06_02_qa_train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating question answer pairs from text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import importlib\n",
    "import os\n",
    "\n",
    "from tensorflow.keras.layers import Input, Embedding, GRU, Bidirectional, Dense, Lambda\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import pickle as pkl\n",
    "\n",
    "from utils.write import training_data, test_data, collapse_documents, expand_answers, _read_data, get_glove\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run params\n",
    "SECTION = 'write'\n",
    "RUN_ID = '0001'\n",
    "DATA_NAME = 'qa'\n",
    "RUN_FOLDER = 'run/{}/'.format(SECTION)\n",
    "RUN_FOLDER += '_'.join([RUN_ID, DATA_NAME])\n",
    "\n",
    "if not os.path.exists(RUN_FOLDER):\n",
    "    os.mkdir(RUN_FOLDER)\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'viz'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'images'))\n",
    "    os.mkdir(os.path.join(RUN_FOLDER, 'weights'))\n",
    "\n",
    "mode =  'build' #'load' #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "training_data_gen = training_data()\n",
    "# training_data_gen = [next(training_data_gen)]\n",
    "test_data_gen = test_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = next(training_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "document_tokens\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "\n",
      "\n",
      "question_input_tokens\n",
      " [2 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "\n",
      "\n",
      "answer_masks\n",
      " [[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "\n",
      "\n",
      "answer_labels\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1\n",
      " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "\n",
      "\n",
      "question_output_tokens\n",
      " [1 1 1 1 1 1 3 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "\n",
    "print('document_tokens\\n', t['document_tokens'][idx])\n",
    "print('\\n')\n",
    "print('question_input_tokens\\n', t['question_input_tokens'][idx])\n",
    "print('\\n')\n",
    "print('answer_masks\\n', t['answer_masks'][idx])\n",
    "print('\\n')\n",
    "print('answer_labels\\n', t['answer_labels'][idx])\n",
    "print('\\n')\n",
    "print('question_output_tokens\\n', t['question_output_tokens'][idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLOVE\n",
      "VOCAB_SIZE:  9984\n",
      "EMBEDDING_DIMENS:  100\n"
     ]
    }
   ],
   "source": [
    "# GloVe\n",
    "\n",
    "glove = get_glove()\n",
    "\n",
    "VOCAB_SIZE = glove.shape[0]\n",
    "EMBEDDING_DIMENS = glove.shape[1]\n",
    "\n",
    "print('GLOVE')\n",
    "print('VOCAB_SIZE: ', VOCAB_SIZE)\n",
    "print('EMBEDDING_DIMENS: ', EMBEDDING_DIMENS)\n",
    "\n",
    "GRU_UNITS = 100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_DOC_SIZE = None\n",
    "MAX_ANSWER_SIZE = None\n",
    "MAX_Q_SIZE = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document_tokens = Input(shape=(MAX_DOC_SIZE,), name=\"document_tokens\")\n",
    "\n",
    "embedding = Embedding(input_dim = VOCAB_SIZE, output_dim = EMBEDDING_DIMENS, weights=[glove], mask_zero = True, name = 'embedding')\n",
    "document_emb = embedding(document_tokens)\n",
    "\n",
    "answer_outputs = Bidirectional(GRU(GRU_UNITS, return_sequences=True), name = 'answer_outputs')(document_emb)\n",
    "answer_tags = Dense(2, activation = 'softmax', name = 'answer_tags')(answer_outputs)\n",
    "\n",
    "encoder_input_mask = Input(shape=(MAX_ANSWER_SIZE, MAX_DOC_SIZE), name=\"encoder_input_mask\")\n",
    "encoder_inputs = Lambda(lambda x: K.batch_dot(x[0], x[1]), name=\"encoder_inputs\")([encoder_input_mask, answer_outputs])\n",
    "encoder_cell = GRU(2 * GRU_UNITS, name = 'encoder_cell')(encoder_inputs)\n",
    "\n",
    "decoder_inputs = Input(shape=(MAX_Q_SIZE,), name=\"decoder_inputs\")\n",
    "decoder_emb = embedding(decoder_inputs)\n",
    "decoder_emb.trainable = False\n",
    "decoder_cell = GRU(2 * GRU_UNITS, return_sequences = True, name = 'decoder_cell')\n",
    "decoder_states = decoder_cell(decoder_emb, initial_state = [encoder_cell])\n",
    "\n",
    "decoder_projection = Dense(VOCAB_SIZE, name = 'decoder_projection', activation = 'softmax', use_bias = False)\n",
    "decoder_outputs = decoder_projection(decoder_states)\n",
    "\n",
    "total_model = Model([document_tokens, decoder_inputs, encoder_input_mask], [answer_tags, decoder_outputs])\n",
    "plot_model(total_model, to_file=os.path.join(RUN_FOLDER ,'viz/model.png'),show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, None, 100])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_model = Model(document_tokens, [answer_tags])\n",
    "decoder_initial_state_model = Model([document_tokens, encoder_input_mask], [encoder_cell])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "document_tokens (InputLayer)    [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, None, 100)    998400      document_tokens[0][0]            \n",
      "                                                                 decoder_inputs[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "answer_outputs (Bidirectional)  (None, None, 200)    121200      embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "encoder_input_mask (InputLayer) [(None, None, None)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "decoder_inputs (InputLayer)     [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder_inputs (Lambda)         (None, None, 200)    0           encoder_input_mask[0][0]         \n",
