.000296 0.000462 0.000534 0.000281 0.000342 \n",
- "5 0.000318 0.000448 0.000290 0.000472 0.000533 0.000257 0.000319 \n",
- "6 0.000307 0.000469 0.000310 0.000387 0.000455 0.000272 0.000300 \n",
- "7 0.000331 0.000356 0.000347 0.000416 0.000542 0.000339 0.000294 \n",
- "8 0.000270 0.000397 0.000348 0.000472 0.000438 0.000314 0.000438 \n",
- "9 0.000565 0.000403 0.000453 0.000351 0.000524 0.000256 0.000438 \n",
+ " 0 1 2 3 4 \\\n",
+ "0 4.112116e-07 6.431073e-07 1.849602e-07 2.901857e-07 1.896978e-07 \n",
+ "1 3.001465e-04 8.451400e-03 7.237033e-05 3.140152e-05 2.350010e-04 \n",
+ "2 9.228524e-06 3.372166e-05 9.086550e-06 6.295843e-05 4.948483e-05 \n",
+ "3 1.625279e-03 4.320768e-04 7.980427e-04 4.398703e-03 7.204175e-04 \n",
+ "4 6.679990e-07 1.118133e-07 1.356601e-07 3.105948e-07 9.812925e-08 \n",
+ "5 2.491103e-08 1.568859e-07 6.609765e-08 3.131541e-07 3.274807e-07 \n",
+ "6 1.175133e-05 6.302856e-06 1.538858e-05 1.975774e-06 6.743026e-06 \n",
+ "7 1.037626e-07 1.353383e-08 7.288905e-09 1.098443e-08 4.959495e-08 \n",
+ "8 6.833109e-05 3.645496e-05 2.202850e-05 1.781727e-05 3.195088e-05 \n",
+ "9 1.659851e-05 3.445375e-06 7.167127e-05 2.247873e-05 5.783238e-06 \n",
"\n",
- " 7 8 9 ... 2480 2481 2482 2483 \\\n",
- "0 0.000442 0.000352 0.000247 ... 0.000194 0.000569 0.000420 0.000373 \n",
- "1 0.000448 0.000364 0.000277 ... 0.000256 0.000471 0.000404 0.000398 \n",
- "2 0.000440 0.000351 0.000313 ... 0.000339 0.000456 0.000469 0.000364 \n",
- "3 0.000354 0.000326 0.000421 ... 0.000227 0.000492 0.000341 0.000370 \n",
- "4 0.000409 0.000261 0.000323 ... 0.000244 0.000466 0.000441 0.000425 \n",
- "5 0.000403 0.000274 0.000332 ... 0.000234 0.000485 0.000468 0.000434 \n",
- "6 0.000425 0.000256 0.000382 ... 0.000231 0.000504 0.000467 0.000423 \n",
- "7 0.000569 0.000331 0.000409 ... 0.000312 0.000499 0.000434 0.000398 \n",
- "8 0.000399 0.000407 0.000308 ... 0.000232 0.000470 0.000366 0.000372 \n",
- "9 0.000368 0.000306 0.000347 ... 0.000273 0.000518 0.000302 0.000396 \n",
+ " 5 6 7 8 9 ... \\\n",
+ "0 9.998808e-01 1.181713e-07 7.831349e-08 1.391216e-06 1.022416e-07 ... \n",
+ "1 1.294978e-04 4.570283e-04 4.366606e-05 1.534946e-04 2.121972e-04 ... \n",
+ "2 7.433358e-05 6.279901e-04 8.630683e-06 6.407512e-06 8.860663e-05 ... \n",
+ "3 6.877603e-03 2.316440e-03 2.695289e-02 1.449860e-03 4.616880e-03 ... \n",
+ "4 2.302669e-06 3.953598e-07 7.584490e-08 8.587980e-08 2.130783e-07 ... \n",
+ "5 1.259794e-07 2.869727e-08 1.484190e-08 1.604501e-08 3.322065e-08 ... \n",
+ "6 8.401215e-06 9.915345e-05 3.187398e-06 1.144623e-05 3.738823e-06 ... \n",
+ "7 5.956720e-08 1.022102e-07 1.365160e-07 3.209238e-08 4.519928e-08 ... \n",
+ "8 1.572230e-04 1.019967e-05 1.786529e-05 2.262647e-05 3.677092e-05 ... \n",
+ "9 4.164083e-05 8.055934e-05 1.418358e-05 1.055363e-04 3.648655e-05 ... \n",
"\n",
- " 2484 2485 2486 2487 2488 2489 \n",
- "0 0.000480 0.000375 0.000383 0.000410 0.000335 0.000665 \n",
- "1 0.000457 0.000335 0.000326 0.000356 0.000480 0.000487 \n",
- "2 0.000552 0.000466 0.000343 0.000358 0.000483 0.000465 \n",
- "3 0.000574 0.000298 0.000363 0.000369 0.000392 0.000447 \n",
- "4 0.000542 0.000402 0.000442 0.000384 0.000347 0.000664 \n",
- "5 0.000532 0.000414 0.000427 0.000351 0.000377 0.000657 \n",
- "6 0.000502 0.000372 0.000400 0.000304 0.000335 0.000635 \n",
- "7 0.000591 0.000427 0.000272 0.000311 0.000340 0.000704 \n",
- "8 0.000496 0.000479 0.000340 0.000398 0.000394 0.000606 \n",
- "9 0.000354 0.000395 0.000404 0.000408 0.000450 0.000533 \n",
+ " 357 358 359 360 361 \\\n",
+ "0 4.974994e-08 7.632985e-08 4.758867e-08 7.957695e-08 1.858005e-07 \n",
+ "1 1.281680e-04 1.202347e-05 7.841870e-05 3.458156e-04 2.887429e-04 \n",
+ "2 1.801482e-05 1.693699e-05 1.927819e-05 1.354420e-04 1.218900e-04 \n",
+ "3 4.829602e-04 2.176314e-04 6.519357e-04 1.142669e-03 5.279170e-03 \n",
+ "4 5.218141e-08 1.580854e-07 6.571570e-08 1.611555e-07 1.264892e-06 \n",
+ "5 2.739106e-08 2.026524e-08 2.147384e-08 2.954245e-07 2.366482e-07 \n",
+ "6 5.627634e-06 1.611392e-05 8.483434e-06 8.323984e-06 1.623187e-05 \n",
+ "7 1.253997e-08 2.981331e-08 1.712155e-08 6.362873e-08 3.363054e-08 \n",
+ "8 7.263287e-06 3.531726e-05 8.178367e-06 2.379867e-05 4.802139e-05 \n",
+ "9 6.429980e-06 2.886483e-05 8.141491e-06 2.397202e-05 1.312219e-05 \n",
"\n",
- "[10 rows x 2490 columns]"
+ " 362 363 364 365 366 \n",
+ "0 3.643930e-08 5.673511e-08 3.031996e-08 2.925358e-07 8.484311e-08 \n",
+ "1 6.659747e-05 3.862405e-05 8.485643e-05 1.376286e-04 2.540933e-03 \n",
+ "2 1.574835e-05 1.220086e-05 1.585285e-05 2.775633e-05 2.249711e-05 \n",
+ "3 7.605951e-04 2.147494e-04 4.885770e-04 1.646929e-03 6.149090e-04 \n",
+ "4 5.802754e-08 2.287778e-08 6.092194e-08 1.229654e-07 1.394618e-07 \n",
+ "5 2.395059e-08 1.943951e-08 2.001486e-08 4.482562e-08 3.366723e-08 \n",
+ "6 5.388600e-06 4.647758e-06 6.885765e-06 1.631825e-06 2.885853e-06 \n",
+ "7 2.021101e-08 1.268721e-08 2.116206e-08 4.744892e-08 2.007199e-08 \n",
+ "8 6.168165e-06 2.953384e-05 6.080852e-06 1.518107e-05 4.603200e-05 \n",
+ "9 5.940739e-06 3.801329e-06 6.381523e-06 7.229489e-06 1.309865e-06 \n",
+ "\n",
+ "[10 rows x 367 columns]"
]
},
- "execution_count": 66,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "results_df = pd.read_csv(path+'cls_output-finetune/test_results.tsv', header=None, sep='\\t')\n",
+ "results_df = pd.read_csv(path+'cls_output-methodname-sota/test_results.tsv', header=None, sep='\\t')\n",
"results_df.head(10)"
]
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(80, 2490)"
+ "(75, 367)"
]
},
- "execution_count": 67,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -407,129 +419,125 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(2490, 1)"
+ "(367, 1)"
]
},
- "execution_count": 68,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "vocab_label_df = pd.read_csv(path+'label_vocab.csv', header=None)\n",
+ "vocab_label_df = pd.read_csv(path+'sparse_fname2_vocab-label.txt', header=None)\n",
"vocab_label_df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"n=10\n",
- "preds = []\n",
+ "preds = []; probs = []\n",
"for idx, row in results_df.iterrows():\n",
" top_n = list(np.argsort(-row)[:n])\n",
- " preds.append(top_n[:n])"
+ " preds.append(top_n[:n])\n",
+ " probs.append(row[top_n[:n]])"
]
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
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]
},
- "execution_count": 70,
+ "execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
@@ -540,62 +548,840 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": 116,
"metadata": {},
"outputs": [
{
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]
},
- "execution_count": 71,
+ "execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "label_keras_df = pd.read_csv(path+'keras_cls_split_magret_label_val.txt', header=None)\n",
- "label_keras_df.shape"
+ "probs"
]
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(37, 1)"
+ "(75, 1)"
]
},
- "execution_count": 58,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "label_pytorch_df = pd.read_csv(path+'pytorch_cls_split_magret_label_val.txt', header=None)\n",
- "label_pytorch_df.shape"
+ "label_keras_df = pd.read_csv(path+'sparse_fname2_split_magret_label_val.txt', header=None)\n",
+ "label_keras_df.shape"
]
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(234, 1)"
+ "(621, 5)"
]
},
- "execution_count": 59,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
+ "source": [
+ "label_pytorch_df = pd.read_csv(path+'sparse_split_magret_label_val.txt', header=None)\n",
+ "label_pytorch_df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] File b'../sparse/sklearn_cls_split_magret_label_val.txt' does not exist: b'../sparse/sklearn_cls_split_magret_label_val.txt'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlabel_skl_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'sklearn_cls_split_magret_label_val.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mlabel_skl_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1120\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1123\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1851\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1854\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'../sparse/sklearn_cls_split_magret_label_val.txt' does not exist: b'../sparse/sklearn_cls_split_magret_label_val.txt'"
+ ]
+ }
+ ],
"source": [
"label_skl_df = pd.read_csv(path+'sklearn_cls_split_magret_label_val.txt', header=None)\n",
"label_skl_df.shape"
@@ -603,7 +1389,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -612,13 +1398,20 @@
"label_dfs = [label_keras_df]\n",
"for label_df in label_dfs:\n",
" for idx, row in label_df.iterrows():\n",
- " labels.append(vocab_label_df.index[vocab_label_df[0]==row[0]][0])\n",
+ " labels.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n",
" labels_str.append(row[0])"
]
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
"metadata": {
"scrolled": true
},
@@ -626,89 +1419,84 @@
{
"data": {
"text/plain": [
- "['binary_accuracy',\n",
- " 'on_train_end',\n",
- " 'on_epoch_end',\n",
- " '__call__',\n",
- " '__call__',\n",
- " '__call__',\n",
- " '__call__',\n",
- " 'he_normal',\n",
- " 'hinge',\n",
- " 'get',\n",
+ "['deserialize',\n",
+ " 'set_model',\n",
+ " 'get_monitor_value',\n",
+ " 'is_indexed_slices',\n",
+ " 'from_config',\n",
" '__init__',\n",
+ " '__call__',\n",
+ " 'get_config',\n",
+ " 'glorot_normal',\n",
" 'call',\n",
- " 'ask_to_proceed_with_overwrite',\n",
- " 'validate_file',\n",
- " '__iter__',\n",
- " 'NASNetMobile',\n",
+ " 'update',\n",
+ " '__init__',\n",
+ " 'add',\n",
+ " 'get_word_index',\n",
" 'decode_predictions',\n",
" 'preprocess_input',\n",
" '__init__',\n",
- " 'minimum',\n",
+ " '_merge_function',\n",
+ " '_merge_function',\n",
+ " 'add',\n",
+ " 'subtract',\n",
+ " 'multiply',\n",
+ " 'dot',\n",
" 'call',\n",
- " 'get_config',\n",
- " 'get_config',\n",
- " 'set_weights',\n",
+ " 'call',\n",
+ " '__init__',\n",
+ " 'compute_output_shape',\n",
" 'trainable_weights',\n",
+ " 'updates',\n",
" 'get_config',\n",
- " '_pooling_function',\n",
" '__init__',\n",
- " 'get_config',\n",
" '__init__',\n",
- " 'state_size',\n",
+ " 'step',\n",
+ " 'get_config',\n",
" 'call',\n",
- " 'from_config',\n",
- " 'from_config',\n",
" 'get_config',\n",
+ " 'compute_mask',\n",
+ " 'from_config',\n",
+ " 'losses',\n",
+ " 'call',\n",
+ " 'call',\n",
+ " 'bias_initializer',\n",
+ " 'noised',\n",
+ " '__init__',\n",
+ " '__init__',\n",
" 'compute_output_shape',\n",
- " 'get_uid',\n",
- " 'min',\n",
- " 'sign',\n",
- " 'binary_crossentropy',\n",
- " 'gradients',\n",
- " 'batch_get_value',\n",
- " 'batch_set_value',\n",
- " '_get_dynamic_axis_num',\n",
- " '_get_available_gpus',\n",
- " 'eye',\n",
+ " 'cast_to_floatx',\n",
+ " 'image_dim_ordering',\n",
+ " 'eval',\n",
+ " 'ndim',\n",
+ " 'gather',\n",
+ " 'argmax',\n",
+ " 'softmax',\n",
+ " '__init__',\n",
+ " 'infer_outputs',\n",
+ " '_has_nchw_support',\n",
+ " '_to_tensor',\n",
+ " 'eval',\n",
" 'min',\n",
- " 'std',\n",
- " 'any',\n",
- " 'logsumexp',\n",
- " 'reshape',\n",
- " 'permute_dimensions',\n",
- " 'batch_get_value',\n",
+ " 'flatten',\n",
+ " 'batch_flatten',\n",
+ " 'get_value',\n",
" 'sigmoid',\n",
- " 'dropout',\n",
- " '_preprocess_padding',\n",
- " 'random_uniform',\n",
- " 'foldl',\n",
- " '_is_explicit_shape',\n",
- " 'is_tensor',\n",
- " 'ones_like',\n",
- " 'count_params',\n",
- " 'std',\n",
- " 'argmin',\n",
+ " 'is_placeholder',\n",
+ " 'ones',\n",
+ " 'identity',\n",
+ " 'update_sub',\n",
+ " 'sum',\n",
" 'arange',\n",
- " '__call__',\n",
+ " 'ctc_cost',\n",
" 'foldr',\n",
- " '__init__',\n",
- " 'state_updates',\n",
- " 'trainable_weights',\n",
- " 'summary',\n",
- " 'layers',\n",
- " 'predict_proba',\n",
- " 'pickle_model',\n",
- " 'get_input_shape_at',\n",
- " 'get_output_mask_at',\n",
- " 'input',\n",
- " 'output',\n",
- " 'get_config',\n",
- " 'evaluate_generator']"
+ " 'save_img',\n",
+ " 'predict_classes',\n",
+ " 'model_from_json',\n",
+ " '_node_key']"
]
},
- "execution_count": 73,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -719,12 +1507,12 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -745,7 +1533,7 @@
"width = 1\n",
"\n",
"plt.bar(indexes, values, width)\n",
- "plt.xticks(indexes + width * 0.5, labels, rotation=90)\n",
+ "plt.xticks(indexes, labels, rotation=90)\n",
"plt.show()"
]
},
@@ -758,16 +1546,16 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'uniform_'"
+ "'clone'"
]
},
- "execution_count": 75,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -778,833 +1566,864 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'[CLS] FunctionDef arguments arg self arg optimizer Assign Attribute optimizer Name Name With withitem Call Attribute name scope Name Attribute name Attribute class Name Assign Attribute iterations Name Call Attribute variable Name Num keyword Str keyword Str'"
+ "'[CLS] FunctionDef arguments arg self arg args If Call Name Attribute data Name Name Expr Call Attribute update Attribute data Name Starred Name Raise Name'"
]
},
- "execution_count": 76,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "snippet = pd.read_csv(path+'keras_cls_split_magret_tk_val.txt', header=None)\n",
+ "snippet = pd.read_csv(path+'sparse_fname2_split_magret_tk_val.txt', header=None)\n",
"snippet.loc[10][0]"
]
},
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": 130,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Label = binary_accuracy\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. call\n",
- " 2. get_config\n",
- " 3. batch_flatten\n",
- " 4. test_build_repr\n",
- " 5. to_yaml\n",
- " 6. cosine_proximity\n",
- "\n",
- "Label = on_train_end\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. cumprod\n",
- " 4. conv1d_args_preprocessor\n",
- " 5. tmpdata\n",
- " 6. random_normal\n",
- "\n",
- "Label = on_epoch_end\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. from_config\n",
- " 3. random_uniform\n",
- " 4. int_shape\n",
- " 5. update_sub\n",
- " 6. _check_test_data\n",
- "\n",
- "Label = __call__\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. update_sub\n",
- " 4. hard_sigmoid\n",
- " 5. test_ovo_fit_on_list\n",
- " 6. activity_regularizer\n",
- "\n",
- "Label = __call__\n",
+ "[CLS] FunctionDef arguments arg config arg custom objects NameConstant Return Call Name Name keyword Call Name keyword Name keyword Str\n",
+ "Label = deserialize\n",
+ "Pred =\n",
+ "---- 0. deserialize (1.0)\n",
+ " 1. model_from_config (0.0)\n",
+ " 2. from_config (0.0)\n",
+ " 3. unpickle_model (0.0)\n",
+ " 4. model_from_yaml (0.0)\n",
+ " 5. clear_session (0.0)\n",
+ " 6. call (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg model For Name callback Attribute callbacks Name Expr Call Attribute set model Name Name\n",
+ "Label = set_model\n",
+ "Pred =\n",
+ " 0. set_params (0.867)\n",
+ " 1. on_train_begin (0.015)\n",
+ " 2. __setstate__ (0.013)\n",
+ " 3. clone_model (0.008)\n",
+ " 4. pickle_model (0.008)\n",
+ " 5. __init__ (0.006)\n",
+ " 6. __enter__ (0.004)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg logs Assign Name monitor value Call Attribute get Name Attribute monitor Name If Compare Name Is NameConstant Expr Call Attribute warn Name BinOp Str Mod Tuple Attribute monitor Name Call Attribute join Str Call Name Call Attribute keys Name Name Return Name\n",
+ "Label = get_monitor_value\n",
+ "Pred =\n",
+ " 0. on_train_begin (0.961)\n",
+ " 1. on_train_end (0.01)\n",
+ " 2. __init__ (0.004)\n",
+ " 3. on_epoch_end (0.002)\n",
+ " 4. on_batch_begin (0.001)\n",
+ " 5. _wait_queue (0.001)\n",
+ " 6. on_batch_end (0.001)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg grad Return Compare Attribute name Call Name Name Eq Str\n",
+ "Label = is_indexed_slices\n",
+ "Pred =\n",
+ " 0. _get_available_gpus (0.06)\n",
+ " 1. print_tensor (0.052)\n",
+ " 2. stop_gradient (0.041)\n",
+ " 3. from_config (0.031)\n",
+ " 4. update_add (0.03)\n",
+ " 5. binary_accuracy (0.027)\n",
+ " 6. update (0.023)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg cls arg config If Compare Str In Name Expr Call Attribute pop Name Str Return Call Name keyword Name Name\n",
+ "Label = from_config\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. random_normal\n",
- " 5. from_config\n",
- " 6. test_ovo_fit_on_list\n",
+ "---- 0. from_config (1.0)\n",
+ " 1. name_scope (0.0)\n",
+ " 2. batchnorm_args_preprocessor (0.0)\n",
+ " 3. deserialize (0.0)\n",
+ " 4. model_from_config (0.0)\n",
+ " 5. MobileNetV2 (0.0)\n",
+ " 6. AtrousConvolution1D (0.0)\n",
"\n",
- "Label = __call__\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. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. from_config\n",
- " 5. random_normal\n",
- " 6. zeros_like\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. init_pool_generator (0.0)\n",
+ " 4. _get_executor_init (0.0)\n",
+ " 5. predict_proba (0.0)\n",
+ " 6. to_yaml (0.0)\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. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. selu\n",
- " 4. softmax\n",
- " 5. from_config\n",
- " 6. test_ovo_fit_on_list\n",
- "\n",
- "Label = he_normal\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. from_config\n",
- " 3. call\n",
- " 4. update_sub\n",
- " 5. softmax\n",
- " 6. compute_mask\n",
- "\n",
- "Label = hinge\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. call\n",
- " 2. sqrt\n",
- " 3. batch_flatten\n",
- " 4. get_config\n",
- " 5. test_omp_reaches_least_squares\n",
- " 6. deserialize\n",
- "\n",
- "Label = get\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. int_shape\n",
- " 2. clip\n",
- " 3. random_normal\n",
- " 4. call\n",
- " 5. get_config\n",
- "---- 6. get\n",
+ "---- 0. __call__ (0.995)\n",
+ " 1. truncated_normal (0.001)\n",
+ " 2. transform (0.0)\n",
+ " 3. constant (0.0)\n",
+ " 4. random_normal_variable (0.0)\n",
+ " 5. forward (0.0)\n",
+ " 6. random_uniform (0.0)\n",
"\n",
- "Label = __init__\n",
+ "[CLS] FunctionDef arguments arg self Return Dict Str Str Str Attribute mean Name Attribute stddev Name Attribute seed Name\n",
+ "Label = get_config\n",
"Pred =\n",
- "---- 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. from_config\n",
- " 4. softmax\n",
- " 5. squared_hinge\n",
- " 6. conv1d_args_preprocessor\n",
- "\n",
+ "---- 0. get_config (1.0)\n",
+ " 1. _updated_config (0.0)\n",
+ " 2. _preprocess_conv3d_kernel (0.0)\n",
+ " 3. dropped_inputs (0.0)\n",
+ " 4. cell (0.0)\n",
+ " 5. infer_outputs (0.0)\n",
+ " 6. serialize_keras_object (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg seed NameConstant Expr Str Return Call Name keyword Num keyword Str keyword Str keyword Name\n",
+ "Label = glorot_normal\n",
+ "Pred =\n",
+ " 0. he_normal (0.209)\n",
+ " 1. lecun_normal (0.198)\n",
+ " 2. lecun_uniform (0.198)\n",
+ " 3. glorot_uniform (0.193)\n",
+ " 4. he_uniform (0.19)\n",
+ " 5. _postprocess_conv3d_output (0.0)\n",
+ " 6. _preprocess_conv2d_input (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg x Assign Name output Call Attribute dot Name Name Attribute W Name If Attribute bias Name AugAssign Name output Add Attribute b Name Assign Name output Call Attribute max Name Name keyword Num Return Name\n",
"Label = call\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- "---- 2. call\n",
- " 3. normalize_padding\n",
- " 4. std\n",
- " 5. conv1d_args_preprocessor\n",
- " 6. expand_dims\n",
- "\n",
- "Label = ask_to_proceed_with_overwrite\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. plot_model\n",
- " 5. gradients\n",
- " 6. update_sub\n",
- "\n",
- "Label = validate_file\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. squared_hinge\n",
- " 3. decode_predictions\n",
- " 4. _jaccard\n",
- " 5. update_sub\n",
- " 6. test_one_hot_encoder_pandas\n",
- "\n",
- "Label = __iter__\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. _merge_function\n",
- " 3. VGG16\n",
- " 4. is_keras_tensor\n",
- " 5. model_from_json\n",
- " 6. test_checksubparams_negative_subpopulation\n",
- "\n",
- "Label = NASNetMobile\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. decode_predictions\n",
- " 2. preprocess_input\n",
- " 3. get_config\n",
- " 4. call\n",
- " 5. int_shape\n",
- " 6. wrap_future_result\n",
- "\n",
+ " 0. __call__ (0.554)\n",
+ "---- 1. call (0.434)\n",
+ " 2. recurrent_conv (0.001)\n",
+ " 3. max (0.0)\n",
+ " 4. trainable_weights (0.0)\n",
+ " 5. _get_noise_shape (0.0)\n",
+ " 6. _merge_function (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg args If Call Name Attribute data Name Name Expr Call Attribute update Attribute data Name Starred Name Raise Name\n",
+ "Label = update\n",
+ "Pred =\n",
+ " 0. h5wrapper (0.323)\n",
+ " 1. __contains__ (0.099)\n",
+ " 2. __setattr__ (0.055)\n",
+ " 3. close (0.04)\n",
+ " 4. __init__ (0.024)\n",
+ " 5. InceptionV3 (0.02)\n",
+ " 6. preprocess_input (0.02)\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__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. predict_proba (0.0)\n",
+ " 3. preprocess_input (0.0)\n",
+ " 4. score (0.0)\n",
+ " 5. init_pool_generator (0.0)\n",
+ " 6. random_normal_variable (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg n arg values NameConstant Expr Call Attribute update Name BinOp Attribute seen so far Name Add Name Name\n",
+ "Label = add\n",
+ "Pred =\n",
+ " 0. set_params (0.157)\n",
+ " 1. on_batch_begin (0.083)\n",
+ " 2. save (0.034)\n",
+ " 3. _logcosh (0.032)\n",
+ " 4. on_epoch_begin (0.028)\n",
+ " 5. forward (0.021)\n",
+ " 6. get (0.019)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg path Str Expr Str Assign Name path Call Name Name keyword Str keyword Str With withitem Call Name Name Name f Return Call Attribute load Name Name\n",
+ "Label = get_word_index\n",
+ "Pred =\n",
+ "---- 0. get_word_index (0.999)\n",
+ " 1. model_from_yaml (0.0)\n",
+ " 2. is_sparse (0.0)\n",
+ " 3. noised (0.0)\n",
+ " 4. moving_average_update (0.0)\n",
+ " 5. handle_value (0.0)\n",
+ " 6. l1_l2 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg args arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n",
"Label = decode_predictions\n",
"Pred =\n",
- " 0. __init__\n",
- "---- 1. decode_predictions\n",
- " 2. preprocess_input\n",
- " 3. get_config\n",
- " 4. int_shape\n",
- " 5. get_output_at\n",
- " 6. concatenate\n",
+ "---- 0. decode_predictions (1.0)\n",
+ " 1. DenseNet201 (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. DenseNet121 (0.0)\n",
+ " 4. InceptionResNetV2 (0.0)\n",
+ " 5. VGG16 (0.0)\n",
+ " 6. InceptionV3 (0.0)\n",
"\n",
+ "[CLS] FunctionDef arguments arg args arg kwargs Return Call Attribute preprocess input Name Starred Name keyword Name Name\n",
"Label = preprocess_input\n",
"Pred =\n",
- " 0. __init__\n",
- "---- 1. preprocess_input\n",
- " 2. decode_predictions\n",
- " 3. get_config\n",
- " 4. call\n",
- " 5. int_shape\n",
- " 6. wrap_future_result\n",
+ "---- 0. preprocess_input (1.0)\n",
+ " 1. decode_predictions (0.0)\n",
+ " 2. Xception (0.0)\n",
+ " 3. __init__ (0.0)\n",
+ " 4. DenseNet121 (0.0)\n",
+ " 5. _init_subclassed_network (0.0)\n",
+ " 6. ResNet50 (0.0)\n",
"\n",
+ "[CLS] FunctionDef arguments arg self arg kwargs Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n",
"Label = __init__\n",
"Pred =\n",
- "---- 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. _preprocess_conv3d_kernel\n",
- " 5. random_normal\n",
- " 6. update_sub\n",
- "\n",
- "Label = minimum\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. from_config\n",
- " 5. random_normal\n",
- " 6. batch_flatten\n",
- "\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. predict_proba (0.0)\n",
+ " 4. to_yaml (0.0)\n",
+ " 5. score (0.0)\n",
+ " 6. init_pool_generator (0.0)\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 AugAssign Name output Add Subscript Name Index Name Return Name\n",
+ "Label = _merge_function\n",
+ "Pred =\n",
+ "---- 0. _merge_function (1.0)\n",
+ " 1. call (0.0)\n",
+ " 2. get_uid (0.0)\n",
+ " 3. get_updates_for (0.0)\n",
+ " 4. compute_mask (0.0)\n",
+ " 5. __call__ (0.0)\n",
+ " 6. trainable_weights (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg inputs Return Call Attribute concatenate Name Name keyword Attribute axis Name\n",
+ "Label = _merge_function\n",
+ "Pred =\n",
+ " 0. call (1.0)\n",
+ " 1. __call__ (0.0)\n",
+ " 2. compute_mask (0.0)\n",
+ " 3. _get_noise_shape (0.0)\n",
+ "---- 4. _merge_function (0.0)\n",
+ " 5. step (0.0)\n",
+ " 6. argmin (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg inputs arg kwargs Expr Str Return Call Call Name keyword Name Name\n",
+ "Label = add\n",
+ "Pred =\n",
+ " 0. average (0.343)\n",
+ " 1. maximum (0.326)\n",
+ " 2. minimum (0.323)\n",
+ " 3. h5wrapper (0.0)\n",
+ " 4. predict_generator (0.0)\n",
+ " 5. concatenate (0.0)\n",
+ " 6. ResNet50 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg inputs arg kwargs Expr Str Return Call Call Name keyword Name Name\n",
+ "Label = subtract\n",
+ "Pred =\n",
+ " 0. average (0.343)\n",
+ " 1. maximum (0.326)\n",
+ " 2. minimum (0.323)\n",
+ " 3. h5wrapper (0.0)\n",
+ " 4. predict_generator (0.0)\n",
+ " 5. concatenate (0.0)\n",
+ " 6. ResNet50 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg inputs arg kwargs Expr Str Return Call Call Name keyword Name Name\n",
+ "Label = multiply\n",
+ "Pred =\n",
+ " 0. average (0.343)\n",
+ " 1. maximum (0.326)\n",
+ " 2. minimum (0.323)\n",
+ " 3. h5wrapper (0.0)\n",
+ " 4. predict_generator (0.0)\n",
+ " 5. concatenate (0.0)\n",
+ " 6. ResNet50 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg inputs arg axes arg normalize arg kwargs NameConstant Expr Str Return Call Call Name keyword Name keyword Name keyword Name Name\n",
+ "Label = dot\n",
+ "Pred =\n",
+ " 0. average (0.345)\n",
+ " 1. maximum (0.325)\n",
+ " 2. minimum (0.32)\n",
+ " 3. h5wrapper (0.0)\n",
+ " 4. predict_generator (0.0)\n",
+ " 5. concatenate (0.0)\n",
+ " 6. ResNet50 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg inputs Return Call Attribute relu Name Name keyword Attribute alpha Name\n",
"Label = call\n",
"Pred =\n",
- " 0. __init__\n",
- "---- 1. call\n",
- " 2. get_config\n",
- " 3. softmax\n",
- " 4. selu\n",
- " 5. batch_flatten\n",
- " 6. _weighted_percentile\n",
+ "---- 0. call (1.0)\n",
+ " 1. __call__ (0.0)\n",
+ " 2. step (0.0)\n",
+ " 3. _get_noise_shape (0.0)\n",
+ " 4. dropped_inputs (0.0)\n",
+ " 5. _merge_function (0.0)\n",
+ " 6. trainable (0.0)\n",
"\n",
- "Label = get_config\n",
+ "[CLS] FunctionDef arguments arg self arg inputs Return Call Attribute softmax Name Name keyword Attribute axis Name\n",
+ "Label = call\n",
"Pred =\n",
- "---- 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. call\n",
- " 4. _preprocess_padding\n",
- " 5. cumprod\n",
- " 6. batch_flatten\n",
+ "---- 0. call (1.0)\n",
+ " 1. __call__ (0.0)\n",
+ " 2. _get_noise_shape (0.0)\n",
+ " 3. step (0.0)\n",
+ " 4. compute_mask (0.0)\n",
+ " 5. dropped_inputs (0.0)\n",
+ " 6. get_losses_for (0.0)\n",
"\n",
- "Label = get_config\n",
+ "[CLS] FunctionDef arguments arg self arg layer arg kwargs Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n",
+ "Label = __init__\n",
"Pred =\n",
- "---- 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. call\n",
- " 4. _preprocess_padding\n",
- " 5. cumprod\n",
- " 6. batch_flatten\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. predict_proba (0.0)\n",
+ " 3. preprocess_input (0.0)\n",
+ " 4. to_yaml (0.0)\n",
+ " 5. score (0.0)\n",
+ " 6. random_normal_variable (0.0)\n",
"\n",
- "Label = set_weights\n",
+ "[CLS] FunctionDef arguments arg self arg input shape Assign Name child input shape BinOp Tuple Subscript Name Index Num Add Subscript Name Slice Num Assign Name child output shape Call Attribute compute output shape Attribute layer Name Name Assign Name timesteps Subscript Name Index Num Return BinOp Tuple Subscript Name Index Num Name Subscript Name Slice Num\n",
+ "Label = compute_output_shape\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. compute_mask\n",
- " 3. call\n",
- " 4. batch_flatten\n",
- " 5. int_shape\n",
- " 6. VGG16\n",
+ "---- 0. compute_output_shape (1.0)\n",
+ " 1. _get_noise_shape (0.0)\n",
+ " 2. range_less_than (0.0)\n",
+ " 3. _to_snake_case (0.0)\n",
+ " 4. foldl (0.0)\n",
+ " 5. infer_outputs (0.0)\n",
+ " 6. init_pool_generator (0.0)\n",
"\n",
+ "[CLS] FunctionDef arguments arg self If Call Name Attribute forward layer Name Str Return BinOp Attribute trainable weights Attribute forward layer Name Add Attribute trainable weights Attribute backward layer Name Return List Name\n",
"Label = trainable_weights\n",
"Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. non_trainable_weights\n",
- " 4. call\n",
- " 5. _equal_similarities_and_preferences\n",
- " 6. format\n",
- "\n",
+ "---- 0. trainable_weights (1.0)\n",
+ " 1. non_trainable_weights (0.0)\n",
+ " 2. get_weights (0.0)\n",
+ " 3. weights (0.0)\n",
+ " 4. trainable (0.0)\n",
+ " 5. next_sample (0.0)\n",
+ " 6. updates (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self If Call Name Attribute forward layer Name Str Return BinOp Attribute updates Attribute forward layer Name Add Attribute updates Attribute backward layer Name Return List Name\n",
+ "Label = updates\n",
+ "Pred =\n",
+ "---- 0. updates (0.999)\n",
+ " 1. state_updates (0.0)\n",
+ " 2. get_updates_for (0.0)\n",
+ " 3. non_trainable_weights (0.0)\n",
+ " 4. _uses_dynamic_learning_phase (0.0)\n",
+ " 5. stateful (0.0)\n",
+ " 6. get_weights (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self Assign Name config Dict Str Str Str Str Attribute return sequences Name Attribute return state Name Attribute go backwards Name Attribute stateful 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. __init__\n",
- " 2. softmax\n",
- " 3. _preprocess_padding\n",
- " 4. call\n",
- " 5. batch_flatten\n",
- " 6. selu\n",
+ "---- 0. get_config (1.0)\n",
+ " 1. stop_gradient (0.0)\n",
+ " 2. _updated_config (0.0)\n",
+ " 3. serialize_keras_object (0.0)\n",
+ " 4. infer_outputs (0.0)\n",
+ " 5. process_layer (0.0)\n",
+ " 6. cell (0.0)\n",
"\n",
- "Label = _pooling_function\n",
+ "[CLS] FunctionDef arguments arg self arg pool size arg strides arg padding arg data format arg kwargs Num NameConstant Str Str Expr Call Attribute init Call Name Name Name Name Name Name Name keyword Name Attribute legacy pooling1d support Name\n",
+ "Label = __init__\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. from_config\n",
- " 5. random_normal\n",
- " 6. selu\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. predict_proba (0.0)\n",
+ " 4. score (0.0)\n",
+ " 5. to_yaml (0.0)\n",
+ " 6. random_normal_variable (0.0)\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. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. test_ovo_fit_on_list\n",
- " 5. compute_mask\n",
- " 6. squared_hinge\n",
- "\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. predict_proba (0.0)\n",
+ " 4. score (0.0)\n",
+ " 5. random_normal_variable (0.0)\n",
+ " 6. _get_executor_init (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg inputs arg states Assign Name constants Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name states Subscript Name Slice UnaryOp Attribute num constants Name Return Call Attribute call Attribute cell Name Name Name keyword Name keyword Name\n",
+ "Label = step\n",
+ "Pred =\n",
+ "---- 0. step (1.0)\n",
+ " 1. _step (0.0)\n",
+ " 2. function (0.0)\n",
+ " 3. call (0.0)\n",
+ " 4. pickle_model (0.0)\n",
+ " 5. one_hot (0.0)\n",
+ " 6. _merge_function (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self Assign Name config Call Attribute get config Call Name Name Name Assign Subscript Name Index Str Attribute output padding Name Return Name\n",
"Label = get_config\n",
"Pred =\n",
- "---- 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. call\n",
- " 4. batch_flatten\n",
- " 5. get_variable_shape\n",
- " 6. cumprod\n",
+ "---- 0. get_config (1.0)\n",
+ " 1. infer_outputs (0.0)\n",
+ " 2. stop_gradient (0.0)\n",
+ " 3. _updated_config (0.0)\n",
+ " 4. _preprocess_conv3d_kernel (0.0)\n",
+ " 5. _get_noise_shape (0.0)\n",
+ " 6. process_layer (0.0)\n",
"\n",
- "Label = __init__\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. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. test_ovo_fit_on_list\n",
- " 5. conv1d_args_preprocessor\n",
- " 6. selu\n",
+ "---- 0. call (1.0)\n",
+ " 1. _pooling_function (0.0)\n",
+ " 2. _get_noise_shape (0.0)\n",
+ " 3. compute_mask (0.0)\n",
+ " 4. __call__ (0.0)\n",
+ " 5. step (0.0)\n",
+ " 6. _merge_function (0.0)\n",
"\n",
- "Label = state_size\n",
+ "[CLS] FunctionDef arguments arg self Assign Name config Dict Str Str Attribute cropping Name Attribute data format 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. __init__\n",
- " 1. get_config\n",
- " 2. softmax\n",
- " 3. non_trainable_weights\n",
- " 4. call\n",
- " 5. test_float_class_labels\n",
- " 6. conv1d_args_preprocessor\n",
- "\n",
+ "---- 0. get_config (1.0)\n",
+ " 1. stop_gradient (0.0)\n",
+ " 2. infer_outputs (0.0)\n",
+ " 3. _updated_config (0.0)\n",
+ " 4. dropped_inputs (0.0)\n",
+ " 5. process_layer (0.0)\n",
+ " 6. serialize_keras_object (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg inputs arg mask If Call Name Name Name Assign Name mask Subscript Name Index Num Assign Name output mask IfExp Attribute return sequences Name Name NameConstant If Attribute return state Name Assign Name state mask ListComp NameConstant comprehension Name Attribute states Name Return BinOp List Name Add Name Return Name\n",
+ "Label = compute_mask\n",
+ "Pred =\n",
+ "---- 0. compute_mask (1.0)\n",
+ " 1. call (0.0)\n",
+ " 2. get_index (0.0)\n",
+ " 3. _step (0.0)\n",
+ " 4. not_equal (0.0)\n",
+ " 5. _get_noise_shape (0.0)\n",
+ " 6. compute_output_shape (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg cls arg config arg custom objects NameConstant ImportFrom alias Assign Name cell Call Name Call Attribute pop Name Str keyword Name Assign Name num constants Call Attribute pop Name Str NameConstant Assign Name layer Call Name Name keyword Name Assign Attribute num constants Name Name Return Name Name\n",
+ "Label = from_config\n",
+ "Pred =\n",
+ "---- 0. from_config (1.0)\n",
+ " 1. name_scope (0.0)\n",
+ " 2. deserialize (0.0)\n",
+ " 3. batchnorm_args_preprocessor (0.0)\n",
+ " 4. MobileNetV2 (0.0)\n",
+ " 5. __init__ (0.0)\n",
+ " 6. predict_generator (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self Assign Name layer losses Attribute losses Call Name Name Name If Call Name Attribute cell Name Name Return BinOp Attribute losses Attribute cell Name Add Name Return Name Name\n",
+ "Label = losses\n",
+ "Pred =\n",
+ "---- 0. losses (1.0)\n",
+ " 1. get_losses_for (0.0)\n",
+ " 2. get_config (0.0)\n",
+ " 3. _reshape_sequence (0.0)\n",
+ " 4. permute_dimensions (0.0)\n",
+ " 5. states (0.0)\n",
+ " 6. __call__ (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg inputs arg mask arg training arg initial state NameConstant NameConstant NameConstant Assign Attribute dropout mask Attribute cell Name NameConstant Assign Attribute recurrent dropout mask Attribute cell Name NameConstant Return Call Attribute call Call Name Name Name Name keyword Name keyword Name keyword Name\n",
"Label = call\n",
"Pred =\n",
- " 0. __init__\n",
- "---- 1. call\n",
- " 2. get_config\n",
- " 3. softmax\n",
- " 4. test_ovo_fit_on_list\n",
- " 5. conv1d_args_preprocessor\n",
- " 6. noised\n",
+ "---- 0. call (1.0)\n",
+ " 1. compute_mask (0.0)\n",
+ " 2. _get_noise_shape (0.0)\n",
+ " 3. step (0.0)\n",
+ " 4. _merge_function (0.0)\n",
+ " 5. _init_subclassed_network (0.0)\n",
+ " 6. argmin (0.0)\n",
"\n",
- "Label = from_config\n",
+ "[CLS] FunctionDef arguments arg self arg inputs arg mask arg training arg initial state NameConstant NameConstant NameConstant Assign Attribute dropout mask Attribute cell Name NameConstant Assign Attribute recurrent dropout mask Attribute cell Name NameConstant Return Call Attribute call Call Name Name Name Name keyword Name keyword Name keyword Name\n",
+ "Label = call\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- "---- 2. from_config\n",
- " 3. softmax\n",
- " 4. call\n",
- " 5. batch_flatten\n",
- " 6. update_sub\n",
- "\n",
- "Label = from_config\n",
+ "---- 0. call (1.0)\n",
+ " 1. compute_mask (0.0)\n",
+ " 2. _get_noise_shape (0.0)\n",
+ " 3. step (0.0)\n",
+ " 4. _merge_function (0.0)\n",
+ " 5. _init_subclassed_network (0.0)\n",
+ " 6. argmin (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg arg args arg kwargs Return Call Attribute concatenate Name List Call Attribute bias initializer Name Tuple Attribute units Name Starred Name keyword Name Call Call Attribute Ones Name Tuple Attribute units Name Starred Name keyword Name Call Attribute bias initializer Name Tuple BinOp Attribute units Name Mult Num Starred Name keyword Name\n",
+ "Label = bias_initializer\n",
+ "Pred =\n",
+ "---- 0. bias_initializer (1.0)\n",
+ " 1. DenseNet169 (0.0)\n",
+ " 2. InceptionV3 (0.0)\n",
+ " 3. wrapper (0.0)\n",
+ " 4. constant (0.0)\n",
+ " 5. compute_output_shape (0.0)\n",
+ " 6. VGG19 (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments Assign Name stddev Call Attribute sqrt Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name Return BinOp Name Mult Call Attribute random normal Name keyword Call Attribute shape Name Name keyword Num keyword Name\n",
+ "Label = noised\n",
+ "Pred =\n",
+ "---- 0. noised (0.752)\n",
+ " 1. ctc_create_skip_idxs (0.143)\n",
+ " 2. softsign (0.006)\n",
+ " 3. std (0.005)\n",
+ " 4. _logcosh (0.004)\n",
+ " 5. transform (0.004)\n",
+ " 6. l2_normalize (0.003)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg rate arg noise shape arg seed arg kwargs NameConstant NameConstant Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute rate Name Name Assign Attribute noise shape Name Name Assign Attribute seed Name Name Assign Attribute supports masking Name NameConstant\n",
+ "Label = __init__\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- "---- 2. from_config\n",
- " 3. softmax\n",
- " 4. is_shutting_down\n",
- " 5. call\n",
- " 6. int_shape\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. predict_proba (0.0)\n",
+ " 4. init_pool_generator (0.0)\n",
+ " 5. to_yaml (0.0)\n",
+ " 6. random_normal_variable (0.0)\n",
"\n",
- "Label = get_config\n",
+ "[CLS] FunctionDef arguments arg self arg mask value arg kwargs Num Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute supports masking Name NameConstant Assign Attribute mask value Name Name\n",
+ "Label = __init__\n",
"Pred =\n",
- "---- 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. _preprocess_padding\n",
- " 4. call\n",
- " 5. cumprod\n",
- " 6. batch_flatten\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. predict_proba (0.0)\n",
+ " 4. random_normal_variable (0.0)\n",
+ " 5. to_yaml (0.0)\n",
+ " 6. score (0.0)\n",
"\n",
+ "[CLS] FunctionDef arguments arg self 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 = compute_output_shape\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- "---- 2. compute_output_shape\n",
- " 3. non_trainable_weights\n",
- " 4. batch_flatten\n",
- " 5. _merge_function\n",
- " 6. default_double_backwards_fn\n",
- "\n",
- "Label = get_uid\n",
+ "---- 0. compute_output_shape (1.0)\n",
+ " 1. build (0.0)\n",
+ " 2. _get_noise_shape (0.0)\n",
+ " 3. size (0.0)\n",
+ " 4. reshape (0.0)\n",
+ " 5. __init__ (0.0)\n",
+ " 6. _to_snake_case (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return Call Attribute asarray Name Name keyword Name\n",
+ "Label = cast_to_floatx\n",
+ "Pred =\n",
+ " 0. softplus (0.204)\n",
+ " 1. permute_dimensions (0.175)\n",
+ " 2. reshape (0.037)\n",
+ " 3. batch_get_value (0.029)\n",
+ " 4. softmax (0.028)\n",
+ " 5. transform (0.024)\n",
+ " 6. __call__ (0.022)\n",
+ "\n",
+ "[CLS] FunctionDef arguments Expr Str If Compare Name Eq Str Return Str Return Str\n",
+ "Label = image_dim_ordering\n",
+ "Pred =\n",
+ " 0. _preprocess_padding (0.314)\n",
+ " 1. _convert_dtype_string (0.109)\n",
+ " 2. _convert_string_dtype (0.058)\n",
+ " 3. set_image_dim_ordering (0.055)\n",
+ " 4. is_placeholder (0.039)\n",
+ " 5. unpack_singleton (0.037)\n",
+ " 6. maximum (0.025)\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 (0.98)\n",
+ "---- 1. eval (0.007)\n",
+ " 2. is_tensor (0.001)\n",
+ " 3. int_or_none (0.001)\n",
+ " 4. is_placeholder (0.0)\n",
+ " 5. set_value (0.0)\n",
+ " 6. ctc_create_skip_idxs (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Assign Name shape Call Name Name Return Call Name Name\n",
+ "Label = ndim\n",
+ "Pred =\n",
+ " 0. reshape (0.796)\n",
+ " 1. _is_explicit_shape (0.034)\n",
+ " 2. _reshape_batch (0.027)\n",
+ " 3. zeros (0.008)\n",
+ " 4. size (0.007)\n",
+ " 5. print_layer_summary (0.006)\n",
+ " 6. model_from_yaml (0.005)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg reference arg indices If Compare Call Name GtE Num Return Call Attribute gather Attribute ops Name Name Name Assign Name num classes Subscript Attribute shape Name Index Num Assign Name one hot matrix Call Attribute one hot Attribute ops Name Name Name Return Call Attribute times Name Name Name keyword BinOp Call Name Attribute shape Name Sub Num\n",
+ "Label = gather\n",
+ "Pred =\n",
+ "---- 0. gather (0.976)\n",
+ " 1. one_hot (0.004)\n",
+ " 2. ctc_create_skip_idxs (0.002)\n",
+ " 3. save (0.001)\n",
+ " 4. _generate_dropout_mask (0.001)\n",
+ " 5. range_less_than (0.001)\n",
+ " 6. _reshape_batch (0.0)\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. argmax (0.966)\n",
+ " 1. argmin (0.031)\n",
+ " 2. expand_dims (0.0)\n",
+ " 3. classification_error (0.0)\n",
+ " 4. squeeze (0.0)\n",
+ " 5. softmax (0.0)\n",
+ " 6. cumsum (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg axis UnaryOp USub Num Return Call Attribute softmax Name Name keyword Name\n",
+ "Label = softmax\n",
+ "Pred =\n",
+ "---- 0. softmax (0.999)\n",
+ " 1. argmin (0.0)\n",
+ " 2. expand_dims (0.0)\n",
+ " 3. argmax (0.0)\n",
+ " 4. concatenate (0.0)\n",
+ " 5. cumprod (0.0)\n",
+ " 6. l2_normalize (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg input arg shape arg name Str Expr Call Attribute init Call Name Name Name List Name keyword NameConstant keyword Name Assign Attribute from shape Name Attribute shape Name Assign Attribute target shape Name Name\n",
+ "Label = __init__\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. slice\n",
- " 3. call\n",
- " 4. random_uniform\n",
- " 5. squeeze\n",
- " 6. project\n",
- "\n",
+ "---- 0. __init__ (1.0)\n",
+ " 1. on_train_begin (0.0)\n",
+ " 2. preprocess_input (0.0)\n",
+ " 3. compute_output_shape (0.0)\n",
+ " 4. random_normal_variable (0.0)\n",
+ " 5. predict_proba (0.0)\n",
+ " 6. build (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self Return List Call Attribute output variable Name Attribute target shape Name Attribute dtype Subscript Attribute inputs Name Index Num List\n",
+ "Label = infer_outputs\n",
+ "Pred =\n",
+ "---- 0. infer_outputs (1.0)\n",
+ " 1. reset_states (0.0)\n",
+ " 2. compute_output_shape (0.0)\n",
+ " 3. get_config (0.0)\n",
+ " 4. eval (0.0)\n",
+ " 5. states (0.0)\n",
+ " 6. call (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments Expr Str Assign Name explicitly on cpu Call Name Str Assign Name gpus available Compare Call Name Call Name Gt Num Return BoolOp And UnaryOp Not Name Name\n",
+ "Label = _has_nchw_support\n",
+ "Pred =\n",
+ " 0. has_seq_axis (0.156)\n",
+ " 1. normalize (0.035)\n",
+ " 2. output (0.033)\n",
+ " 3. deserialize (0.024)\n",
+ " 4. _normalize_device_name (0.021)\n",
+ " 5. validate_file (0.02)\n",
+ " 6. handle_value (0.019)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg dtype Expr Str Return Call Attribute convert to tensor Name Name keyword Name\n",
+ "Label = _to_tensor\n",
+ "Pred =\n",
+ " 0. ones_like (0.217)\n",
+ " 1. zeros_like (0.189)\n",
+ " 2. sqrt (0.033)\n",
+ " 3. softplus (0.027)\n",
+ " 4. cast (0.027)\n",
+ " 5. map_fn (0.023)\n",
+ " 6. _convert_string_dtype (0.022)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return Call Attribute eval Call Name Name keyword Call Name\n",
+ "Label = eval\n",
+ "Pred =\n",
+ "---- 0. eval (0.999)\n",
+ " 1. _get_dynamic_axis_num (0.0)\n",
+ " 2. infer_outputs (0.0)\n",
+ " 3. count_params (0.0)\n",
+ " 4. has_seq_axis (0.0)\n",
+ " 5. ctc_create_skip_idxs (0.0)\n",
+ " 6. _get_available_devices (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute reduce min Name Name Name Name\n",
"Label = min\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. update_sub\n",
- " 3. call\n",
- " 4. random_normal\n",
- " 5. batch_flatten\n",
- " 6. stop_gradient\n",
- "\n",
- "Label = sign\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. min\n",
- " 3. call\n",
- " 4. test_k_means_copyx\n",
- " 5. preprocess_input\n",
- " 6. batch_flatten\n",
- "\n",
- "Label = binary_crossentropy\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. _compute_class_weight_dictionary\n",
- " 4. assert_grid_iter_equals_getitem\n",
- " 5. test_deterministic_vocabulary\n",
- " 6. __add__\n",
- "\n",
- "Label = gradients\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. from_config\n",
- " 3. call\n",
- " 4. test_non_positive_precomputed_distances\n",
- " 5. softmax\n",
- " 6. random_uniform\n",
- "\n",
- "Label = batch_get_value\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. activity_regularizer\n",
- " 3. call\n",
- " 4. softmax\n",
- " 5. update_sub\n",
- " 6. noised\n",
- "\n",
- "Label = batch_set_value\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. _get_current_tf_device\n",
- " 4. sqrt\n",
- " 5. hard_sigmoid\n",
- " 6. compute_mask\n",
- "\n",
- "Label = _get_dynamic_axis_num\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. decode_predictions\n",
- " 3. int_shape\n",
- " 4. call\n",
- " 5. test_base_chain_fit_and_predict_with_sparse_data_and_cv\n",
- " 6. selu\n",
- "\n",
- "Label = _get_available_gpus\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. decode_predictions\n",
- " 4. get\n",
- " 5. softmax\n",
- " 6. int_shape\n",
- "\n",
- "Label = eye\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. random_normal\n",
- " 4. softmax\n",
- "---- 5. eye\n",
- " 6. logsumexp\n",
- "\n",
- "Label = min\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. sqrt\n",
- " 4. logsumexp\n",
- " 5. softmax\n",
- " 6. update_sub\n",
- "\n",
- "Label = std\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. update_sub\n",
- " 4. model\n",
- " 5. softmax\n",
- " 6. _preprocess_conv3d_input\n",
- "\n",
- "Label = any\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. logsumexp\n",
- " 4. softmax\n",
- " 5. selu\n",
- " 6. batch_flatten\n",
- "\n",
- "Label = logsumexp\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- "---- 3. logsumexp\n",
- " 4. softmax\n",
- " 5. batch_flatten\n",
- " 6. sqrt\n",
- "\n",
- "Label = reshape\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. logsumexp\n",
- " 4. test_fit\n",
- " 5. test_ovo_fit_on_list\n",
- " 6. predict_generator\n",
- "\n",
- "Label = permute_dimensions\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. logsumexp\n",
- " 4. decode_predictions\n",
- " 5. softmax\n",
- " 6. _preprocess_conv3d_input\n",
- "\n",
- "Label = batch_get_value\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. int_shape\n",
- " 4. softmax\n",
- " 5. cosine_proximity\n",
- " 6. random_normal\n",
- "\n",
+ "---- 0. min (0.986)\n",
+ " 1. max (0.004)\n",
+ " 2. sum (0.002)\n",
+ " 3. mean (0.001)\n",
+ " 4. prod (0.001)\n",
+ " 5. logsumexp (0.0)\n",
+ " 6. in_test_phase (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return Call Attribute reshape Name Name List UnaryOp USub Num\n",
+ "Label = flatten\n",
+ "Pred =\n",
+ " 0. reshape (0.591)\n",
+ "---- 1. flatten (0.134)\n",
+ " 2. expand_dims (0.095)\n",
+ " 3. tile (0.024)\n",
+ " 4. handle_value (0.008)\n",
+ " 5. convert_nested_time_distributed (0.006)\n",
+ " 6. softplus (0.006)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Assign Name x Call Attribute reshape Name Name Call Attribute stack Name List UnaryOp USub Num Call Name Subscript Call Name Name Slice Num Return Name\n",
+ "Label = batch_flatten\n",
+ "Pred =\n",
+ " 0. _reshape_batch (0.473)\n",
+ " 1. repeat (0.204)\n",
+ " 2. slice (0.011)\n",
+ " 3. _normalize_device_name (0.011)\n",
+ " 4. _canonical_to_params (0.01)\n",
+ " 5. tile (0.01)\n",
+ " 6. reshape (0.009)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return Call Attribute eval Name keyword Call Name\n",
+ "Label = get_value\n",
+ "Pred =\n",
+ " 0. eval (0.991)\n",
+ " 1. count_params (0.002)\n",
+ " 2. get_variable_shape (0.0)\n",
+ " 3. update (0.0)\n",
+ " 4. softsign (0.0)\n",
+ " 5. std (0.0)\n",
+ " 6. Xception (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return Call Attribute sigmoid Attribute nn Name Name\n",
"Label = sigmoid\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. int_shape\n",
- " 4. softmax\n",
- " 5. eval\n",
- " 6. logsumexp\n",
- "\n",
- "Label = dropout\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. noised\n",
- " 4. update_sub\n",
- " 5. int_shape\n",
- " 6. random_normal\n",
- "\n",
- "Label = _preprocess_padding\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. eye\n",
- " 5. stop_gradient\n",
- " 6. test_ovr_fit_predict_svc\n",
- "\n",
- "Label = random_uniform\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. call\n",
- " 2. get_config\n",
- " 3. random_normal\n",
- " 4. test_ovo_fit_on_list\n",
- "---- 5. random_uniform\n",
- " 6. temporal_padding\n",
- "\n",
- "Label = foldl\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. zeros_like\n",
- " 5. from_config\n",
- " 6. preprocess_input\n",
- "\n",
- "Label = _is_explicit_shape\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. int_shape\n",
- " 2. clip\n",
- " 3. call\n",
- " 4. _get_current_tf_device\n",
- " 5. model_from_json\n",
- " 6. on_train_begin\n",
- "\n",
- "Label = is_tensor\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. int_shape\n",
- " 4. softmax\n",
- " 5. normalize_padding\n",
- " 6. from_config\n",
- "\n",
- "Label = ones_like\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. zeros_like\n",
- " 3. call\n",
- " 4. min\n",
- " 5. sqrt\n",
- " 6. softmax\n",
- "\n",
- "Label = count_params\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. compute_mask\n",
- " 3. call\n",
- " 4. set_floatx\n",
- " 5. X_64bit\n",
- " 6. foldr\n",
- "\n",
- "Label = std\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. zeros_like\n",
- " 4. sqrt\n",
- " 5. softmax\n",
- " 6. logsumexp\n",
- "\n",
- "Label = argmin\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. zeros_like\n",
- " 4. _preprocess_conv3d_input\n",
