neural-machine-translation/50.bertmultilanguage-encoder-lstm-decoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip\n",
    "# !unzip multi_cased_L-12_H-768_A-12.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import bert\n",
    "from bert import run_classifier\n",
    "from bert import optimization\n",
    "from bert import tokenization\n",
    "from bert import modeling\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('dataset.json') as fopen:\n",
    "    data = json.load(fopen)\n",
    "    \n",
    "train_X = data['train_X']\n",
    "train_Y = data['train_Y']\n",
    "test_X = data['test_X']\n",
    "test_Y = data['test_Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/tokenization.py:125: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "BERT_VOCAB = 'multi_cased_L-12_H-768_A-12/vocab.txt'\n",
    "BERT_INIT_CHKPNT = 'multi_cased_L-12_H-768_A-12/bert_model.ckpt'\n",
    "BERT_CONFIG = 'multi_cased_L-12_H-768_A-12/bert_config.json'\n",
    "\n",
    "tokenizer = tokenization.FullTokenizer(\n",
    "      vocab_file=BERT_VOCAB, do_lower_case=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "GO = 101\n",
    "EOS = 102"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unidecode import unidecode\n",
    "\n",
    "def get_inputs(x, y):\n",
    "    input_ids, input_masks, segment_ids, ys = [], [], [], []\n",
    "    for i in tqdm(range(len(x))):\n",
    "        tokens_a = tokenizer.tokenize(unidecode(x[i]))\n",
    "        tokens_b = tokenizer.tokenize(unidecode(y[i]))\n",
    "        tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\n",
    "        \n",
    "        segment_id = [0] * len(tokens)\n",
    "        input_id = tokenizer.convert_tokens_to_ids(tokens)\n",
    "        input_mask = [1] * len(input_id)\n",
    "\n",
    "        input_ids.append(input_id)\n",
    "        input_masks.append(input_mask)\n",
    "        segment_ids.append(segment_id)\n",
    "        \n",
    "        r = tokenizer.convert_tokens_to_ids(tokens_b + [\"[SEP]\"])\n",
    "        if len([k for k in r if k == 0]):\n",
    "            print(y[i], i)\n",
    "            break\n",
    "        \n",
    "        ys.append(r)\n",
    "        \n",
    "    return input_ids, input_masks, segment_ids, ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200000/200000 [02:39<00:00, 1255.89it/s]\n"
     ]
    }
   ],
   "source": [
    "train_input_ids, train_input_masks, train_segment_ids, train_Y = get_inputs(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [00:04<00:00, 1089.86it/s]\n"
     ]
    }
   ],
   "source": [
    "test_input_ids, test_input_masks, test_segment_ids, test_Y = get_inputs(test_X, test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)\n",
    "epoch = 20\n",
    "batch_size = 16\n",
    "warmup_proportion = 0.1\n",
    "num_train_steps = len(train_input_ids)\n",
    "num_warmup_steps = int(num_train_steps * warmup_proportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        size_layer,\n",
    "        num_layers,\n",
    "        learning_rate = 2e-5,\n",
    "        training = True,\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
    "        self.segment_ids = tf.placeholder(tf.int32, [None, None])\n",
    "        self.input_masks = 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",
    "        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",
    "        \n",
    "        model = modeling.BertModel(\n",
    "            config=bert_config,\n",
    "            is_training=training,\n",
    "            input_ids=self.X,\n",
    "            input_mask=self.input_masks,\n",
    "            token_type_ids=self.segment_ids,\n",
    "            use_one_hot_embeddings=False)\n",
    "        \n",
    "        output_layer = model.get_sequence_output()\n",
    "        pooled_output = model.get_pooled_output()\n",
    "        embedding = model.get_embedding_table()\n",
    "        \n",
    "        dense = tf.layers.Dense(bert_config.vocab_size)\n",
    "        \n",
    "        def cells(size_layer=size_layer, reuse=False):\n",
    "            return tf.nn.rnn_cell.LSTMCell(size_layer,initializer=tf.orthogonal_initializer(),reuse=reuse)\n",
    "        \n",
    "        lstm_state = tf.nn.rnn_cell.LSTMStateTuple(c=pooled_output, h=pooled_output)\n",
    "        \n",
    "        encoder_state = tuple([lstm_state] * num_layers)\n",
    "        decoder_cells = tf.nn.rnn_cell.MultiRNNCell([cells(size_layer) for _ in range(num_layers)])\n",
    "        \n",
    "        training_helper = tf.contrib.seq2seq.TrainingHelper(\n",
    "                inputs = tf.nn.embedding_lookup(embedding, decoder_input),\n",
    "                sequence_length = self.Y_seq_len,\n",
    "                time_major = False)\n",
    "        training_decoder = tf.contrib.seq2seq.BasicDecoder(\n",
    "                cell = decoder_cells,\n",
    "                helper = training_helper,\n",