      "                                                                 answer_outputs[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "encoder_cell (GRU)              (None, 200)          241200      encoder_inputs[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "decoder_cell (GRU)              (None, None, 200)    181200      embedding[1][0]                  \n",
      "                                                                 encoder_cell[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "answer_tags (Dense)             (None, None, 2)      402         answer_outputs[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "decoder_projection (Dense)      (None, None, 9984)   1996800     decoder_cell[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 3,539,202\n",
      "Trainable params: 3,539,202\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "total_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### INFERENCE MODEL ####\n",
    "\n",
    "decoder_inputs_dynamic = Input(shape=(1,), name=\"decoder_inputs_dynamic\")\n",
    "decoder_emb_dynamic = embedding(decoder_inputs_dynamic)\n",
    "decoder_init_state_dynamic = Input(shape=(2 * GRU_UNITS,), name = 'decoder_init_state_dynamic') #the embedding of the previous word\n",
    "decoder_states_dynamic = decoder_cell(decoder_emb_dynamic, initial_state = [decoder_init_state_dynamic])\n",
    "decoder_outputs_dynamic = decoder_projection(decoder_states_dynamic)\n",
    "\n",
    "question_model = Model([decoder_inputs_dynamic, decoder_init_state_dynamic], [decoder_outputs_dynamic, decoder_states_dynamic])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### COMPILE TRAINING MODEL ####\n",
    "\n",
    "opti = Adam(lr=0.001)\n",
    "total_model.compile(loss=['sparse_categorical_crossentropy', 'sparse_categorical_crossentropy']\n",
    "                    , optimizer=opti\n",
    "                    , loss_weights = [1,1]) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_loss_history = []\n",
    "test_loss_history = []\n",
    "\n",
    "EPOCHS = 2000\n",
    "start_epoch = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "0: Train Loss: [5.876297, 1.2322317, 4.6440654] | Test Loss: [9.901741, 0.73573875, 9.166002]\n",
      "1: Train Loss: [5.7138276, 0.7673969, 4.9464307] | Test Loss: [9.58865, 0.47302073, 9.115629]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-92aaf55a5e8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m         training_loss = total_model.train_on_batch(\n\u001b[1;32m     13\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'document_tokens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question_input_tokens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'answer_masks'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'answer_labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question_output_tokens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         )\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    971\u001b[0m       outputs = training_v2_utils.train_on_batch(\n\u001b[1;32m    972\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m           class_weight=class_weight, reset_metrics=reset_metrics)\n\u001b[0m\u001b[1;32m    974\u001b[0m       outputs = (outputs['total_loss'] + outputs['output_losses'] +\n\u001b[1;32m    975\u001b[0m                  outputs['metrics'])\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    262\u001b[0m       \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m       \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m       output_loss_metrics=model._output_loss_metrics)\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, inputs, targets, sample_weights, output_loss_metrics)\u001b[0m\n\u001b[1;32m    309\u001b[0m           \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m           \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m           output_loss_metrics=output_loss_metrics))\n\u001b[0m\u001b[1;32m    312\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36m_process_single_batch\u001b[0;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[1;32m    250\u001b[0m               \u001b[0moutput_loss_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_loss_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m               \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m               training=training))\n\u001b[0m\u001b[1;32m    253\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mtotal_loss\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         raise ValueError('The model cannot be run '\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36m_model_loss\u001b[0;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m   \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m   \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    889\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    890\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    706\u001b[0m     return self._run_internal_graph(\n\u001b[1;32m    707\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m         convert_kwargs_to_constants=base_layer_utils.call_context().saving)\n\u001b[0m\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[0;34m(self, inputs, training, mask, convert_kwargs_to_constants)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m           \u001b[0;31m# Compute outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 860\u001b[0;31m           \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomputed_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m           \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/wrappers.