- " 5. int_shape\n",
- " 6. equal\n",
+ " 0. tanh (0.525)\n",
+ " 1. softplus (0.335)\n",
+ " 2. softsign (0.104)\n",
+ " 3. ctc_create_skip_idxs (0.003)\n",
+ " 4. gather (0.002)\n",
+ " 5. is_placeholder (0.001)\n",
+ " 6. softmax (0.001)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x Expr Str Return BoolOp And Call Name Name Str Attribute theano placeholder Name\n",
+ "Label = is_placeholder\n",
+ "Pred =\n",
+ "---- 0. is_placeholder (0.996)\n",
+ " 1. get_json_type (0.0)\n",
+ " 2. sparse_top_k_categorical_accuracy (0.0)\n",
+ " 3. has_seq_axis (0.0)\n",
+ " 4. softsign (0.0)\n",
+ " 5. _get_available_gpus (0.0)\n",
+ " 6. ctc_create_skip_idxs (0.0)\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 (0.998)\n",
+ " 1. ones_like (0.0)\n",
+ " 2. zeros (0.0)\n",
+ " 3. random_binomial (0.0)\n",
+ " 4. constant (0.0)\n",
+ " 5. _preprocess_conv2d_input (0.0)\n",
+ " 6. _preprocess_conv3d_input (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg name NameConstant Expr Str Return Call Attribute copy Name keyword Name\n",
+ "Label = identity\n",
+ "Pred =\n",
+ "---- 0. identity (0.802)\n",
+ " 1. zeros_like (0.115)\n",
+ " 2. ones_like (0.009)\n",
+ " 3. repeat (0.003)\n",
+ " 4. map_fn (0.003)\n",
+ " 5. _logcosh (0.003)\n",
+ " 6. softplus (0.003)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg decrement Return Tuple Name BinOp Name Sub Name\n",
+ "Label = update_sub\n",
+ "Pred =\n",
+ "---- 0. update_sub (0.992)\n",
+ " 1. shape (0.0)\n",
+ " 2. in_test_phase (0.0)\n",
+ " 3. update_add (0.0)\n",
+ " 4. next_sample (0.0)\n",
+ " 5. on_train_end (0.0)\n",
+ " 6. get_index (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg x arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute sum Name Name keyword Name keyword Name\n",
+ "Label = sum\n",
+ "Pred =\n",
+ "---- 0. sum (0.894)\n",
+ " 1. prod (0.025)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 2. std (0.014)\n",
+ " 3. min (0.014)\n",
+ " 4. logsumexp (0.013)\n",
+ " 5. max (0.013)\n",
+ " 6. mean (0.012)\n",
"\n",
+ "[CLS] FunctionDef arguments arg start arg stop arg step arg dtype NameConstant Num Str Expr Str Return Call Attribute arange Name Name keyword Name keyword Name keyword Name\n",
"Label = arange\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. temporal_padding\n",
- " 3. concatenate\n",
- " 4. call\n",
- " 5. activity_regularizer\n",
- " 6. update_sub\n",
- "\n",
- "Label = __call__\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. preprocess_input\n",
- " 5. from_config\n",
- " 6. int_shape\n",
- "\n",
+ " 0. map_fn (0.387)\n",
+ " 1. truncated_normal (0.065)\n",
+ " 2. slice (0.064)\n",
+ " 3. predict_generator (0.033)\n",
+ " 4. evaluate_generator (0.026)\n",
+ " 5. eye (0.014)\n",
+ " 6. _wait_queue (0.014)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg predict arg Y Assign Tuple Name log probs Name mask Call Name Name Call Name Name Assign Name common factor Call Attribute max Name Name Assign Name total log prob BinOp Call Attribute log Name Call Attribute sum Name Subscript Call Attribute exp Name BinOp Name Sub Name Index Call Attribute nonzero Name Add Name Return UnaryOp USub Name\n",
+ "Label = ctc_cost\n",
+ "Pred =\n",
+ " 0. mean_absolute_error (0.124)\n",
+ " 1. compute_mask (0.069)\n",
+ " 2. mean_squared_error (0.067)\n",
+ " 3. predict_proba (0.052)\n",
+ " 4. handle_value (0.051)\n",
+ " 5. categorical_hinge (0.037)\n",
+ " 6. in_top_k (0.029)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg fn arg elems arg initializer arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name initializer Subscript Name Index UnaryOp USub Num Assign Name elems Subscript Name Slice UnaryOp Num Return Subscript Call Attribute foldr Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name Index Num\n",
"Label = foldr\n",
"Pred =\n",
- " 0. __init__\n",
- " 1. call\n",
- " 2. sqrt\n",
- " 3. preprocess_input\n",
- " 4. temporal_padding\n",
- " 5. random_normal\n",
- " 6. decode_predictions\n",
- "\n",
- "Label = __init__\n",
- "Pred =\n",
- "---- 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. decode_predictions\n",
- " 4. squared_hinge\n",
- " 5. softmax\n",
- " 6. is_keras_tensor\n",
- "\n",
- "Label = state_updates\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. softmax\n",
- " 4. int_shape\n",
- " 5. test_k_means_random_init_not_precomputed\n",
- " 6. test_float_class_labels\n",
- "\n",
- "Label = trainable_weights\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. non_trainable_weights\n",
- " 4. save\n",
- " 5. stop_gradient\n",
- " 6. test_sparse_and_verbose\n",
- "\n",
- "Label = summary\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. call\n",
- " 2. get_config\n",
- " 3. softmax\n",
- " 4. test_ovo_fit_on_list\n",
- " 5. sqrt\n",
- " 6. VGG16\n",
- "\n",
- "Label = layers\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. _merge_function\n",
- " 4. batch_flatten\n",
- " 5. softmax\n",
- " 6. int_shape\n",
- "\n",
- "Label = predict_proba\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. sqrt\n",
- " 3. call\n",
- " 4. update_sub\n",
- " 5. warnings_to_stdout\n",
- " 6. softmax\n",
- "\n",
- "Label = pickle_model\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. update\n",
- " 2. get_config\n",
- " 3. _pairwise\n",
- " 4. get_context\n",
- " 5. int_shape\n",
- " 6. mean_absolute_error\n",
- "\n",
- "Label = get_input_shape_at\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. from_config\n",
- " 3. softmax\n",
- " 4. create_mv\n",
- " 5. _merge_function\n",
- " 6. test_polynomial_feature_array_order\n",
- "\n",
- "Label = get_output_mask_at\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. from_config\n",
- " 3. softmax\n",
- " 4. create_mv\n",
- " 5. _merge_function\n",
- " 6. test_polynomial_feature_array_order\n",
- "\n",
- "Label = input\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. _merge_function\n",
- " 3. softmax\n",
- " 4. compute_mask\n",
- " 5. VGG16\n",
- " 6. int_shape\n",
- "\n",
- "Label = output\n",
- "Pred =\n",
- " 0. get_config\n",
- " 1. __init__\n",
- " 2. _merge_function\n",
- " 3. compute_mask\n",
- " 4. softmax\n",
- " 5. VGG16\n",
- " 6. int_shape\n",
- "\n",
- "Label = get_config\n",
- "Pred =\n",
- "---- 0. get_config\n",
- " 1. __init__\n",
- " 2. softmax\n",
- " 3. call\n",
- " 4. squared_hinge\n",
- " 5. batch_flatten\n",
- " 6. _preprocess_padding\n",
- "\n",
- "Label = evaluate_generator\n",
- "Pred =\n",
- " 0. __init__\n",
- " 1. get_config\n",
- " 2. call\n",
- " 3. random_normal\n",
- " 4. temporal_padding\n",
- " 5. tmpdata\n",
- " 6. update_sub\n",
+ "---- 0. foldr (0.996)\n",
+ " 1. foldl (0.002)\n",
+ " 2. clip (0.0)\n",
+ " 3. ctc_step (0.0)\n",
+ " 4. ones (0.0)\n",
+ " 5. map_fn (0.0)\n",
+ " 6. ask_to_proceed_with_overwrite (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg path 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 = save_img\n",
+ "Pred =\n",
+ " 0. truncated_normal (0.048)\n",
+ " 1. get_word_index (0.033)\n",
+ " 2. random_normal_variable (0.031)\n",
+ " 3. ones (0.031)\n",
+ " 4. ask_to_proceed_with_overwrite (0.03)\n",
+ " 5. set_image_dim_ordering (0.022)\n",
+ " 6. random_uniform_variable (0.022)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg self arg x arg batch size arg verbose Num Num Expr Str Assign Name proba Call Attribute predict Name Name keyword Name keyword Name If Compare Subscript Attribute shape Name Index UnaryOp USub Num Gt Num Return Call Attribute argmax Name keyword UnaryOp Num Return Call Attribute astype Compare Name Num Str\n",
+ "Label = predict_classes\n",
+ "Pred =\n",
+ " 0. predict_proba (0.997)\n",
+ " 1. predict_generator (0.0)\n",
+ " 2. predict (0.0)\n",
+ " 3. on_train_end (0.0)\n",
+ " 4. argmax (0.0)\n",
+ " 5. handle_value (0.0)\n",
+ " 6. update_sub (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg json string arg custom objects NameConstant Expr Str Assign Name config Call Attribute loads Name Name ImportFrom alias Return Call Name Name keyword Name\n",
+ "Label = model_from_json\n",
+ "Pred =\n",
+ " 0. model_from_yaml (0.992)\n",
+ " 1. model_from_config (0.001)\n",
+ " 2. get (0.0)\n",
+ " 3. truncated_normal (0.0)\n",
+ " 4. _reshape_batch (0.0)\n",
+ " 5. is_all_none (0.0)\n",
+ " 6. any (0.0)\n",
+ "\n",
+ "[CLS] FunctionDef arguments arg layer arg node index Expr Str Return BinOp BinOp Attribute name Name Add Str Call Name Name Name\n",
+ "Label = _node_key\n",
+ "Pred =\n",
+ " 0. _make_node_key (0.983)\n",
+ " 1. get_input_shape_at (0.001)\n",
+ " 2. get_output_shape_at (0.001)\n",
+ " 3. get_output_at (0.001)\n",
+ " 4. get_input_mask_at (0.001)\n",
+ " 5. get_output_mask_at (0.001)\n",
+ " 6. get_input_at (0.001)\n",
"\n"
]
}
@@ -1612,20 +2431,21 @@
"source": [
"pred_str = []; score = 0; rank = []\n",
"for idx, r in enumerate(preds):\n",
- " #print(snippet.loc[idx][0])\n",
+ " print(snippet.loc[idx][0])\n",
" print(\"Label =\", labels_str[idx])\n",
" preds_ = []\n",
" print(\"Pred =\")\n",
" correct = False\n",
+ " \n",
" for i in range(7):\n",
" p = vocab_label_df.loc[r[i]][0] \n",
" if p==labels_str[idx]:\n",
" score +=1\n",
" rank.append(i+1)\n",
- " print(\"---- {}. {}\".format(i,p))\n",
+ " print(\"---- {}. {} ({})\".format(i,p,np.around(probs[idx][r[i]],3)))\n",
" correct = True\n",
" else:\n",
- " print(\" {}. {}\".format(i,p))\n",
+ " print(\" {}. {} ({})\".format(i,p,np.around(probs[idx][r[i]],3)))\n",
" preds_.append(p)\n",
" if correct == False:\n",
" rank.append(i)\n",
@@ -1635,36 +2455,56 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 129,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0"
+ ]
+ },
+ "execution_count": 129,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.around(probs[idx][r[i]],3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.38333333333333336"
+ "0.68"
]
},
- "execution_count": 78,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "score/len(labels)"
+ "score/len(preds)"
]
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.20202020202020202"
+ "0.3712871287128713"
]
},
- "execution_count": 79,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -1675,7 +2515,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -1684,16 +2524,16 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
- "m = io.mmread('../sparse/adj/0_sparse_fname2_split_magret_adj.mtx').toarray()"
+ "m = io.mmread('../sparse/adj/1_sparse_fname2_split_magret_adj.mtx').toarray()"
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
@@ -1702,22 +2542,22 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 40,
+ "execution_count": 86,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1735,7 +2575,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 87,
"metadata": {},
"outputs": [
{
@@ -1744,7 +2584,7 @@
"2"
]
},
- "execution_count": 41,
+ "execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
@@ -1755,7 +2595,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
@@ -1764,7 +2604,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
@@ -1773,32 +2613,176 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"G = nx.from_numpy_matrix(m)\n",
- "shuffled_adj = nx.adjacency_matrix(G, nodelist=shuffle_idx).todense()"
+ "#shuffled_adj = nx.adjacency_matrix(G, nodelist=shuffle_idx).todense()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20"
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "emb_len = len(snippet.loc[1][0].split(' '))\n",
+ "emb_len"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "G = nx.from_numpy_matrix(m[:emb_len,:emb_len])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "edges = list(G.edges(0))\n",
+ "G.remove_edges_from(edges)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "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",
+ "plt.imshow(m[:emb_len,:emb_len])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['[CLS]',\n",
+ " 'FunctionDef',\n",
+ " 'arguments',\n",
+ " 'arg',\n",
+ " 'self',\n",
+ " 'arg',\n",
+ " 'model',\n",
+ " 'For',\n",
+ " 'Name',\n",
+ " 'callback',\n",
+ " 'Attribute',\n",
+ " 'callbacks',\n",
+ " 'Name',\n",
+ " 'Expr',\n",
+ " 'Call',\n",
+ " 'Attribute',\n",
+ " 'set',\n",
+ " 'model',\n",
+ " 'Name',\n",
+ " 'Name']"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snippet.loc[1][0].split(' ')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "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),snippet.loc[1][0].split(' '))), font_size=20, font_family='sans-serif')\n",
+ "\n",
+ "plt.axis('off')\n",
+ "plt.show()"
]
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 51,
+ "execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1817,7 +2801,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
@@ -1826,22 +2810,22 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 53,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
diff --git a/notebook/Inspect Predictions - Var Naming.ipynb b/notebook/Inspect Predictions - Var Naming.ipynb
index e35baca..e48ad15 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": 1,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 104,
"metadata": {},
"outputs": [
{
@@ -41,7 +41,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 142,
"metadata": {},
"outputs": [
{
@@ -396,7 +396,7 @@
"[10 rows x 1156 columns]"
]
},
- "execution_count": 5,
+ "execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
@@ -408,7 +408,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 143,
"metadata": {},
"outputs": [
{
@@ -417,7 +417,7 @@
"(39552, 1156)"
]
},
- "execution_count": 6,
+ "execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
@@ -428,7 +428,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 144,
"metadata": {},
"outputs": [
{
@@ -514,7 +514,7 @@
"9 call"
]
},
- "execution_count": 7,
+ "execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
@@ -526,7 +526,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 145,
"metadata": {},
"outputs": [
{
@@ -535,7 +535,7 @@
"(618, 5)"
]
},
- "execution_count": 8,
+ "execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
@@ -547,7 +547,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 146,
"metadata": {},
"outputs": [
{
@@ -556,7 +556,7 @@
"618"
]
},
- "execution_count": 9,
+ "execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
@@ -567,7 +567,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 147,
"metadata": {},
"outputs": [],
"source": [
@@ -576,7 +576,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
@@ -600,7 +600,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 149,
"metadata": {},
"outputs": [
{
@@ -609,7 +609,7 @@
"(618, 64, 7)"
]
},
- "execution_count": 12,
+ "execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
@@ -620,7 +620,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 150,
"metadata": {
"scrolled": true
},
@@ -631,7 +631,7 @@
"(618, 5)"
]
},
- "execution_count": 13,
+ "execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
@@ -642,7 +642,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
@@ -658,7 +658,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 152,
"metadata": {
"scrolled": true
},
@@ -1286,7 +1286,7 @@
" ['shape', '[PAD]', '[PAD]', '[PAD]']]"
]
},
- "execution_count": 15,
+ "execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
@@ -1297,7 +1297,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 153,
"metadata": {},
"outputs": [
{
@@ -1306,7 +1306,7 @@
"'[PAD]'"
]
},
- "execution_count": 16,
+ "execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
@@ -1317,7 +1317,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 154,
"metadata": {},
"outputs": [
{
@@ -1326,7 +1326,7 @@
"(618, 1)"
]
},
- "execution_count": 17,
+ "execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
@@ -1338,7 +1338,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 155,
"metadata": {},
"outputs": [
{
@@ -1349,3161 +1349,2535 @@
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute input layers Name Assign Name input tensor Call Name keyword Attribute batch input shape Name keyword Attribute dtype Name keyword Attribute sparse Name keyword Attribute name Name Expr Call Attribute append Name Name Assign Name newly created input layer Subscript Attribute keras history Name Index Num Assign Subscript Name Index Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['tensor', 'tensor', 'tensor', 'tensor']\n",
+ " 2. ['input', 'input', 'input', 'input']\n",
"\n",
"1\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name In Name Expr Call Attribute append Name Subscript Name Index 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",
+ " 1. ['g', 'value', 'value', 'value']\n",
+ " 2. ['layer', 'data', 'data', 'data']\n",
"\n",
"2\n",
"[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Name Sub Call Attribute max Name Name keyword Name keyword NameConstant Assign Name s Call Attribute sum Name Name keyword Name keyword NameConstant Return BinOp Name Div Name Raise Call Name BinOp Str Mod Name\n",
"Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new e\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['p', 'log', 'log', 'log']\n",
+ " 2. ['v', 't', 't', 't']\n",
"\n",
"3\n",
"[CLS] BinOp Name Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword NameConstant\n",
"Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "random max\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['random', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['scan', 'normal', 'normal', 'normal']\n",
+ " 2. ['convolution', 'spec', 'spec', 'spec']\n",
"\n",
"4\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name alpha Num Assign Name scale Num Return BinOp Name Mult Call Attribute elu Name 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",
+ " 1. ['a', 'format', 'format', 'format']\n",
+ " 2. ['negative', 'true', 'true', 'true']\n",
"\n",
"5\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Return Call Name Name keyword Call Name keyword Name keyword Str\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'config', 'config', 'config']\n",
+ " 2. ['cls', 'string', 'string', 'string']\n",
"\n",
"6\n",
"[CLS] If Call Name Name If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name Return Name Raise Call Name Str Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pop', 'params', 'params', 'params']\n",
+ " 2. ['extend', 'function', 'function', 'function']\n",
"\n",
"7\n",
"[CLS] If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pop', 'params', 'params', 'params']\n",
+ " 2. ['append', 'config', 'config', 'config']\n",
"\n",
"8\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute flatten Name Name Call Attribute cast Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute floatx Name Call Attribute floatx Name\n",
"Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cast cast\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cast', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['mean', 'true', 'true', 'true']\n",
+ " 2. ['equal', 'mean', 'mean', 'mean']\n",
"\n",
"9\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['y', 'true', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "y y\n",
- "true true\n",
- "true [PAD]\n",
- "true [PAD]\n",
" 0. ['y', 'true', 'true', 'true']\n",
+ " 1. ['self', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['true', 'train', 'train', 'train']\n",
"\n",
"10\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg model For Name callback Attribute callbacks Name Expr Call Attribute set model 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",
+ " 1. ['model', 'model', 'model', 'model']\n",
+ " 2. ['layer', 'weights', 'weights', 'weights']\n",
"\n",
"11\n",
"[CLS] If Name Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Assign Attribute stateful metrics Name Call Name\n",
"Label = ['stateful', 'metrics', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "metrics metrics\n",
- "metrics [PAD]\n",
- "metrics [PAD]\n",
" 0. ['stateful', 'metrics', 'metrics', 'metrics']\n",
+ " 1. ['metrics', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['append', 'stateful', 'stateful', 'stateful']\n",
"\n",
"12\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name target Subscript Attribute params Name Index Str Assign Name target Subscript Attribute params Name Index Str\n",
"Label = ['use', 'steps', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs use\n",
- "[PAD] steps\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'sequences', 'sequences', 'sequences']\n",
+ " 2. ['stateful', 'function', 'function', 'function']\n",
"\n",
"13\n",
"[CLS] 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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['extend', 'dim', 'dim', 'dim']\n",
"\n",
"14\n",
"[CLS] Call Name 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",
- "monitor monitor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['monitor', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['float32', 'size', 'size', 'size']\n",
+ " 2. ['warn', 'weights', 'weights', 'weights']\n",
"\n",
"15\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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\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",
+ " 1. ['model', 'epoch', 'epoch', 'epoch']\n",
+ " 2. ['path', 'format', 'format', 'format']\n",
"\n",
"16\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg root arg path arg field arg headers arg send as json Str Str Str NameConstant NameConstant Expr Call Attribute init Call Name Name Name Assign Attribute root Name Name Assign Attribute path Name Name Assign Attribute field Name Name Assign Attribute headers Name Name Assign Attribute send as json 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",
+ " 1. ['model', 'name', 'name', 'name']\n",
+ " 2. ['path', 'fn', 'fn', 'fn']\n",
"\n",
"17\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg schedule arg verbose Num Expr Call Attribute init Call Name Name Name Assign Attribute schedule Name Name Assign Attribute verbose 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",
+ " 1. ['model', 'weights', 'weights', 'weights']\n",
+ " 2. ['path', 'format', 'format', 'format']\n",
"\n",
"18\n",
"[CLS] If Compare Name NotEq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name embeddings freq Num\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pop', 'format', 'format', 'format']\n",
+ " 2. ['update', 'data', 'data', 'data']\n",
"\n",
"19\n",
"[CLS] If Compare Name Eq Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Num Assign Attribute update freq Name Name\n",
"Label = ['update', 'freq', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data update\n",
- "[PAD] freq\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['type', 'data', 'data', 'data']\n",
+ " 2. ['embeddings', 'format', 'format', 'format']\n",
"\n",
"20\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stack', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['transpose', 'dims', 'dims', 'dims']\n",
"\n",
"21\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotIn List Str Str Str Expr Call Attribute warn Name BinOp Str Mod Attribute mode Name Name Assign Attribute mode Name Str\n",
"Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mode mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['merge', 'mode', 'mode', 'mode']\n",
+ " 2. ['verbose', 'format', 'format', 'format']\n",
"\n",
"22\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Mod Tuple Attribute monitor Name Call Attribute join Str Call Name Call Attribute keys Name Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['update', 'function', 'function', 'function']\n",
+ " 2. ['post', 'weight', 'weight', 'weight']\n",
"\n",
"23\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name k Attribute keys Name\n",
"Label = ['logs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k logs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 'values', 'values', 'values']\n",
+ " 2. ['result', 'key', 'key', 'key']\n",
"\n",
"24\n",
"[CLS] If Compare Name IsNot NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Attribute on train begin Name Lambda arguments arg logs NameConstant\n",
"Label = ['on', 'train', 'begin', '[PAD]']\n",
"Pred =\n",
- "on on\n",
- "[PAD] train\n",
- "[PAD] begin\n",
- "[PAD] [PAD]\n",
" 0. ['on', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['initial', 'begin', 'begin', 'begin']\n",
+ " 2. ['end', 'batch', 'batch', 'batch']\n",
"\n",
"25\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x Assign Name regularization Num If Attribute l1 Name AugAssign Name regularization Add Call Attribute sum Name BinOp Attribute l1 Name Mult Call Attribute abs Name Name If Attribute l2 Name AugAssign Name regularization Call Attribute sum Name BinOp Attribute l2 Name Call Attribute square Name 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",
+ " 1. ['l1', 'sum', 'sum', 'sum']\n",
+ " 2. ['xs', 't', 't', 't']\n",
"\n",
"26\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Dict Str Str Attribute max value Name 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",
+ " 1. ['x', 'value', 'value', 'value']\n",
+ " 2. ['model', 'config', 'config', 'config']\n",
"\n",
"27\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg w Return BinOp Name Div BinOp Call Attribute epsilon Name Add Call Attribute sqrt Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword 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",
+ " 1. ['x', 'true', 'true', 'true']\n",
+ " 2. ['a', 'sum', 'sum', 'sum']\n",
"\n",
"28\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n",
"Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sum sum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reduce', 'sum', 'sum', 'sum']\n",
+ " 2. ['mean', 'function', 'function', 'function']\n",
"\n",
"29\n",
"[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['string', 'types', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "string string\n",
- "[PAD] types\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['string', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cell', 'types', 'types', 'types']\n",
+ " 2. ['data', 'tensor', 'tensor', 'tensor']\n",
"\n",
"30\n",
"[CLS] 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",
+ " 1. ['value', 'value', 'value', 'value']\n",
+ " 2. ['x', 'size', 'size', 'size']\n",
"\n",
"31\n",
"[CLS] Return Dict Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute stddev Name Attribute seed Name\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['random', 'normal', 'normal', 'normal']\n",
+ " 2. ['truncated', 'uniform', 'uniform', 'uniform']\n",
"\n",
"32\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str AugAssign Name scale Div Call Name Num Name If Compare Attribute mode Name Str AugAssign Name scale Call Name Num Name AugAssign Name scale Call Name Num BinOp Call Name BinOp Name Add Name Num\n",
"Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mode mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['merge', 'mode', 'mode', 'mode']\n",
"\n",
"33\n",
"[CLS] Return 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",
- "truncated truncated\n",
- "[PAD] normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['truncated', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['random', 'normal', 'normal', 'normal']\n",
+ " 2. ['uniform', 'uniform', 'uniform', 'uniform']\n",
"\n",
"34\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num AugAssign Name num rows Mult Name\n",
"Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "a dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['a', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['s', 'out', 'out', 'out']\n",
+ " 2. ['i', 'size', 'size', 'size']\n",
"\n",
"35\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Expr Call Attribute seed Attribute random Name Attribute seed Name\n",
"Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['random', 'normal', 'normal', 'normal']\n",
+ " 2. ['activation', 'uniform', 'uniform', 'uniform']\n",
"\n",
"36\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num\n",
"Label = ['receptive', 'field', 'size', '[PAD]']\n",
"Pred =\n",
- "y receptive\n",
- "[PAD] field\n",
- "[PAD] size\n",
- "[PAD] [PAD]\n",
" 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'shape', 'shape', 'shape']\n",
+ " 2. ['batch', 'size', 'size', 'size']\n",
"\n",
"37\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sum', 'sum', 'sum', 'sum']\n",
+ " 2. ['reduce', 'mean', 'mean', 'mean']\n",
"\n",
"38\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name BinOp Name Sub Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sum', 'normal', 'normal', 'normal']\n",
+ " 2. ['max', 'function', 'function', 'function']\n",
"\n",
"39\n",
"[CLS] BinOp Num Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['max', 'normal', 'normal', 'normal']\n",
+ " 2. ['sum', 'sum', 'sum', 'sum']\n",
"\n",
"40\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name pos Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp USub Num Assign Name neg Call Attribute max Name BinOp BinOp Num Sub Name Name keyword UnaryOp Num Return Call Attribute maximum Name Num BinOp BinOp Name Name Add Num\n",
"Label = ['y', 'true', '[PAD]', '[PAD]']\n",
- "Pred =\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "y y\n",
- "[PAD] true\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'true', 'true', 'true']\n",
+ " 2. ['self', 'train', 'train', 'train']\n",
"\n",
"41\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",
- "maximum maximum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['maximum', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['ndim', 'dims', 'dims', 'dims']\n",
+ " 2. ['clip', 'shape', 'shape', 'shape']\n",
"\n",
"42\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",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sum', 'mean', 'mean', 'mean']\n",
+ " 2. ['any', 'sum', 'sum', 'sum']\n",
"\n",
"43\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",
- "y y\n",
- "[PAD] true\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'true', 'true', 'true']\n",
+ " 2. ['u', 'train', 'train', 'train']\n",
"\n",
"44\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute switch Name Call Attribute greater equal Name Name Name BinOp BinOp Name Mult Name Div Name Name\n",
"Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new g\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['all', 'element', 'element', 'element']\n",
+ " 2. ['v', 't', 't', 't']\n",
"\n",
"45\n",
"[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num\n",
"Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "clipvalue clipnorm\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['clipvalue', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dynamic', 'axes', 'axes', 'axes']\n",
+ " 2. ['delta', 'data', 'data', 'data']\n",
"\n",
"46\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name ListComp Call Attribute sum Name Call Attribute square Name Name comprehension Name g Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['items', 'sum', 'sum', 'sum']\n",
+ " 2. ['sum', 'list', 'list', 'list']\n",
"\n",
"47\n",
"[CLS] Raise Call Name 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",
+ " 1. ['axis', 'shape', 'shape', 'shape']\n",
+ " 2. ['name', 'tensor', 'tensor', 'tensor']\n",
"\n",
"48\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n",
"Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "iterations iterations\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cudnn', 'size', 'size', 'size']\n",
+ " 2. ['name', 'iterations', 'iterations', 'iterations']\n",
"\n",
"49\n",
"[CLS] 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",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['lr', 'decay', 'decay', 'decay']\n",
+ " 2. ['gain', 'size', 'size', 'size']\n",
"\n",
"50\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 = ['new', 'p', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p new\n",
- "[PAD] p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 't', 't', 't']\n",
+ " 2. ['t', 'p', 'p', 'p']\n",
"\n",
"51\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Call Attribute sqrt Name BinOp Name Add Attribute epsilon Name Div Call Attribute sqrt Name BinOp Name Attribute epsilon Name\n",
"Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p update\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 't', 't', 't']\n",
+ " 2. ['lr', 'p', 'p', 'p']\n",
"\n",
"52\n",
"[CLS] Dict Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Attribute rho Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n",
"Label = ['get', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'value', 'value', 'value']\n",
+ " 2. ['cast', 'function', 'function', 'function']\n",
"\n",
"53\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n",
"Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "iterations iterations\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cudnn', 'size', 'size', 'size']\n",
+ " 2. ['name', 'iterations', 'iterations', 'iterations']\n",
"\n",
"54\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",
- "lr lr\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['lr', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['decay', 'decay', 'decay', 'decay']\n",
+ " 2. ['t', 't', 't', 't']\n",
"\n",
"55\n",
"[CLS] 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",
+ "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Pred =\n",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['lr', 'decay', 'decay', 'decay']\n",
+ " 2. ['gain', 'size', 'size', 'size']\n",
"\n",
"56\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Attribute cast Name Attribute iterations Name Call Attribute floatx Name Add Num\n",
"Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "t t\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['t', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['lr', 't', 't', 't']\n",
+ " 2. ['decay', 'decay', 'decay', 'decay']\n",
"\n",
"57\n",
"[CLS] BinOp Name Mult BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name Div BinOp Num Call Attribute pow Name Attribute beta 1 Name Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pow', 't', 't', 't']\n",
+ " 2. ['maximum', '1', '1', '1']\n",
"\n",
"58\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pow', 't', 't', 't']\n",
+ " 2. ['maximum', '1', '1', '1']\n",
"\n",
"59\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg lr arg beta 1 arg beta 2 arg epsilon arg decay arg kwargs Num Num Num 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",
+ " 1. ['model', 'value', 'value', 'value']\n",
+ " 2. ['lr', 'format', 'format', 'format']\n",
"\n",
"60\n",
"[CLS] BinOp Num Div 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",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['lr', 'decay', 'decay', 'decay']\n",
+ " 2. ['gain', 't', 't', 't']\n",
"\n",
"61\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",
- "m m\n",
- "[PAD] t\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['m', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['lr', 't', 't', 't']\n",
+ " 2. ['t', '1', '1', '1']\n",
"\n",
"62\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",
- "ndim epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'names', 'names', 'names']\n",
+ " 2. ['momentum', 'dim', 'dim', 'dim']\n",
"\n",
"63\n",
"[CLS] BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ndim epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['momentum', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['value', 'names', 'names', 'names']\n",
"\n",
"64\n",
"[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp BinOp Name Add Num Attribute schedule decay Name\n",
"Label = ['beta', '1', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "beta beta\n",
- "[PAD] 1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['decay', '1', '1', '1']\n",
+ " 2. ['pow', 't', 't', 't']\n",
"\n",
"65\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",
- "beta beta\n",
- "[PAD] 1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['momentum', '1', '1', '1']\n",
+ " 2. ['lr', 't', 't', 't']\n",
"\n",
"66\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 2 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 2 Name Call Attribute square Name Name\n",
"Label = ['v', 't', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v v\n",
- "t t\n",
- "t [PAD]\n",
- "t [PAD]\n",
" 0. ['v', 't', 't', 't']\n",
+ " 1. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['lr', '2', '2', '2']\n",
"\n",
"67\n",
"[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 2 Name Name\n",
"Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pow pow\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['pow', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sqrt', '1', '1', '1']\n",
+ " 2. ['maximum', 't', 't', 't']\n",
"\n",
"68\n",
"[CLS] Dict Str Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['get', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cast', 'value', 'value', 'value']\n",
+ " 2. ['output', 'function', 'function', 'function']\n",
"\n",
"69\n",
"[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str\n",
"Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "toarray lower\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['toarray', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['keys', 'shape', 'shape', 'shape']\n",
+ " 2. ['as', 'size', 'size', 'size']\n",
"\n",
"70\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Str Tuple Attribute input dim Name\n",
"Label = ['input', 'dim', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input input\n",
- "[PAD] dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['return', 'dim', 'dim', 'dim']\n",
+ " 2. ['stateful', 'spec', 'spec', 'spec']\n",
"\n",
"71\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['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",
+ " 1. ['bias', 'weight', 'weight', 'weight']\n",
+ " 2. ['b', 'bias', 'bias', 'bias']\n",
"\n",
"72\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",
+ " 1. ['layer', 'format', 'format', 'format']\n",
+ " 2. ['model', 'bias', 'bias', 'bias']\n",
"\n",
"73\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name keyword Attribute init Name keyword Str keyword Attribute W regularizer Name keyword Attribute W 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",
+ " 1. ['init', 'weight', 'weight', 'weight']\n",
+ " 2. ['pooling', 'function', 'function', 'function']\n",
"\n",
"74\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs ImportFrom alias If Compare Str In Name Assign Name rate Call Attribute pop Name Str Assign Name rate Num Assign Subscript Name Index Str Name Expr Call Attribute warn Name Str Return Call Name Starred Name keyword Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'args', 'args', 'args']\n",
+ " 2. ['cls', 'format', 'format', 'format']\n",
"\n",
"75\n",
"[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['outputs', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['inputs', 'layers', 'layers', 'layers']\n",
"\n",
"76\n",
"[CLS] Raise Call Name BinOp BinOp 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 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",
+ " 1. ['batch', 'name', 'name', 'name']\n",
+ " 2. ['[PAD]', 'list', 'list', 'list']\n",
"\n",
"77\n",
"[CLS] BinOp 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 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",
+ " 1. ['batch', 'name', 'name', 'name']\n",
+ " 2. ['[PAD]', 'list', 'list', 'list']\n",
"\n",
"78\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 Tuple Name Attribute units Name Str Call Name Attribute shape 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",
+ " 1. ['shape', 'shape', 'shape', 'shape']\n",
+ " 2. ['batch', 'size', 'size', 'size']\n",
"\n",
"79\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Attribute return sequences Name Attribute return state Name Attribute go backwards Name Attribute stateful Name Attribute unroll Name Attribute implementation Name\n",
"Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 'sequences', 'sequences', 'sequences']\n",
+ " 2. ['last', 'config', 'config', 'config']\n",
"\n",
"80\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute kernel size Name Index Num keyword Attribute padding Name keyword Subscript Attribute strides Name Index Num keyword Subscript Attribute dilation rate Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "output [PAD]\n",
" 0. ['conv', 'output', 'output', 'output']\n",
+ " 1. ['deconv', 'length', 'length', 'length']\n",
+ " 2. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"81\n",
"[CLS] Tuple Subscript Name Index Num Subscript Name Index Num Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filters filters\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['units', 'dim', 'dim', 'dim']\n",
+ " 2. ['n', 'size', 'size', 'size']\n",
"\n",
"82\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",
- "str str\n",
- "[PAD] val\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['str', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['line', 'val', 'val', 'val']\n",
+ " 2. ['data', 'state', 'state', 'state']\n",
"\n",
"83\n",
"[CLS] If BoolOp Or Compare Name Lt BinOp Call Name Subscript Name Slice Num Sub Num Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Str\n",
"Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info signature\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'shape', 'shape', 'shape']\n",
+ " 2. ['output', 'out', 'out', 'out']\n",
"\n",
"84\n",
"[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name signature Add BinOp BinOp Str Name Str If Call Name Name Attribute ndarray Name Assign Name str val Str Assign Name str val Call Name Name If Compare Call Name Name Gt Num Assign Name str val BinOp Subscript Name Slice Num Str AugAssign Name signature Name\n",
"Label = ['string', 'types', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "string string\n",
- "[PAD] types\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['string', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['is', 'types', 'types', 'types']\n",
+ " 2. ['function', 'tensor', 'tensor', 'tensor']\n",
"\n",
"85\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg new arg Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Name Str Name Str\n",
"Label = ['old', 'arg', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "value old\n",
- "[PAD] arg\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['value', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'name', 'name', 'name']\n",
+ " 2. ['dim', 'names', 'names', 'names']\n",
"\n",
"86\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str\n",
"Label = ['legacy', 'dropout', 'support', '[PAD]']\n",
"Pred =\n",
- "legacy legacy\n",
- "support dropout\n",
- "support support\n",
- "support [PAD]\n",
" 0. ['legacy', 'support', 'support', 'support']\n",
+ " 1. ['support', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['x', 'conv2d', 'conv2d', 'conv2d']\n",
"\n",
"87\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str Assign Name length NameConstant\n",
"Label = ['length', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "length length\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['length', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'length', 'length', 'length']\n",
+ " 2. ['[PAD]', 'size', 'size', 'size']\n",
"\n",
"88\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",
+ " 1. ['filter', 'shape', 'shape', 'shape']\n",
+ " 2. ['noise', 'size', 'size', 'size']\n",
"\n",
"89\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Call Attribute pop Name Str\n",
"Label = ['kernel', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "size size\n",
- "size [PAD]\n",
- "size [PAD]\n",
" 0. ['kernel', 'size', 'size', 'size']\n",
+ " 1. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['filter', 'kernel', 'kernel', 'kernel']\n",
"\n",
"90\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",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pattern', 'shape', 'shape', 'shape']\n",
+ " 2. ['data', 'kernel', 'kernel', 'kernel']\n",
"\n",
"91\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",
- "kernel kernel\n",
- "[PAD] size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'size', 'size', 'size']\n",
+ " 2. ['new', 'kernel', 'kernel', 'kernel']\n",
"\n",
"92\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 Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant keyword Name\n",
"Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n",
"Pred =\n",
- "legacy legacy\n",
- "support deconv2d\n",
- "support support\n",
- "support [PAD]\n",
" 0. ['legacy', 'support', 'support', 'support']\n",
+ " 1. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['support', 'conv2d', 'conv2d', 'conv2d']\n",
"\n",
"93\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str If Compare Name Eq Str Assign Subscript Name Index Str NameConstant Expr Call Attribute append Name Tuple Str Str Expr Call Attribute warn Name Str keyword Num\n",
"Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'size', 'size', 'size']\n",
+ " 2. ['init', 'kernel', 'kernel', 'kernel']\n",
"\n",
"94\n",
"[CLS] If Call Name Subscript Name Index Num Tuple Name Name Assert Call Name Subscript Name Index Num Name Assert Compare Str In Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name params Name Name Assign Subscript Name Index Str Name Return Tuple List Name Name List\n",
"Label = ['opt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer opt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'size', 'size', 'size']\n",
+ " 2. ['n', 'layer', 'layer', 'layer']\n",
"\n",
"95\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",
- "k device\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'length', 'length', 'length']\n",
+ " 2. ['a', 'input', 'input', 'input']\n",
"\n",
"96\n",
"[CLS] If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Subscript Name Index Name\n",
"Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info n\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['idx', 'dim', 'dim', 'dim']\n",
+ " 2. ['i', 'data', 'data', 'data']\n",
"\n",
"97\n",
- "[CLS] Return BinOp Tuple BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Attribute start Name Add Attribute base shape Name\n",
+ "[CLS] Return BinOp Tuple BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Attribute start Name Add Attribute base shape Name\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "end end\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['end', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['start', 'dim', 'dim', 'dim']\n",
+ " 2. ['max', 'end', 'end', 'end']\n",
"\n",
"98\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Tuple Name Assign Subscript Name Slice 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",
+ " 1. ['inbound', 'shape', 'shape', 'shape']\n",
+ " 2. ['trainable', 'input', 'input', 'input']\n",
"\n",
"99\n",
"[CLS] BoolOp And Name Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Num Call Name Subscript Name Index Num Name\n",
"Label = ['version', 'info', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape version\n",
- "[PAD] info\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['keras', 'size', 'size', 'size']\n",
+ " 2. ['args', 'format', 'format', 'format']\n",
"\n",
"100\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Str Name In Attribute data Name Assign Name val Call Attribute loads 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",
+ " 1. ['join', 'format', 'format', 'format']\n",
+ " 2. ['data', 'data', 'data', 'data']\n",
"\n",
"101\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Attribute count params Name Name comprehension Name p Call Name Name\n",
"Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sum sum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['extend', 'sum', 'sum', 'sum']\n",
+ " 2. ['zeros', 'weights', 'weights', 'weights']\n",
"\n",
"102\n",
"[CLS] If Compare Call Name Name Gt Num 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",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['key', 'i', 'i', 'i']\n",
+ " 2. ['o', 'list', 'list', 'list']\n",
"\n",
"103\n",
"[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute W OK Name Assign Name datadir base Call Attribute join Attribute path Name Str Str\n",
"Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "save access\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['save', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['exists', 'scope', 'scope', 'scope']\n",
+ " 2. ['load', 'types', 'types', 'types']\n",
"\n",
"104\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg file hash arg algorithm arg chunk size Str Num Expr Str If BoolOp Or Compare Name Is Str BoolOp And Compare Name Str Compare Call Name Name Num Assign Name hasher Str Assign Name hasher Str If Compare Call Name Call Name Name Name Name Eq Call Name Name Return NameConstant Return NameConstant\n",
"Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self fpath\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['model', 'fn', 'fn', 'fn']\n",
+ " 2. ['fname', 'id', 'id', 'id']\n",
"\n",
"105\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return BoolOp And Compare Attribute stop signal Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal 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",
+ " 1. ['stop', 'signal', 'signal', 'signal']\n",
+ " 2. ['variables', 'stop', 'stop', 'stop']\n",
"\n",
"106\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['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",
+ " 1. ['model', 'fn', 'fn', 'fn']\n",
+ " 2. ['index', 'array', 'array', 'array']\n",
"\n",
"107\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple 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",
+ " 1. ['seqs', 'out', 'out', 'out']\n",
+ " 2. ['model', 'fn', 'fn', 'fn']\n",
"\n",
"108\n",
"[CLS] While NameConstant Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num If BoolOp Or Compare Attribute unfinished tasks Attribute queue Name Eq Num Call Attribute is set Attribute stop signal Name Return\n",
"Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append sleep\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['set', 'running', 'running', 'running']\n",
+ " 2. ['expand', 'tasks', 'tasks', 'tasks']\n",
"\n",
"109\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name Attribute random seed 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",
+ " 1. ['seqs', 'out', 'out', 'out']\n",
+ " 2. ['model', 'format', 'format', 'format']\n",
"\n",
"110\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Expr Call Attribute add Name Str\n",
"Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "backend backend\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['backend', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['image', 'size', 'size', 'size']\n",
+ " 2. ['device', 'format', 'format', 'format']\n",
"\n",
"111\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num If Compare Name Str Assign Name pad BinOp Name Sub Num\n",
"Label = ['pad', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pad pad\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['pad', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'pad', 'pad', 'pad']\n",
+ " 2. ['axes', 'size', 'size', 'size']\n",
"\n",
"112\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num Name keyword Tuple Name\n",
"Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "seed y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'shape', 'shape', 'shape']\n",
+ " 2. ['value', 'value', 'value', 'value']\n",
"\n",
"113\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute zeros Name BinOp Tuple Name Add Name keyword Attribute float32 Name\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",
+ " 1. ['y', 'train', 'train', 'train']\n",
+ " 2. ['dtype', 'placeholder', 'placeholder', 'placeholder']\n",
"\n",
"114\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",
- "inputs built\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['built', 'inputs', 'inputs', 'inputs']\n",
+ " 2. ['layers', 'metadata', 'metadata', 'metadata']\n",
"\n",
"115\n",
"[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Str ListComp Call Name Name comprehension Name ishape Attribute input shapes Name Assign Name inputlabels Str\n",
"Label = ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputlabels inputlabels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cache', 'inputlabels', 'inputlabels', 'inputlabels']\n",
+ " 2. ['is', 'config', 'config', 'config']\n",
"\n",
"116\n",
"[CLS] keyword 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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"117\n",
"[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name fn Call Attribute get Name Name If Compare Name Is NameConstant Raise Call Name BinOp BinOp BinOp Str Add Name Str Name\n",
"Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "fn fn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['fn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['metric', 'fn', 'fn', 'fn']\n",
+ " 2. ['index', 'name', 'name', 'name']\n",
"\n",
"118\n",
"[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Attribute width Name Sub Name\n",
"Label = ['bar', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info bar\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 'size', 'size', 'size']\n",
+ " 2. ['bar', 't', 't', 't']\n",
"\n",
"119\n",
"[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Tuple BinOp Name FloorDiv Num BinOp BinOp Name Num Num BinOp Name Num If Compare Name Num Assign Name eta format BinOp Str Tuple BinOp Name Num BinOp Name Num Assign Name eta format BinOp Str Name\n",
"Label = ['eta', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "eta eta\n",
- "[PAD] format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['eta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dim', 'format', 'format', 'format']\n",
+ " 2. ['format', 'data', 'data', 'data']\n",
"\n",
"120\n",
"[CLS] If Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name info Add BinOp Str Mult BinOp Name Sub Attribute total width Name\n",
"Label = ['total', 'width', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "verbose total\n",
- "[PAD] width\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['verbose', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['min', 't', 't', 't']\n",
+ " 2. ['delta', 'updates', 'updates', 'updates']\n",
"\n",
"121\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Num Assign Name fpath Call Attribute join Attribute path Name Name BinOp Str Add Call Name Name Assign Tuple Subscript Name ExtSlice Slice BinOp BinOp Name Sub Num Mult Num BinOp Name Num Slice Slice Slice Subscript Name Slice BinOp BinOp Name Num Num BinOp Name Num Call Name Name\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "chunk i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['chunk', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'out', 'out', 'out']\n",
+ " 2. ['i', 'dir', 'dir', 'dir']\n",
"\n",
"122\n",
"[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x train 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",
- "open open\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['open', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'test', 'test', 'test']\n",
+ " 2. ['device', 'train', 'train', 'train']\n",
"\n",
"123\n",
"[CLS] Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['transpose', 'train', 'train', 'train']\n",
+ " 2. ['dimshuffle', 'test', 'test', 'test']\n",
"\n",
"124\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Tuple Num Assign Name d Call Attribute load Name Name Assign Name d Call Attribute load Name Name keyword Str Assign Name d decoded Dict For Tuple Name k Name v Call Attribute items Name Assign Subscript Name Index Call Attribute decode Name Str Name Assign Name d Name\n",
"Label = ['version', 'info', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs version\n",
- "[PAD] info\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['input', 'spec', 'spec', 'spec']\n",
+ " 2. ['recurrent', 'updates', 'updates', 'updates']\n",
"\n",
"125\n",
"[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Call Name Name comprehension Name x Name\n",
"Label = ['num', 'words', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] words\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['gpus', 'words', 'words', 'words']\n",
+ " 2. ['words', 'tensors', 'tensors', 'tensors']\n",
"\n",
"126\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Str Expr Str Assign Name path Call Name Name keyword Str keyword Str Assign Name f Call Name Name Assign Name data Call Attribute load Name Name Expr Call Attribute close Name Return Name\n",
"Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "path path\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['path', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'format', 'format', 'format']\n",
+ " 2. ['model', 'metadata', 'metadata', 'metadata']\n",
"\n",
"127\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",
- "xs xs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['xs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['padding', 'shape', 'shape', 'shape']\n",
+ " 2. ['w', 'val', 'val', 'val']\n",
"\n",
"128\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"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "xs xs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['xs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['w', 'xs', 'xs', 'xs']\n",
+ " 2. ['bad', 'tensors', 'tensors', 'tensors']\n",
"\n",
"129\n",
"[CLS] ListComp ListComp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Compare Name LtE Lt Name Name comprehension Name x Name\n",
"Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w w\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['s', 'ndim', 'ndim', 'ndim']\n",
+ " 2. ['xs', 'axes', 'axes', 'axes']\n",
"\n",
"130\n",
"[CLS] If BoolOp And UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute FunctionType Name UnaryOp Call Name Attribute build fn Name Attribute MethodType Name Expr Call Attribute append Name Attribute call Attribute build fn Name Expr Call Attribute append Name Attribute build fn Name\n",
"Label = ['build', 'fn', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "build build\n",
- "[PAD] fn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['build', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['fn', 'fn', 'fn', 'fn']\n",
+ " 2. ['is', 'build', 'build', 'build']\n",
"\n",
"131\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name output Call Name Attribute metrics names Attribute model Name Name If Compare Name Eq Str Return 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",
+ " 1. ['i', 'name', 'name', 'name']\n",
+ " 2. ['n', 'names', 'names', 'names']\n",
"\n",
"132\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",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'args', 'args', 'args']\n",
+ " 2. ['f', 'metadata', 'metadata', 'metadata']\n",
"\n",
"133\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute VGG19 Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'args', 'args', 'args']\n",
+ " 2. ['f', 'metadata', 'metadata', 'metadata']\n",
"\n",
"134\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'args', 'args', 'args']\n",
+ " 2. ['f', 'metadata', 'metadata', 'metadata']\n",
"\n",
"135\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute DenseNet121 Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'args', 'args', 'args']\n",
+ " 2. ['f', 'metadata', 'metadata', 'metadata']\n",
"\n",
"136\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'args', 'args', 'args']\n",
+ " 2. ['f', 'metadata', 'metadata', 'metadata']\n",
"\n",
"137\n",
"[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name Num Expr Call Attribute append Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str 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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['is', 'tensor', 'tensor', 'tensor']\n",
"\n",
"138\n",
"[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str 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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['is', 'tensor', 'tensor', 'tensor']\n",
"\n",
"139\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",
+ " 1. ['args', 'shape', 'shape', 'shape']\n",
+ " 2. ['num', 'size', 'size', 'size']\n",
"\n",
"140\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List BinOp Name Sub Num Add Call Name Call Name BinOp Name Num\n",
"Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes dims\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dims', 'dims', 'dims', 'dims']\n",
+ " 2. ['ins', 'size', 'size', 'size']\n",
"\n",
"141\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",
+ " 1. ['args', 'shape', 'shape', 'shape']\n",
+ " 2. ['num', 'size', 'size', 'size']\n",
"\n",
"142\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Subscript Name Index Num comprehension Name s Name Compare Name IsNot NameConstant\n",
"Label = ['batch', 'sizes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "batch batch\n",
- "[PAD] sizes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['batch', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 'shape', 'shape', 'shape']\n",
+ " 2. ['filter', 'states', 'states', 'states']\n",
"\n",
"143\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Call Name Name NotEq Num Raise Call Name Str Return BinOp Subscript Name Index Num Sub Subscript Name Index 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",
+ " 1. ['variable', 'pad', 'pad', 'pad']\n",
+ " 2. ['x', 'nodes', 'nodes', 'nodes']\n",
"\n",
"144\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Attribute axes Name Index Name Mod Call Attribute ndim 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",
+ " 1. ['update', 'axes', 'axes', 'axes']\n",
+ " 2. ['axes', 'i', 'i', 'i']\n",
"\n",
"145\n",
"[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n",
"Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'axes', 'axes', 'axes']\n",
+ " 2. ['ndim', 'ndim', 'ndim', 'ndim']\n",
"\n",
"146\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Expr Str Return Call Call Name keyword Name Name\n",
"Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['args', 'inputs', 'inputs', 'inputs']\n",
+ " 2. ['f', 'list', 'list', 'list']\n",
"\n",
"147\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Call Name Attribute alpha 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",
+ " 1. ['x', 'config', 'config', 'config']\n",
+ " 2. ['layer', 'value', 'value', 'value']\n",
"\n",
"148\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"149\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute shared axes Name Assign Subscript Name Index BinOp Name Sub Num Num Assign Subscript Attribute param broadcast Name Index BinOp Name Num NameConstant\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",
+ " 1. ['v', 'axes', 'axes', 'axes']\n",
+ " 2. ['axes', 'i', 'i', 'i']\n",
"\n",
"150\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name If Compare Name NotIn Attribute shared axes Name Assign Subscript Name Index Name Subscript Name Index 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",
+ " 1. ['axes', 'axes', 'axes', 'axes']\n",
+ " 2. ['o', 'i', 'i', 'i']\n",
"\n",
"151\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",
+ " 1. ['x', 'value', 'value', 'value']\n",
+ " 2. ['layer', 'config', 'config', 'config']\n",
"\n",
"152\n",
"[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast to floatx Name Name\n",
"Label = ['max', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype max\n",
- "[PAD] value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['w', 't', 't', 't']\n",
+ " 2. ['result', 'dtype', 'dtype', 'dtype']\n",
"\n",
"153\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute layer Name Str Return Attribute updates Attribute 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",
+ " 1. ['layer', 'layer', 'layer', 'layer']\n",
+ " 2. ['cls', 'weights', 'weights', 'weights']\n",
"\n",
"154\n",
"[CLS] Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Attribute layer Name Call Attribute get config Attribute layer 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",
+ " 1. ['batch', 'scope', 'scope', 'scope']\n",
+ " 2. ['from', 'format', 'format', 'format']\n",
"\n",
"155\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Num\n",
"Label = ['inner', 'input', 'shape', '[PAD]']\n",
"Pred =\n",
- "inner inner\n",
- "mask input\n",
- "mask shape\n",
- "mask [PAD]\n",
" 0. ['inner', 'mask', 'mask', 'mask']\n",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['depthwise', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"156\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n",
"Label = ['output', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['output', 'shape', 'shape', 'shape']\n",
+ " 1. ['inner', 'mask', 'mask', 'mask']\n",
+ " 2. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"157\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n",
"Label = ['get', 'shape', 'tuple', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] shape\n",
- "[PAD] tuple\n",
- "[PAD] [PAD]\n",
" 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'shape', 'shape', 'shape']\n",
+ " 2. ['expand', 'dims', 'dims', 'dims']\n",
"\n",
"158\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['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",
+ " 1. ['layer', 'layer', 'layer', 'layer']\n",
+ " 2. ['model', 'value', 'value', 'value']\n",
"\n",
"159\n",
"[CLS] Return BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute forward layer Name Add Call Attribute get weights Attribute backward layer Name\n",
"Label = ['get', 'weights', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] weights\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['initial', 'weights', 'weights', 'weights']\n",
+ " 2. ['state', 'layer', 'layer', 'layer']\n",
"\n",
"160\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Return BinOp BinOp Name Add Name Call Attribute copy Name Name\n",
"Label = ['merge', 'mode', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "merge merge\n",
- "[PAD] mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['merge', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['return', 'mode', 'mode', 'mode']\n",
+ " 2. ['mode', 'state', 'state', 'state']\n",
"\n",
"161\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['get', 'for', 'for', 'for']\n",
+ " 2. ['init', 'list', 'list', 'list']\n",
"\n",
"162\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Name FloorDiv Num Add Num\n",
"Label = ['pivot', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num pivot\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['new', 'axes', 'axes', 'axes']\n",
+ " 2. ['y', 'spec', 'spec', 'spec']\n",
"\n",
"163\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp UnaryOp Not Attribute merge mode Name List NameConstant NameConstant NameConstant\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",
+ " 1. ['mode', 'mode', 'mode', 'mode']\n",
+ " 2. ['state', 'state', 'state', 'state']\n",
"\n",
"164\n",
"[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Add Name Name\n",
"Label = ['get', 'updates', 'for', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "for updates\n",
- "for for\n",
- "for [PAD]\n",
" 0. ['get', 'for', 'for', 'for']\n",
+ " 1. ['init', 'losses', 'losses', 'losses']\n",
+ " 2. ['call', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"165\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute losses Attribute forward layer Name Add Attribute losses Attribute backward layer Name\n",
"Label = ['forward', 'layer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "forward forward\n",
- "[PAD] layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['forward', '[PAD]', '[PAD]', '[PAD]']\n",
- "\n",
+ " 1. ['call', 'layer', 'layer', 'layer']\n",
+ " 2. ['layer', 'losses', 'losses', 'losses']\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"166\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['mean', 'list', 'list', 'list']\n",
"\n",
"167\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Name keyword Tuple NameConstant Name comprehension Name dim Name\n",
"Label = ['state', 'spec', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] spec\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['states', 'spec', 'spec', 'spec']\n",
+ " 2. ['input', 'dim', 'dim', 'dim']\n",
"\n",
"168\n",
"[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Attribute go backwards Name Attribute stateful Name\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stateful', 'sequences', 'sequences', 'sequences']\n",
+ " 2. ['scale', 'size', 'size', 'size']\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['mean', 'list', 'list', 'list']\n",
"\n",
"170\n",
"[CLS] If BoolOp And Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute built Name Return List Attribute kernel Name Attribute recurrent kernel Name Attribute bias Name\n",
"Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reset trainable\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['reset', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stateful', 'after', 'after', 'after']\n",
+ " 2. ['use', 'bias', 'bias', 'bias']\n",
"\n",
"171\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n",
"Label = ['recurrent', 'kernel', 'z', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "kernel kernel\n",
- "kernel z\n",
- "kernel [PAD]\n",
" 0. ['recurrent', 'kernel', 'kernel', 'kernel']\n",
+ " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['reset', 'i', 'i', 'i']\n",
"\n",
"172\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name BinOp Attribute units Name Mult Num\n",
"Label = ['kernel', 'r', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] r\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'f', 'f', 'f']\n",
+ " 2. ['bias', 'r', 'r', 'r']\n",
"\n",
"173\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', 'r', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] r\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'c', 'c', 'c']\n",
+ " 2. ['input', 'i', 'i', 'i']\n",
"\n",
"174\n",
"[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num\n",
"Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['return', 'state', 'state', 'state']\n",
+ " 2. ['state', 'sequences', 'sequences', 'sequences']\n",
"\n",
"175\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"176\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",
- "units units\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['units', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['filters', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['gain', 'c', 'c', 'c']\n",
"\n",
"177\n",
"[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num Assign Name c Subscript Name Index Num\n",
"Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reset', 'state', 'state', 'state']\n",
+ " 2. ['run', 'sequences', 'sequences', 'sequences']\n",
"\n",
"178\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name output Call Attribute transpose Name Name Tuple Num Num Num Assign Name output Subscript Name Index UnaryOp USub Num\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['use', 'sequences', 'sequences', 'sequences']\n",
+ " 2. ['reset', 'state', 'state', 'state']\n",
"\n",
"179\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",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['outputs', 'shape', 'shape', 'shape']\n",
+ " 2. ['x', 'size', 'size', 'size']\n",
"\n",
"180\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "output [PAD]\n",
" 0. ['conv', 'output', 'output', 'output']\n",
+ " 1. ['deconv', 'length', 'length', 'length']\n",
+ " 2. ['resize', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"181\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",
- "data data\n",
- "format format\n",
- "format [PAD]\n",
- "format [PAD]\n",
" 0. ['data', 'format', 'format', 'format']\n",
+ " 1. ['type', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['mode', 'data', 'data', 'data']\n",
"\n",
"182\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"183\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
- "Pred =\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "conv conv\n",
- "output output\n",
- "output length\n",
- "output [PAD]\n",
+ "Pred =\n",
" 0. ['conv', 'output', 'output', 'output']\n",
+ " 1. ['deconv', 'length', 'length', 'length']\n",
+ " 2. ['resize', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"184\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', 'dim3', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cols len\n",
- "[PAD] dim3\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cols', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['rows', 'length', 'length', 'length']\n",
+ " 2. ['length', 'dim', 'dim', 'dim']\n",
"\n",
"185\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute pooling function Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format 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",
+ " 1. ['x', 'function', 'function', 'function']\n",
+ " 2. ['layer', 'size', 'size', 'size']\n",
"\n",
"186\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name\n",
"Label = ['pooling', 'function', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pooling pooling\n",
- "[PAD] function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['pooling', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['add', 'function', 'function', 'function']\n",
+ " 2. ['constant', 'weight', 'weight', 'weight']\n",
"\n",
"187\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg kwargs Tuple Num Num Num NameConstant Str NameConstant Expr Call Attribute init Call Name Name Name Name Name Name Name keyword Name Attribute legacy pooling3d 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",
+ " 1. ['layer', 'size', 'size', 'size']\n",
+ " 2. ['x', 'function', 'function', 'function']\n",
"\n",
"188\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",
+ " 1. ['layer', 'format', 'format', 'format']\n",
+ " 2. ['model', 'size', 'size', 'size']\n",
"\n",
"189\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['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",
+ " 1. ['model', 'format', 'format', 'format']\n",
+ " 2. ['layer', 'layer', 'layer', 'layer']\n",
"\n",
"190\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute data format 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",
+ " 1. ['layer', 'config', 'config', 'config']\n",
+ " 2. ['cls', 'format', 'format', 'format']\n",
"\n",
"191\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute mean Name Name keyword List Num Num Return Call Attribute mean Name Name keyword List Num 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",
+ " 1. ['x', 'format', 'format', 'format']\n",
+ " 2. ['seqs', 'size', 'size', 'size']\n",
"\n",
"192\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute max Name Name keyword List Num Num Return Call Attribute max Name Name keyword List Num 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",
+ " 1. ['model', 'format', 'format', 'format']\n",
+ " 2. ['x', 'data', 'data', 'data']\n",
"\n",
"193\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"194\n",
"[CLS] Raise 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",
+ " 1. ['join', 'size', 'size', 'size']\n",
+ " 2. ['state', 'format', 'format', 'format']\n",
"\n",
"195\n",
"[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Subscript Name Index Name keyword Attribute padding Attribute cell Name keyword Subscript Attribute strides Attribute cell Name Index Name keyword Subscript Attribute dilation rate Attribute cell Name Index Name\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "output [PAD]\n",
" 0. ['conv', 'output', 'output', 'output']\n",
+ " 1. ['deconv', 'length', 'length', 'length']\n",
+ " 2. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"196\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['get', 'for', 'for', 'for']\n",
+ " 2. ['init', 'list', 'list', 'list']\n",
"\n",
"197\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state arg constants 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",
+ " 1. ['step', 'function', 'function', 'function']\n",
+ " 2. ['inputs', 'size', 'size', 'size']\n",
"\n",
"198\n",
"[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['outputs', 'layers', 'layers', 'layers']\n",
+ " 2. ['layers', 'uid', 'uid', 'uid']\n",
"\n",
"199\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg states Assign Name constants Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name states Subscript Name Slice UnaryOp Attribute num constants Name Return Call Attribute call Attribute cell Name Name Name keyword Name keyword Name\n",
"Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['num', 'inputs', 'inputs', 'inputs']\n",
+ " 2. ['constants', 'constants', 'constants', 'constants']\n",
"\n",
"200\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name state shape BinOp Subscript Name Slice Num Add Subscript Name Slice Num\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stateful', 'state', 'state', 'state']\n",
+ " 2. ['reverse', 'shape', 'shape', 'shape']\n",
"\n",
"201\n",
"[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Tuple Name BinOp Attribute filters Name Mult Num\n",
"Label = ['kernel', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['bias', 'size', 'size', 'size']\n",
+ " 2. ['filters', 'shape', 'shape', 'shape']\n",
"\n",
"202\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice Attribute filters Name\n",
"Label = ['kernel', 'i', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'i', 'i', 'i']\n",
+ " 2. ['bias', 'f', 'f', 'f']\n",
"\n",
"203\n",
"[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['bias', 'size', 'size', 'size']\n",
"\n",
"204\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['recurrent', 'kernel', 'o', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "kernel kernel\n",
- "kernel o\n",
- "kernel [PAD]\n",
" 0. ['recurrent', 'kernel', 'kernel', 'kernel']\n",
+ " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['bias', 'c', 'c', 'c']\n",
"\n",
"205\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",
- "bias bias\n",
- "[PAD] c\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'c', 'c', 'c']\n",
+ " 2. ['kernel', 'f', 'f', 'f']\n",
"\n",
"206\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['bias', 'o', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] o\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'c', 'c', 'c']\n",
+ " 2. ['kernel', 'f', 'f', 'f']\n",
"\n",
"207\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel i Name Attribute bias i Name keyword Attribute padding Name\n",
"Label = ['input', 'conv', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input input\n",
- "[PAD] conv\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['conv', 'conv', 'conv', 'conv']\n",
+ " 2. ['outputs', 'i', 'i', 'i']\n",
"\n",
"208\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",
+ " 1. ['init', 'losses', 'losses', 'losses']\n",
+ " 2. ['get', 'list', 'list', 'list']\n",
"\n",
"209\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['mean', 'list', 'list', 'list']\n",
"\n",
"210\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Assign Name outputs Call Attribute conv1d Name Name Attribute kernel Name keyword Subscript Attribute strides Name Index Num keyword Attribute padding Name keyword Attribute data format Name keyword Subscript Attribute dilation rate Name Index Num\n",
"Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "rank rank\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['rank', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'transpose', 'transpose', 'transpose']\n",
+ " 2. ['ndim', 'format', 'format', 'format']\n",
"\n",
"211\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Assign Name new dim Call Attribute conv output length Name Subscript Name Index Name Subscript Attribute kernel size Name Index Name keyword Attribute padding Name keyword Subscript Attribute strides Name Index Name keyword Subscript Attribute dilation rate Name Index Name Expr Call Attribute append 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",
+ " 1. ['dim', 'dim', 'dim', 'dim']\n",
+ " 2. ['o', 'length', 'length', 'length']\n",
"\n",
"212\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"213\n",
"[CLS] 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 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",
+ " 1. ['call', 'generator', 'generator', 'generator']\n",
+ " 2. ['fit', 'loop', 'loop', 'loop']\n",
"\n",
"214\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n",
"Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['rank', 'padding', 'padding', 'padding']\n",
+ " 2. ['pow', 'size', 'size', 'size']\n",
"\n",
"215\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['depthwise', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['[PAD]', 'weight', 'weight', 'weight']\n",
"\n",
"216\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Tuple Name h axis Name w axis Tuple Num Num Assign Tuple Name h axis Name w axis Tuple Num 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",
+ " 1. ['mode', 'format', 'format', 'format']\n",
+ " 2. ['type', 'data', 'data', 'data']\n",
"\n",
"217\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Assign Name output shape Tuple Name Name Name Attribute filters Name\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",
+ " 1. ['mode', 'format', 'format', 'format']\n",
+ " 2. ['merge', 'data', 'data', 'data']\n",
"\n",
"218\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d transpose Name Name Attribute kernel Name Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['conv', 'out', 'out', 'out']\n",
+ " 2. ['cols', 'outputs', 'outputs', 'outputs']\n",
"\n",
"219\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'bias', 'bias', 'bias']\n",
+ " 2. ['conv', 'out', 'out', 'out']\n",
"\n",
"220\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n",
"Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['rank', 'padding', 'padding', 'padding']\n",
+ " 2. ['pow', 'size', 'size', 'size']\n",
"\n",
"221\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] 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\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",
+ " 1. ['recurrent', 'bias', 'bias', 'bias']\n",
+ " 2. ['use', 'i', 'i', 'i']\n",
"\n",
"222\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias 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",
+ " 1. ['bias', 'weight', 'weight', 'weight']\n",
+ " 2. ['parameter', 'bias', 'bias', 'bias']\n",
"\n",
"223\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute deconv length Name Name Name Name Attribute padding Name Name\n",
"Label = ['out', 'height', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "out out\n",
- "[PAD] height\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['out', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['width', 'out', 'out', 'out']\n",
+ " 2. ['output', 'width', 'width', 'width']\n",
"\n",
"224\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Name Assign Name output shape Tuple Name Name Name Name Attribute filters Name\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",
+ " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 1. ['mode', 'format', 'format', 'format']\n",
+ " 2. ['merge', 'data', 'data', 'data']\n",
"\n",
"225\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "length [PAD]\n",
" 0. ['deconv', 'length', 'length', 'length']\n",
+ " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['append', 'output', 'output', 'output']\n",
"\n",
"226\n",
"[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "length [PAD]\n",
" 0. ['deconv', 'length', 'length', 'length']\n",
+ " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['stateful', 'output', 'output', 'output']\n",
"\n",
"227\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "length [PAD]\n",
" 0. ['deconv', 'length', 'length', 'length']\n",
+ " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['append', 'output', 'output', 'output']\n",
"\n",
"228\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call 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 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",
+ " 1. ['call', 'name', 'name', 'name']\n",
+ " 2. ['encode', 'generator', 'generator', 'generator']\n",
"\n",
"229\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",
- "data channel\n",
- "[PAD] axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['format', 'format', 'format', 'format']\n",
+ " 2. ['tf', 'data', 'data', 'data']\n",
"\n",
"230\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] 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\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",
+ " 1. ['recurrent', 'bias', 'bias', 'bias']\n",
+ " 2. ['use', 'i', 'i', 'i']\n",
"\n",
"231\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 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",
+ " 1. ['call', 'name', 'name', 'name']\n",
+ " 2. ['encode', 'generator', 'generator', 'generator']\n",
"\n",
"232\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute depthwise kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute dilation rate Name keyword Attribute data format Name\n",
"Label = ['depthwise', 'conv2d', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "conv2d depthwise\n",
- "[PAD] conv2d\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['separable', 'conv2d', 'conv2d', 'conv2d']\n",
+ " 2. ['conv3d', 'transpose', 'transpose', 'transpose']\n",
"\n",
"233\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",
- "use use\n",
- "bias bias\n",
- "bias [PAD]\n",
- "bias [PAD]\n",
" 0. ['use', 'bias', 'bias', 'bias']\n",
+ " 1. ['reset', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['outputs', 'after', 'after', 'after']\n",
"\n",
"234\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Num BinOp Num Add Attribute rank Name\n",
"Label = ['spatial', 'axes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "spatial spatial\n",
- "axes axes\n",
- "axes [PAD]\n",
- "axes [PAD]\n",
" 0. ['spatial', 'axes', 'axes', 'axes']\n",
+ " 1. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['new', 'rank', 'rank', 'rank']\n",
"\n",
"235\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute repeat elements Name Name Subscript Attribute size Name Index Num keyword 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",
+ " 1. ['layer', 'size', 'size', 'size']\n",
+ " 2. ['model', 'function', 'function', 'function']\n",
"\n",
"236\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute resize images Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation 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",
+ " 1. ['layer', 'size', 'size', 'size']\n",
+ " 2. ['seqs', 'format', 'format', 'format']\n",
"\n",
"237\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n",
"Label = ['resize', 'images', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "resize resize\n",
- "[PAD] images\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['resize', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['conv', 'length', 'length', 'length']\n",
+ " 2. ['deconv', 'output', 'output', 'output']\n",
"\n",
"238\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Str 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",
+ " 1. ['call', 'params', 'params', 'params']\n",
+ " 2. ['encode', 'list', 'list', 'list']\n",
"\n",
"239\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n",
"Label = ['dim3', 'padding', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "width dim3\n",
- "[PAD] padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['width', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dim2', 'cropping', 'cropping', 'cropping']\n",
+ " 2. ['height', 'padding', 'padding', 'padding']\n",
"\n",
"240\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num 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",
+ " 1. ['x', 'format', 'format', 'format']\n",
+ " 2. ['args', 'size', 'size', 'size']\n",
"\n",
"241\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Tuple Name Name Tuple Name Name Tuple Name Name\n",
"Label = ['normalized', 'cropping', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args normalized\n",
- "[PAD] cropping\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'shape', 'shape', 'shape']\n",
+ " 2. ['legacy', 'input', 'input', 'input']\n",
"\n",
"242\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",
- "input input\n",
- "length length\n",
- "length [PAD]\n",
- "length [PAD]\n",
" 0. ['input', 'length', 'length', 'length']\n",
+ " 1. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['outputs', 'uid', 'uid', 'uid']\n",
"\n",
"243\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv1d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides 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",
+ " 1. ['outputs', 'out', 'out', 'out']\n",
+ " 2. ['conv', 'output', 'output', 'output']\n",
"\n",
"244\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Str Raise Call Name BinOp Str Add Name\n",
"Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'names', 'names', 'names']\n",
+ " 2. ['shape', 'padding', 'padding', 'padding']\n",
"\n",
"245\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute kernel size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['output', 'row', '[PAD]', '[PAD]']\n",
- "Pred =\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "cols output\n",
- "[PAD] row\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['cols', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['rows', 'length', 'length', 'length']\n",
+ " 2. ['output', 'dim', 'dim', 'dim']\n",
"\n",
"246\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv2d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name Tuple Attribute output row Name Attribute output col Name Attribute data format 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",
+ " 1. ['conv', 'out', 'out', 'out']\n",
+ " 2. ['x', 'size', 'size', 'size']\n",
"\n",
"247\n",
"[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n",
"Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['axis', 'layers', 'layers', 'layers']\n",
+ " 2. ['name', 'shape', 'shape', 'shape']\n",
"\n",
"248\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute gamma 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 Assign Attribute gamma Name NameConstant\n",
"Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "gamma scale\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['gamma', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['use', 'weight', 'weight', 'weight']\n",
+ " 2. ['center', 'gamma', 'gamma', 'gamma']\n",
"\n",
"249\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute beta initializer Name keyword Attribute beta regularizer Name keyword Attribute beta constraint Name\n",
"Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "beta beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['depthwise', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['bias', 'weight', 'weight', 'weight']\n",
"\n",
"250\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Str keyword Attribute moving mean initializer Name keyword NameConstant\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",
+ " 1. ['parameter', 'weight', 'weight', 'weight']\n",
+ " 2. ['zeros', 'function', 'function', 'function']\n",
"\n",
"251\n",
"[CLS] BinOp Name Div BinOp Name Sub BinOp Num Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ndim epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['rank', 't', 't', 't']\n",
+ " 2. ['sqrt', 'dims', 'dims', 'dims']\n",
"\n",
"252\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['mean', 'list', 'list', 'list']\n",
"\n",
"253\n",
"[CLS] Return 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",
- "state state\n",
- "[PAD] size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'size', 'size', 'size']\n",
+ " 2. ['output', 'state', 'state', 'state']\n",
"\n",
"254\n",
"[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute cells Name Slice UnaryOp USub Num Attribute cells Name\n",
"Label = ['reverse', 'state', 'order', '[PAD]']\n",
"Pred =\n",
- "reverse reverse\n",
- "state state\n",
- "state order\n",
- "state [PAD]\n",
" 0. ['reverse', 'state', 'state', 'state']\n",
+ " 1. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['return', 'order', 'order', 'order']\n",
"\n",
"255\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",
- "input constants\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['mask', 'shape', 'shape', 'shape']\n",
+ " 2. ['shape', 'input', 'input', 'input']\n",
"\n",
"256\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute cells Name If Call Name Name Name AugAssign Name weights Add Attribute non trainable weights Name\n",
"Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cell cell\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layer', 'layer', 'layer', 'layer']\n",
+ " 2. ['state', 'dim', 'dim', 'dim']\n",
"\n",
"257\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs NameConstant Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Call Attribute get losses for Name Name AugAssign Name losses Add 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",
+ " 1. ['cell', 'losses', 'losses', 'losses']\n",
+ " 2. ['cls', 'function', 'function', 'function']\n",
"\n",
"258\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Attribute states Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name Return Attribute states 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",
+ " 1. ['cls', 'size', 'size', 'size']\n",
+ " 2. ['states', 'function', 'function', 'function']\n",
"\n",
"259\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['state', 'size', 'size', 'size']\n",
+ " 2. ['tile', 'spec', 'spec', 'spec']\n",
"\n",
"260\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Tuple Subscript Name Index Num Name comprehension Name dim Name\n",
"Label = ['state', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['batch', 'shape', 'shape', 'shape']\n",
+ " 2. ['output', 'input', 'input', 'input']\n",
"\n",
"261\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n",
"Label = ['input', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mask input\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['mask', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['input', 'shape', 'shape', 'shape']\n",
+ " 2. ['inputs', 'mask', 'mask', 'mask']\n",
"\n",
"262\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Str Return ListComp Call Attribute tile Name Name List Num Name comprehension Name dim Attribute state size Attribute cell Name Return List Call Attribute tile Name Name List Num Attribute state size Attribute cell Name\n",
"Label = ['state', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['call', 'size', 'size', 'size']\n",
+ " 2. ['bn', 'dim', 'dim', 'dim']\n",
"\n",
"263\n",
"[CLS] If Call Name Name Name If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Assign Name initial state Subscript Name Slice Num Assign Name initial state Subscript Name Slice Num UnaryOp USub Attribute num constants Name If Compare Call Name Name Eq Num Assign Name initial state NameConstant Assign Name inputs Subscript Name Index Num\n",
"Label = ['num', 'constants', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] constants\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['initial', 'constants', 'constants', 'constants']\n",
+ " 2. ['constants', 'function', 'function', 'function']\n",
"\n",
"264\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Name Str Call Name Attribute shape 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",
+ " 1. ['shape', 'shape', 'shape', 'shape']\n",
+ " 2. ['batch', 'size', 'size', 'size']\n",
"\n",
"265\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",
+ " 1. ['shape', 'names', 'names', 'names']\n",
+ " 2. ['batch', 'nodes', 'nodes', 'nodes']\n",
"\n",
"266\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",
+ " 1. ['batch', 'names', 'names', 'names']\n",
+ " 2. ['shape', 'dim', 'dim', 'dim']\n",
"\n",
"267\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Subscript Name Index Str Attribute num constants Name\n",
"Label = ['num', 'constants', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] constants\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['constants', 'constants', 'constants', 'constants']\n",
+ " 2. ['initial', 'function', 'function', 'function']\n",
"\n",
"268\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"269\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name\n",
"Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cell cell\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['trainable', 'layer', 'layer', 'layer']\n",
+ " 2. ['forward', 'weights', 'weights', 'weights']\n",
"\n",
"270\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name h Call Attribute bias add Name Name Attribute bias 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",
+ " 1. ['input', 'bias', 'bias', 'bias']\n",
+ " 2. ['use', 'i', 'i', 'i']\n",
"\n",
"271\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n",
"Label = ['kernel', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'o', 'o', 'o']\n",
+ " 2. ['h', 'c', 'c', 'c']\n",
"\n",
"272\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute recurrent dropout Name keyword Name keyword Num\n",
"Label = ['ones', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones ones\n",
- "[PAD] like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'like', 'like', 'like']\n",
+ " 2. ['dropout', 'dropout', 'dropout', 'dropout']\n",
"\n",
"273\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",
- "recurrent matrix\n",
- "[PAD] inner\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'h', 'h', 'h']\n",
+ " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n",
"\n",
"274\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Subscript Name ExtSlice Slice Slice BinOp Num Attribute units Name\n",
"Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['h', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['kernel', 'h', 'h', 'h']\n",
"\n",
"275\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name BinOp Name Mult Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Attribute units Name\n",
"Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'h', 'h', 'h']\n",
+ " 2. ['h', 'kernel', 'kernel', 'kernel']\n",
"\n",
"276\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name 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",
+ " 1. ['call', 'normal', 'normal', 'normal']\n",
+ " 2. ['fit', 'initializer', 'initializer', 'initializer']\n",
"\n",
"277\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",
+ " 1. ['init', 'losses', 'losses', 'losses']\n",
+ " 2. ['get', 'list', 'list', 'list']\n",
"\n",
"278\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['init', 'normal', 'normal', 'normal']\n",
+ " 2. ['get', 'initializer', 'initializer', 'initializer']\n",
"\n",
"279\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",
- "cls cls\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cls', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'data', 'data', 'data']\n",
+ " 2. ['prefix', 'format', 'format', 'format']\n",
"\n",
"280\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute bias initializer Name Tuple Attribute units Name Starred Name keyword Name Call Call Attribute Ones Name Tuple Attribute units Name Starred Name keyword Name Call Attribute bias initializer Name Tuple BinOp Attribute units Name Mult Num Starred Name keyword Name\n",
"Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "concatenate concatenate\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['bias', 'initializer', 'initializer', 'initializer']\n",
+ " 2. ['stack', 'weight', 'weight', 'weight']\n",
"\n",
"281\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",
+ " 1. ['recurrent', 'o', 'o', 'o']\n",
+ " 2. ['h', 'c', 'c', 'c']\n",
"\n",
"282\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Call Attribute ones like Name Name Attribute dropout Name keyword Name keyword Num\n",
"Label = ['dropout', 'mask', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dropout dropout\n",
- "[PAD] mask\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dropout', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'mask', 'mask', 'mask']\n",
+ " 2. ['mask', 'dropout', 'dropout', 'dropout']\n",
"\n",
"283\n",
"[CLS] If BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant Assign Attribute recurrent dropout mask Name Call Name Call Attribute ones like Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n",
- "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n",
+ "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Pred =\n",
- "recurrent recurrent\n",
- "dropout dropout\n",
- "dropout [PAD]\n",
- "dropout [PAD]\n",
" 0. ['recurrent', 'dropout', 'dropout', 'dropout']\n",
+ " 1. ['dropout', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['reset', 'mask', 'mask', 'mask']\n",
