abstractive-summarization/4.lstm-birnn-seq2seq-beam-luong.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import collections\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('ctexts.json','r') as fopen:\n",
    "    ctexts = json.load(fopen)\n",
    "    \n",
    "with open('headlines.json','r') as fopen:\n",
    "    headlines = json.load(fopen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "def topic_modelling(string, n = 500):\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    tf = vectorizer.fit_transform([string])\n",
    "    tf_features = vectorizer.get_feature_names()\n",
    "    compose = TruncatedSVD(1).fit(tf)\n",
    "    return ' '.join([tf_features[i] for i in compose.components_[0].argsort()[: -n - 1 : -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/decomposition/truncated_svd.py:192: RuntimeWarning: invalid value encountered in true_divide\n",
      "  self.explained_variance_ratio_ = exp_var / full_var\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.3 s, sys: 29.8 s, total: 50 s\n",
      "Wall time: 20 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "h, c = [], []\n",
    "for i in range(len(ctexts)):\n",
    "    try:\n",
    "        c.append(topic_modelling(ctexts[i]))\n",
    "        h.append(headlines[i])\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(words, n_words):\n",
    "    count = [['PAD', 0], ['GO', 1], ['EOS', 2], ['UNK', 3]]\n",
    "    count.extend(collections.Counter(words).most_common(n_words))\n",
    "    dictionary = dict()\n",
    "    for word, _ in count:\n",
    "        dictionary[word] = len(dictionary)\n",
    "    data = list()\n",
    "    unk_count = 0\n",
    "    for word in words:\n",
    "        index = dictionary.get(word, 0)\n",
    "        if index == 0:\n",
    "            unk_count += 1\n",
    "        data.append(index)\n",
    "    count[0][1] = unk_count\n",
    "    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "    return data, count, dictionary, reversed_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab from size: 49585\n",
      "Most common words [('dot', 4394), ('the', 4379), ('comma', 4349), ('to', 4280), ('in', 4268), ('of', 4262)]\n",
      "Sample data [5, 7, 4, 6, 10, 9, 11, 8, 15, 2062] ['the', 'to', 'dot', 'comma', 'and', 'of', 'on', 'in', 'was', 'festival']\n"
     ]
    }
   ],
   "source": [
    "concat_from = ' '.join(c).split()\n",
    "vocabulary_size_from = len(list(set(concat_from)))\n",
    "data_from, count_from, dictionary_from, rev_dictionary_from = build_dataset(concat_from, vocabulary_size_from)\n",
    "print('vocab from size: %d'%(vocabulary_size_from))\n",
    "print('Most common words', count_from[4:10])\n",
    "print('Sample data', data_from[:10], [rev_dictionary_from[i] for i in data_from[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab to size: 8534\n",
      "Most common words [('to', 1388), ('in', 1196), ('comma', 876), ('s', 785), ('for', 733), ('of', 596)]\n",
      "Sample data [2797, 14, 2798, 2799, 656, 2800, 5, 1642, 657, 2086] ['daman', 'and', 'diu', 'revokes', 'mandatory', 'rakshabandhan', 'in', 'offices', 'order', 'malaika']\n"
     ]
    }
   ],
   "source": [
    "concat_to = ' '.join(h).split()\n",
    "vocabulary_size_to = len(list(set(concat_to)))\n",
    "data_to, count_to, dictionary_to, rev_dictionary_to = build_dataset(concat_to, vocabulary_size_to)\n",
    "print('vocab to size: %d'%(vocabulary_size_to))\n",
    "print('Most common words', count_to[4:10])\n",
    "print('Sample data', data_to[:10], [rev_dictionary_to[i] for i in data_to[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'daman and diu revokes mandatory rakshabandhan in offices order EOS'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(h)):\n",
    "    h[i] = h[i] + ' EOS'\n",
    "h[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "GO = dictionary_from['GO']\n",
    "PAD = dictionary_from['PAD']\n",
    "EOS = dictionary_from['EOS']\n",
    "UNK = dictionary_from['UNK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str_idx(corpus, dic, UNK=3):\n",
    "    X = []\n",
    "    for i in corpus:\n",
    "        ints = []\n",
    "        for k in i.split():\n",
    "            ints.append(dic.get(k, UNK))\n",
    "        X.append(ints)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = str_idx(c, dictionary_from)\n",
    "Y = str_idx(h, dictionary_to)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Summarization:\n",
    "    def __init__(self, size_layer, num_layers, embedded_size, \n",
    "                 from_dict_size, to_dict_size, batch_size,\n",
    "                 grad_clip=5.0, beam_width=5, force_teaching_ratio=0.5):\n",
    "        \n",
    "        def lstm_cell(size, reuse=False):\n",