    "                initial_state = encoder_state,\n",
    "                output_layer = dense)\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.training_logits = training_decoder_output.rnn_output\n",
    "        \n",
    "        predicting_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(\n",
    "                embedding = embedding,\n",
    "                start_tokens = tf.tile(tf.constant([GO], dtype=tf.int32), [batch_size]),\n",
    "                end_token = EOS)\n",
    "        predicting_decoder = tf.contrib.seq2seq.BasicDecoder(\n",
    "                cell = decoder_cells,\n",
    "                helper = predicting_helper,\n",
    "                initial_state = encoder_state,\n",
    "                output_layer = dense)\n",
    "        predicting_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(\n",
    "                decoder = predicting_decoder,\n",
    "                impute_finished = True,\n",
    "                maximum_iterations = 2 * tf.reduce_max(self.X_seq_len))\n",
    "        self.fast_result = predicting_decoder_output.sample_id\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.training_logits,\n",
    "                                                     targets = self.Y,\n",
    "                                                     weights = masks)\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
    "        y_t = tf.argmax(self.training_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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py:507: calling count_nonzero (from tensorflow.python.ops.math_ops) with axis is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "reduction_indices is deprecated, use axis instead\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:409: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
      "\n",
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From <ipython-input-11-c9333c9a86f4>:35: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-11-c9333c9a86f4>:40: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:958: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:962: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/seq2seq/python/ops/decoder.py:420: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Model(bert_config.hidden_size, 2)\n",
    "\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import re\n",
    "\n",
    "def get_assignment_map_from_checkpoint(tvars, init_checkpoint):\n",
    "    \"\"\"Compute the union of the current variables and checkpoint variables.\"\"\"\n",
    "    assignment_map = {}\n",
    "    initialized_variable_names = {}\n",
    "\n",
    "    name_to_variable = collections.OrderedDict()\n",
    "    for var in tvars:\n",
    "        name = var.name\n",
    "        m = re.match('^(.*):\\\\d+$', name)\n",
    "        if m is not None:\n",
    "            name = m.group(1)\n",
    "        name_to_variable[name] = var\n",
    "\n",
    "    init_vars = tf.train.list_variables(init_checkpoint)\n",
    "\n",
    "    assignment_map = collections.OrderedDict()\n",
    "    for x in init_vars:\n",
    "        (name, var) = (x[0], x[1])\n",
    "        if 'bert/' + name in name_to_variable:\n",
    "            assignment_map[name] = name_to_variable['bert/' + name]\n",
    "            initialized_variable_names[name] = 1\n",
    "            initialized_variable_names[name + ':0'] = 1\n",
    "        elif name in name_to_variable:\n",
    "            assignment_map[name] = name_to_variable[name]\n",
    "            initialized_variable_names[name] = 1\n",
    "            initialized_variable_names[name + ':0'] = 1\n",
    "        \n",
    "\n",
    "    return (assignment_map, initialized_variable_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvars = tf.trainable_variables()\n",
    "\n",
    "checkpoint = BERT_INIT_CHKPNT\n",
    "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
    "                                                                                checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from multi_cased_L-12_H-768_A-12/bert_model.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver(var_list = assignment_map)\n",
    "saver.restore(sess, checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pad_sequences = tf.keras.preprocessing.sequence.pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:38:03<00:00,  2.12it/s, accuracy=0.163, cost=5.76] \n",
      "test minibatch loop: 100%|██████████| 313/313 [00:45<00:00,  6.95it/s, accuracy=0.163, cost=5.62]\n",
      "train minibatch loop:   0%|          | 0/12500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, training loss: 6.346653, training acc: 0.080043, valid loss: 5.756541, valid acc: 0.151906\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:35<00:00,  2.13it/s, accuracy=0.231, cost=4.79]\n",