"\n",
"284\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n",
"Label = ['ones', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones ones\n",
- "[PAD] like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'like', 'like', 'like']\n",
+ " 2. ['set', 'dropout', 'dropout', 'dropout']\n",
"\n",
"285\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel f Name\n",
"Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i f\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['c', 'i', 'i', 'i']\n",
+ " 2. ['o', 'c', 'c', 'c']\n",
"\n",
"286\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",
- "activation recurrent\n",
- "[PAD] activation\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['activation', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['recurrent', 'activation', 'activation', 'activation']\n",
+ " 2. ['append', 'i', 'i', 'i']\n",
"\n",
"287\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n",
"Label = ['z1', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent z1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'r', 'r', 'r']\n",
+ " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n",
"\n",
"288\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"289\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name 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",
+ " 1. ['call', 'name', 'name', 'name']\n",
+ " 2. ['encode', 'losses', 'losses', 'losses']\n",
"\n",
"290\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n",
"Label = ['stddev', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "rate stddev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['rate', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stddev', 't', 't', 't']\n",
+ " 2. ['y', 'axes', 'axes', 'axes']\n",
"\n",
"291\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"292\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name If Compare Attribute data format Name Eq Str Assign Name noise shape Tuple Subscript Name Index Num Subscript Name Index Num Num Num Assign Name noise shape Tuple Subscript Name Index Num 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",
+ " 1. ['x', 'format', 'format', 'format']\n",
+ " 2. ['layer', 'shape', 'shape', 'shape']\n",
"\n",
"293\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Num Num Subscript Name Index Num\n",
"Label = ['noise', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "noise noise\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['noise', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['filter', 'shape', 'shape', 'shape']\n",
+ " 2. ['new', 'size', 'size', 'size']\n",
"\n",
"294\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str\n",
"Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "steps unknown\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['steps', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['batch', 'epoch', 'epoch', 'epoch']\n",
+ " 2. ['do', 'per', 'per', 'per']\n",
"\n",
"295\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"296\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"297\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n arg kwargs Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute n Name Name Assign Attribute input spec Name Call Name keyword 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",
+ " 1. ['model', 'spec', 'spec', 'spec']\n",
+ " 2. ['layer', 'format', 'format', 'format']\n",
"\n",
"298\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Return Tuple Subscript Name Index Num Attribute n Name Subscript Name Index 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",
+ " 1. ['model', 'format', 'format', 'format']\n",
+ " 2. ['layer', 'size', 'size', 'size']\n",
"\n",
"299\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",
+ " 1. ['model', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['y', 'format', 'format', 'format']\n",
"\n",
"300\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Name comprehension Name shape Name\n",
"Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight xs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['xs', 'placeholder', 'placeholder', 'placeholder']\n",
+ " 2. ['data', 'shape', 'shape', 'shape']\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",
+ " 1. ['append', 'config', 'config', 'config']\n",
+ " 2. ['backend', 'list', 'list', 'list']\n",
"\n",
"302\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str If Call Name Subscript Subscript Name Index Str Index Name Name Assign Name arg dict Subscript Subscript Name Index Str Index Name If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Str Assign Subscript Subscript Name Index Str Index Name Call Attribute array Name Subscript Name Index Str\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",
+ " 1. ['k', 'config', 'config', 'config']\n",
+ " 2. ['layer', 'data', 'data', 'data']\n",
"\n",
"303\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Name Attribute units 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"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "add add\n",
- "weight weight\n",
- "weight [PAD]\n",
- "weight [PAD]\n",
+ "Pred =\n",
" 0. ['add', 'weight', 'weight', 'weight']\n",
+ " 1. ['compile', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['pooling', 'kernel', 'kernel', 'kernel']\n",
"\n",
"304\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias 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",
+ " 1. ['bias', 'weight', 'weight', 'weight']\n",
+ " 2. ['parameter', 'bias', 'bias', 'bias']\n",
"\n",
"305\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute asarray Name Name keyword 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",
+ " 1. ['value', 'value', 'value', 'value']\n",
+ " 2. ['tensor', 'tensor', 'tensor', 'tensor']\n",
"\n",
"306\n",
"[CLS] BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant\n",
"Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "function Function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['function', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'function', 'function', 'function']\n",
+ " 2. ['parameter', 'value', 'value', 'value']\n",
"\n",
"307\n",
"[CLS] If Compare Name Eq Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Return Attribute float32 Name\n",
"Label = ['float16', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype float16\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['float64', 'dtype', 'dtype', 'dtype']\n",
+ " 2. ['float32', 'tensor', 'tensor', 'tensor']\n",
"\n",
"308\n",
"[CLS] BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name\n",
"Label = ['Constant', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "constant Constant\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['constant', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['parameter', 'normal', 'normal', 'normal']\n",
+ " 2. ['variable', 'dim', 'dim', 'dim']\n",
"\n",
"309\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Attribute shape Name Index Num Add Subscript Attribute shape 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",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['[PAD]', 'size', 'size', 'size']\n",
"\n",
"310\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input Name keyword Name keyword Call Name Name keyword Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['outputs', 'input', 'input', 'input']\n",
+ " 2. ['output', 'spec', 'spec', 'spec']\n",
"\n",
"311\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index BinOp Name Add 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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['is', 'sparse', 'sparse', 'sparse']\n",
"\n",
"312\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'seed', 'seed', 'seed']\n",
+ " 2. ['[PAD]', 'value', 'value', 'value']\n",
"\n",
"313\n",
"[CLS] For Name Name If Compare Name Is NameConstant Raise Call Name Str\n",
@@ -4512,101 +3886,81 @@
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "uniform bernoulli\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['uniform', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['normal', 'normal', 'normal', 'normal']\n",
+ " 2. ['randint', 'uniform', 'uniform', 'uniform']\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'seed', 'seed', 'seed']\n",
+ " 2. ['[PAD]', 'value', 'value', 'value']\n",
"\n",
"316\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name Name keyword Call Attribute uniform Attribute initializer Name Name keyword Name keyword Name keyword Name\n",
"Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['v', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'out', 'out', 'out']\n",
+ " 2. ['out', 't', 't', 't']\n",
"\n",
"317\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name keyword Name keyword Call Attribute normal Attribute initializer Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['v', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 't', 't', 't']\n",
+ " 2. ['parameter', 'out', 'out', 'out']\n",
"\n",
"318\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",
- "uniform normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['uniform', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['normal', 'normal', 'normal', 'normal']\n",
+ " 2. ['randint', 'uniform', 'uniform', 'uniform']\n",
"\n",
"319\n",
"[CLS] For Name Attribute [MASK] [MASK] [MASK] [MASK] Name If BoolOp Or Compare Name Eq Attribute InferredDimension Name Compare Name Attribute FreeDimension Name Raise Call 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",
+ " 1. ['tensor', 'shape', 'shape', 'shape']\n",
+ " 2. ['value', 'tensor', 'tensor', 'tensor']\n",
"\n",
"320\n",
"[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Call Name BinOp Call Name Name Sub Num\n",
"Label = ['permutation', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output permutation\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['permutation', 'shape', 'shape', 'shape']\n",
+ " 2. ['i', 'dim', 'dim', 'dim']\n",
"\n",
"321\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Call Name Name Sub Num BinOp Call Name Name Num\n",
"Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['[PAD]', 'axes', 'axes', 'axes']\n",
+ " 2. ['pattern', 'dims', 'dims', 'dims']\n",
"\n",
"322\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Is NameConstant Attribute InferredDimension Name Name comprehension Name Name\n",
"Label = ['new', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'shape', 'shape', 'shape']\n",
+ " 2. ['result', 'value', 'value', 'value']\n",
"\n",
"323\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Compare Name GtE Num Name BinOp Name Add Call 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",
+ " 1. ['update', 'shape', 'shape', 'shape']\n",
+ " 2. ['extend', 'value', 'value', 'value']\n",
"\n",
"324\n",
"[CLS] Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name\n",
@@ -4615,71 +3969,57 @@
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name rep Call Name Name If BoolOp And Compare Name GtE Name Compare Subscript Name Index Name IsNot NameConstant Assign Name tmp BinOp List Name Mult Name Assign Name x Call Attribute splice Name Starred Name keyword BinOp Name Sub Name AugAssign Name i Add Num\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",
+ " 1. ['w', 't', 't', 't']\n",
+ " 2. ['a', 'size', 'size', 'size']\n",
"\n",
"326\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute all axes Attribute Axis Name\n",
"Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['axis', 'axes', 'axes', 'axes']\n",
+ " 2. ['a', 'axis', 'axis', 'axis']\n",
"\n",
"327\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute element select Name Name Call Name Name Call Name Name\n",
"Label = ['any', 'matrix', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "result any\n",
- "[PAD] matrix\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['result', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['all', 'matrix', 'matrix', 'matrix']\n",
+ " 2. ['out', 'out', 'out', 'out']\n",
"\n",
"328\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg increment Assign Name result BinOp Name Add Name Return Call Attribute assign Name 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",
+ " 1. ['a', 'test', 'test', 'test']\n",
+ " 2. ['size', 'size', 'size', 'size']\n",
"\n",
"329\n",
"[CLS] If BoolOp And Compare Call Name Name Eq Call Name Name Compare Subscript Call Name Name Index Num Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name List Num\n",
"Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "gamma beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['gamma', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['mean', 'shape', 'shape', 'shape']\n",
+ " 2. ['var', 'img', 'img', 'img']\n",
"\n",
"330\n",
"[CLS] BoolOp Or Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name Call Name GeneratorExp Compare Name Attribute FreeDimension Name comprehension Name Attribute shape Name\n",
"Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inferreddimension InferredDimension\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inferreddimension', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['freedimension', 'shape', 'shape', 'shape']\n",
+ " 2. ['ndim', 'spec', 'spec', 'spec']\n",
"\n",
"331\n",
"[CLS] Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name\n",
"Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "freedimension InferredDimension\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['freedimension', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inferreddimension', 'shape', 'shape', 'shape']\n",
+ " 2. ['ndim', 'spec', 'spec', 'spec']\n",
"\n",
"332\n",
"[CLS] BinOp Call Name ListComp UnaryOp USub Num comprehension Name Call Name BinOp Name Sub Name Add Name\n",
@@ -4688,2881 +4028,2317 @@
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Name comprehension Name i Call Name Name\n",
"Label = ['current', 'layout', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape current\n",
- "[PAD] layout\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['result', 'list', 'list', 'list']\n",
"\n",
"334\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg initial states arg go backwards arg mask arg constants arg unroll arg input length NameConstant NameConstant NameConstant NameConstant NameConstant\n",
"Label = ['step', 'function', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "step step\n",
- "function function\n",
- "function [PAD]\n",
- "function [PAD]\n",
" 0. ['step', 'function', 'function', 'function']\n",
+ " 1. ['batch', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['function', 'step', 'step', 'step']\n",
"\n",
"335\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",
- "slice slice\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['slice', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dims', 'slice', 'slice', 'slice']\n",
+ " 2. ['activation', 'dims', 'dims', 'dims']\n",
"\n",
"336\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute element select Attribute ops Name Name 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",
+ " 1. ['update', 'slice', 'slice', 'slice']\n",
+ " 2. ['set', 'shape', 'shape', 'shape']\n",
"\n",
"337\n",
"[CLS] If BoolOp And Compare Name Is NameConstant UnaryOp Not Call Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Name Index Num\n",
"Label = ['num', 'time', 'step', '[PAD]']\n",
"Pred =\n",
- "input num\n",
- "[PAD] time\n",
- "[PAD] step\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['batch', 'length', 'length', 'length']\n",
+ " 2. ['mask', 'size', 'size', 'size']\n",
"\n",
"338\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Name Name Eq Num Expr Call Attribute append Name Call Attribute broadcast as Attribute sequence Name Name Name Expr Call Attribute append Name Name\n",
"Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i c\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['s', 'i', 'i', 'i']\n",
+ " 2. ['o', 'o', 'o', 'o']\n",
"\n",
"339\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name new states Call Name Name BinOp Call Name Name Add Call Name Name\n",
"Label = ['new', 'output', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output new\n",
- "[PAD] output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'length', 'length', 'length']\n",
+ " 2. ['outputs', 'shape', 'shape', 'shape']\n",
"\n",
"340\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute element select Name Name Name Name comprehension Tuple Name n Name s Call Name Name Name\n",
"Label = ['new', 'states', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n",
- "[PAD] states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['batch', 'states', 'states', 'states']\n",
+ " 2. ['initial', 'p', 'p', 'p']\n",
"\n",
"341\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name 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",
+ " 1. ['y', 'shape', 'shape', 'shape']\n",
+ " 2. ['pool', 'out', 'out', 'out']\n",
"\n",
"342\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['transpose', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['append', 'shape', 'shape', 'shape']\n",
"\n",
"343\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword List NameConstant Name Name keyword Subscript Attribute shape Name Index Num\n",
"Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "convolution convolution\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['convolution', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['scan', 'transpose', 'transpose', 'transpose']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 2. ['rnn', 'normal', 'normal', 'normal']\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",
- "depthwise depthwise\n",
- "[PAD] kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['depthwise', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['recurrent', 'img', 'img', 'img']\n",
"\n",
"345\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format Tuple Num Num Num Str 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",
+ " 1. ['self', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'shape', 'shape', 'shape']\n",
"\n",
"346\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute clip Name Name Call Name BinOp Num Sub Call 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",
+ " 1. ['x', 'dimensions', 'dimensions', 'dimensions']\n",
+ " 2. ['out', 'axes', 'axes', 'axes']\n",
"\n",
"347\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute one hot Name Name Subscript Attribute shape Name Index Name keyword Name\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "targets target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['targets', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['result', 'counter', 'counter', 'counter']\n",
+ " 2. ['feed', 'dict', 'dict', 'dict']\n",
"\n",
"348\n",
"[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Expr Call Attribute append Name Name Expr Call Attribute append Name Name\n",
"Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape arguments\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['axis', 'out', 'out', 'out']\n",
+ " 2. ['ndarray', 'axes', 'axes', 'axes']\n",
"\n",
"349\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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['extend', 'params', 'params', 'params']\n",
"\n",
"350\n",
"[CLS] If Compare Call Name Name Gt Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute combine Name ListComp Attribute output Name comprehension Name Name\n",
"Label = ['unrelated', 'updates', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input unrelated\n",
- "[PAD] updates\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['metrics', 'updates', 'updates', 'updates']\n",
+ " 2. ['unrelated', 'spec', 'spec', 'spec']\n",
"\n",
"351\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute arguments Attribute loss Name If Compare Name In Name Assign Subscript Name Index Name Subscript Name Index Name Raise Call Name BinOp Str Mod Attribute name Name\n",
"Label = ['argument', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "key argument\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['key', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layer', 'name', 'name', 'name']\n",
+ " 2. ['argument', 'config', 'config', 'config']\n",
"\n",
"352\n",
"[CLS] If Compare Subscript Name Index Num Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Subscript Name Index Name Subscript Name Index Num Assign Name prefix shape Call Name Name Assign Name x Call Attribute splice Name Call Attribute constant Name keyword Num keyword Name Name keyword Name Assign Name base shape Attribute shape Name\n",
"Label = ['prefix', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x prefix\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['xs', 'shape', 'shape', 'shape']\n",
+ " 2. ['w', 'value', 'value', 'value']\n",
"\n",
"353\n",
"[CLS] Assert 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",
+ " 1. ['axis', 'shape', 'shape', 'shape']\n",
+ " 2. ['[PAD]', 'axes', 'axes', 'axes']\n",
"\n",
"354\n",
"[CLS] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Expr Call Attribute append Name Attribute value Name Expr Call Attribute append Name Call Name Name\n",
"Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n",
- "Pred =\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "parameter Parameter\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['parameter', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['constant', 'value', 'value', 'value']\n",
+ " 2. ['function', 'spec', 'spec', 'spec']\n",
"\n",
"355\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg message Str Return Call Attribute user function Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x 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",
+ " 1. ['a', 'function', 'function', 'function']\n",
+ " 2. ['pool', 'test', 'test', 'test']\n",
"\n",
"356\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Subscript Name Index BinOp Name Add Name\n",
"Label = ['condition', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "slice condition\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['slice', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['result', 'shape', 'shape', 'shape']\n",
+ " 2. ['output', 'length', 'length', 'length']\n",
"\n",
"357\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format If Compare Name Eq Str Assign Name x Call Attribute transpose Name Name Tuple Num Num Num Return 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",
+ " 1. ['kernel', 'format', 'format', 'format']\n",
+ " 2. ['a', 'data', 'data', 'data']\n",
"\n",
"358\n",
"[CLS] If Call Name Name Str Return Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Return Num\n",
"Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "in dynamic\n",
- "[PAD] axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['in', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['tile', 'shape', 'shape', 'shape']\n",
+ " 2. ['get', 'like', 'like', 'like']\n",
"\n",
"359\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Slice Index Name Index Name Tuple UnaryOp USub Num 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",
+ " 1. ['extend', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['set', 'shape', 'shape', 'shape']\n",
"\n",
"360\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Index Name Slice Tuple UnaryOp USub Num 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",
+ " 1. ['reshape', 'shape', 'shape', 'shape']\n",
+ " 2. ['extend', 'kernel', 'kernel', 'kernel']\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",
- "shape result\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['shape', 'shape', 'shape', 'shape']\n",
+ " 1. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['input', 'mask', 'mask', 'mask']\n",
"\n",
"362\n",
"[CLS] BinOp Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Mult Call Attribute prod Name Call Attribute asarray Name Attribute target 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",
+ " 1. ['arange', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['num', 'shape', 'shape', 'shape']\n",
"\n",
"363\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name List Name keyword NameConstant 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",
+ " 1. ['call', 'transpose', 'transpose', 'transpose']\n",
+ " 2. ['encode', 'shape', 'shape', 'shape']\n",
"\n",
"364\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",
- "output output\n",
- "variable variable\n",
- "variable [PAD]\n",
- "variable [PAD]\n",
" 0. ['output', 'variable', 'variable', 'variable']\n",
+ " 1. ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['get', 'value', 'value', 'value']\n",
"\n",
"365\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg state arg root gradients Return Call Attribute Value Attribute cntk py Name Call Attribute data 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",
+ " 1. ['model', 'value', 'value', 'value']\n",
+ " 2. ['layer', 'size', 'size', 'size']\n",
"\n",
"366\n",
"[CLS] FunctionDef arguments Expr Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get default graph Name If Compare Name NotIn Name Assign Name phase Call Attribute placeholder with default Name NameConstant keyword Tuple keyword Str Assign Subscript Name Index Name Name Return Subscript Name Index Name\n",
"Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "phase graph\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['phase', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['learning', 'graph', 'graph', 'graph']\n",
+ " 2. ['g', 'phase', 'phase', 'phase']\n",
"\n",
"367\n",
"[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword NameConstant Assign Name num thread Call Name Call Attribute get Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword Name keyword NameConstant\n",
"Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['encode', 'fn', 'fn', 'fn']\n",
+ " 2. ['lower', 'size', 'size', 'size']\n",
"\n",
"368\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute eval Call Name Name keyword Call 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",
+ " 1. ['kernel', 'shape', 'shape', 'shape']\n",
+ " 2. ['a', 'kernel', 'kernel', 'kernel']\n",
"\n",
"369\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",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['concatenate', 'format', 'format', 'format']\n",
+ " 2. ['stack', 'sum', 'sum', 'sum']\n",
"\n",
"370\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",
- "a a\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['a', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'input', 'input', 'input']\n",
+ " 2. ['m', 'list', 'list', 'list']\n",
"\n",
"371\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Subscript Name Index Num Eq BinOp Call Name Name Sub Num NameConstant NameConstant\n",
"Label = ['adj', 'x', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "adj adj\n",
- "[PAD] x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['adj', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['num', 'pad', 'pad', 'pad']\n",
+ " 2. ['new', 'size', 'size', 'size']\n",
"\n",
"372\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute reduce max Name Name 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",
+ " 1. ['a', 'devices', 'devices', 'devices']\n",
+ " 2. ['axis', 'list', 'list', 'list']\n",
"\n",
"373\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['append', 'shape', 'shape', 'shape']\n",
+ " 2. ['mean', 'function', 'function', 'function']\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",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['y', 'train', 'train', 'train']\n",
+ " 2. ['a', 'true', 'true', 'true']\n",
"\n",
"375\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute greater equal Name 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",
+ " 1. ['y', 'train', 'train', 'train']\n",
+ " 2. ['a', 'true', 'true', 'true']\n",
"\n",
"376\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name Name Name Name keyword Name keyword Name\n",
"Label = ['fused', 'batch', 'norm', '[PAD]']\n",
"Pred =\n",
- "max fused\n",
- "[PAD] batch\n",
- "[PAD] norm\n",
- "[PAD] [PAD]\n",
" 0. ['max', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['avg', 'conv2d', 'conv2d', 'conv2d']\n",
+ " 2. ['separable', 'transpose', 'transpose', 'transpose']\n",
"\n",
"377\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",
- "beta beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['gamma', 'beta', 'beta', 'beta']\n",
+ " 2. ['broadcast', 'gamma', 'gamma', 'gamma']\n",
"\n",
"378\n",
"[CLS] If Compare Name Lt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num If Name AugAssign Name axis Mod Name Assign Name axis Num\n",
"Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axis rank\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axis', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'axis', 'axis', 'axis']\n",
+ " 2. ['gamma', 'shape', 'shape', 'shape']\n",
"\n",
"379\n",
"[CLS] If Call Name ListComp Call Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return Call Attribute sparse concat Name Name Name Return Call Attribute concat Name ListComp Call Name Name comprehension Name x 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",
+ " 1. ['a', 'shape', 'shape', 'shape']\n",
+ " 2. ['w', 'test', 'test', 'test']\n",
"\n",
"380\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape Expr Str Return Call Attribute reshape Name 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",
+ " 1. ['y', 'train', 'train', 'train']\n",
+ " 2. ['a', 'input', 'input', 'input']\n",
"\n",
"381\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pattern Expr Str Return Call Attribute transpose Name Name keyword 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",
+ " 1. ['kernel', 'true', 'true', 'true']\n",
+ " 2. ['a', 'img', 'img', 'img']\n",
"\n",
"382\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Subscript Name Index Name keyword Name\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "normal split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['normal', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['parameter', 'normal', 'normal', 'normal']\n",
+ " 2. ['add', 'weight', 'weight', 'weight']\n",
"\n",
"383\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name x Call Attribute reshape Name Name Call Attribute stack Name List UnaryOp USub Num Call Name Subscript Call Name Name Slice Num Return 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",
+ " 1. ['a', 'train', 'train', 'train']\n",
+ " 2. ['y', 'true', 'true', 'true']\n",
"\n",
"384\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num Tuple Num Num 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",
+ " 1. ['kernel', 'size', 'size', 'size']\n",
+ " 2. ['padding', 'padding', 'padding', 'padding']\n",
"\n",
"385\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute outputs Name Add List Attribute updates op Name Attribute fetches Name\n",
"Label = ['fetches', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs fetches\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'shape', 'shape', 'shape']\n",
+ " 2. ['size', 'kernel', 'kernel', 'kernel']\n",
"\n",
"386\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",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dims', 'axes', 'axes', 'axes']\n",
+ " 2. ['ins', 'shape', 'shape', 'shape']\n",
"\n",
"387\n",
"[CLS] If Compare Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Name Sub Num Assign Name mask Call Name Name\n",
"Label = ['get', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['get', 'shape', 'shape', 'shape']\n",
+ " 1. ['append', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['mask', 'mask', 'mask', 'mask']\n",
"\n",
"388\n",
"[CLS] UnaryOp USub Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Call Attribute log Name Name Name\n",
"Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reduce reduce\n",
- "sum sum\n",
- "sum [PAD]\n",
- "sum [PAD]\n",
" 0. ['reduce', 'sum', 'sum', 'sum']\n",
+ " 1. ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['log', 'reduce', 'reduce', 'reduce']\n",
"\n",
"389\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",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['seed', 'mask', 'mask', 'mask']\n",
+ " 2. ['kernel', 'true', 'true', 'true']\n",
"\n",
"390\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'seed', 'seed', 'seed']\n",
+ " 2. ['[PAD]', 'value', 'value', 'value']\n",
"\n",
"391\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",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['encode', 'size', 'size', 'size']\n",
+ " 2. ['dimshuffle', 'dtype', 'dtype', 'dtype']\n",
"\n",
"392\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",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['encode', 'list', 'list', 'list']\n",
+ " 2. ['dimshuffle', 'format', 'format', 'format']\n",
"\n",
"393\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Name Eq Str Assign Name padding Str If Compare Name Str Assign Name padding Str Raise Call Name BinOp Str Add Call Name Name Return Name\n",
"Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'padding', 'padding', 'padding']\n",
+ " 2. ['[PAD]', 'pad', 'pad', 'pad']\n",
"\n",
"394\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",
- "h left\n",
- "[PAD] pad\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['h', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['left', 'pad', 'pad', 'pad']\n",
+ " 2. ['d', 'size', 'size', 'size']\n",
"\n",
"395\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute convolution Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword 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",
+ " 1. ['pool', 'shape', 'shape', 'shape']\n",
+ " 2. ['kernel', 'x', 'x', 'x']\n",
"\n",
"396\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",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['output', 'shape', 'shape', 'shape']\n",
+ " 1. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['mask', 'input', 'input', 'input']\n",
"\n",
"397\n",
"[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Call Attribute shape Name Name Index Num Add Call Name Subscript Name Slice Num Assign Name output shape Call Attribute stack Name Call Name Name\n",
"Label = ['output', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['output', 'shape', 'shape', 'shape']\n",
+ " 1. ['size', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['state', 'size', 'size', 'size']\n",
"\n",
"398\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",
+ " 1. ['w', 'img', 'img', 'img']\n",
+ " 2. ['conv', 'out', 'out', 'out']\n",
"\n",
"399\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name strides BinOp BinOp Tuple Num Add BinOp Name Mult Num Tuple Num Assign Name spatial start dim Num Assign Name strides BinOp Tuple Num Num BinOp Name Num\n",
"Label = ['spatial', 'start', 'dim', '[PAD]']\n",
"Pred =\n",
- "spatial spatial\n",
- "[PAD] start\n",
- "[PAD] dim\n",
- "[PAD] [PAD]\n",
" 0. ['spatial', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['conv', 'dim', 'dim', 'dim']\n",
+ " 2. ['num', 'dims', 'dims', 'dims']\n",
"\n",
"400\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",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['padding', 'size', 'size', 'size']\n",
+ " 2. ['dims', 'shape', 'shape', 'shape']\n",
"\n",
"401\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",
+ " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['image', 'shape', 'shape', 'shape']\n",
"\n",
"402\n",
"[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num 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",
+ "Pred =\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
" 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['volume', 'shape', 'shape', 'shape']\n",
+ " 2. ['filter', 'size', 'size', 'size']\n",
"\n",
"403\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 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",
+ " 1. ['filter', 'shape', 'shape', 'shape']\n",
+ " 2. ['noise', 'size', 'size', 'size']\n",
"\n",
"404\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv3d transpose Attribute nn Name Name Name Name Name keyword Name keyword 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",
+ " 1. ['conv', 'img', 'img', 'img']\n",
+ " 2. ['recurrent', 'array', 'array', 'array']\n",
"\n",
"405\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute max pool Attribute nn Name Name Name Name keyword Name keyword Name If Compare Name Str Assign Name x Call Attribute avg pool 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",
+ " 1. ['pool', 'x', 'x', 'x']\n",
+ " 2. ['assign', 'out', 'out', 'out']\n",
"\n",
"406\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",
+ " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['image', 'shape', 'shape', 'shape']\n",
"\n",
"407\n",
"[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Num Num Num Num Subscript Name Index Num Assign Name new shape BinOp Tuple Num Add Name\n",
"Label = ['new', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'shape', 'shape', 'shape']\n",
+ " 2. ['filter', 'size', 'size', 'size']\n",
"\n",
"408\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['value', 'seed', 'seed', 'seed']\n",
+ " 2. ['[PAD]', 'value', 'value', 'value']\n",
"\n",
"409\n",
"[CLS] Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n",
"Label = ['expand', 'dims', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "expand expand\n",
- "dims dims\n",
- "dims [PAD]\n",
- "dims [PAD]\n",
" 0. ['expand', 'dims', 'dims', 'dims']\n",
+ " 1. ['float32', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['concatenate', 'shape', 'shape', 'shape']\n",
"\n",
"410\n",
"[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name log prob Call Attribute ctc greedy decoder Name keyword Name keyword Name Assign Tuple Name decoded Name log prob Call Attribute ctc beam search decoder Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['decoded', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "decoded decoded\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['decoded', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['l', 'fn', 'fn', 'fn']\n",
+ " 2. ['stop', 'out', 'out', 'out']\n",
"\n",
"411\n",
"[CLS] If Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return Name\n",
"Label = ['dense', 'from', 'sparse', '[PAD]']\n",
"Pred =\n",
- "sparse dense\n",
- "tensor from\n",
- "tensor sparse\n",
- "tensor [PAD]\n",
" 0. ['sparse', 'tensor', 'tensor', 'tensor']\n",
+ " 1. ['ones', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['to', 'to', 'to', 'to']\n",
"\n",
"412\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg ndim arg dtype arg sparse arg name NameConstant NameConstant NameConstant NameConstant NameConstant\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",
+ " 1. ['[PAD]', 'shape', 'shape', 'shape']\n",
+ " 2. ['value', 'ndim', 'ndim', 'ndim']\n",
"\n",
"413\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return BoolOp And Call Name Name Str Attribute theano placeholder 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",
+ " 1. ['value', 'placeholder', 'placeholder', 'placeholder']\n",
+ " 2. ['self', 'function', 'function', 'function']\n",
"\n",
"414\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute zeros Name Name Name Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'shape', 'shape', 'shape']\n",
+ " 2. ['size', 'size', 'size', 'size']\n",
"\n",
"415\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",
- "normal normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['normal', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['uniform', 'normal', 'normal', 'normal']\n",
+ " 2. ['randint', 'uniform', 'uniform', 'uniform']\n",
"\n",
"416\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg indices Expr Str Assign Name y Subscript Name Index Name If BoolOp And Call Name Name Str Call Name Name Str Assign Attribute keras shape Name BinOp Attribute keras shape Name Add Subscript Attribute keras shape Name Slice Num Return Name\n",
"Label = ['reference', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x reference\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['y', 'shape', 'shape', 'shape']\n",
+ " 2. ['a', 'train', 'train', 'train']\n",
"\n",
"417\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",
- "dtype dtype\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'dtype', 'dtype', 'dtype']\n",
+ " 2. ['monitor', 'format', 'format', 'format']\n",
"\n",
"418\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Return Call Attribute var Name Name keyword Name keyword 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",
+ " 1. ['a', 'dtype', 'dtype', 'dtype']\n",
+ " 2. ['inputs', 'shape', 'shape', 'shape']\n",
"\n",
"419\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Return Call Attribute argmin Name Name keyword Name keyword 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",
+ " 1. ['self', 'true', 'true', 'true']\n",
+ " 2. ['a', 'size', 'size', 'size']\n",
"\n",
"420\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Assign Name z Call Attribute neq Name Name Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name Return 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",
+ " 1. ['a', 'shape', 'shape', 'shape']\n",
+ " 2. ['y', 'train', 'train', 'train']\n",
"\n",
"421\n",
"[CLS] Return Tuple Name Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Pow Num\n",
"Label = ['inv', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cast inv\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cast', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['arange', 'shape', 'shape', 'shape']\n",
+ " 2. ['pow', 'dims', 'dims', 'dims']\n",
"\n",
"422\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute bn Attribute nnet Name Name Name Name Name Name Name Name\n",
"Label = ['batch', 'normalization', 'test', '[PAD]']\n",
"Pred =\n",
- "batch batch\n",
- "normalization normalization\n",
- "normalization test\n",
- "normalization [PAD]\n",
" 0. ['batch', 'normalization', 'normalization', 'normalization']\n",
+ " 1. ['is', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['dnn', 'test', 'test', 'test']\n",
"\n",
"423\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"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ndim ndim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['max', 'ndim', 'ndim', 'ndim']\n",
+ " 2. ['data', 'format', 'format', 'format']\n",
"\n",
"424\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute dnn Attribute cuda Attribute sandbox Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Str Name\n",
"Label = ['dnn', 'batch', 'normalization', 'test']\n",
"Pred =\n",
- "dnn dnn\n",
- "[PAD] batch\n",
- "[PAD] normalization\n",
- "[PAD] test\n",
" 0. ['dnn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dimshuffle', 'batch', 'batch', 'batch']\n",
+ " 2. ['split', 'normalization', 'normalization', 'normalization']\n",
"\n",
"425\n",
"[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute hstack Attribute basic Name Name keyword Str Raise Call Name Str 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",
+ " 1. ['out', 'out', 'out', 'out']\n",
+ " 2. ['conv', 'output', 'output', 'output']\n",
"\n",
"426\n",
"[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute uses learning phase Name Assign Attribute uses learning phase Name NameConstant\n",
"Label = ['uses', 'learning', 'phase', '[PAD]']\n",
"Pred =\n",
- "uses uses\n",
- "learning learning\n",
- "learning phase\n",
- "learning [PAD]\n",
" 0. ['uses', 'learning', 'learning', 'learning']\n",
+ " 1. ['learning', 'phase', 'phase', 'phase']\n",
+ " 2. ['phase', '[PAD]', '[PAD]', '[PAD]']\n",
"\n",
"427\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Subscript Attribute keras shape Name Index UnaryOp Num Mult Name\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['keras', 'shape', 'shape', 'shape']\n",
+ " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['return', 'size', 'size', 'size']\n",
"\n",
"428\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num BinOp BinOp Subscript Name Index Num Add 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",
+ " 1. ['filter', 'shape', 'shape', 'shape']\n",
+ " 2. ['volume', 'size', 'size', 'size']\n",
"\n",
"429\n",
"[CLS] ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num 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",
+ " 1. ['kernel', 'size', 'size', 'size']\n",
+ " 2. ['[PAD]', 'shape', 'shape', 'shape']\n",
"\n",
"430\n",
"[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute keras shape Name Index Num BinOp Subscript Attribute keras shape Name Index Num Add Call Name Name Subscript Attribute keras shape Name Index Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['keras', 'shape', 'shape', 'shape']\n",
+ " 1. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['output', 'keras', 'keras', 'keras']\n",
"\n",
"431\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Name NameConstant Call Name Name BinOp Subscript Name Index Num Add Name Call Name Name BinOp Subscript Name Index Num Name Call Name NameConstant\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",
+ " 1. ['new', 'shape', 'shape', 'shape']\n",
+ " 2. ['indices', 'size', 'size', 'size']\n",
"\n",
"432\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n",
"Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['h', 'shape', 'shape', 'shape']\n",
+ " 2. ['d', 'keras', 'keras', 'keras']\n",
"\n",
"433\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num IsNot NameConstant Assign Name w BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num Assign Name w NameConstant\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['keras', 'shape', 'shape', 'shape']\n",
+ " 1. ['input', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['shape', 'size', 'size', 'size']\n",
"\n",
"434\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n",
"Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w w\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['h', 'shape', 'shape', 'shape']\n",
+ " 2. ['d', 'keras', 'keras', 'keras']\n",
"\n",
"435\n",
"[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Subscript Name Index Num Index Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "shape [PAD]\n",
" 0. ['keras', 'shape', 'shape', 'shape']\n",
+ " 1. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['input', 'size', 'size', 'size']\n",
"\n",
"436\n",
"[CLS] GeneratorExp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name comprehension Name i Call Name Attribute ndim 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",
+ " 1. ['keras', 'shape', 'shape', 'shape']\n",
+ " 2. ['ndim', 'axes', 'axes', 'axes']\n",
"\n",
"437\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Attribute shape Call Attribute get value Name keyword NameConstant keyword 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",
+ " 1. ['x', 'value', 'value', 'value']\n",
+ " 2. ['variable', 'spec', 'spec', 'spec']\n",
"\n",
"438\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute function Name Name Name keyword Name keyword NameConstant keyword Str keyword Name keyword Name\n",
"Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "function function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['function', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pool', 'function', 'function', 'function']\n",
+ " 2. ['dtype', 'shape', 'shape', 'shape']\n",
"\n",
"439\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",
+ " 1. ['x', 'function', 'function', 'function']\n",
+ " 2. ['inputs', 'value', 'value', 'value']\n",
"\n",
"440\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute switch Name Subscript Name Index Name 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",
+ " 1. ['set', 'value', 'value', 'value']\n",
+ " 2. ['update', 'scope', 'scope', 'scope']\n",
"\n",
"441\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Call Attribute scan Name Name keyword List Name Name keyword BinOp List Name Add Name keyword Name keyword Name\n",
"Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ret results\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ret', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['results', 'mask', 'mask', 'mask']\n",
+ " 2. ['last', 'out', 'out', 'out']\n",
"\n",
"442\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name states Subscript Name Slice Num Assign Name outputs Name Assign Name states List\n",
"Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inputs', 'state', 'state', 'state']\n",
+ " 2. ['initial', 'outputs', 'outputs', 'outputs']\n",
"\n",
"443\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",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sqrt', 'shape', 'shape', 'shape']\n",
+ " 2. ['extend', 'function', 'function', 'function']\n",
"\n",
"444\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute squeeze Name Subscript Name Index UnaryOp USub Num comprehension Name state Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'state', 'state', 'state']\n",
+ " 2. ['constants', 'shape', 'shape', 'shape']\n",
"\n",
"445\n",
"[CLS] If Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub Name For Name Call Name Name Assign Name condition Call Name Name\n",
"Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape ndim\n",
- "[PAD] diff\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['axes', 'shape', 'shape', 'shape']\n",
+ " 2. ['masks', 'axes', 'axes', 'axes']\n",
"\n",
"446\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call 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",
+ " 1. ['y', 'test', 'test', 'test']\n",
+ " 2. ['new', 'dtype', 'dtype', 'dtype']\n",
"\n",
"447\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute to one hot Attribute extra ops Name Name keyword Subscript Attribute shape Name Index UnaryOp USub Num\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "targets target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['targets', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['input', 'length', 'length', 'length']\n",
+ " 2. ['last', 'hot', 'hot', 'hot']\n",
"\n",
"448\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis NameConstant Assign Name square sum Call Attribute sum Name Call Attribute square Name Name keyword Name keyword NameConstant Assign Name norm Call Attribute sqrt Name Call Attribute maximum Name Name Call Name Return BinOp Name Div 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",