    "            return tf.nn.rnn_cell.LSTMCell(size, initializer=tf.orthogonal_initializer(),reuse=reuse)\n",
    "        \n",
    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
    "        self.Y = tf.placeholder(tf.int32, [None, None])\n",
    "        self.X_seq_len = tf.count_nonzero(self.X, 1, dtype=tf.int32)\n",
    "        self.Y_seq_len = tf.count_nonzero(self.Y, 1, dtype=tf.int32)\n",
    "        batch_size = tf.shape(self.X)[0]\n",
    "        \n",
    "        encoder_embeddings = tf.Variable(tf.random_uniform([from_dict_size, embedded_size], -1, 1))\n",
    "        decoder_embeddings = tf.Variable(tf.random_uniform([to_dict_size, embedded_size], -1, 1))\n",
    "        encoder_embedded = tf.nn.embedding_lookup(encoder_embeddings, self.X)\n",
    "        \n",
    "        for n in range(num_layers):\n",
    "            (out_fw, out_bw), (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n",
    "                cell_fw = lstm_cell(size_layer // 2),\n",
    "                cell_bw = lstm_cell(size_layer // 2),\n",
    "                inputs = encoder_embedded,\n",
    "                sequence_length = self.X_seq_len,\n",
    "                dtype = tf.float32,\n",
    "                scope = 'bidirectional_rnn_%d'%(n))\n",
    "            encoder_embedded = tf.concat((out_fw, out_bw), 2)\n",
    "        bi_state_c = tf.concat((state_fw.c, state_bw.c), -1)\n",
    "        bi_state_h = tf.concat((state_fw.h, state_bw.h), -1)\n",
    "        bi_lstm_state = tf.nn.rnn_cell.LSTMStateTuple(c=bi_state_c, h=bi_state_h)\n",
    "        encoder_state = tuple([bi_lstm_state] * num_layers)\n",
    "        \n",
    "        with tf.variable_scope('decode'):\n",
    "            attention_mechanism = tf.contrib.seq2seq.LuongAttention(\n",
    "            num_units = size_layer, \n",
    "            memory = encoder_embedded,\n",
    "            memory_sequence_length = self.X_seq_len)\n",
    "            decoder_cell = tf.contrib.seq2seq.AttentionWrapper(\n",
    "                cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell(size_layer) for _ in range(num_layers)]),\n",
    "                attention_mechanism = attention_mechanism,\n",
    "                attention_layer_size = size_layer)\n",
    "            main = tf.strided_slice(self.Y, [0, 0], [batch_size, -1], [1, 1])\n",
    "            decoder_input = tf.concat([tf.fill([batch_size, 1], GO), main], 1)\n",
    "            training_helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(\n",
    "            inputs = tf.nn.embedding_lookup(decoder_embeddings, decoder_input),\n",
    "                sequence_length = self.Y_seq_len,\n",
    "                embedding = decoder_embeddings,\n",
    "                sampling_probability = 1 - force_teaching_ratio,\n",
    "                time_major = False)\n",
    "            training_decoder = tf.contrib.seq2seq.BasicDecoder(\n",
    "                cell = decoder_cell,\n",
    "                helper = training_helper,\n",
    "                initial_state = decoder_cell.zero_state(batch_size, tf.float32).clone(cell_state=encoder_state),\n",
    "                output_layer = tf.layers.Dense(to_dict_size))\n",
    "            training_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(\n",
    "                decoder = training_decoder,\n",
    "                impute_finished = True,\n",
    "                maximum_iterations = tf.reduce_max(self.Y_seq_len))\n",
    "            self.logits = training_decoder_output.rnn_output\n",
    "            \n",
    "        with tf.variable_scope('decode', reuse=True):\n",
    "            encoder_out_tiled = tf.contrib.seq2seq.tile_batch(encoder_embedded, beam_width)\n",
    "            encoder_state_tiled = tf.contrib.seq2seq.tile_batch(encoder_state, beam_width)\n",
    "            X_seq_len_tiled = tf.contrib.seq2seq.tile_batch(self.X_seq_len, beam_width)\n",
    "            attention_mechanism = tf.contrib.seq2seq.LuongAttention(\n",
    "                num_units = size_layer, \n",
    "                memory = encoder_out_tiled,\n",
    "                memory_sequence_length = X_seq_len_tiled)\n",
    "            decoder_cell = tf.contrib.seq2seq.AttentionWrapper(\n",
    "                cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell(size_layer, reuse=True) for _ in range(num_layers)]),\n",
    "                attention_mechanism = attention_mechanism,\n",
    "                attention_layer_size = size_layer)\n",
    "            predicting_decoder = tf.contrib.seq2seq.BeamSearchDecoder(\n",
    "                cell = decoder_cell,\n",
    "                embedding = decoder_embeddings,\n",