      "test minibatch loop: 100%|██████████| 313/313 [00:44<00:00,  7.09it/s, accuracy=0.229, cost=4.58]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, training loss: 5.131091, training acc: 0.197364, valid loss: 4.706825, valid acc: 0.234835\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:10<00:00,  2.14it/s, accuracy=0.29, cost=4.26] \n",
      "test minibatch loop: 100%|██████████| 313/313 [00:44<00:00,  7.10it/s, accuracy=0.331, cost=4.05]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2, training loss: 4.417070, training acc: 0.265139, valid loss: 4.205559, valid acc: 0.291394\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:08<00:00,  2.14it/s, accuracy=0.326, cost=3.98]\n",
      "test minibatch loop: 100%|██████████| 313/313 [00:44<00:00,  7.11it/s, accuracy=0.343, cost=3.71]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3, training loss: 4.012529, training acc: 0.311701, valid loss: 3.900843, valid acc: 0.325425\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop:  60%|██████    | 7519/12500 [58:18<41:03,  2.02it/s, accuracy=0.301, cost=3.79]  IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "train minibatch loop:  90%|█████████ | 11261/12500 [1:27:24<09:18,  2.22it/s, accuracy=0.337, cost=3.68]IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "train minibatch loop:  14%|█▍        | 1725/12500 [13:24<1:33:25,  1.92it/s, accuracy=0.355, cost=3.42]IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:03<00:00,  2.15it/s, accuracy=0.377, cost=3.6] \n",
      "test minibatch loop: 100%|██████████| 313/313 [00:44<00:00,  7.10it/s, accuracy=0.396, cost=3.3] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5, training loss: 3.518369, training acc: 0.372840, valid loss: 3.498968, valid acc: 0.376734\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:07<00:00,  2.15it/s, accuracy=0.39, cost=3.43] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 6, training loss: 3.337285, training acc: 0.395921, valid loss: 3.367668, valid acc: 0.393659\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:42<00:00,  2.13it/s, accuracy=0.426, cost=3.3] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 7, training loss: 3.183454, training acc: 0.415893, valid loss: 3.250831, valid acc: 0.408419\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "train minibatch loop: 100%|██████████| 12500/12500 [1:38:27<00:00,  2.12it/s, accuracy=0.415, cost=3.18]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 8, training loss: 3.049351, training acc: 0.433559, valid loss: 3.166048, valid acc: 0.420588\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:38:07<00:00,  2.12it/s, accuracy=0.422, cost=3.09]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9, training loss: 2.930445, training acc: 0.449600, valid loss: 3.079593, valid acc: 0.432120\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:11<00:00,  2.14it/s, accuracy=0.449, cost=2.97]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10, training loss: 2.823860, training acc: 0.464009, valid loss: 3.006260, valid acc: 0.441914\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:08<00:00,  2.14it/s, accuracy=0.475, cost=2.88]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 11, training loss: 2.727184, training acc: 0.477504, valid loss: 2.951655, valid acc: 0.450500\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:04<00:00,  2.15it/s, accuracy=0.466, cost=2.8] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 12, training loss: 2.637947, training acc: 0.490476, valid loss: 2.894938, valid acc: 0.458457\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:00<00:00,  2.15it/s, accuracy=0.466, cost=2.74]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 13, training loss: 2.555691, training acc: 0.502384, valid loss: 2.853183, valid acc: 0.465069\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:10<00:00,  2.14it/s, accuracy=0.458, cost=2.64]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 14, training loss: 2.479316, training acc: 0.513608, valid loss: 2.815553, valid acc: 0.469557\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:28<00:00,  2.14it/s, accuracy=0.481, cost=2.61]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 15, training loss: 2.407951, training acc: 0.524401, valid loss: 2.787772, valid acc: 0.475006\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:16<00:00,  2.14it/s, accuracy=0.506, cost=2.51]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 16, training loss: 2.340384, training acc: 0.534496, valid loss: 2.757216, valid acc: 0.480557\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 12500/12500 [1:36:58<00:00,  2.15it/s, accuracy=0.508, cost=2.44]\n",
      "test minibatch loop: 100%|██████████| 313/313 [00:43<00:00,  7.12it/s, accuracy=0.486, cost=2.64]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 17, training loss: 2.275915, training acc: 0.544488, valid loss: 2.735755, valid acc: 0.483841\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop:  97%|█████████▋| 12166/12500 [1:34:17<02:39,  2.10it/s, accuracy=0.565, cost=2.19]IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "train minibatch loop:  25%|██▍       | 3111/12500 [24:08<1:11:48,  2.18it/s, accuracy=0.574, cost=2.05]IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "train minibatch loop: 100%|██████████| 12500/12500 [1:37:16<00:00,  2.14it/s, accuracy=0.54, cost=2.33]  \n",