+ " 1. ['a', 'sum', 'sum', 'sum']\n",
+ " 2. ['self', 'true', 'true', 'true']\n",
"\n",
"449\n",
"[CLS] If Compare Name Lt Num Try Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Str ExceptHandler Name Return Call Attribute zeros like Name Name keyword Str\n",
"Label = ['zeros', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones zeros\n",
- "[PAD] like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['zeros', 'like', 'like', 'like']\n",
+ " 2. ['max', 'normal', 'normal', 'normal']\n",
"\n",
"450\n",
"[CLS] Index Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Name\n",
"Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "arange arange\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'subtensor', 'subtensor', 'subtensor']\n",
+ " 2. ['transpose', 'function', 'function', 'function']\n",
"\n",
"451\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Str Assign Name th padding Str Raise Call Name Str Call Name Name\n",
"Label = ['th', 'padding', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding th\n",
- "padding padding\n",
- "padding [PAD]\n",
- "padding [PAD]\n",
" 0. ['padding', 'padding', 'padding', 'padding']\n",
+ " 1. ['th', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['tf', 'pad', 'pad', 'pad']\n",
"\n",
"452\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Return Call Name Name ExceptHandler Name Return NameConstant\n",
"Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['value', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'value', 'value', 'value']\n",
+ " 2. ['seed', 'list', 'list', 'list']\n",
"\n",
"453\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n",
"Label = ['filter', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filter filter\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['filter', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['image', 'shape', 'shape', 'shape']\n",
+ " 2. ['volume', 'tensor', 'tensor', 'tensor']\n",
"\n",
"454\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n",
"Label = ['filter', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filter filter\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['filter', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['image', 'shape', 'shape', 'shape']\n",
+ " 2. ['volume', 'tensor', 'tensor', 'tensor']\n",
"\n",
"455\n",
"[CLS] BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript 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",
+ " 1. ['[PAD]', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'pad', 'pad', 'pad']\n",
"\n",
"456\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",
+ " 1. ['keras', 'shape', 'shape', 'shape']\n",
+ " 2. ['[PAD]', 'size', 'size', 'size']\n",
"\n",
"457\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n",
"Label = ['conv', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "out out\n",
- "out [PAD]\n",
- "out [PAD]\n",
" 0. ['conv', 'out', 'out', 'out']\n",
+ " 1. ['pool', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['expected', 'width', 'width', 'width']\n",
"\n",
"458\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",
+ " 1. ['[PAD]', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'format', 'format', 'format']\n",
"\n",
"459\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",
+ " 1. ['[PAD]', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n",
"\n",
"460\n",
"[CLS] ExtSlice Slice Slice 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\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",
+ " 1. ['[PAD]', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'out', 'out', 'out']\n",
"\n",
"461\n",
- "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n",
+ "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Label = ['conv', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x conv\n",
- "[PAD] out\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pool', 'out', 'out', 'out']\n",
+ " 2. ['conv', 'kernel', 'kernel', 'kernel']\n",
"\n",
"462\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant 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",
+ " 1. ['kernel', 'size', 'size', 'size']\n",
+ " 2. ['self', 'function', 'function', 'function']\n",
"\n",
"463\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['keras', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['kernel', 'shape', 'shape', 'shape']\n",
+ " 2. ['noise', 'size', 'size', 'size']\n",
"\n",
"464\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple 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",
+ " 1. ['kernel', 'size', 'size', 'size']\n",
+ " 2. ['a', 'format', 'format', 'format']\n",
"\n",
"465\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple 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",
+ " 1. ['self', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'length', 'length', 'length']\n",
"\n",
"466\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute AbstractConv2d gradInputs Attribute abstract conv Attribute nnet Name keyword NameConstant keyword Name keyword Name keyword Name keyword UnaryOp Not Name keyword Name\n",
"Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "op op\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['op', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['result', 'updates', 'updates', 'updates']\n",
+ " 2. ['new', 'gradinputs', 'gradinputs', 'gradinputs']\n",
"\n",
"467\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute shape Call Attribute eval Name\n",
"Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n",
"Pred =\n",
- "kernel pointwise\n",
- "[PAD] kernel\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['depthwise', 'shape', 'shape', 'shape']\n",
+ " 2. ['recurrent', 'kernel', 'kernel', 'kernel']\n",
"\n",
"468\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp BoolOp And Compare Subscript Name Index Num Gt Num 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",
- "h w\n",
- "pad pad\n",
- "pad [PAD]\n",
- "pad [PAD]\n",
" 0. ['h', 'pad', 'pad', 'pad']\n",
+ " 1. ['w', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['d', 'out', 'out', 'out']\n",
"\n",
"469\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",
- "pool pool\n",
- "[PAD] 2d\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['pool', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['function', '3d', '3d', '3d']\n",
+ " 2. ['pooling', 'weight', 'weight', 'weight']\n",
"\n",
"470\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pool 3d Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str Raise Call Name Str Name\n",
"Label = ['pool', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pool pool\n",
- "out out\n",
- "out [PAD]\n",
- "out [PAD]\n",
" 0. ['pool', 'out', 'out', 'out']\n",
+ " 1. ['out', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['conv', 'pool', 'pool', 'pool']\n",
"\n",
"471\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n",
"Label = ['pool', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x pool\n",
- "[PAD] out\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['pool', 'out', 'out', 'out']\n",
+ " 2. ['conv', 'kernel', 'kernel', 'kernel']\n",
"\n",
"472\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",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['kernel', 'input', 'input', 'input']\n",
"\n",
"473\n",
"[CLS] If Compare Name Eq Str If Compare Call Name Name 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",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['kernel', 'x', 'x', 'x']\n",
"\n",
"474\n",
"[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple 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",
+ " 1. ['output', 'shape', 'shape', 'shape']\n",
+ " 2. ['a', 'out', 'out', 'out']\n",
"\n",
"475\n",
"[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Mult Num Add Num\n",
"Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "arange arange\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'normal', 'normal', 'normal']\n",
+ " 2. ['reshape', 'subtensor', 'subtensor', 'subtensor']\n",
"\n",
"476\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name skip idxs BinOp BinOp Call Attribute arange Name BinOp BinOp Subscript Attribute shape Name Index Num Sub Num FloorDiv Num Mult Num Add Num Assign Name non repeats Call Attribute neq Name Subscript Name Index Name Subscript Name Index BinOp Name Num Return Subscript Name Index Call Attribute nonzero Name\n",
"Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "y Y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'train', 'train', 'train']\n",
+ " 2. ['path', 'true', 'true', 'true']\n",
"\n",
"477\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Subscript Name Slice Name Sub Name\n",
"Label = ['p', 'prev', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "log p\n",
- "[PAD] prev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['log', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['out', 'log', 'log', 'log']\n",
+ " 2. ['output', 't', 't', 't']\n",
"\n",
"478\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute inc subtensor Name Subscript Name Index BinOp Name Add Num Subscript Name Index Name\n",
"Label = ['p', 'prev', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p p\n",
- "[PAD] prev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['result', 'p', 'p', 'p']\n",
+ " 2. ['i', 'values', 'values', 'values']\n",
"\n",
"479\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Compare Name Lt Call Attribute dimshuffle Name Num Str BitAnd Subscript Compare Name Call Attribute dimshuffle Name Num Str ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num\n",
"Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel mask\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['strides', 'out', 'out', 'out']\n",
+ " 2. ['h', 'kernel', 'kernel', 'kernel']\n",
"\n",
"480\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",
- "fn initializer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['fn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['initializer', 'fn', 'fn', 'fn']\n",
+ " 2. ['args', 'size', 'size', 'size']\n",
"\n",
"481\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name\n",
"Label = ['foldl', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "foldr foldl\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['foldr', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['foldl', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['sort', 'foldr', 'foldr', 'foldr']\n",
"\n",
"482\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Subscript Name ExtSlice Index BinOp BinOp Name Mult Name Add Name Slice Slice\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",
+ " 1. ['extend', 'params', 'params', 'params']\n",
+ " 2. ['set', 'weights', 'weights', 'weights']\n",
"\n",
"483\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",
+ " 1. ['args', 'format', 'format', 'format']\n",
+ " 2. ['cls', 'data', 'data', 'data']\n",
"\n",
"484\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",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['append', 'names', 'names', 'names']\n",
+ " 2. ['pop', 'params', 'params', 'params']\n",
"\n",
"485\n",
"[CLS] 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",
+ " 1. ['warn', 'names', 'names', 'names']\n",
+ " 2. ['keys', 'scope', 'scope', 'scope']\n",
"\n",
"486\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BoolOp Or Call Name Attribute call Name Str Call Name Name Str\n",
"Label = ['compute', 'previous', 'mask', '[PAD]']\n",
"Pred =\n",
- "name compute\n",
- "[PAD] previous\n",
- "[PAD] mask\n",
- "[PAD] [PAD]\n",
" 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['uses', 'learning', 'learning', 'learning']\n",
+ " 2. ['dynamic', 'phase', 'phase', 'phase']\n",
"\n",
"487\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name nodes by depth Name layers Name layers by depth Call Name Attribute inputs Name Attribute outputs Name\n",
"Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "outputs nodes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inbound', 'layer', 'layer', 'layer']\n",
+ " 2. ['node', 'index', 'index', 'index']\n",
"\n",
"488\n",
"[CLS] If BoolOp And UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Attribute stateful Name Return List\n",
"Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "trainable trainable\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reset', 'sequences', 'sequences', 'sequences']\n",
+ " 2. ['inputs', 'format', 'format', 'format']\n",
"\n",
"489\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Name ListComp BoolOp And Call Name Name Str Attribute stateful Name comprehension Name layer Attribute layers 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",
+ " 1. ['layer', 'layer', 'layer', 'layer']\n",
+ " 2. ['cls', 'names', 'names', 'names']\n",
"\n",
"490\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name Call Name Call Name Name Assign Name masks Call Name Name\n",
"Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "masks masks\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['masks', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['output', 'masks', 'masks', 'masks']\n",
+ " 2. ['ndim', 'shape', 'shape', 'shape']\n",
"\n",
"491\n",
"[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Name Str Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n",
"Label = ['input', 'layers', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layers input\n",
- "[PAD] layers\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layers', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['input', 'layers', 'layers', 'layers']\n",
+ " 2. ['outputs', 'uid', 'uid', 'uid']\n",
"\n",
"492\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute name Name Add BinOp Str Mod Tuple Name Name\n",
"Label = ['shape', 'key', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] key\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['dim', 'key', 'key', 'key']\n",
+ " 2. ['eta', 'format', 'format', 'format']\n",
"\n",
"493\n",
"[CLS] 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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['function', 'axes', 'axes', 'axes']\n",
+ " 2. ['init', 'kernel', 'kernel', 'kernel']\n",
"\n",
"494\n",
"[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant\n",
"Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "activity activity\n",
- "[PAD] regularizer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['activity', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['attrs', 'regularizer', 'regularizer', 'regularizer']\n",
+ " 2. ['clipvalue', 'function', 'function', 'function']\n",
"\n",
"495\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Call Name 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",
+ " 1. ['a', 'x', 'x', 'x']\n",
+ " 2. ['val', 'train', 'train', 'train']\n",
"\n",
"496\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",
+ " 1. ['input', 'shape', 'shape', 'shape']\n",
+ " 2. ['inputs', 'shapes', 'shapes', 'shapes']\n",
"\n",
"497\n",
"[CLS] BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute arguments Name\n",
- "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "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",
+ " 1. ['batch', 'names', 'names', 'names']\n",
+ " 2. ['val', 'name', 'name', 'name']\n",
"\n",
"498\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node data If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute append Subscript Name Index Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'data', 'data', 'data']\n",
+ " 2. ['self', 'layer', 'layer', 'layer']\n",
"\n",
"499\n",
"[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Name Name Call Attribute backend 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",
+ " 1. ['batch', 'config', 'config', 'config']\n",
+ " 2. ['axis', 'scope', 'scope', 'scope']\n",
"\n",
"500\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Eq Attribute name Name Return Attribute 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",
+ " 1. ['module', 'format', 'format', 'format']\n",
+ " 2. ['mode', 'scope', 'scope', 'scope']\n",
"\n",
"501\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name depth Call Attribute items Name If Compare Name NotIn Name Assign Subscript Name Index Name List Expr Call Attribute append Subscript Name Index Name Name\n",
"Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer node\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['chunk', 'layer', 'layer', 'layer']\n",
+ " 2. ['inbound', 'dict', 'dict', 'dict']\n",
"\n",
"502\n",
"[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Attribute input tensors Name If Compare Name NotIn Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute name Name Str Call Name Name For Name x Attribute output tensors Name Expr Call Attribute append Name Name Expr Call Attribute append Name Attribute 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",
+ " 1. ['layer', 'x', 'x', 'x']\n",
+ " 2. ['a', 'layer', 'layer', 'layer']\n",
"\n",
"503\n",
"[CLS] Raise 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",
- "inbound count\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['count', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['get', 'names', 'names', 'names']\n",
"\n",
"504\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",
- "inbound count\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['count', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['get', 'names', 'names', 'names']\n",
"\n",
"505\n",
"[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp BoolOp Or Name List List Attribute history Name\n",
"Label = ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "callbacks callbacks\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['info', 'values', 'values', 'values']\n",
+ " 2. ['args', 'tensor', 'tensor', 'tensor']\n",
"\n",
"506\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",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['o', 'i', 'i', 'i']\n",
+ " 2. ['k', 'axes', 'axes', 'axes']\n",
"\n",
"507\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['o', 'weights', 'weights', 'weights']\n",
+ " 2. ['w', 'o', 'o', 'o']\n",
"\n",
"508\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num\n",
"Label = ['val', 'outs', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "[PAD] outs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['y', 'outs', 'outs', 'outs']\n",
+ " 2. ['mask', 'shape', 'shape', 'shape']\n",
"\n",
"509\n",
"[CLS] If Name If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute inbound nodes Name Index UnaryOp USub Num NotEq Num Raise Call Name Str Assign Attribute outputs Name List Subscript Attribute output tensors Subscript Attribute inbound nodes Name Index UnaryOp Num Index Num Assign Attribute inputs Name Call Attribute get source inputs Name Subscript Attribute outputs Name Index Num\n",
"Label = ['output', 'tensors', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] tensors\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inbound', 'tensors', 'tensors', 'tensors']\n",
+ " 2. ['state', 'layers', 'layers', 'layers']\n",
"\n",
"510\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Gt Num Return Call Attribute argmax Name keyword UnaryOp Num Return Call Attribute astype Compare Name Num 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",
+ " 1. ['dynamic', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'shape', 'shape', 'shape']\n",
"\n",
"511\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",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['model', 'config', 'config', 'config']\n",
+ " 2. ['metric', 'p', 'p', 'p']\n",
"\n",
"512\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name build input shape Call Attribute get Name Str Assign Name layer configs Subscript Name Index Str Assign Name name Name build input shape NameConstant Assign Name layer configs Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cls name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['cls', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layer', 'layer', 'layer', 'layer']\n",
+ " 2. ['embeddings', 'names', 'names', 'names']\n",
"\n",
"513\n",
"[CLS] Assign Subscript Name Index 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",
- "encode encode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['encode', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['decode', 'name', 'name', 'name']\n",
+ " 2. ['pop', 'weights', 'weights', 'weights']\n",
"\n",
"514\n",
"[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute backend Name Str\n",
"Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "encode encode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['encode', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['split', 'config', 'config', 'config']\n",
+ " 2. ['dimshuffle', 'list', 'list', 'list']\n",
"\n",
"515\n",
"[CLS] If Call Name Name Str If Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str Assign Name name BinOp BinOp Call Name Attribute name Name Add Str Call Name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Call Name Name\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['encode', 'name', 'name', 'name']\n",
+ " 2. ['get', 'size', 'size', 'size']\n",
"\n",
"516\n",
"[CLS] Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['encode', 'format', 'format', 'format']\n",
+ " 2. ['dimshuffle', 'size', 'size', 'size']\n",
"\n",
"517\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Attribute name Name Add 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",
+ " 1. ['input', 'input', 'input', 'input']\n",
+ " 2. ['batch', 'name', 'name', 'name']\n",
"\n",
"518\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] List For Name value Name Expr Call Attribute append Name Call Name Name Return Name\n",
"Label = ['deserialized', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data deserialized\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['axis', 'metrics', 'metrics', 'metrics']\n",
+ " 2. ['weights', 'weights', 'weights', 'weights']\n",
"\n",
"519\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute decode Subscript Name Index Str Str Assign Name original backend NameConstant\n",
"Label = ['original', 'backend', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "original original\n",
- "[PAD] backend\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['original', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'fn', 'fn', 'fn']\n",
+ " 2. ['overwrite', 'names', 'names', 'names']\n",
"\n",
- "520\n",
+ "520\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"[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",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['cell', 'weights', 'weights', 'weights']\n",
+ " 2. ['v', 'layer', 'layer', 'layer']\n",
"\n",
"521\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Name 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",
+ " 1. ['join', 'size', 'size', 'size']\n",
+ " 2. ['[PAD]', 'list', 'list', 'list']\n",
"\n",
"522\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",
+ " 1. ['batch', 'dim', 'dim', 'dim']\n",
+ " 2. ['ndarray', 'name', 'name', 'name']\n",
"\n",
"523\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Expr Str If Call Name Name Name Raise Call Name Str ImportFrom alias Return Call Name Name keyword Name\n",
"Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['identifier', 'string', 'string', 'string']\n",
+ " 2. ['name', 'config', 'config', 'config']\n",
"\n",
"524\n",
"[CLS] BinOp Str Mod Tuple Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Name comprehension Name x Name\n",
"Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "join join\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['join', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['format', 'weight', 'weight', 'weight']\n",
+ " 2. ['add', 'nodes', 'nodes', 'nodes']\n",
"\n",
"525\n",
"[CLS] While Compare BinOp Str Mod Tuple Name Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute extend Name ListComp Call Attribute decode Name Str comprehension Name n Subscript Attribute attrs Name Index BinOp Str Tuple Name Name AugAssign Name chunk id Add Num\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['args', 'size', 'size', 'size']\n",
"\n",
"526\n",
"[CLS] comprehension Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute attrs Name Index BinOp Str Mod Tuple Name Name\n",
"Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k n\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['m', 'length', 'length', 'length']\n",
+ " 2. ['s', 'id', 'id', 'id']\n",
"\n",
"527\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",
- "name i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layer', 'name', 'name', 'name']\n",
+ " 2. ['sw', 'layer', 'layer', 'layer']\n",
"\n",
"528\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name In List Str Str Assign Name weights 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",
+ " 1. ['[PAD]', 'format', 'format', 'format']\n",
+ " 2. ['mode', 'weights', 'weights', 'weights']\n",
"\n",
"529\n",
"[CLS] Assert BoolOp And Compare Subscript Name Index Num Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Subscript Name Slice Num Tuple Subscript Attribute kernel size Name Index Num Num\n",
"Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filters filters\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['output', 'format', 'format', 'format']\n",
"\n",
"530\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str If Compare Attribute data format Name Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\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",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['[PAD]', 'data', 'data', 'data']\n",
"\n",
"531\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num 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",
+ " 1. ['type', 'format', 'format', 'format']\n",
+ " 2. ['format', 'data', 'data', 'data']\n",
"\n",
"532\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num 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",
+ " 1. ['type', 'format', 'format', 'format']\n",
+ " 2. ['format', 'data', 'data', 'data']\n",
"\n",
"533\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",
- "concatenate concatenate\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['identity', 'weight', 'weight', 'weight']\n",
+ " 2. ['sum', 'kernel', 'kernel', 'kernel']\n",
"\n",
"534\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str Call Name Call Attribute prod 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",
+ " 1. ['batch', 'size', 'size', 'size']\n",
+ " 2. ['start', 'dim', 'dim', 'dim']\n",
"\n",
"535\n",
"[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Name\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "convert reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['convert', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['unbroadcast', 'spec', 'spec', 'spec']\n",
"\n",
"536\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\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",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['[PAD]', 'data', 'data', 'data']\n",
"\n",
"537\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",
- "transpose transpose\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['transpose', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['arange', 'shape', 'shape', 'shape']\n",
"\n",
"538\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",
- "transpose transpose\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['transpose', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n",
+ " 2. ['arange', 'shape', 'shape', 'shape']\n",
"\n",
"539\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func arg n gates Expr Str Return Call Attribute hstack Name ListComp Call Name Name comprehension Name k Call Attribute hsplit Name Name Name\n",
"Label = ['kernels', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self kernels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['path', 'format', 'format', 'format']\n",
+ " 2. ['k', 'metrics', 'metrics', 'metrics']\n",
"\n",
"540\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Attribute reshape Attribute T Name Attribute shape Name keyword Name\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 't', 't', 't']\n",
+ " 2. ['x', 'mask', 'mask', 'mask']\n",
"\n",
"541\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Mult Subscript Name Index Num Num\n",
"Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "expand tile\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['expand', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['maximum', 'dims', 'dims', 'dims']\n",
+ " 2. ['float32', 'shape', 'shape', 'shape']\n",
"\n",
"542\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num Lambda arguments arg k Attribute T Name Name\n",
"Label = ['recurrent', 'kernels', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] kernels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['conv', 't', 't', 't']\n",
+ " 2. ['num', 'mask', 'mask', 'mask']\n",
"\n",
"543\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",
- "source source\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['source', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['args', 'size', 'size', 'size']\n",
+ " 2. ['padding', 'shape', 'shape', 'shape']\n",
"\n",
"544\n",
"[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original backend Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original backend NameConstant\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['args', 'size', 'size', 'size']\n",
"\n",
"545\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",
+ " 1. ['batch', 'names', 'names', 'names']\n",
+ " 2. ['shape', 'dim', 'dim', 'dim']\n",
"\n",
"546\n",
"[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original keras version Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original keras version Str\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['data', 'size', 'size', 'size']\n",
+ " 2. ['args', 'format', 'format', 'format']\n",
"\n",
"547\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Call Attribute setdefault Name Attribute name Name List Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['stateful', 'dict', 'dict', 'dict']\n",
+ " 2. ['name', 'scope', 'scope', 'scope']\n",
"\n",
"548\n",
"[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name comprehension Name weight name Name\n",
"Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "asarray asarray\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['asarray', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['concatenate', 'values', 'values', 'values']\n",
+ " 2. ['prod', 'shape', 'shape', 'shape']\n",
"\n",
"549\n",
"[CLS] BinOp BinOp BinOp 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 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",
+ " 1. ['batch', 'dim', 'dim', 'dim']\n",
+ " 2. ['ndarray', 'scope', 'scope', 'scope']\n",
"\n",
"550\n",
"[CLS] BinOp 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 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",
+ " 1. ['batch', 'name', 'name', 'name']\n",
+ " 2. ['[PAD]', 'format', 'format', 'format']\n",
"\n",
"551\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript 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",
+ " 1. ['extend', 'value', 'value', 'value']\n",
+ " 2. ['update', 'shape', 'shape', 'shape']\n",
"\n",
"552\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Add ListComp BinOp Str Name comprehension Name n Name\n",
"Label = ['callback', 'metrics', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight callback\n",
- "[PAD] metrics\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['batch', 'names', 'names', 'names']\n",
+ " 2. ['dim', 'metrics', 'metrics', 'metrics']\n",
"\n",
"553\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['o', 'weights', 'weights', 'weights']\n",
+ " 2. ['w', 'o', 'o', 'o']\n",
"\n",
"554\n",
"[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute evaluate generator Name Name Name keyword Num Assign Name val outs Call Attribute evaluate Name Name Name keyword Name keyword Name keyword Num\n",
"Label = ['val', 'outs', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "[PAD] outs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['outs', 'outs', 'outs', 'outs']\n",
+ " 2. ['y', 'val', 'val', 'val']\n",
"\n",
"555\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['w', 'weights', 'weights', 'weights']\n",
+ " 2. ['o', 'l', 'l', 'l']\n",
"\n",
"556\n",
"[CLS] If Compare Name Is NameConstant If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n",
"Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "steps steps\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['steps', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['epoch', 'epoch', 'epoch', 'epoch']\n",
+ " 2. ['do', 'per', 'per', 'per']\n",
"\n",
"557\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Subscript Name Index Num Index Num If Call Name Name Name Assign Name batch size 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",
- "batch batch\n",
- "size size\n",
- "size [PAD]\n",
- "size [PAD]\n",
" 0. ['batch', 'size', 'size', 'size']\n",
+ " 1. ['size', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['mask', 'shape', 'shape', 'shape']\n",
"\n",
"558\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n",
"Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "concatenate average\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['sum', 'weight', 'weight', 'weight']\n",
+ " 2. ['extend', 'values', 'values', 'values']\n",
"\n",
"559\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num 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",
+ " 1. ['pop', 'size', 'size', 'size']\n",
+ " 2. ['update', 'shape', 'shape', 'shape']\n",
"\n",
"560\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num 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",
+ " 1. ['mean', 'size', 'size', 'size']\n",
+ " 2. ['keys', 'shape', 'shape', 'shape']\n",
"\n",
"561\n",
"[CLS] If Compare Name Gt Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name Expr Call Attribute start Name keyword Name keyword Name Assign Name output generator Call Attribute get Name If Name Assign Name output generator Call Name Name Assign Name output generator Name\n",
"Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "enqueuer enqueuer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['val', 'enqueuer', 'enqueuer', 'enqueuer']\n",
+ " 2. ['progbar', 'metrics', 'metrics', 'metrics']\n",
"\n",
"562\n",
"[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name\n",
"Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "enqueuer enqueuer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['val', 'enqueuer', 'enqueuer', 'enqueuer']\n",
+ " 2. ['progbar', 'outs', 'outs', 'outs']\n",
"\n",
"563\n",
"[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name\n",
"Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "progbar progbar\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['progbar', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['mask', 'shape', 'shape', 'shape']\n",
+ " 2. ['input', 'out', 'out', 'out']\n",
"\n",
"564\n",
"[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name name BinOp BinOp Name Add Str Call Name Call Attribute get uid Name Name\n",
"Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n",
- "Pred =\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "prefix prefix\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
" 0. ['prefix', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'name', 'name', 'name']\n",
+ " 2. ['fn', 'fn', 'fn', 'fn']\n",
"\n",
"565\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return BinOp BinOp Attribute name Name Add Str Call Name Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'name', 'name', 'name']\n",
+ " 2. ['cls', 'layer', 'layer', 'layer']\n",
"\n",
"566\n",
"[CLS] If Compare Name IsNot NameConstant With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute add loss Name Call Name Name\n",
"Label = ['name', 'scope', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] scope\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['device', 'scope', 'scope', 'scope']\n",
+ " 2. ['backend', 'config', 'config', 'config']\n",
"\n",
"567\n",
"[CLS] BoolOp And Compare Name IsNot NameConstant Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['max', 'ndim', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "delta max\n",
- "[PAD] ndim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['delta', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inferreddimension', 'batch', 'batch', 'batch']\n",
+ " 2. ['min', 't', 't', 't']\n",
"\n",
"568\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 max 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",
+ " 1. ['batch', 'ndim', 'ndim', 'ndim']\n",
+ " 2. ['ndim', 'value', 'value', 'value']\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 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",
+ " 1. ['ndim', 'ndim', 'ndim', 'ndim']\n",
+ " 2. ['batch', 'dtype', 'dtype', 'dtype']\n",
"\n",
"570\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim 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",
+ " 1. ['batch', 'ndim', 'ndim', 'ndim']\n",
+ " 2. ['start', 'axes', 'axes', 'axes']\n",
"\n",
"571\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",
+ " 1. ['shape', 'names', 'names', 'names']\n",
+ " 2. ['batch', 'nodes', 'nodes', 'nodes']\n",
"\n",
"572\n",
"[CLS] If BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant With withitem Call Attribute name scope Name Str Assign Name regularization losses ListComp Call Attribute activity regularizer Name Name comprehension Name x Call Name Name Expr Call Attribute add loss Name Name keyword Call Name Name\n",
"Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "activity activity\n",
- "[PAD] regularizer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['activity', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['activation', 'regularizer', 'regularizer', 'regularizer']\n",
+ " 2. ['run', 'losses', 'losses', 'losses']\n",
"\n",
"573\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Name IsNot NameConstant If Call Name Name Name If Call Name GeneratorExp Compare Name NameConstant comprehension Name m 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 Return NameConstant\n",
"Label = ['supports', 'masking', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound supports\n",
- "[PAD] masking\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['built', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['trainable', 'tensor', 'tensor', 'tensor']\n",
"\n",
"574\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp Str Add Attribute name Name Str\n",
"Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound inbound\n",
- "nodes nodes\n",
- "nodes [PAD]\n",
- "nodes [PAD]\n",
" 0. ['inbound', 'nodes', 'nodes', 'nodes']\n",
+ " 1. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['built', 'names', 'names', 'names']\n",
"\n",
"575\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Call Name Attribute inbound nodes Name NotEq Num Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Str Return Call Attribute get node attribute at index Name Num Str Str 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",
+ " 1. ['layer', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['model', 'data', 'data', 'data']\n",
"\n",
"576\n",
"[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n",
"Label = ['input', 'shapes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is input\n",
- "[PAD] shapes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inbound', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['input', 'keras', 'keras', 'keras']\n",
"\n",
"577\n",
"[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n",
"Label = ['output', 'shapes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is output\n",
- "[PAD] shapes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['inbound', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['input', 'keras', 'keras', 'keras']\n",
"\n",
"578\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",
+ " 1. ['batch', 'name', 'name', 'name']\n",
+ " 2. ['[PAD]', 'list', 'list', 'list']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"\n",
"579\n",
"[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['batch', 'input', 'shape', '[PAD]']\n",
"Pred =\n",
- "dtype batch\n",
- "[PAD] input\n",
- "[PAD] shape\n",
- "[PAD] [PAD]\n",
" 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['function', 'shape', 'shape', 'shape']\n",
+ " 2. ['keras', 'function', 'function', 'function']\n",
"\n",
"580\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg shape arg ndim arg max ndim arg min ndim arg axes NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'value', 'value', 'value']\n",
+ " 2. ['max', 'ndim', 'ndim', 'ndim']\n",
"\n",
"581\n",
"[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute dtype Name Str\n",
"Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype dtype\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'dtype', 'dtype', 'dtype']\n",
+ " 2. ['decay', 'size', 'size', 'size']\n",
"\n",
"582\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outbound layer Attribute name Attribute outbound layer Name Assign Name outbound layer NameConstant\n",
"Label = ['outbound', 'layer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful outbound\n",
- "[PAD] layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['arguments', 'layer', 'layer', 'layer']\n",
+ " 2. ['inputs', 'weights', 'weights', 'weights']\n",
"\n",
"583\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg optimizer arg loss arg metrics arg loss weights arg sample weight mode arg weighted metrics arg target tensors arg kwargs 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",
+ " 1. ['model', 'weights', 'weights', 'weights']\n",
+ " 2. ['args', 'format', 'format', 'format']\n",
"\n",
"584\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['states', 'layers', 'layers', 'layers']\n",
+ " 2. ['layers', 'uid', 'uid', 'uid']\n",
"\n",
"585\n",
"[CLS] Raise Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layers', 'layers', 'layers', 'layers']\n",
+ " 2. ['states', 'tensor', 'tensor', 'tensor']\n",
"\n",
"586\n",
"[CLS] Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['states', 'layers', 'layers', 'layers']\n",
+ " 2. ['layers', 'uid', 'uid', 'uid']\n",
"\n",
"587\n",
"[CLS] Raise Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['layers', 'layers', 'layers', 'layers']\n",
+ " 2. ['states', 'tensor', 'tensor', 'tensor']\n",
"\n",
"588\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['output', 'names', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "names names\n",
- "names [PAD]\n",
- "names [PAD]\n",
" 0. ['output', 'names', 'names', 'names']\n",
+ " 1. ['count', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['inbound', 'nodes', 'nodes', 'nodes']\n",
"\n",
"589\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['states', 'layers', 'layers', 'layers']\n",
+ " 2. ['layers', 'uid', 'uid', 'uid']\n",
"\n",
"590\n",
"[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name target NameConstant\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "target target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['target', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['idx', 'axes', 'axes', 'axes']\n",
+ " 2. ['axes', 'shape', 'shape', 'shape']\n",
"\n",
"591\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Call Name Name keyword BinOp Name Add Str keyword Call Attribute is sparse Name Subscript Attribute outputs Name Index Name keyword Call Attribute dtype Name Subscript Attribute outputs Name Index Name\n",
"Label = ['placeholder', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add placeholder\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['placeholder', 'weight', 'weight', 'weight']\n",
+ " 2. ['variable', 'target', 'target', 'target']\n",
"\n",
"592\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Eq Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n",
"Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound get\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'nodes', 'nodes', 'nodes']\n",
+ " 2. ['backend', 'placeholder', 'placeholder', 'placeholder']\n",
"\n",
"593\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n",
"Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['target', 'placeholder', 'placeholder', 'placeholder']\n",
+ " 2. ['g', 'weight', 'weight', 'weight']\n",
"\n",
"594\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",
+ " 1. ['set', 'weight', 'weight', 'weight']\n",
+ " 2. ['extend', 'placeholder', 'placeholder', 'placeholder']\n",
"\n",
"595\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",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['set', 'weight', 'weight', 'weight']\n",
+ " 2. ['user', 'placeholder', 'placeholder', 'placeholder']\n",
"\n",
"596\n",
"[CLS] BoolOp Or Compare Subscript Name Index UnaryOp USub Num Eq Num Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Attribute binary crossentropy Name\n",
"Label = ['loss', 'functions', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "values loss\n",
- "[PAD] functions\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['values', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['shape', 'fn', 'fn', 'fn']\n",
+ " 2. ['fn', 'i', 'i', 'i']\n",
"\n",
"597\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",
- "new all\n",
- "[PAD] inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'shape', 'shape', 'shape']\n",
+ " 2. ['additional', 'value', 'value', 'value']\n",
"\n",
"598\n",
"[CLS] If Call Name GeneratorExp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name v Name If UnaryOp Not Call Name GeneratorExp Call Attribute is tensor Name Name comprehension Name v Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name\n",
"Label = ['is', 'tensor', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is is\n",
- "[PAD] tensor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['ndarray', 'tensor', 'tensor', 'tensor']\n",
+ " 2. ['run', 'keras', 'keras', 'keras']\n",
"\n",
"599\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes NameConstant Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes Attribute feed input shapes Name\n",
"Label = ['is', 'graph', 'network', '[PAD]']\n",
"Pred =\n",
- "trainable is\n",
- "[PAD] graph\n",
- "[PAD] network\n",
- "[PAD] [PAD]\n",
" 0. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'updates', 'updates', 'updates']\n",
+ " 2. ['input', 'names', 'names', 'names']\n",
"\n",
"600\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg y arg batch size arg epochs arg verbose arg callbacks arg validation split arg validation data arg shuffle arg class weight arg sample weight arg initial epoch arg steps per epoch arg validation steps arg kwargs NameConstant NameConstant NameConstant Num Num NameConstant Num NameConstant NameConstant NameConstant NameConstant Num 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",
+ " 1. ['model', 'format', 'format', 'format']\n",
+ " 2. ['path', 'data', 'data', 'data']\n",
"\n",
"601\n",
"[CLS] If Compare Call Name Name Eq Num Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Name val sample weight Name Raise Call Name BinOp Str Mod Call Name Name\n",
"Label = ['val', 'x', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "x x\n",
- "x [PAD]\n",
- "x [PAD]\n",
" 0. ['val', 'x', 'x', 'x']\n",
+ " 1. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 2. ['sample', 'val', 'val', 'val']\n",
"\n",
"602\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Name Add Name Name List Num\n",
"Label = ['val', 'ins', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ins val\n",
- "[PAD] ins\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['ins', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['val', 'ins', 'ins', 'ins']\n",
+ " 2. ['pattern', 'size', 'size', 'size']\n",
"\n",
"603\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Tuple Call Name Name Num Name Call Name Name Name\n",
"Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['x', 'x', 'x', 'x']\n",
+ " 2. ['y', 'test', 'test', 'test']\n",
"\n",
"604\n",
"[CLS] If BoolOp And Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Gt Name Compare BinOp Subscript Attribute shape Subscript Name Index Num Index Num Mod Name NotEq Num Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute shape 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",
+ " 1. ['[PAD]', 'size', 'size', 'size']\n",
+ " 2. ['keras', 'layers', 'layers', 'layers']\n",
"\n",
"605\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",
+ " 1. ['args', 'size', 'size', 'size']\n",
+ " 2. ['[PAD]', 'shape', 'shape', 'shape']\n",
"\n",
"606\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 = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "model self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['model', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['self', 'format', 'format', 'format']\n",
+ " 2. ['path', 'model', 'model', 'model']\n",
"\n",
"607\n",
"[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Attribute name Attribute class Subscript Name Index Name Eq Str Attribute values Subscript Name Index Name Subscript Name Index Name comprehension Name x Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp Str Add Subscript Attribute args Name Index Num Str Call Name Name\n",
"Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['config', 'data', 'data', 'data']\n",
+ " 2. ['new', 'layer', 'layer', 'layer']\n",
"\n",
"608\n",
"[CLS] ListComp IfExp Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Attribute values Name Name comprehension Name x 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",
+ " 1. ['data', 'format', 'format', 'format']\n",
+ " 2. ['[PAD]', 'data', 'data', 'data']\n",
"\n",
"609\n",
"[CLS] BoolOp And Compare Subscript Name Index Name IsNot NameConstant UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name\n",
"Label = ['is', 'tensor', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is is\n",
- "[PAD] tensor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['get', 'sparse', 'sparse', 'sparse']\n",
+ " 2. ['array', 'tensor', 'tensor', 'tensor']\n",
"\n",
"610\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name ref dim Call Name Name Name If BoolOp And Compare Name NotEq Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Subscript Name Index Name Str Call Name Name Str Call Name Name\n",
"Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dim dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['dim', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['chunk', 'dim', 'dim', 'dim']\n",
+ " 2. ['w', 'layer', 'layer', 'layer']\n",
"\n",
"611\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Name Call Attribute get Name Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
" 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n",
+ " 1. ['name', 'config', 'config', 'config']\n",
+ " 2. ['o', 'scope', 'scope', 'scope']\n",
"\n",
"612\n",
"[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name y 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",
+ " 1. ['[PAD]', 'shape', 'shape', 'shape']\n",
+ " 2. ['name', 'spec', 'spec', 'spec']\n",
"\n",
"613\n",
"[CLS] Call Name BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w 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",
+ " 1. ['[PAD]', 'spec', 'spec', 'spec']\n",
+ " 2. ['name', 'shape', 'shape', 'shape']\n",
"\n",
"614\n",
"[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w 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",
+ " 1. ['[PAD]', 'format', 'format', 'format']\n",
+ " 2. ['name', 'spec', 'spec', 'spec']\n",
"\n",
"615\n",
"[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Call Name Attribute shape Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute shape Name Str Call Name 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",
+ " 1. ['input', 'shape', 'shape', 'shape']\n",
+ " 2. ['times', 'layers', 'layers', 'layers']\n",
"\n",
"616\n",
"[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape 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",
+ " 1. ['name', 'shape', 'shape', 'shape']\n",
+ " 2. ['axis', 'size', 'size', 'size']\n",
"\n",
"617\n",
"[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute shape 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",
+ " 1. ['keras', 'size', 'size', 'size']\n",
+ " 2. ['kernel', 'shape', 'shape', 'shape']\n",
"\n"
]
}
],
"source": [
- "n=1\n",
+ "n=3\n",
"pred_str = []; score = 0; score_no_pad=0; rank =[]; skipped = 0\n",
"for idx in range(618):\n",
" print(idx)\n",
@@ -7582,7 +6358,7 @@
" 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",
+ " #print(p_, labels_str[idx][k])\n",
" if p_==labels_str[idx][k]:\n",
" score +=1\n",
" if p_ != '[PAD]':\n",
@@ -7595,7 +6371,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 156,
"metadata": {},
"outputs": [
{
@@ -7604,7 +6380,7 @@
"3"
]
},
- "execution_count": 19,
+ "execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
@@ -7615,16 +6391,16 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "2064"
+ "2299"
]
},
- "execution_count": 20,
+ "execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
@@ -7635,7 +6411,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 158,
"metadata": {
"scrolled": true
},
@@ -7648,3401 +6424,1584 @@
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute input layers Name Assign Name input tensor Call Name keyword Attribute batch input shape Name keyword Attribute dtype Name keyword Attribute sparse Name keyword Attribute name Name Expr Call Attribute append Name Name Assign Name newly created input layer Subscript Attribute keras history Name Index Num Assign Subscript Name Index Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"1\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name In Name Expr Call Attribute append Name Subscript Name Index Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"2\n",
"[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Name Sub Call Attribute max Name Name keyword Name keyword NameConstant Assign Name s Call Attribute sum Name Name keyword Name keyword NameConstant Return BinOp Name Div Name Raise Call Name BinOp Str Mod Name\n",
"Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new e\n",
- "p e\n",
- "v e\n",
- "k e\n",
- "e e\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"3\n",
"[CLS] BinOp Name Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword NameConstant\n",
"Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "random max\n",
- "scan max\n",
- "convolution max\n",
- "all max\n",
- "input max\n",
- "rnn max\n",
- "in max\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"4\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name alpha Num Assign Name scale Num Return BinOp Name Mult Call Attribute elu Name Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"5\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Return Call Name Name keyword Call Name keyword Name keyword Str\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config name\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"6\n",
"[CLS] If Call Name Name If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name Return Name Raise Call Name Str Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"7\n",
"[CLS] If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"8\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute flatten Name Name Call Attribute cast Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute floatx Name Call Attribute floatx Name\n",
"Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cast cast\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"9\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['y', 'true', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "y y\n",
- "true true\n",
- "true [PAD]\n",
- "[PAD] [PAD]\n",
- "true [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"10\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg model For Name callback Attribute callbacks Name Expr Call Attribute set model Name Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"11\n",
"[CLS] If Name Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Assign Attribute stateful metrics Name Call Name\n",
"Label = ['stateful', 'metrics', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "metrics metrics\n",
- "metrics [PAD]\n",
- "[PAD] [PAD]\n",
- "metrics [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"12\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name target Subscript Attribute params Name Index Str Assign Name target Subscript Attribute params Name Index Str\n",
"Label = ['use', 'steps', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs use\n",
- "name use\n",
- "stateful use\n",
- "run use\n",
- "target use\n",
- "arguments use\n",
- "return use\n",
- "[PAD] steps\n",
- "sequences steps\n",
- "function steps\n",
- "inputs steps\n",
- "kernel steps\n",
- "length steps\n",
- "size steps\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"13\n",
"[CLS] 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",
"14\n",
"[CLS] Call Name 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",
- "monitor monitor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"15\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"16\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg root arg path arg field arg headers arg send as json Str Str Str NameConstant NameConstant Expr Call Attribute init Call Name Name Name Assign Attribute root Name Name Assign Attribute path Name Name Assign Attribute field Name Name Assign Attribute headers Name Name Assign Attribute send as json Name Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"17\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg schedule arg verbose Num Expr Call Attribute init Call Name Name Name Assign Attribute schedule Name Name Assign Attribute verbose Name Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"18\n",
"[CLS] If Compare Name NotEq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name embeddings freq Num\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"19\n",
"[CLS] If Compare Name Eq Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Num Assign Attribute update freq Name Name\n",
"Label = ['update', 'freq', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data update\n",
- "type update\n",
- "embeddings update\n",
- "state update\n",
- "delta update\n",
- "stopped update\n",
- "output update\n",
- "[PAD] freq\n",
- "data freq\n",
- "format freq\n",
- "epoch freq\n",
- "spec freq\n",
- "names freq\n",
- "size freq\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"20\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"21\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotIn List Str Str Str Expr Call Attribute warn Name BinOp Str Mod Attribute mode Name Name Assign Attribute mode Name Str\n",
"Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mode mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"22\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Mod Tuple Attribute monitor Name Call Attribute join Str Call Name Call Attribute keys Name Name\n",
"Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"23\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name k Attribute keys Name\n",
"Label = ['logs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k logs\n",
- "new logs\n",
- "result logs\n",
- "key logs\n",
- "use logs\n",
- "mask logs\n",
- "training logs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"24\n",
"[CLS] If Compare Name IsNot NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Attribute on train begin Name Lambda arguments arg logs NameConstant\n",
"Label = ['on', 'train', 'begin', '[PAD]']\n",
"Pred =\n",
- "on on\n",
- "[PAD] train\n",
- "begin train\n",
- "batch train\n",
- "end train\n",
- "train train\n",
- "[PAD] begin\n",
- "begin begin\n",
- "[PAD] [PAD]\n",
"\n",
"25\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x Assign Name regularization Num If Attribute l1 Name AugAssign Name regularization Add Call Attribute sum Name BinOp Attribute l1 Name Mult Call Attribute abs Name Name If Attribute l2 Name AugAssign Name regularization Call Attribute sum Name BinOp Attribute l2 Name Call Attribute square Name 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",
"26\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Dict Str Str Attribute max value Name 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",
"27\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg w Return BinOp Name Div BinOp Call Attribute epsilon Name Add Call Attribute sqrt Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"28\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n",
"Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sum sum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"29\n",
"[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['string', 'types', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "string string\n",
- "[PAD] types\n",
- "types types\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"30\n",
"[CLS] 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",
"31\n",
"[CLS] Return Dict Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute stddev Name Attribute seed Name\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"32\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str AugAssign Name scale Div Call Name Num Name If Compare Attribute mode Name Str AugAssign Name scale Call Name Num Name AugAssign Name scale Call Name Num BinOp Call Name BinOp Name Add Name Num\n",
"Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mode mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"33\n",
"[CLS] Return 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",
- "truncated truncated\n",
- "[PAD] normal\n",
- "normal normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"34\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num AugAssign Name num rows Mult Name\n",
"Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "a dim\n",
- "s dim\n",
- "i dim\n",
- "w dim\n",
- "x dim\n",
- "m dim\n",
- "n dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"35\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Expr Call Attribute seed Attribute random Name Attribute seed Name\n",
"Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "seed seed\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"36\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num\n",
"Label = ['receptive', 'field', 'size', '[PAD]']\n",
"Pred =\n",
- "y receptive\n",
- "x receptive\n",
- "batch receptive\n",
- "output receptive\n",
- "biases receptive\n",
- "inner receptive\n",
- "image receptive\n",
- "[PAD] field\n",
- "shape field\n",
- "size field\n",
- "mask field\n",
- "batch field\n",
- "out field\n",
- "length field\n",
- "[PAD] size\n",
- "shape size\n",
- "size size\n",
- "[PAD] [PAD]\n",
"\n",
"37\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"38\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name BinOp Name Sub Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"39\n",
"[CLS] BinOp Num Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword UnaryOp USub Num\n",
"Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"40\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name pos Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp USub Num Assign Name neg Call Attribute max Name BinOp BinOp Num Sub Name Name keyword UnaryOp Num Return Call Attribute maximum Name Num BinOp BinOp Name Name Add Num\n",
"Label = ['y', 'true', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "y y\n",
- "[PAD] true\n",
- "true true\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"41\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",
- "maximum maximum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"42\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",
- "mean mean\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"43\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",
- "y y\n",
- "[PAD] true\n",
- "true true\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"44\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute switch Name Call Attribute greater equal Name Name Name BinOp BinOp Name Mult Name Div Name Name\n",
"Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new g\n",
- "all g\n",
- "v g\n",
- "element g\n",
- "w g\n",
- "sum g\n",
- "select g\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"45\n",
"[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num\n",
"Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "clipvalue clipnorm\n",
- "dynamic clipnorm\n",
- "delta clipnorm\n",
- "dtype clipnorm\n",
- "num clipnorm\n",
- "verbose clipnorm\n",
- "min clipnorm\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"46\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name ListComp Call Attribute sum Name Call Attribute square Name Name comprehension Name g Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"47\n",
"[CLS] Raise Call Name 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",
"48\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n",
"Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "iterations iterations\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"49\n",
"[CLS] 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",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"50\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 = ['new', 'p', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p new\n",
- "new new\n",
- "[PAD] p\n",
- "t p\n",
- "p p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"51\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Call Attribute sqrt Name BinOp Name Add Attribute epsilon Name Div Call Attribute sqrt Name BinOp Name Attribute epsilon Name\n",
"Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p update\n",
- "new update\n",
- "lr update\n",
- "t update\n",
- "v update\n",
- "w update\n",
- "norm update\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"52\n",
"[CLS] Dict Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Attribute rho Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n",
"Label = ['get', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] value\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"53\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n",
"Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "iterations iterations\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"54\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",
- "lr lr\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"55\n",
"[CLS] 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",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"56\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Attribute cast Name Attribute iterations Name Call Attribute floatx Name Add Num\n",
"Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "t t\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"57\n",
"[CLS] BinOp Name Mult BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name Div BinOp Num Call Attribute pow Name Attribute beta 1 Name Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"58\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"59\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg lr arg beta 1 arg beta 2 arg epsilon arg decay arg kwargs Num Num Num NameConstant Num\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"60\n",
"[CLS] BinOp Num Div 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",
- "decay decay\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"61\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",
- "m m\n",
- "[PAD] t\n",
- "t t\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"62\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",
- "ndim epsilon\n",
- "value epsilon\n",
- "momentum epsilon\n",
- "sqrt epsilon\n",
- "inputs epsilon\n",
- "lr epsilon\n",
- "beta epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"63\n",
"[CLS] BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ndim epsilon\n",
- "momentum epsilon\n",
- "value epsilon\n",
- "sqrt epsilon\n",
- "inputs epsilon\n",
- "activation epsilon\n",
- "seen epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"64\n",
"[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp BinOp Name Add Num Attribute schedule decay Name\n",
"Label = ['beta', '1', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "beta beta\n",
- "[PAD] 1\n",
- "1 1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"65\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",
- "beta beta\n",
- "[PAD] 1\n",
- "1 1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"66\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 2 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 2 Name Call Attribute square Name Name\n",
"Label = ['v', 't', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v v\n",
- "t t\n",
- "t [PAD]\n",
- "[PAD] [PAD]\n",
- "t [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"67\n",
"[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 2 Name Name\n",
"Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pow pow\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"68\n",
"[CLS] Dict Str Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['get', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] value\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"69\n",
"[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str\n",
"Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "toarray lower\n",
- "keys lower\n",
- "as lower\n",
- "get lower\n",
- "lower lower\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"70\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Str Tuple Attribute input dim Name\n",
"Label = ['input', 'dim', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input input\n",
- "[PAD] dim\n",
- "dim dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"71\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['add', 'weight', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add add\n",
- "[PAD] weight\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"72\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",
"73\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name keyword Attribute init Name keyword Str keyword Attribute W regularizer Name keyword Attribute W constraint Name\n",
"Label = ['add', 'weight', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add add\n",
- "[PAD] weight\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"74\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs ImportFrom alias If Compare Str In Name Assign Name rate Call Attribute pop Name Str Assign Name rate Num Assign Subscript Name Index Str Name Expr Call Attribute warn Name Str Return Call Name Starred Name keyword Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"75\n",
"[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"76\n",
"[CLS] Raise Call Name BinOp BinOp 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 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",
"77\n",
"[CLS] BinOp 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 Str\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"78\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 Tuple Name Attribute units Name Str Call Name Attribute shape Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"79\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Attribute return sequences Name Attribute return state Name Attribute go backwards Name Attribute stateful Name Attribute unroll Name Attribute implementation Name\n",
"Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"80\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute kernel size Name Index Num keyword Attribute padding Name keyword Subscript Attribute strides Name Index Num keyword Subscript Attribute dilation rate Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "length length\n",
- "output [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"81\n",
"[CLS] Tuple Subscript Name Index Num Subscript Name Index Num Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filters filters\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"82\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",
- "str str\n",
- "[PAD] val\n",
- "val val\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"83\n",
"[CLS] If BoolOp Or Compare Name Lt BinOp Call Name Subscript Name Slice Num Sub Num Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Str\n",
"Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info signature\n",
- "x signature\n",
- "output signature\n",
- "f signature\n",
- "signature signature\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"84\n",
"[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name signature Add BinOp BinOp Str Name Str If Call Name Name Attribute ndarray Name Assign Name str val Str Assign Name str val Call Name Name If Compare Call Name Name Gt Num Assign Name str val BinOp Subscript Name Slice Num Str AugAssign Name signature Name\n",
"Label = ['string', 'types', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "string string\n",
- "[PAD] types\n",
- "types types\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"85\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg new arg Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Name Str Name Str\n",
"Label = ['old', 'arg', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "value old\n",
- "x old\n",
- "dim old\n",
- "self old\n",
- "prefix old\n",
- "kernel old\n",
- "layer old\n",
- "[PAD] arg\n",
- "name arg\n",
- "names arg\n",
- "length arg\n",
- "batch arg\n",
- "weights arg\n",
- "dim arg\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"86\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str\n",
"Label = ['legacy', 'dropout', 'support', '[PAD]']\n",
"Pred =\n",
- "legacy legacy\n",
- "support dropout\n",
- "[PAD] dropout\n",
- "conv2d dropout\n",
- "pooling2d dropout\n",
- "generator dropout\n",
- "conv1d dropout\n",
- "spatialdropout1d dropout\n",
- "support support\n",
- "support [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"87\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str Assign Name length NameConstant\n",
"Label = ['length', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "length length\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"88\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",
- "filter kernel\n",
- "noise kernel\n",
- "volume kernel\n",
- "kernel kernel\n",
- "[PAD] size\n",
- "shape size\n",
- "size size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"89\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Call Attribute pop Name Str\n",
"Label = ['kernel', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "size size\n",
- "size [PAD]\n",
- "[PAD] [PAD]\n",
- "size [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"90\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",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"91\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",
- "kernel kernel\n",
- "[PAD] size\n",
- "size size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"92\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 Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant keyword Name\n",
"Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n",
"Pred =\n",
- "legacy legacy\n",
- "support deconv2d\n",
- "[PAD] deconv2d\n",
- "conv2d deconv2d\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pooling2d deconv2d\n",
- "conv1d deconv2d\n",
- "generator deconv2d\n",
- "cropping2d deconv2d\n",
- "support support\n",
- "support [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"93\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str If Compare Name Eq Str Assign Subscript Name Index Str NameConstant Expr Call Attribute append Name Tuple Str Str Expr Call Attribute warn Name Str keyword Num\n",
"Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel value\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"94\n",
"[CLS] If Call Name Subscript Name Index Num Tuple Name Name Assert Call Name Subscript Name Index Num Name Assert Compare Str In Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name params Name Name Assign Subscript Name Index Str Name Return Tuple List Name Name List\n",
"Label = ['opt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer opt\n",
- "inputs opt\n",
- "n opt\n",
- "outputs opt\n",
- "c opt\n",
- "opt opt\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"95\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",
- "k device\n",
- "x device\n",
- "a device\n",
- "m device\n",
- "dim device\n",
- "n device\n",
- "v device\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"96\n",
"[CLS] If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Subscript Name Index Name\n",
"Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info n\n",
- "idx n\n",
- "i n\n",
- "line n\n",
- "prefix n\n",
- "dim n\n",
- "w n\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"97\n",
"[CLS] Return BinOp Tuple BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Attribute start Name Add Attribute base shape Name\n",
"Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "end end\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"98\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Tuple Name Assign Subscript Name Slice Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"99\n",
"[CLS] BoolOp And Name Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Num Call Name Subscript Name Index Num Name\n",
"Label = ['version', 'info', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape version\n",
- "keras version\n",
- "args version\n",
- "dynamic version\n",
- "values version\n",
- "version version\n",
- "[PAD] info\n",
- "size info\n",
- "format info\n",
- "shape info\n",
- "axes info\n",
- "spec info\n",
- "layers info\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"100\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Str Name In Attribute data Name Assign Name val Call Attribute loads Name Name\n",
"Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"101\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Attribute count params Name Name comprehension Name p Call Name Name\n",
"Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sum sum\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"102\n",
"[CLS] If Compare Call Name Name Gt Num 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",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"103\n",
"[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute W OK Name Assign Name datadir base Call Attribute join Attribute path Name Str Str\n",
"Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "save access\n",
- "exists access\n",
- "load access\n",
- "deconv access\n",
- "in access\n",
- "string access\n",
- "get access\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"104\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg file hash arg algorithm arg chunk size Str Num Expr Str If BoolOp Or Compare Name Is Str BoolOp And Compare Name Str Compare Call Name Name Num Assign Name hasher Str Assign Name hasher Str If Compare Call Name Call Name Name Name Name Eq Call Name Name Return NameConstant Return NameConstant\n",
"Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self fpath\n",
- "model fpath\n",
- "fname fpath\n",
- "uid fpath\n",
- "cls fpath\n",
- "path fpath\n",
- "fpath fpath\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"105\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return BoolOp And Compare Attribute stop signal Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"106\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"107\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"108\n",
"[CLS] While NameConstant Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num If BoolOp Or Compare Attribute unfinished tasks Attribute queue Name Eq Num Call Attribute is set Attribute stop signal Name Return\n",
"Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append sleep\n",
- "set sleep\n",
- "expand sleep\n",
- "stop sleep\n",
- "update sleep\n",
- "is sleep\n",
- "reset sleep\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"109\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name Attribute random seed Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"110\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Expr Call Attribute add Name Str\n",
"Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "backend backend\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"111\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num If Compare Name Str Assign Name pad BinOp Name Sub Num\n",
"Label = ['pad', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pad pad\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"112\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num Name keyword Tuple Name\n",
"Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "seed y\n",
- "x y\n",
- "value y\n",
- "kernel y\n",
- "pool y\n",
- "normal y\n",
- "recurrent y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"113\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute zeros Name BinOp Tuple Name Add Name keyword Attribute float32 Name\n",
"Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x y\n",
- "y y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"114\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",
- "inputs built\n",
- "built built\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"115\n",
"[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Str ListComp Call Name Name comprehension Name ishape Attribute input shapes Name Assign Name inputlabels Str\n",
"Label = ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputlabels inputlabels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"116\n",
"[CLS] keyword 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",
"117\n",
- "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name fn Call Attribute get Name Name If Compare Name Is NameConstant Raise Call Name BinOp BinOp BinOp Str Add Name Str Name\n",
+ "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name fn Call Attribute get Name Name If Compare Name Is NameConstant Raise Call Name BinOp BinOp BinOp Str Add Name Str Name\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "fn fn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"118\n",
"[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Attribute width Name Sub Name\n",
"Label = ['bar', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "info bar\n",
- "new bar\n",
- "bar bar\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"119\n",
"[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Tuple BinOp Name FloorDiv Num BinOp BinOp Name Num Num BinOp Name Num If Compare Name Num Assign Name eta format BinOp Str Tuple BinOp Name Num BinOp Name Num Assign Name eta format BinOp Str Name\n",
"Label = ['eta', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "eta eta\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"120\n",
"[CLS] If Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name info Add BinOp Str Mult BinOp Name Sub Attribute total width Name\n",
"Label = ['total', 'width', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "verbose total\n",
- "min total\n",
- "delta total\n",
- "dynamic total\n",
- "stopped total\n",
- "monitor total\n",
- "clipvalue total\n",
- "[PAD] width\n",
- "t width\n",
- "updates width\n",
- "names width\n",
- "nodes width\n",
- "epoch width\n",
- "axes width\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"121\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Num Assign Name fpath Call Attribute join Attribute path Name Name BinOp Str Add Call Name Name Assign Tuple Subscript Name ExtSlice Slice BinOp BinOp Name Sub Num Mult Num BinOp Name Num Slice Slice Slice Subscript Name Slice BinOp BinOp Name Num Num BinOp Name Num Call Name Name\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "chunk i\n",
- "name i\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"122\n",
"[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x train 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",
- "open open\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"123\n",
"[CLS] Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"124\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Tuple Num Assign Name d Call Attribute load Name Name Assign Name d Call Attribute load Name Name keyword Str Assign Name d decoded Dict For Tuple Name k Name v Call Attribute items Name Assign Subscript Name Index Call Attribute decode Name Str Name Assign Name d Name\n",
"Label = ['version', 'info', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs version\n",
- "input version\n",
- "recurrent version\n",
- "version version\n",
- "[PAD] info\n",
- "spec info\n",
- "updates info\n",
- "kernel info\n",
- "regularizer info\n",
- "axes info\n",
- "dropout info\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"125\n",
"[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Call Name Name comprehension Name x Name\n",
"Label = ['num', 'words', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] words\n",
- "words words\n",
- "[PAD] [PAD]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
"\n",
"126\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Str Expr Str Assign Name path Call Name Name keyword Str keyword Str Assign Name f Call Name Name Assign Name data Call Attribute load Name Name Expr Call Attribute close Name Return Name\n",
"Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "path path\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"127\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",
- "xs xs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"128\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",
- "xs xs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"129\n",
"[CLS] ListComp ListComp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Compare Name LtE Lt Name Name comprehension Name x Name\n",
"Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w w\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"130\n",
"[CLS] If BoolOp And UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute FunctionType Name UnaryOp Call Name Attribute build fn Name Attribute MethodType Name Expr Call Attribute append Name Attribute call Attribute build fn Name Expr Call Attribute append Name Attribute build fn Name\n",
"Label = ['build', 'fn', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "build build\n",
- "[PAD] fn\n",
- "fn fn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"131\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name output Call Name Attribute metrics names Attribute model Name Name If Compare Name Eq Str Return Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"132\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",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"133\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute VGG19 Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"134\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"135\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute DenseNet121 Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"136\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n",
"Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args args\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"137\n",
"[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name Num Expr Call Attribute append Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str 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",
"138\n",
"[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str 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",
"139\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",
"140\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List BinOp Name Sub Num Add Call Name Call Name BinOp Name Num\n",
"Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes dims\n",
- "dims dims\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"141\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",
"142\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Subscript Name Index Num comprehension Name s Name Compare Name IsNot NameConstant\n",
"Label = ['batch', 'sizes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "batch batch\n",
- "[PAD] sizes\n",
- "shape sizes\n",
- "states sizes\n",
- "batch sizes\n",
- "keras sizes\n",
- "train sizes\n",
- "dim sizes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"143\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Call Name Name NotEq Num Raise Call Name Str Return BinOp Subscript Name Index Num Sub Subscript Name Index Num\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"144\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Attribute axes Name Index Name Mod Call Attribute ndim 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",
"145\n",
"[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n",
"Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"146\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Expr Str Return Call Call Name keyword Name Name\n",
"Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"147\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Call Name Attribute alpha 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",
"148\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",
"149\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute shared axes Name Assign Subscript Name Index BinOp Name Sub Num Num Assign Subscript Attribute param broadcast Name Index BinOp Name Num NameConstant\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"150\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name If Compare Name NotIn Attribute shared axes Name Assign Subscript Name Index Name Subscript Name Index Name\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"151\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",
"152\n",
"[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast to floatx Name Name\n",
"Label = ['max', 'value', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype max\n",
- "w max\n",
- "result max\n",
- "x max\n",
- "momentum max\n",
- "init max\n",
- "beta max\n",
- "[PAD] value\n",
- "t value\n",
- "dtype value\n",
- "1 value\n",
- "array value\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"153\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute layer Name Str Return Attribute updates Attribute 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",
"154\n",
"[CLS] Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Attribute layer Name Call Attribute get config Attribute layer Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"155\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Num\n",
"Label = ['inner', 'input', 'shape', '[PAD]']\n",
"Pred =\n",
- "inner inner\n",
- "mask input\n",
- "shape input\n",
- "[PAD] input\n",
- "length input\n",
- "input input\n",
- "mask shape\n",
- "shape shape\n",
- "mask [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"156\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n",
"Label = ['output', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "mask [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "mask [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"157\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n",
"Label = ['get', 'shape', 'tuple', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] tuple\n",
- "shape tuple\n",
- "dims tuple\n",
- "subtensor tuple\n",
- "tuple tuple\n",
- "[PAD] [PAD]\n",
"\n",
"158\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"159\n",
"[CLS] Return BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute forward layer Name Add Call Attribute get weights Attribute backward layer Name\n",
"Label = ['get', 'weights', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] weights\n",
- "weights weights\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"160\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Return BinOp BinOp Name Add Name Call Attribute copy Name Name\n",
"Label = ['merge', 'mode', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "merge merge\n",
- "[PAD] mode\n",
- "mode mode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"161\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"162\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Name FloorDiv Num Add Num\n",
"Label = ['pivot', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num pivot\n",
- "new pivot\n",
- "y pivot\n",
- "axes pivot\n",
- "batch pivot\n",
- "expected pivot\n",
- "spatial pivot\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"163\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp UnaryOp Not Attribute merge mode Name List NameConstant NameConstant NameConstant\n",
"Label = ['output', 'mask', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] mask\n",
- "mode mask\n",
- "state mask\n",
- "size mask\n",
- "mask mask\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"164\n",
"[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Add Name Name\n",
"Label = ['get', 'updates', 'for', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "for updates\n",
- "losses updates\n",
- "[PAD] updates\n",
- "updates updates\n",
- "for for\n",
- "for [PAD]\n",
- "losses [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"165\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute losses Attribute forward layer Name Add Attribute losses Attribute backward layer Name\n",
"Label = ['forward', 'layer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "forward forward\n",
- "[PAD] layer\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"166\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",
"167\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Name keyword Tuple NameConstant Name comprehension Name dim Name\n",
"Label = ['state', 'spec', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] spec\n",
- "spec spec\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"168\n",
"[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Attribute go backwards Name Attribute stateful Name\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "sequences sequences\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] If BoolOp And Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute built Name Return List Attribute kernel Name Attribute recurrent kernel Name Attribute bias Name\n",
"Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reset trainable\n",
- "stateful trainable\n",
- "use trainable\n",
- "run trainable\n",
- "trainable trainable\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"171\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n",
"Label = ['recurrent', 'kernel', 'z', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "kernel kernel\n",
- "kernel z\n",
- "[PAD] z\n",
- "i z\n",
- "r z\n",
- "f z\n",
- "z z\n",
- "kernel [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"172\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name BinOp Attribute units Name Mult Num\n",
"Label = ['kernel', 'r', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] r\n",
- "f r\n",
- "r r\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"173\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', 'r', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] r\n",
- "c r\n",
- "i r\n",
- "o r\n",
- "h r\n",
- "f r\n",
- "r r\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"174\n",
"[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num\n",
"Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"175\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",
"176\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",
- "units units\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"177\n",
"[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num Assign Name c Subscript Name Index Num\n",
"Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful stateful\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"178\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name output Call Attribute transpose Name Name Tuple Num Num Num Assign Name output Subscript Name Index UnaryOp USub Num\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "sequences sequences\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"179\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",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"180\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "length length\n",
- "output [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"181\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",
- "data data\n",
- "format format\n",
- "format [PAD]\n",
- "[PAD] [PAD]\n",
- "format [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"182\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",
"183\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "length length\n",
- "output [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"184\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', 'dim3', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cols len\n",
- "rows len\n",
- "length len\n",
- "output len\n",
- "len len\n",
- "[PAD] dim3\n",
- "length dim3\n",
- "dim dim3\n",
- "output dim3\n",
- "width dim3\n",
- "rows dim3\n",
- "col dim3\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"185\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute pooling function Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format 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",
"186\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name\n",
"Label = ['pooling', 'function', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pooling pooling\n",
- "[PAD] function\n",
- "function function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"187\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg kwargs Tuple Num Num Num NameConstant Str NameConstant Expr Call Attribute init Call Name Name Name Name Name Name Name keyword Name Attribute legacy pooling3d support Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"188\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",
"189\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] 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 = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"190\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute data format 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",
"191\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute mean Name Name keyword List Num Num Return Call Attribute mean Name Name keyword List Num Num\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"192\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute max Name Name keyword List Num Num Return Call Attribute max Name Name keyword List Num Num\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"193\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",
"194\n",
"[CLS] Raise 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",
"195\n",
"[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Subscript Name Index Name keyword Attribute padding Attribute cell Name keyword Subscript Attribute strides Attribute cell Name Index Name keyword Subscript Attribute dilation rate Attribute cell Name Index Name\n",
"Label = ['conv', 'output', 'length', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "output output\n",
- "output length\n",
- "length length\n",
- "output [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"196\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"197\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state arg constants 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",
"198\n",
"[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"199\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg states Assign Name constants Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name states Subscript Name Slice UnaryOp Attribute num constants Name Return Call Attribute call Attribute cell Name Name Name keyword Name keyword Name\n",
"Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"200\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name state shape BinOp Subscript Name Slice Num Add Subscript Name Slice Num\n",
"Label = ['return', 'sequences', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "return return\n",
- "[PAD] sequences\n",
- "state sequences\n",
- "shape sequences\n",
- "sequences sequences\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"201\n",
"[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Tuple Name BinOp Attribute filters Name Mult Num\n",
"Label = ['kernel', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] size\n",
- "size size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"202\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice Attribute filters Name\n",