    "                start_tokens = tf.tile(tf.constant([GO], dtype=tf.int32), [batch_size]),\n",
    "                end_token = EOS,\n",
    "                initial_state = decoder_cell.zero_state(batch_size * beam_width, tf.float32).clone(cell_state = encoder_state_tiled),\n",
    "                beam_width = beam_width,\n",
    "                output_layer = tf.layers.Dense(to_dict_size, _reuse=True),\n",
    "                length_penalty_weight = 0.0)\n",
    "            predicting_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(\n",
    "                decoder = predicting_decoder,\n",
    "                impute_finished = False,\n",
    "                maximum_iterations = tf.reduce_max(self.X_seq_len))\n",
    "            self.predicting_ids = predicting_decoder_output.predicted_ids[:, :, 0]\n",
    "        \n",
    "        masks = tf.sequence_mask(self.Y_seq_len, tf.reduce_max(self.Y_seq_len), dtype=tf.float32)\n",
    "        self.cost = tf.contrib.seq2seq.sequence_loss(logits = self.logits,\n",
    "                                                     targets = self.Y,\n",
    "                                                     weights = masks)\n",
    "        \n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate = 1e-3).minimize(self.cost)\n",
    "        y_t = tf.argmax(self.logits,axis=2)\n",
    "        y_t = tf.cast(y_t, tf.int32)\n",
    "        self.prediction = tf.boolean_mask(y_t, masks)\n",
    "        mask_label = tf.boolean_mask(self.Y, masks)\n",
    "        correct_pred = tf.equal(self.prediction, mask_label)\n",
    "        correct_index = tf.cast(correct_pred, tf.float32)\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_layer = 128\n",
    "num_layers = 2\n",
    "embedded_size = 128\n",
    "batch_size = 8\n",
    "epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gradients_impl.py:112: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Summarization(size_layer, num_layers, embedded_size, len(dictionary_from), \n",
    "                len(dictionary_to), batch_size)\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_sentence_batch(sentence_batch, pad_int):\n",
    "    padded_seqs = []\n",
    "    seq_lens = []\n",
    "    max_sentence_len = max([len(sentence) for sentence in sentence_batch])\n",
    "    for sentence in sentence_batch:\n",
    "        padded_seqs.append(sentence + [pad_int] * (max_sentence_len - len(sentence)))\n",
    "        seq_lens.append(len(sentence))\n",
    "    return padded_seqs, seq_lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [08:17<00:00,  1.60it/s, accuracy=0.155, cost=7.32] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:05<00:00,  4.96it/s, accuracy=0.104, cost=6.92] \n",
      "train minibatch loop:   0%|          | 0/522 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, avg loss: 7.358761, avg accuracy: 0.109815\n",
      "epoch: 0, avg loss test: 7.344269, avg accuracy test: 0.115052\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [05:20<00:00,  1.48it/s, accuracy=0.115, cost=6.88] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:05<00:00,  5.89it/s, accuracy=0.109, cost=7.13] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, avg loss: 6.912264, avg accuracy: 0.114700\n",
      "epoch: 1, avg loss test: 7.437581, avg accuracy test: 0.111710\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [08:20<00:00,  1.15it/s, accuracy=0.105, cost=6.85] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:10<00:00,  3.13it/s, accuracy=0.119, cost=7.24] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2, avg loss: 6.751335, avg accuracy: 0.117069\n",
      "epoch: 2, avg loss test: 7.482199, avg accuracy test: 0.113114\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [08:26<00:00,  1.09it/s, accuracy=0.075, cost=6.69] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:10<00:00,  3.22it/s, accuracy=0.136, cost=8.28] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3, avg loss: 6.515499, avg accuracy: 0.120910\n",
      "epoch: 3, avg loss test: 7.572859, avg accuracy test: 0.111479\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [06:16<00:00,  1.22it/s, accuracy=0.0986, cost=6.54]\n",
      "test minibatch loop: 100%|██████████| 28/28 [00:10<00:00,  2.61it/s, accuracy=0.1, cost=7.18]   \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 4, avg loss: 6.185435, avg accuracy: 0.126369\n",
      "epoch: 4, avg loss test: 7.748631, avg accuracy test: 0.110879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [08:16<00:00,  1.15it/s, accuracy=0.125, cost=5.85] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:10<00:00,  3.29it/s, accuracy=0.113, cost=8.61] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5, avg loss: 5.796789, avg accuracy: 0.132073\n",