      "test minibatch loop: 100%|██████████| 313/313 [00:44<00:00,  7.10it/s, accuracy=0.49, cost=2.58] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 19, training loss: 2.158188, training acc: 0.562544, valid loss: 2.700762, valid acc: 0.489972\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "for EPOCH in range(epoch):\n",
    "\n",
    "    train_acc, train_loss, test_acc, test_loss = [], [], [], []\n",
    "    pbar = tqdm(\n",
    "        range(0, len(train_input_ids), batch_size), desc = 'train minibatch loop'\n",
    "    )\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(train_input_ids))\n",
    "        batch_x = train_input_ids[i: index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_mask = train_input_masks[i: index]\n",
    "        batch_mask = pad_sequences(batch_mask, padding='post')\n",
    "        batch_segment = train_segment_ids[i: index]\n",
    "        batch_segment = pad_sequences(batch_segment, padding='post')\n",
    "        batch_y = pad_sequences(train_Y[i: index], padding='post')\n",
    "        acc, cost, _ = sess.run(\n",
    "            [model.accuracy, model.cost, model.optimizer],\n",
    "            feed_dict = {\n",
    "                model.Y: batch_y,\n",
    "                model.X: batch_x,\n",
    "                model.input_masks: batch_mask,\n",
    "                model.segment_ids: batch_segment\n",
    "            },\n",
    "        )\n",
    "        train_loss.append(cost)\n",
    "        train_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "        \n",
    "    pbar = tqdm(range(0, len(test_input_ids), batch_size), desc = 'test minibatch loop')\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(test_input_ids))\n",
    "        batch_x = test_input_ids[i: index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_y = pad_sequences(test_Y[i: index], padding='post')\n",
    "        batch_mask = test_input_masks[i: index]\n",
    "        batch_mask = pad_sequences(batch_mask, padding='post')\n",
    "        batch_segment = test_segment_ids[i: index]\n",
    "        batch_segment = pad_sequences(batch_segment, padding='post')\n",
    "        acc, cost = sess.run(\n",
    "            [model.accuracy, model.cost],\n",
    "            feed_dict = {\n",
    "                model.Y: batch_y,\n",
    "                model.X: batch_x,\n",
    "                model.input_masks: batch_mask,\n",
    "                model.segment_ids: batch_segment\n",
    "            },\n",
    "        )\n",
    "        test_loss.append(cost)\n",
    "        test_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "        \n",
    "    train_loss = np.mean(train_loss)\n",
    "    train_acc = np.mean(train_acc)\n",
    "    test_loss = np.mean(test_loss)\n",
    "    test_acc = np.mean(test_acc)\n",
    "    print(\n",
    "        'epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\\n'\n",
    "        % (EPOCH, train_loss, train_acc, test_loss, test_acc)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensor2tensor.utils import bleu_hook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 313/313 [04:26<00:00,  1.18it/s]\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for i in tqdm(range(0, len(test_X), batch_size)):\n",
    "    index = min(i + batch_size, len(test_X))\n",
    "    batch_x = test_input_ids[i: index]\n",
    "    batch_x = pad_sequences(batch_x, padding='post')\n",
    "    batch_y = pad_sequences(test_Y[i: index], padding='post')\n",
    "    batch_mask = test_input_masks[i: index]\n",
    "    batch_mask = pad_sequences(batch_mask, padding='post')\n",
    "    batch_segment = test_segment_ids[i: index]\n",
    "    batch_segment = pad_sequences(batch_segment, padding='post')\n",
    "    feed = {\n",
    "        model.X: batch_x,\n",
    "        model.input_masks: batch_mask,\n",
    "        model.segment_ids: batch_segment\n",
    "    }\n",
    "    p = sess.run(model.fast_result,feed_dict = feed)\n",
    "    result = []\n",
    "    for row in p:\n",
    "        result.append([i for i in row if i > 3 and i not in [101, 102]])\n",
    "    results.extend(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rights = []\n",
    "for r in test_Y:\n",
    "    rights.append([i for i in r if i > 3 and i not in [101, 102]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11384286"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_hook.compute_bleu(reference_corpus = rights,\n",
    "                       translation_corpus = results)"
   ]
  }
 ],
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