"Label = ['kernel', 'i', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] i\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"203\n",
"[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent kernel\n",
- "kernel kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"204\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['recurrent', 'kernel', 'o', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "kernel kernel\n",
- "kernel o\n",
- "[PAD] o\n",
- "c o\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "o o\n",
- "kernel [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"205\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",
- "bias bias\n",
- "[PAD] c\n",
- "c c\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"206\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num\n",
"Label = ['bias', 'o', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] o\n",
- "c o\n",
- "f o\n",
- "i o\n",
- "o o\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"207\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel i Name Attribute bias i Name keyword Attribute padding Name\n",
"Label = ['input', 'conv', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input input\n",
- "[PAD] conv\n",
- "conv conv\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
- "208\n",
+ "208\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"[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",
"209\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",
"210\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Assign Name outputs Call Attribute conv1d Name Name Attribute kernel Name keyword Subscript Attribute strides Name Index Num keyword Attribute padding Name keyword Attribute data format Name keyword Subscript Attribute dilation rate Name Index Num\n",
"Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "rank rank\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"211\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Assign Name new dim Call Attribute conv output length Name Subscript Name Index Name Subscript Attribute kernel size Name Index Name keyword Attribute padding Name keyword Subscript Attribute strides Name Index Name keyword Subscript Attribute dilation rate Name Index Name Expr Call Attribute append Name Name\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"212\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",
"213\n",
"[CLS] 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 keyword Name\n",
"Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "init init\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"214\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n",
"Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"215\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"216\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Tuple Name h axis Name w axis Tuple Num Num Assign Tuple Name h axis Name w axis Tuple Num Num\n",
"Label = ['data', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"217\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Assign Name output shape Tuple Name Name Name Attribute filters Name\n",
"Label = ['data', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"218\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d transpose Name Name Attribute kernel Name Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"219\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"220\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n",
"Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"221\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"222\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n",
"Label = ['add', 'weight', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add add\n",
- "[PAD] weight\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"223\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute deconv length Name Name Name Name Attribute padding Name Name\n",
"Label = ['out', 'height', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "out out\n",
- "[PAD] height\n",
- "out height\n",
- "width height\n",
- "height height\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"224\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Name Assign Name output shape Tuple Name Name Name Name Attribute filters Name\n",
"Label = ['data', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"225\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"226\n",
"[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"227\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n",
"Label = ['deconv', 'length', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "deconv deconv\n",
- "length length\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"228\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call 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 keyword Name\n",
"Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "init init\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"229\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",
- "data channel\n",
- "format channel\n",
- "tf channel\n",
- "output channel\n",
- "pad channel\n",
- "axis channel\n",
- "left channel\n",
- "[PAD] axis\n",
- "format axis\n",
- "data axis\n",
- "size axis\n",
- "shape axis\n",
- "sequences axis\n",
- "val axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"230\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] 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\n",
"Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"231\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 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",
"232\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute depthwise kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute dilation rate Name keyword Attribute data format Name\n",
"Label = ['depthwise', 'conv2d', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "conv2d depthwise\n",
- "separable depthwise\n",
- "conv3d depthwise\n",
- "pool depthwise\n",
- "convolution depthwise\n",
- "function depthwise\n",
- "conv1d depthwise\n",
- "[PAD] conv2d\n",
- "conv2d conv2d\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"233\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",
- "use use\n",
- "bias bias\n",
- "bias [PAD]\n",
- "[PAD] [PAD]\n",
- "bias [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"234\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Num BinOp Num Add Attribute rank Name\n",
"Label = ['spatial', 'axes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "spatial spatial\n",
- "axes axes\n",
- "axes [PAD]\n",
- "[PAD] [PAD]\n",
- "axes [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"235\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute repeat elements Name Name Subscript Attribute size Name Index Num keyword 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",
"236\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute resize images Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"237\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n",
"Label = ['resize', 'images', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "resize resize\n",
- "[PAD] images\n",
- "length images\n",
- "output images\n",
- "volumes images\n",
- "images images\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"238\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Str keyword Name\n",
"Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "init init\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"239\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n",
"Label = ['dim3', 'padding', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "width dim3\n",
- "dim2 dim3\n",
- "height dim3\n",
- "dim1 dim3\n",
- "padding dim3\n",
- "cropping dim3\n",
- "dim3 dim3\n",
- "[PAD] padding\n",
- "cropping padding\n",
- "padding padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"240\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num 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",
"241\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Tuple Name Name Tuple Name Name Tuple Name Name\n",
"Label = ['normalized', 'cropping', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "args normalized\n",
- "data normalized\n",
- "legacy normalized\n",
- "shape normalized\n",
- "attrs normalized\n",
- "input normalized\n",
- "state normalized\n",
- "[PAD] cropping\n",
- "shape cropping\n",
- "input cropping\n",
- "support cropping\n",
- "spec cropping\n",
- "format cropping\n",
- "dim cropping\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"242\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",
- "input input\n",
- "length length\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
- "length [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"243\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv1d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name\n",
"Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"244\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Str Raise Call Name BinOp Str Add Name\n",
"Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"245\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute kernel size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n",
"Label = ['output', 'row', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cols output\n",
- "rows output\n",
- "output output\n",
- "[PAD] row\n",
- "length row\n",
- "dim row\n",
- "output row\n",
- "width row\n",
- "rows row\n",
- "col row\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"246\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv2d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name Tuple Attribute output row Name Attribute output col Name Attribute data format Name\n",
"Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"247\n",
"[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n",
"Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape axis\n",
- "axis axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"248\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute gamma 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 Assign Attribute gamma Name NameConstant\n",
"Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "gamma scale\n",
- "use scale\n",
- "center scale\n",
- "normalize scale\n",
- "add scale\n",
- "scale scale\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"249\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute beta initializer Name keyword Attribute beta regularizer Name keyword Attribute beta constraint Name\n",
"Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "beta beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"250\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Str keyword Attribute moving mean initializer Name keyword NameConstant\n",
"Label = ['add', 'weight', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add add\n",
- "[PAD] weight\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"251\n",
"[CLS] BinOp Name Div BinOp Name Sub BinOp Num Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ndim epsilon\n",
- "rank epsilon\n",
- "sqrt epsilon\n",
- "value epsilon\n",
- "start epsilon\n",
- "beta epsilon\n",
- "name epsilon\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"252\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",
"253\n",
"[CLS] Return 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",
- "state state\n",
- "[PAD] size\n",
- "size size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"254\n",
"[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute cells Name Slice UnaryOp USub Num Attribute cells Name\n",
"Label = ['reverse', 'state', 'order', '[PAD]']\n",
"Pred =\n",
- "reverse reverse\n",
- "state state\n",
- "state order\n",
- "[PAD] order\n",
- "order order\n",
- "state [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"255\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",
- "input constants\n",
- "mask constants\n",
- "shape constants\n",
- "batch constants\n",
- "output constants\n",
- "state constants\n",
- "data constants\n",
- "[PAD] shape\n",
- "shape shape\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"256\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute cells Name If Call Name Name Name AugAssign Name weights Add Attribute non trainable weights Name\n",
"Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cell cell\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"257\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs NameConstant Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Call Attribute get losses for Name Name AugAssign Name losses Add 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",
"258\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Attribute states Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name Return Attribute states Name Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"259\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"260\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Tuple Subscript Name Index Num Name comprehension Name dim Name\n",
"Label = ['state', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"261\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n",
"Label = ['input', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "mask input\n",
- "input input\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"262\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Str Return ListComp Call Attribute tile Name Name List Num Name comprehension Name dim Attribute state size Attribute cell Name Return List Call Attribute tile Name Name List Num Attribute state size Attribute cell Name\n",
"Label = ['state', 'size', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state state\n",
- "[PAD] size\n",
- "size size\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"263\n",
"[CLS] If Call Name Name Name If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Assign Name initial state Subscript Name Slice Num Assign Name initial state Subscript Name Slice Num UnaryOp USub Attribute num constants Name If Compare Call Name Name Eq Num Assign Name initial state NameConstant Assign Name inputs Subscript Name Index Num\n",
"Label = ['num', 'constants', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] constants\n",
- "constants constants\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"264\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Name Str Call Name Attribute shape Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"265\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",
"266\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",
"267\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Subscript Name Index Str Attribute num constants Name\n",
"Label = ['num', 'constants', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "num num\n",
- "[PAD] constants\n",
- "constants constants\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"268\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",
"269\n",
"[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name\n",
"Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cell cell\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"270\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name h Call Attribute bias add Name Name Attribute bias Name\n",
"Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "bias bias\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"271\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n",
"Label = ['kernel', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel kernel\n",
- "[PAD] h\n",
- "o h\n",
- "c h\n",
- "f h\n",
- "h h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"272\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute recurrent dropout Name keyword Name keyword Num\n",
"Label = ['ones', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones ones\n",
- "[PAD] like\n",
- "like like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"273\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",
- "recurrent matrix\n",
- "x matrix\n",
- "kernel matrix\n",
- "h matrix\n",
- "conv matrix\n",
- "w matrix\n",
- "output matrix\n",
- "[PAD] inner\n",
- "h inner\n",
- "kernel inner\n",
- "o inner\n",
- "c inner\n",
- "f inner\n",
- "r inner\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"274\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Subscript Name ExtSlice Slice Slice BinOp Num Attribute units Name\n",
"Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] h\n",
- "kernel h\n",
- "h h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"275\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name BinOp Name Mult Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Attribute units Name\n",
"Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] h\n",
- "h h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"276\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name 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",
"277\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",
"278\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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"279\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",
- "cls cls\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"280\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute bias initializer Name Tuple Attribute units Name Starred Name keyword Name Call Call Attribute Ones Name Tuple Attribute units Name Starred Name keyword Name Call Attribute bias initializer Name Tuple BinOp Attribute units Name Mult Num Starred Name keyword Name\n",
"Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "concatenate concatenate\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"281\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",
- "o c\n",
- "c c\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"282\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Call Attribute ones like Name Name Attribute dropout Name keyword Name keyword Num\n",
"Label = ['dropout', 'mask', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dropout dropout\n",
- "[PAD] mask\n",
- "mask mask\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"283\n",
"[CLS] If BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant Assign Attribute recurrent dropout mask Name Call Name Call Attribute ones like Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n",
"Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "dropout dropout\n",
- "dropout [PAD]\n",
- "[PAD] [PAD]\n",
- "dropout [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"284\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n",
"Label = ['ones', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones ones\n",
- "[PAD] like\n",
- "like like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"285\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel f Name\n",
"Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i f\n",
- "c f\n",
- "o f\n",
- "h f\n",
- "activation f\n",
- "recurrent f\n",
- "f f\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"286\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",
- "activation recurrent\n",
- "recurrent recurrent\n",
- "[PAD] activation\n",
- "activation activation\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"287\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n",
"Label = ['z1', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent z1\n",
- "x z1\n",
- "kernel z1\n",
- "bias z1\n",
- "r z1\n",
- "h z1\n",
- "conv z1\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"288\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",
"289\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name 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",
"290\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n",
"Label = ['stddev', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "rate stddev\n",
- "stddev stddev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"291\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",
"292\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name If Compare Attribute data format Name Eq Str Assign Name noise shape Tuple Subscript Name Index Num Subscript Name Index Num Num Num Assign Name noise shape Tuple Subscript Name Index Num 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",
"293\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Num Num Subscript Name Index Num\n",
"Label = ['noise', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "noise noise\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"294\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str\n",
"Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "steps unknown\n",
- "batch unknown\n",
- "do unknown\n",
- "epoch unknown\n",
- "is unknown\n",
- "use unknown\n",
- "pad unknown\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"295\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",
"296\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",
"297\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n arg kwargs Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute n Name Name Assign Attribute input spec Name Call Name keyword Num\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 input shape Return Tuple Subscript Name Index Num Attribute n Name Subscript Name Index Num\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"299\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",
"300\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Name comprehension Name shape Name\n",
"Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight xs\n",
- "xs xs\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] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str If Call Name Subscript Subscript Name Index Str Index Name Name Assign Name arg dict Subscript Subscript Name Index Str Index Name If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Str Assign Subscript Subscript Name Index Str Index Name Call Attribute array Name Subscript Name Index Str\n",
"Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x key\n",
- "k key\n",
- "layer key\n",
- "a key\n",
- "key key\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"303\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Name Attribute units 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",
- "weight weight\n",
- "weight [PAD]\n",
- "[PAD] [PAD]\n",
- "weight [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"304\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n",
"Label = ['add', 'weight', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add add\n",
- "[PAD] weight\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"305\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute asarray Name Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"306\n",
"[CLS] BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant\n",
"Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "function Function\n",
- "value Function\n",
- "parameter Function\n",
- "ndarray Function\n",
- "shape Function\n",
- "run Function\n",
- "is Function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"307\n",
"[CLS] If Compare Name Eq Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Return Attribute float32 Name\n",
"Label = ['float16', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype float16\n",
- "float64 float16\n",
- "float32 float16\n",
- "name float16\n",
- "monitor float16\n",
- "int float16\n",
- "unknown float16\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"308\n",
"[CLS] BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name\n",
"Label = ['Constant', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "constant Constant\n",
- "parameter Constant\n",
- "variable Constant\n",
- "zeros Constant\n",
- "uniform Constant\n",
- "normal Constant\n",
- "function Constant\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"309\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Attribute shape Name Index Num Add Subscript Attribute shape 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",
"310\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input Name keyword Name keyword Call Name Name keyword Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input x\n",
- "outputs x\n",
- "output x\n",
- "feed x\n",
- "inputs x\n",
- "cell x\n",
- "node x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"311\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index BinOp Name Add Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"312\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"313\n",
"[CLS] For Name Name If Compare Name Is NameConstant Raise Call Name Str\n",
@@ -11051,118 +8010,51 @@
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "uniform bernoulli\n",
- "normal bernoulli\n",
- "randint bernoulli\n",
- "parameter bernoulli\n",
- "[PAD] bernoulli\n",
- "pool bernoulli\n",
- "convolution bernoulli\n",
- "[PAD] [PAD]\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"316\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name Name keyword Call Attribute uniform Attribute initializer Name Name keyword Name keyword Name keyword Name\n",
"Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v p\n",
- "value p\n",
- "out p\n",
- "new p\n",
- "parameter p\n",
- "beta p\n",
- "param p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"317\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name keyword Name keyword Call Attribute normal Attribute initializer Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "v p\n",
- "value p\n",
- "parameter p\n",
- "beta p\n",
- "normal p\n",
- "new p\n",
- "param p\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"318\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",
- "uniform normal\n",
- "normal normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"319\n",
"[CLS] For Name Attribute [MASK] [MASK] [MASK] [MASK] Name If BoolOp Or Compare Name Eq Attribute InferredDimension Name Compare Name Attribute FreeDimension Name Raise Call Name Str\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] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Call Name BinOp Call Name Name Sub Num\n",
"Label = ['permutation', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output permutation\n",
- "permutation permutation\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"321\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Call Name Name Sub Num BinOp Call Name Name Num\n",
"Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"322\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Is NameConstant Attribute InferredDimension Name Name comprehension Name Name\n",
"Label = ['new', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"323\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Compare Name GtE Num Name BinOp Name Add Call Name Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"324\n",
"[CLS] Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name\n",
@@ -11171,92 +8063,36 @@
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name rep Call Name Name If BoolOp And Compare Name GtE Name Compare Subscript Name Index Name IsNot NameConstant Assign Name tmp BinOp List Name Mult Name Assign Name x Call Attribute splice Name Starred Name keyword BinOp Name Sub Name AugAssign Name i Add Num\n",
"Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x i\n",
- "w i\n",
- "a i\n",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"326\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute all axes Attribute Axis Name\n",
"Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axes axis\n",
- "axis axis\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"327\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute element select Name Name Call Name Name Call Name Name\n",
"Label = ['any', 'matrix', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "result any\n",
- "all any\n",
- "out any\n",
- "y any\n",
- "new any\n",
- "output any\n",
- "batch any\n",
- "[PAD] matrix\n",
- "matrix matrix\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"328\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg increment Assign Name result BinOp Name Add Name Return Call Attribute assign Name Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"329\n",
"[CLS] If BoolOp And Compare Call Name Name Eq Call Name Name Compare Subscript Call Name Name Index Num Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name List Num\n",
"Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "gamma beta\n",
- "mean beta\n",
- "var beta\n",
- "out beta\n",
- "val beta\n",
- "beta beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"330\n",
"[CLS] BoolOp Or Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name Call Name GeneratorExp Compare Name Attribute FreeDimension Name comprehension Name Attribute shape Name\n",
"Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inferreddimension InferredDimension\n",
- "freedimension InferredDimension\n",
- "ndim InferredDimension\n",
- "kind InferredDimension\n",
- "min InferredDimension\n",
- "index InferredDimension\n",
- "int InferredDimension\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"331\n",
"[CLS] Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name\n",
"Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "freedimension InferredDimension\n",
- "inferreddimension InferredDimension\n",
- "ndim InferredDimension\n",
- "int InferredDimension\n",
- "categorical InferredDimension\n",
- "float32 InferredDimension\n",
- "keras InferredDimension\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"332\n",
"[CLS] BinOp Call Name ListComp UnaryOp USub Num comprehension Name Call Name BinOp Name Sub Name Add Name\n",
@@ -11265,3164 +8101,1450 @@
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Name comprehension Name i Call Name Name\n",
"Label = ['current', 'layout', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape current\n",
- "output current\n",
- "result current\n",
- "axis current\n",
- "mask current\n",
- "input current\n",
- "masks current\n",
- "[PAD] layout\n",
- "shape layout\n",
- "list layout\n",
- "train layout\n",
- "output layout\n",
- "words layout\n",
- "tensors layout\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"334\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg initial states arg go backwards arg mask arg constants arg unroll arg input length NameConstant NameConstant NameConstant NameConstant NameConstant\n",
"Label = ['step', 'function', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "step step\n",
- "function function\n",
- "function [PAD]\n",
- "[PAD] [PAD]\n",
- "function [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"335\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",
- "slice slice\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"336\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute element select Attribute ops Name Name Name Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"337\n",
"[CLS] If BoolOp And Compare Name Is NameConstant UnaryOp Not Call Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Name Index Num\n",
"Label = ['num', 'time', 'step', '[PAD]']\n",
"Pred =\n",
- "input num\n",
- "batch num\n",
- "mask num\n",
- "data num\n",
- "shape num\n",
- "random num\n",
- "op num\n",
- "[PAD] time\n",
- "length time\n",
- "size time\n",
- "shape time\n",
- "mask time\n",
- "input time\n",
- "output time\n",
- "[PAD] step\n",
- "length step\n",
- "size step\n",
- "shape step\n",
- "mask step\n",
- "input step\n",
- "output step\n",
- "[PAD] [PAD]\n",
"\n",
"338\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Name Name Eq Num Expr Call Attribute append Name Call Attribute broadcast as Attribute sequence Name Name Name Expr Call Attribute append Name Name\n",
"Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "i c\n",
- "s c\n",
- "o c\n",
- "n c\n",
- "out c\n",
- "key c\n",
- "a c\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"339\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name new states Call Name Name BinOp Call Name Name Add Call Name Name\n",
"Label = ['new', 'output', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output new\n",
- "inputs new\n",
- "outputs new\n",
- "state new\n",
- "out new\n",
- "i new\n",
- "initial new\n",
- "[PAD] output\n",
- "length output\n",
- "shape output\n",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"340\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute element select Name Name Name Name comprehension Tuple Name n Name s Call Name Name Name\n",
"Label = ['new', 'states', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n",
- "[PAD] states\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"341\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Num Num\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"342\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] 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 = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"343\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword List NameConstant Name Name keyword Subscript Attribute shape Name Index Num\n",
"Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "convolution 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",
- "depthwise depthwise\n",
- "[PAD] kernel\n",
- "kernel kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"345\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format Tuple Num Num Num Str NameConstant\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"346\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute clip Name Name Call Name BinOp Num Sub Call Name\n",
"Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"347\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute one hot Name Name Subscript Attribute shape Name Index Name keyword Name\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "targets target\n",
- "result target\n",
- "feed target\n",
- "index target\n",
- "sample target\n",
- "tiled target\n",
- "y target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"348\n",
"[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Expr Call Attribute append Name Name Expr Call Attribute append Name Name\n",
"Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape arguments\n",
- "axis arguments\n",
- "ndarray arguments\n",
- "out arguments\n",
- "ndim arguments\n",
- "outputs arguments\n",
- "value arguments\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"349\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",
"350\n",
"[CLS] If Compare Call Name Name Gt Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute combine Name ListComp Attribute output Name comprehension Name Name\n",
"Label = ['unrelated', 'updates', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "input unrelated\n",
- "metrics unrelated\n",
- "unrelated unrelated\n",
- "[PAD] updates\n",
- "updates updates\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"351\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute arguments Attribute loss Name If Compare Name In Name Assign Subscript Name Index Name Subscript Name Index Name Raise Call Name BinOp Str Mod Attribute name Name\n",
"Label = ['argument', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "key argument\n",
- "layer argument\n",
- "argument argument\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"352\n",
"[CLS] If Compare Subscript Name Index Num Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Subscript Name Index Name Subscript Name Index Num Assign Name prefix shape Call Name Name Assign Name x Call Attribute splice Name Call Attribute constant Name keyword Num keyword Name Name keyword Name Assign Name base shape Attribute shape Name\n",
"Label = ['prefix', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x prefix\n",
- "xs prefix\n",
- "w prefix\n",
- "postfix prefix\n",
- "idx prefix\n",
- "start prefix\n",
- "tmp prefix\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"353\n",
"[CLS] Assert 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] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Expr Call Attribute append Name Attribute value Name Expr Call Attribute append Name Call Name Name\n",
"Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "parameter Parameter\n",
- "constant Parameter\n",
- "function Parameter\n",
- "variable Parameter\n",
- "run Parameter\n",
- "placeholder Parameter\n",
- "normal Parameter\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"355\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg message Str Return Call Attribute user function Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x 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",
"356\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Subscript Name Index BinOp Name Add Name\n",
"Label = ['condition', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "slice condition\n",
- "result condition\n",
- "output condition\n",
- "new condition\n",
- "batch condition\n",
- "input condition\n",
- "index condition\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"357\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format If Compare Name Eq Str Assign Name x Call Attribute transpose Name Name Tuple Num Num Num Return Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"358\n",
"[CLS] If Call Name Name Str Return Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Return Num\n",
"Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "in dynamic\n",
- "tile dynamic\n",
- "get dynamic\n",
- "string dynamic\n",
- "constant dynamic\n",
- "ones dynamic\n",
- "call dynamic\n",
- "[PAD] axes\n",
- "shape axes\n",
- "like axes\n",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"359\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Slice Index Name Index Name Tuple UnaryOp USub Num Num Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"360\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Index Name Slice Tuple UnaryOp USub Num Num Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\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",
- "shape result\n",
- "output result\n",
- "input result\n",
- "new result\n",
- "random result\n",
- "result result\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"362\n",
"[CLS] BinOp Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Mult Call Attribute prod Name Call Attribute asarray Name Attribute target shape Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"363\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name List Name keyword NameConstant keyword Name\n",
"Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "init init\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"364\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",
- "output output\n",
- "variable variable\n",
- "variable [PAD]\n",
- "[PAD] [PAD]\n",
- "variable [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"365\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg state arg root gradients Return Call Attribute Value Attribute cntk py Name Call Attribute data Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"366\n",
"[CLS] FunctionDef arguments Expr Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get default graph Name If Compare Name NotIn Name Assign Name phase Call Attribute placeholder with default Name NameConstant keyword Tuple keyword Str Assign Subscript Name Index Name Name Return Subscript Name Index Name\n",
"Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "phase graph\n",
- "learning graph\n",
- "g graph\n",
- "training graph\n",
- "new graph\n",
- "graph graph\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"367\n",
"[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword NameConstant Assign Name num thread Call Name Call Attribute get Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword Name keyword NameConstant\n",
"Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"368\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute eval Call Name Name keyword Call Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"369\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",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"370\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",
- "a a\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
"\n",
"371\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Subscript Name Index Num Eq BinOp Call Name Name Sub Num NameConstant NameConstant\n",
"Label = ['adj', 'x', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "adj adj\n",
- "[PAD] x\n",
- "pad x\n",
- "size x\n",
- "y x\n",
- "t x\n",
- "dim x\n",
- "dims x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"372\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute reduce max Name Name Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"373\n",
"[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name\n",
"Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "sqrt sqrt\n",
- "[PAD] [PAD]\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",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"375\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute greater equal Name Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"376\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name Name Name Name keyword Name keyword Name\n",
"Label = ['fused', 'batch', 'norm', '[PAD]']\n",
"Pred =\n",
- "max fused\n",
- "avg fused\n",
- "separable fused\n",
- "conv2d fused\n",
- "convolution fused\n",
- "conv3d fused\n",
- "batch fused\n",
- "[PAD] batch\n",
- "conv2d batch\n",
- "transpose batch\n",
- "pool batch\n",
- "batch batch\n",
- "[PAD] norm\n",
- "conv2d norm\n",
- "transpose norm\n",
- "pool norm\n",
- "batch norm\n",
- "normalization norm\n",
- "pool3d norm\n",
- "[PAD] [PAD]\n",
"\n",
"377\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",
- "beta beta\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"378\n",
"[CLS] If Compare Name Lt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num If Name AugAssign Name axis Mod Name Assign Name axis Num\n",
"Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "axis rank\n",
- "shape rank\n",
- "gamma rank\n",
- "i rank\n",
- "beta rank\n",
- "axes rank\n",
- "dynamic rank\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"379\n",
"[CLS] If Call Name ListComp Call Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return Call Attribute sparse concat Name Name Name Return Call Attribute concat Name ListComp Call Name Name comprehension Name x Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"380\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape Expr Str Return Call Attribute reshape Name Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"381\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pattern Expr Str Return Call Attribute transpose Name Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"382\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Subscript Name Index Name keyword Name\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "normal split\n",
- "parameter split\n",
- "add split\n",
- "compile split\n",
- "shared split\n",
- "random split\n",
- "append split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"383\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name x Call Attribute reshape Name Name Call Attribute stack Name List UnaryOp USub Num Call Name Subscript Call Name Name Slice Num Return Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"384\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num Tuple Num Num NameConstant\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"385\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute outputs Name Add List Attribute updates op Name Attribute fetches Name\n",
"Label = ['fetches', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs fetches\n",
- "x fetches\n",
- "size fetches\n",
- "fetches fetches\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"386\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",
- "axes axes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"387\n",
"[CLS] If Compare Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Name Sub Num Assign Name mask Call Name Name\n",
"Label = ['get', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "get get\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"388\n",
"[CLS] UnaryOp USub Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Call Attribute log Name Name Name\n",
"Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "reduce reduce\n",
- "sum sum\n",
- "sum [PAD]\n",
- "[PAD] [PAD]\n",
- "sum [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"389\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",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"390\n",
- "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n",
+ "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"391\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",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"392\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",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"393\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Name Eq Str Assign Name padding Str If Compare Name Str Assign Name padding Str Raise Call Name BinOp Str Add Call Name Name Return Name\n",
"Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding padding\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"394\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",
- "h left\n",
- "left left\n",
- "[PAD] pad\n",
- "pad pad\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"395\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute convolution Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"396\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",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"397\n",
"[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Call Attribute shape Name Name Index Num Add Call Name Subscript Name Slice Num Assign Name output shape Call Attribute stack Name Call Name Name\n",
"Label = ['output', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"398\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",
"399\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name strides BinOp BinOp Tuple Num Add BinOp Name Mult Num Tuple Num Assign Name spatial start dim Num Assign Name strides BinOp Tuple Num Num BinOp Name Num\n",
"Label = ['spatial', 'start', 'dim', '[PAD]']\n",
"Pred =\n",
- "spatial spatial\n",
- "[PAD] start\n",
- "dim start\n",
- "dims start\n",
- "start start\n",
- "[PAD] dim\n",
- "dim dim\n",
- "[PAD] [PAD]\n",
"\n",
"400\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",
- "strides strides\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"401\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",
"402\n",
"[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num 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",
- "403\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "403\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 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",
"404\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv3d transpose Attribute nn Name Name Name Name Name keyword Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"405\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute max pool Attribute nn Name Name Name Name keyword Name keyword Name If Compare Name Str Assign Name x Call Attribute avg pool 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",
"406\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",
"407\n",
"[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Num Num Num Num Subscript Name Index Num Assign Name new shape BinOp Tuple Num Add Name\n",
"Label = ['new', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "new new\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"408\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",
- "seed seed\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"409\n",
"[CLS] Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n",
"Label = ['expand', 'dims', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "expand expand\n",
- "dims dims\n",
- "dims [PAD]\n",
- "[PAD] [PAD]\n",
- "dims [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"410\n",
"[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name log prob Call Attribute ctc greedy decoder Name keyword Name keyword Name Assign Tuple Name decoded Name log prob Call Attribute ctc beam search decoder Name keyword Name keyword Name keyword Name keyword Name\n",
"Label = ['decoded', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "decoded decoded\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"411\n",
"[CLS] If Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return Name\n",
"Label = ['dense', 'from', 'sparse', '[PAD]']\n",
"Pred =\n",
- "sparse dense\n",
- "ones dense\n",
- "to dense\n",
- "is dense\n",
- "random dense\n",
- "in dense\n",
- "where dense\n",
- "tensor from\n",
- "[PAD] from\n",
- "to from\n",
- "mask from\n",
- "like from\n",
- "types from\n",
- "uniform from\n",
- "tensor sparse\n",
- "[PAD] sparse\n",
- "to sparse\n",
- "mask sparse\n",
- "like sparse\n",
- "types sparse\n",
- "uniform sparse\n",
- "tensor [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"412\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg ndim arg dtype arg sparse arg name NameConstant NameConstant NameConstant NameConstant NameConstant\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"413\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return BoolOp And Call Name Name Str Attribute theano placeholder Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"414\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute zeros Name Name Name Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"415\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",
- "normal normal\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"416\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg indices Expr Str Assign Name y Subscript Name Index Name If BoolOp And Call Name Name Str Call Name Name Str Assign Attribute keras shape Name BinOp Attribute keras shape Name Add Subscript Attribute keras shape Name Slice Num Return Name\n",
"Label = ['reference', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x reference\n",
- "y reference\n",
- "a reference\n",
- "w reference\n",
- "keras reference\n",
- "shape reference\n",