      "epoch: 5, avg loss test: 8.055813, avg accuracy test: 0.110240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [06:33<00:00,  1.53it/s, accuracy=0.143, cost=5.55] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 6, avg loss: 5.362356, avg accuracy: 0.140446\n",
      "epoch: 6, avg loss test: 8.466871, avg accuracy test: 0.104905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [05:14<00:00,  1.87it/s, accuracy=0.15, cost=5.13]  \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 7, avg loss: 4.888377, avg accuracy: 0.163165\n",
      "epoch: 7, avg loss test: 8.884777, avg accuracy test: 0.101707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [05:14<00:00,  1.54it/s, accuracy=0.215, cost=4.5] \n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 8, avg loss: 4.410430, avg accuracy: 0.208496\n",
      "epoch: 8, avg loss test: 9.262277, avg accuracy test: 0.093153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 522/522 [05:12<00:00,  1.80it/s, accuracy=0.224, cost=3.8] \n",
      "test minibatch loop: 100%|██████████| 28/28 [00:09<00:00,  3.09it/s, accuracy=0.0816, cost=10.8]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9, avg loss: 3.975366, avg accuracy: 0.254559\n",
      "epoch: 9, avg loss test: 9.841523, avg accuracy test: 0.082920\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from sklearn.utils import shuffle\n",
    "import time\n",
    "\n",
    "for EPOCH in range(10):\n",
    "    lasttime = time.time()\n",
    "    total_loss, total_accuracy, total_loss_test, total_accuracy_test = 0, 0, 0, 0\n",
    "    train_X, train_Y = shuffle(train_X, train_Y)\n",
    "    test_X, test_Y = shuffle(test_X, test_Y)\n",
    "    pbar = tqdm(range(0, len(train_X), batch_size), desc='train minibatch loop')\n",
    "    for k in pbar:\n",
    "        batch_x, _ = pad_sentence_batch(train_X[k: min(k+batch_size,len(train_X))], PAD)\n",
    "        batch_y, _ = pad_sentence_batch(train_Y[k: min(k+batch_size,len(train_X))], PAD)\n",
    "        acc, loss, _ = sess.run([model.accuracy, model.cost, model.optimizer], \n",
    "                                      feed_dict={model.X:batch_x,\n",
    "                                                model.Y:batch_y})\n",
    "        total_loss += loss\n",
    "        total_accuracy += acc\n",
    "        pbar.set_postfix(cost=loss, accuracy = acc)\n",
    "        \n",
    "    pbar = tqdm(range(0, len(test_X), batch_size), desc='test minibatch loop')\n",
    "    for k in pbar:\n",
    "        batch_x, _ = pad_sentence_batch(test_X[k: min(k+batch_size,len(test_X))], PAD)\n",
    "        batch_y, _ = pad_sentence_batch(test_Y[k: min(k+batch_size,len(test_X))], PAD)\n",
    "        acc, loss = sess.run([model.accuracy, model.cost], \n",
    "                                      feed_dict={model.X:batch_x,\n",
    "                                                model.Y:batch_y})\n",
    "        total_loss_test += loss\n",
    "        total_accuracy_test += acc\n",
    "        pbar.set_postfix(cost=loss, accuracy = acc)\n",
    "        \n",
    "    total_loss /= (len(train_X) / batch_size)\n",
    "    total_accuracy /= (len(train_X) / batch_size)\n",
    "    total_loss_test /= (len(test_X) / batch_size)\n",
    "    total_accuracy_test /= (len(test_X) / batch_size)\n",
    "        \n",
    "    print('epoch: %d, avg loss: %f, avg accuracy: %f'%(EPOCH, total_loss, total_accuracy))\n",
    "    print('epoch: %d, avg loss test: %f, avg accuracy test: %f'%(EPOCH, total_loss_test, total_accuracy_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  17,   21,   14,   47,   36,  251,    5,    5,    5,  375,    2,\n",
       "           2,    2,    2,    2],\n",
       "       [  11, 2257, 2267,   19,  551,    5,  551,    2,    2,    2,    2,\n",
       "           2,    2,    2,    2],\n",
       "       [2414,  609,  466,    4,  292,   17,   21,   14,   47,   82,  251,\n",
       "           2,    2,    2,    2],\n",
       "       [ 356,  356,    4, 2035, 3284,    6,   42,    5,   11,    2,    2,\n",
       "           2,    2,    2,    2]], dtype=int32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(model.predicting_ids, feed_dict = {model.X: batch_x})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1415, 227, 204, 17, 46, 82, 21, 14, 241, 36, 90, 12, 5315, 3401, 5316, 2],\n",
       " [2603, 20, 5779, 5780, 1678, 662, 1528, 5, 597, 2, 0, 0, 0, 0, 0, 0],\n",
       " [8339, 48, 2959, 202, 195, 856, 764, 8340, 2, 0, 0, 0, 0, 0, 0, 0],\n",
       " [4118, 4, 7708, 372, 2753, 26, 814, 229, 4, 359, 6, 80, 7709, 2, 0, 0]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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