- "train reference\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"417\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",
- "dtype dtype\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"418\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Return Call Attribute var Name Name keyword Name keyword Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"419\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Return Call Attribute argmin Name Name keyword Name keyword NameConstant\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"420\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Assign Name z Call Attribute neq Name Name Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name Return Name\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] Return Tuple Name Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Pow Num\n",
"Label = ['inv', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cast inv\n",
- "arange inv\n",
- "pow inv\n",
- "maximum inv\n",
- "zeros inv\n",
- "transpose inv\n",
- "concatenate inv\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"422\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute bn Attribute nnet Name Name Name Name Name Name Name Name\n",
"Label = ['batch', 'normalization', 'test', '[PAD]']\n",
"Pred =\n",
- "batch batch\n",
- "normalization normalization\n",
- "normalization test\n",
- "[PAD] test\n",
- "test test\n",
- "normalization [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"423\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",
- "ndim ndim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"424\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute dnn Attribute cuda Attribute sandbox Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Str Name\n",
"Label = ['dnn', 'batch', 'normalization', 'test']\n",
"Pred =\n",
- "dnn dnn\n",
- "[PAD] batch\n",
- "batch batch\n",
- "[PAD] normalization\n",
- "batch normalization\n",
- "normalization normalization\n",
- "[PAD] test\n",
- "batch test\n",
- "normalization test\n",
- "test test\n",
"\n",
"425\n",
"[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute hstack Attribute basic Name Name keyword Str Raise Call Name Str Name\n",
"Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"426\n",
"[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute uses learning phase Name Assign Attribute uses learning phase Name NameConstant\n",
"Label = ['uses', 'learning', 'phase', '[PAD]']\n",
"Pred =\n",
- "uses uses\n",
- "learning learning\n",
- "learning phase\n",
- "phase phase\n",
- "learning [PAD]\n",
- "phase [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"427\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Subscript Attribute keras shape Name Index UnaryOp Num Mult Name\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"428\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num BinOp BinOp Subscript Name Index Num Add 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",
"429\n",
"[CLS] ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Slice\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"430\n",
"[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute keras shape Name Index Num BinOp Subscript Attribute keras shape Name Index Num Add Call Name Name Subscript Attribute keras shape Name Index Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"431\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Name NameConstant Call Name Name BinOp Subscript Name Index Num Add Name Call Name Name BinOp Subscript Name Index Num Name Call Name NameConstant\n",
"Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output indices\n",
- "new indices\n",
- "indices indices\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"432\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n",
"Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w h\n",
- "h h\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"433\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num IsNot NameConstant Assign Name w BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num Assign Name w NameConstant\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"434\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n",
"Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "w w\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"435\n",
"[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Subscript Name Index Num Index Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "shape shape\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
- "shape [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"436\n",
"[CLS] GeneratorExp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name comprehension Name i Call Name Attribute ndim Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"437\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Attribute shape Call Attribute get value Name keyword NameConstant keyword 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",
"438\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute function Name Name Name keyword Name keyword NameConstant keyword Str keyword Name keyword Name\n",
"Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "function function\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"439\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",
"440\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute switch Name Subscript Name Index Name Name Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"441\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Call Attribute scan Name Name keyword List Name Name keyword BinOp List Name Add Name keyword Name keyword Name\n",
"Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ret results\n",
- "results results\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"442\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name states Subscript Name Slice Num Assign Name outputs Name Assign Name states List\n",
"Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"443\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",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"444\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute squeeze Name Subscript Name Index UnaryOp USub Num comprehension Name state Name\n",
"Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "state states\n",
- "output states\n",
- "constants states\n",
- "states states\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"445\n",
"[CLS] If Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub Name For Name Call Name Name Assign Name condition Call Name Name\n",
"Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape ndim\n",
- "axes ndim\n",
- "masks ndim\n",
- "axis ndim\n",
- "expr ndim\n",
- "output ndim\n",
- "broadcast ndim\n",
- "[PAD] diff\n",
- "shape diff\n",
- "axes diff\n",
- "masks diff\n",
- "expr diff\n",
- "list diff\n",
- "tensors diff\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"446\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"447\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute to one hot Attribute extra ops Name Name keyword Subscript Attribute shape Name Index UnaryOp USub Num\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "targets target\n",
- "input target\n",
- "last target\n",
- "hot target\n",
- "random target\n",
- "result target\n",
- "to target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"448\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis NameConstant Assign Name square sum Call Attribute sum Name Call Attribute square Name Name keyword Name keyword NameConstant Assign Name norm Call Attribute sqrt Name Call Attribute maximum Name Name Call Name Return BinOp Name Div Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"449\n",
"[CLS] If Compare Name Lt Num Try Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Str ExceptHandler Name Return Call Attribute zeros like Name Name keyword Str\n",
"Label = ['zeros', 'like', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ones zeros\n",
- "zeros zeros\n",
- "[PAD] like\n",
- "like like\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"450\n",
"[CLS] Index Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Name\n",
"Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "arange arange\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"451\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Str Assign Name th padding Str Raise Call Name Str Call Name Name\n",
"Label = ['th', 'padding', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "padding th\n",
- "th th\n",
- "padding padding\n",
- "padding [PAD]\n",
- "[PAD] [PAD]\n",
- "padding [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"452\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Return Call Name Name ExceptHandler Name Return NameConstant\n",
"Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "value value\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"453\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n",
"Label = ['filter', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filter filter\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"454\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n",
"Label = ['filter', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filter filter\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"455\n",
"[CLS] BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript 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",
"456\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",
"457\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n",
"Label = ['conv', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "conv conv\n",
- "out out\n",
- "out [PAD]\n",
- "[PAD] [PAD]\n",
- "out [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"458\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",
"459\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"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
+ "Pred =\n",
"\n",
"460\n",
"[CLS] ExtSlice Slice Slice 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\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"461\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n",
"Label = ['conv', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x conv\n",
- "pool conv\n",
- "conv conv\n",
- "[PAD] out\n",
- "out out\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"462\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant Num\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"463\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n",
"Label = ['keras', 'shape', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "keras keras\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"464\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"465\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"466\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute AbstractConv2d gradInputs Attribute abstract conv Attribute nnet Name keyword NameConstant keyword Name keyword Name keyword Name keyword UnaryOp Not Name keyword Name\n",
"Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "op op\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"467\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute shape Call Attribute eval Name\n",
"Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n",
"Pred =\n",
- "kernel pointwise\n",
- "depthwise pointwise\n",
- "recurrent pointwise\n",
- "pointwise pointwise\n",
- "[PAD] kernel\n",
- "shape kernel\n",
- "kernel kernel\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
"\n",
"468\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp BoolOp And Compare Subscript Name Index Num Gt Num 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",
- "h w\n",
- "w w\n",
- "pad pad\n",
- "pad [PAD]\n",
- "[PAD] [PAD]\n",
- "pad [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"469\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",
- "pool pool\n",
- "[PAD] 2d\n",
- "3d 2d\n",
- "weight 2d\n",
- "pool 2d\n",
- "nodes 2d\n",
- "function 2d\n",
- "uniform 2d\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"470\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pool 3d Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str Raise Call Name Str Name\n",
"Label = ['pool', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "pool pool\n",
- "out out\n",
- "out [PAD]\n",
- "[PAD] [PAD]\n",
- "out [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"471\n",
"[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n",
"Label = ['pool', 'out', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x pool\n",
- "pool pool\n",
- "[PAD] out\n",
- "out out\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"472\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",
"473\n",
"[CLS] If Compare Name Eq Str If Compare Call Name Name 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",
"474\n",
"[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple 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",
"475\n",
"[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Mult Num Add Num\n",
"Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "arange arange\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"476\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name skip idxs BinOp BinOp Call Attribute arange Name BinOp BinOp Subscript Attribute shape Name Index Num Sub Num FloorDiv Num Mult Num Add Num Assign Name non repeats Call Attribute neq Name Subscript Name Index Name Subscript Name Index BinOp Name Num Return Subscript Name Index Call Attribute nonzero Name\n",
"Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "y Y\n",
- "self Y\n",
- "path Y\n",
- "x Y\n",
- "num Y\n",
- "predictions Y\n",
- "values Y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"477\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Subscript Name Slice Name Sub Name\n",
"Label = ['p', 'prev', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "log p\n",
- "out p\n",
- "output p\n",
- "y p\n",
- "v p\n",
- "total p\n",
- "result p\n",
- "[PAD] prev\n",
- "log prev\n",
- "t prev\n",
- "sum prev\n",
- "array prev\n",
- "p prev\n",
- "exp prev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"478\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute inc subtensor Name Subscript Name Index BinOp Name Add Num Subscript Name Index Name\n",
"Label = ['p', 'prev', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "p p\n",
- "[PAD] prev\n",
- "p prev\n",
- "values prev\n",
- "i prev\n",
- "log prev\n",
- "o prev\n",
- "width prev\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"479\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Compare Name Lt Call Attribute dimshuffle Name Num Str BitAnd Subscript Compare Name Call Attribute dimshuffle Name Num Str ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num\n",
"Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "kernel mask\n",
- "strides mask\n",
- "h mask\n",
- "pool mask\n",
- "line mask\n",
- "metric mask\n",
- "out mask\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"480\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",
- "fn initializer\n",
- "initializer initializer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"481\n",
- "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name\n",
+ "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Label = ['foldl', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "foldr foldl\n",
- "foldl foldl\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"482\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Subscript Name ExtSlice Index BinOp BinOp Name Mult Name Add Name Slice Slice\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"483\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",
- "args path\n",
- "cls path\n",
- "model path\n",
- "xs path\n",
- "shape path\n",
- "dtype path\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"484\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",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"485\n",
"[CLS] 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",
- "warn warn\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"486\n",
"[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BoolOp Or Call Name Attribute call Name Str Call Name Name Str\n",
"Label = ['compute', 'previous', 'mask', '[PAD]']\n",
"Pred =\n",
- "name compute\n",
- "uses compute\n",
- "dynamic compute\n",
- "optimizer compute\n",
- "clipvalue compute\n",
- "axis compute\n",
- "dtype compute\n",
- "[PAD] previous\n",
- "learning previous\n",
- "phase previous\n",
- "axes previous\n",
- "types previous\n",
- "train previous\n",
- "shape previous\n",
- "[PAD] mask\n",
- "learning mask\n",
- "phase mask\n",
- "axes mask\n",
- "types mask\n",
- "train mask\n",
- "shape mask\n",
- "[PAD] [PAD]\n",
"\n",
"487\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name nodes by depth Name layers Name layers by depth Call Name Attribute inputs Name Attribute outputs Name\n",
"Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "outputs nodes\n",
- "inbound nodes\n",
- "node nodes\n",
- "layer nodes\n",
- "input nodes\n",
- "layers nodes\n",
- "original nodes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"488\n",
"[CLS] If BoolOp And UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Attribute stateful Name Return List\n",
"Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "trainable trainable\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"489\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Name ListComp BoolOp And Call Name Name Str Attribute stateful Name comprehension Name layer Attribute layers Name Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"490\n",
"[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name Call Name Call Name Name Assign Name masks Call Name Name\n",
"Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "masks masks\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"491\n",
"[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Name Str Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n",
"Label = ['input', 'layers', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layers input\n",
- "input input\n",
- "[PAD] layers\n",
- "layers layers\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"492\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute name Name Add BinOp Str Mod Tuple Name Name\n",
"Label = ['shape', 'key', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] key\n",
- "key key\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"493\n",
"[CLS] 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",
- "call call\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"494\n",
"[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant\n",
"Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "activity activity\n",
- "[PAD] regularizer\n",
- "regularizer regularizer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"495\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Call Name Call Name Name Tuple Name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
"\n",
"496\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",
- "input shape\n",
- "inputs shape\n",
- "n shape\n",
- "axis shape\n",
- "outputs shape\n",
- "d shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"497\n",
"[CLS] BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute arguments Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"498\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node data If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute append Subscript Name Index Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"499\n",
"[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Name Name Call Attribute backend Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"500\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Eq Attribute name Name Return Attribute name Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"501\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name depth Call Attribute items Name If Compare Name NotIn Name Assign Subscript Name Index Name List Expr Call Attribute append Subscript Name Index Name Name\n",
"Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer node\n",
- "chunk node\n",
- "inbound node\n",
- "v node\n",
- "n node\n",
- "l node\n",
- "cls node\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"502\n",
"[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Attribute input tensors Name If Compare Name NotIn Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute name Name Str Call Name Name For Name x Attribute output tensors Name Expr Call Attribute append Name Name Expr Call Attribute append Name Attribute name Name\n",
"Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x x\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"503\n",
"[CLS] Raise 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",
- "inbound count\n",
- "count count\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"504\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",
- "inbound count\n",
- "count count\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"505\n",
"[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp BoolOp Or Name List List Attribute history Name\n",
"Label = ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "callbacks callbacks\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"506\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",
- "i i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"507\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"508\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num\n",
"Label = ['val', 'outs', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "[PAD] outs\n",
- "outs outs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"509\n",
"[CLS] If Name If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute inbound nodes Name Index UnaryOp USub Num NotEq Num Raise Call Name Str Assign Attribute outputs Name List Subscript Attribute output tensors Subscript Attribute inbound nodes Name Index UnaryOp Num Index Num Assign Attribute inputs Name Call Attribute get source inputs Name Subscript Attribute outputs Name Index Num\n",
"Label = ['output', 'tensors', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "[PAD] tensors\n",
- "tensors tensors\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"510\n",
"[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Gt Num Return Call Attribute argmax Name keyword UnaryOp Num Return Call Attribute astype Compare Name Num Str\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"511\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",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"512\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name build input shape Call Attribute get Name Str Assign Name layer configs Subscript Name Index Str Assign Name name Name build input shape NameConstant Assign Name layer configs Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "cls name\n",
- "layer name\n",
- "embeddings name\n",
- "data name\n",
- "model name\n",
- "config name\n",
- "metric name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"513\n",
"[CLS] Assign Subscript Name Index 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",
- "encode encode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"514\n",
"[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute backend Name Str\n",
"Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "encode encode\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"515\n",
"[CLS] If Call Name Name Str If Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str Assign Name name BinOp BinOp Call Name Attribute name Name Add Str Call Name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Call Name Name\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"516\n",
"[CLS] Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str\n",
"Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "split split\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"517\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Attribute name Name Add 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",
"518\n",
"[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] List For Name value Name Expr Call Attribute append Name Call Name Name Return Name\n",
"Label = ['deserialized', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data deserialized\n",
- "axis deserialized\n",
- "weights deserialized\n",
- "model deserialized\n",
- "val deserialized\n",
- "metrics deserialized\n",
- "p deserialized\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"519\n",
"[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute decode Subscript Name Index Str Str Assign Name original backend NameConstant\n",
"Label = ['original', 'backend', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "original original\n",
- "[PAD] backend\n",
- "fn backend\n",
- "names backend\n",
- "weight backend\n",
- "size backend\n",
- "layer backend\n",
- "delta backend\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"520\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",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"521\n",
"[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Name 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",
"522\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",
"523\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Expr Str If Call Name Name Name Raise Call Name Str ImportFrom alias Return Call Name Name keyword Name\n",
"Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "config config\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[PAD] [PAD]\n",
"\n",
"524\n",
"[CLS] BinOp Str Mod Tuple Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Name comprehension Name x Name\n",
"Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "join join\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"525\n",
"[CLS] While Compare BinOp Str Mod Tuple Name Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute extend Name ListComp Call Attribute decode Name Str comprehension Name n Subscript Attribute attrs Name Index BinOp Str Tuple Name Name AugAssign Name chunk id Add Num\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"526\n",
"[CLS] comprehension Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute attrs Name Index BinOp Str Mod Tuple Name Name\n",
"Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k n\n",
- "m n\n",
- "s n\n",
- "x n\n",
- "a n\n",
- "chunk n\n",
- "l n\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"527\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",
- "name i\n",
- "layer i\n",
- "sw i\n",
- "cell i\n",
- "o i\n",
- "val i\n",
- "x i\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"528\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name In List Str Str Assign Name weights 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",
"529\n",
"[CLS] Assert BoolOp And Compare Subscript Name Index Num Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Subscript Name Slice Num Tuple Subscript Attribute kernel size Name Index Num Num\n",
"Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "filters filters\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"530\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str If Compare Attribute data format Name Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"531\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n",
"Label = ['data', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"532\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n",
"Label = ['data', 'format', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] format\n",
- "format format\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"533\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",
- "concatenate concatenate\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"534\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str Call Name Call Attribute prod Name Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"535\n",
"[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Name\n",
"Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "convert reshape\n",
- "reshape reshape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"536\n",
"[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\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] 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",
- "transpose transpose\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"538\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",
- "transpose transpose\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"539\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func arg n gates Expr Str Return Call Attribute hstack Name ListComp Call Name Name comprehension Name k Call Attribute hsplit Name Name Name\n",
"Label = ['kernels', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self kernels\n",
- "path kernels\n",
- "k kernels\n",
- "model kernels\n",
- "metrics kernels\n",
- "format kernels\n",
- "loss kernels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"540\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Attribute reshape Attribute T Name Attribute shape Name keyword Name\n",
"Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "k kernel\n",
- "self kernel\n",
- "x kernel\n",
- "tensor kernel\n",
- "y kernel\n",
- "a kernel\n",
- "predictions kernel\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"541\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Mult Subscript Name Index Num Num\n",
"Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "expand tile\n",
- "maximum tile\n",
- "float32 tile\n",
- "unbroadcast tile\n",
- "cast tile\n",
- "filters tile\n",
- "arange tile\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"542\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num Lambda arguments arg k Attribute T Name Name\n",
"Label = ['recurrent', 'kernels', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "recurrent recurrent\n",
- "[PAD] kernels\n",
- "t kernels\n",
- "mask kernels\n",
- "output kernels\n",
- "batch kernels\n",
- "state kernels\n",
- "out kernels\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"543\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",
- "source source\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"544\n",
"[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original backend Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original backend NameConstant\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"545\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",
"546\n",
"[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original keras version Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original keras version Str\n",
"Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "attrs attrs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"547\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Call Attribute setdefault Name Attribute name Name List Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inputs name\n",
- "stateful name\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"548\n",
"[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name comprehension Name weight name Name\n",
"Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "asarray asarray\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"549\n",
"[CLS] BinOp BinOp BinOp 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 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",
"550\n",
"[CLS] BinOp 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 Str\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"551\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript 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",
"552\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Add ListComp BinOp Str Name comprehension Name n Name\n",
"Label = ['callback', 'metrics', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight callback\n",
- "batch callback\n",
- "dim callback\n",
- "callback callback\n",
- "[PAD] metrics\n",
- "names metrics\n",
- "metrics metrics\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"553\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"554\n",
"[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute evaluate generator Name Name Name keyword Num Assign Name val outs Call Attribute evaluate Name Name Name keyword Name keyword Name keyword Num\n",
"Label = ['val', 'outs', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "[PAD] outs\n",
- "outs outs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"555\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",
- "l l\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"556\n",
"[CLS] If Compare Name Is NameConstant If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n",
"Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "steps steps\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 Name Index Num Index Num If Call Name Name Name Assign Name batch size 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",
- "batch batch\n",
- "size size\n",
- "size [PAD]\n",
- "[PAD] [PAD]\n",
- "size [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"558\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n",
"Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "concatenate average\n",
- "sum average\n",
- "extend average\n",
- "asarray average\n",
- "zeros average\n",
- "add average\n",
- "normal average\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"559\n",
"[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"560\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n",
"Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"561\n",
"[CLS] If Compare Name Gt Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name Expr Call Attribute start Name keyword Name keyword Name Assign Name output generator Call Attribute get Name If Name Assign Name output generator Call Name Name Assign Name output generator Name\n",
"Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "enqueuer enqueuer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"562\n",
"[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name\n",
"Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "enqueuer enqueuer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"563\n",
"[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name\n",
"Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "progbar progbar\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"564\n",
"[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name name BinOp BinOp Name Add Str Call Name Call Attribute get uid Name Name\n",
"Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "prefix prefix\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"565\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return BinOp BinOp Attribute name Name Add Str Call Name Name Name\n",
"Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"566\n",
"[CLS] If Compare Name IsNot NameConstant With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute add loss Name Call Name Name\n",
"Label = ['name', 'scope', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] scope\n",
- "scope scope\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"567\n",
"[CLS] BoolOp And Compare Name IsNot NameConstant Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['max', 'ndim', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "delta max\n",
- "inferreddimension max\n",
- "min max\n",
- "where max\n",
- "dtype max\n",
- "initial max\n",
- "num max\n",
- "[PAD] ndim\n",
- "batch ndim\n",
- "t ndim\n",
- "ndim ndim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"568\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 max 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",
"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 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",
"570\n",
"[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim 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 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",
- "572\n",
+ "572\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"[CLS] If BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant With withitem Call Attribute name scope Name Str Assign Name regularization losses ListComp Call Attribute activity regularizer Name Name comprehension Name x Call Name Name Expr Call Attribute add loss Name Name keyword Call Name Name\n",
"Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "activity activity\n",
- "[PAD] regularizer\n",
- "regularizer regularizer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"573\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Name IsNot NameConstant If Call Name Name Name If Call Name GeneratorExp Compare Name NameConstant comprehension Name m 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 Return NameConstant\n",
"Label = ['supports', 'masking', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound supports\n",
- "built supports\n",
- "trainable supports\n",
- "layers supports\n",
- "ndim supports\n",
- "outputs supports\n",
- "shape supports\n",
- "[PAD] masking\n",
- "nodes masking\n",
- "tensor masking\n",
- "spec masking\n",
- "ndim masking\n",
- "layers masking\n",
- "keras masking\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"574\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp Str Add Attribute name Name Str\n",
"Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound inbound\n",
- "nodes nodes\n",
- "nodes [PAD]\n",
- "[PAD] [PAD]\n",
- "nodes [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"575\n",
"[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Call Name Attribute inbound nodes Name NotEq Num Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Str Return Call Attribute get node attribute at index Name Num Str Str Name\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"576\n",
"[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n",
"Label = ['input', 'shapes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is input\n",
- "inbound input\n",
- "input input\n",
- "[PAD] shapes\n",
- "nodes shapes\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "keras shapes\n",
- "tensor shapes\n",
- "shapes shapes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"577\n",
"[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n",
"Label = ['output', 'shapes', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is output\n",
- "inbound output\n",
- "input output\n",
- "class output\n",
- "in output\n",
- "element output\n",
- "add output\n",
- "[PAD] shapes\n",
- "nodes shapes\n",
- "keras shapes\n",
- "tensor shapes\n",
- "shapes shapes\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"578\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",
"579\n",
"[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['batch', 'input', 'shape', '[PAD]']\n",
"Pred =\n",
- "dtype batch\n",
- "function batch\n",
- "keras batch\n",
- "clipvalue batch\n",
- "clipnorm batch\n",
- "optimizer batch\n",
- "predict batch\n",
- "[PAD] input\n",
- "shape input\n",
- "function input\n",
- "size input\n",
- "spec input\n",
- "kernel input\n",
- "value input\n",
- "[PAD] shape\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
"\n",
"580\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg shape arg ndim arg max ndim arg min ndim arg axes NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x self\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"581\n",
"[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute dtype Name Str\n",
"Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dtype dtype\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"582\n",
"[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outbound layer Attribute name Attribute outbound layer Name Assign Name outbound layer NameConstant\n",
"Label = ['outbound', 'layer', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "stateful outbound\n",
- "arguments outbound\n",
- "inputs outbound\n",
- "reset outbound\n",
- "name outbound\n",
- "args outbound\n",
- "layer outbound\n",
- "[PAD] layer\n",
- "layer layer\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"583\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg optimizer arg loss arg metrics arg loss weights arg sample weight mode arg weighted metrics arg target tensors arg kwargs 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",
"584\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"585\n",
"[CLS] Raise Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"586\n",
"[CLS] Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"587\n",
"[CLS] Raise Call Name 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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"588\n",
"[CLS] Call Name BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n",
"Label = ['output', 'names', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "output output\n",
- "names names\n",
- "names [PAD]\n",
- "[PAD] [PAD]\n",
- "names [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"589\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",
- "outputs outputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"590\n",
"[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name target NameConstant\n",
"Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "target target\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"591\n",
"[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Call Name Name keyword BinOp Name Add Str keyword Call Attribute is sparse Name Subscript Attribute outputs Name Index Name keyword Call Attribute dtype Name Subscript Attribute outputs Name Index Name\n",
"Label = ['placeholder', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "add placeholder\n",
- "placeholder placeholder\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"592\n",
"[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Eq Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n",
"Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "inbound get\n",
- "shape get\n",
- "backend get\n",
- "weight get\n",
- "outputs get\n",
- "inputs get\n",
- "assign get\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"593\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n",
"Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "weight weight\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"594\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",
"595\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",
- "append append\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"596\n",
"[CLS] BoolOp Or Compare Subscript Name Index UnaryOp USub Num Eq Num Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Attribute binary crossentropy Name\n",
"Label = ['loss', 'functions', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "values loss\n",
- "shape loss\n",
- "fn loss\n",
- "mean loss\n",
- "dynamic loss\n",
- "i loss\n",
- "version loss\n",
- "[PAD] functions\n",
- "fn functions\n",
- "i functions\n",
- "t functions\n",
- "dim functions\n",
- "data functions\n",
- "initializer functions\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"597\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",
- "new all\n",
- "x all\n",
- "additional all\n",
- "output all\n",
- "progbar all\n",
- "result all\n",
- "value all\n",
- "[PAD] inputs\n",
- "shape inputs\n",
- "value inputs\n",
- "dim inputs\n",
- "size inputs\n",
- "output inputs\n",
- "out inputs\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"598\n",
"[CLS] If Call Name GeneratorExp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name v Name If UnaryOp Not Call Name GeneratorExp Call Attribute is tensor Name Name comprehension Name v Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name\n",
"Label = ['is', 'tensor', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is is\n",
- "[PAD] tensor\n",
- "tensor tensor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"599\n",
"[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes NameConstant Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes Attribute feed input shapes Name\n",
"Label = ['is', 'graph', 'network', '[PAD]']\n",
"Pred =\n",
- "trainable is\n",
- "name is\n",
- "input is\n",
- "inputs is\n",
- "built is\n",
- "losses is\n",
- "layers is\n",
- "[PAD] graph\n",
- "updates graph\n",
- "names graph\n",
- "scope graph\n",
- "placeholder graph\n",
- "input graph\n",
- "config graph\n",
- "[PAD] network\n",
- "updates network\n",
- "names network\n",
- "scope network\n",
- "placeholder network\n",
- "input network\n",
- "config network\n",
- "[PAD] [PAD]\n",
"\n",
"600\n",
"[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg y arg batch size arg epochs arg verbose arg callbacks arg validation split arg validation data arg shuffle arg class weight arg sample weight arg initial epoch arg steps per epoch arg validation steps arg kwargs NameConstant NameConstant NameConstant Num Num NameConstant Num NameConstant NameConstant NameConstant NameConstant Num NameConstant NameConstant\n",
"Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"601\n",
"[CLS] If Compare Call Name Name Eq Num Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Name val sample weight Name Raise Call Name BinOp Str Mod Call Name Name\n",
"Label = ['val', 'x', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val val\n",
- "x x\n",
- "x [PAD]\n",
- "[PAD] [PAD]\n",
- "x [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"602\n",
"[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Name Add Name Name List Num\n",
"Label = ['val', 'ins', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "ins val\n",
- "val val\n",
- "[PAD] ins\n",
- "ins ins\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"603\n",
"[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Tuple Call Name Name Num Name Call Name Name Name\n",
"Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "val y\n",
- "x y\n",
- "y y\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"604\n",
"[CLS] If BoolOp And Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Gt Name Compare BinOp Subscript Attribute shape Subscript Name Index Num Index Num Mod Name NotEq Num Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute shape 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",
"605\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",
"606\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 = ['self', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "model self\n",
- "self self\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"607\n",
"[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Attribute name Attribute class Subscript Name Index Name Eq Str Attribute values Subscript Name Index Name Subscript Name Index Name comprehension Name x Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp Str Add Subscript Attribute args Name Index Num Str Call Name Name\n",
"Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "data data\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"608\n",
"[CLS] ListComp IfExp Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Attribute values Name Name comprehension Name x Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"609\n",
"[CLS] BoolOp And Compare Subscript Name Index Name IsNot NameConstant UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name\n",
"Label = ['is', 'tensor', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "is is\n",
- "[PAD] tensor\n",
- "sparse tensor\n",
- "tensor tensor\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"610\n",
"[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name ref dim Call Name Name Name If BoolOp And Compare Name NotEq Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Subscript Name Index Name Str Call Name Name Str Call Name Name\n",
"Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "dim dim\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"611\n",
"[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Name Call Attribute get Name Name\n",
"Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "x name\n",
- "name name\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"612\n",
"[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name y Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"613\n",
"[CLS] Call Name BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"614\n",
"[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"615\n",
"[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Call Name Attribute shape Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute shape Name Str Call Name 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",
"616\n",
"[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name Str\n",
"Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n",
"Pred =\n",
- "shape shape\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
- "[PAD] [PAD]\n",
"\n",
"617\n",
"[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute shape 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"
]
}
],
"source": [
- "n=7; nb_snips = 618\n",
+ "n=3; nb_snips = 618\n",
"pred_str = []; score = [0]*4; score_full_name=0; score_no_pad = 0; rank =[0]*4; skipped = 0\n",
"for idx in range(nb_snips):\n",
" print(idx)\n",
@@ -14447,7 +9569,7 @@
" 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",
+ " #print(p,l)\n",
" if p == l:\n",
" score[j] += 1\n",
" rank[j] += (i+1)\n",
@@ -14465,19 +9587,16 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[0.49160671462829736,\n",
- " 0.6333676622039135,\n",
- " 0.8661971830985915,\n",
- " 0.9124629080118695]"
+ "[0.701254275940707, 0.754601226993865, 0.8991228070175439, 0.9138187221396731]"
]
},
- "execution_count": 37,
+ "execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
@@ -14488,16 +9607,16 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[0.8682926829268293, 0.9495934959349593, 0.991869918699187, 1.0]"
+ "[0.832520325203252, 0.9203252032520325, 0.9869918699186991, 0.9983739837398374]"
]
},
- "execution_count": 38,
+ "execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
@@ -14508,16 +9627,16 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.8688346883468834"
+ "0.8237127371273713"
]
},
- "execution_count": 39,
+ "execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
@@ -14528,16 +9647,16 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.848780487804878"
+ "0.791869918699187"
]
},
- "execution_count": 40,
+ "execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}