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, initial_state, constants, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minitial_state\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconstants\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBidirectional\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;31m# Applies the same workaround as in `RNN.__call__`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    889\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    890\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/wrappers.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask, initial_state, constants)\u001b[0m\n\u001b[1;32m    642\u001b[0m                              initial_state=forward_state, **kwargs)\n\u001b[1;32m    643\u001b[0m       y_rev = self.backward_layer(backward_inputs,\n\u001b[0;32m--> 644\u001b[0;31m                                   initial_state=backward_state, **kwargs)\n\u001b[0m\u001b[1;32m    645\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m       \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/recurrent.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, initial_state, constants, **kwargs)\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minitial_state\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconstants\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRNN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m     \u001b[0;31m# If any of `initial_state` or `constants` are specified and are Keras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    889\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    890\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/recurrent_v2.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, mask, training, initial_state)\u001b[0m\n\u001b[1;32m    342\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m       last_output, outputs, runtime, states = self._defun_gru_call(\n\u001b[0;32m--> 344\u001b[0;31m           inputs, initial_state, training, mask)\n\u001b[0m\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/recurrent_v2.py\u001b[0m in \u001b[0;36m_defun_gru_call\u001b[0;34m(self, inputs, initial_state, training, mask)\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0mlast_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruntime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcudnn_gru\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcudnn_gru_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m         \u001b[0mlast_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruntime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandard_gru\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mnormal_gru_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m       last_output, outputs, new_h, runtime = gru_with_backend_selection(\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/recurrent_v2.py\u001b[0m in \u001b[0;36mstandard_gru\u001b[0;34m(inputs, init_h, kernel, recurrent_kernel, bias, activation, recurrent_activation, mask, time_major, go_backwards)\u001b[0m\n\u001b[1;32m    480\u001b[0m       \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m       \u001b[0mgo_backwards\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgo_backwards\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m       input_length=timesteps)\n\u001b[0m\u001b[1;32m    483\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mlast_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_states\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_runtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_RUNTIME_CPU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36mrnn\u001b[0;34m(step_function, inputs, initial_states, go_backwards, mask, constants, unroll, input_length, time_major, zero_output_for_mask)\u001b[0m\n\u001b[1;32m   4120\u001b[0m           \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4121\u001b[0m           \u001b[0mloop_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_ta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflat_zero_output\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4122\u001b[0;31m           **while_loop_kwargs)\n\u001b[0m\u001b[1;32m   4123\u001b[0m       \u001b[0;31m# Skip final_outputs[2] which is the output for final timestep.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4124\u001b[0m       \u001b[0mnew_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[0;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure)\u001b[0m\n\u001b[1;32m   2712\u001b[0m                                               list(loop_vars))\n\u001b[1;32m   2713\u001b[0m       \u001b[0;32mwhile\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2714\u001b[0;31m         \u001b[0mloop_vars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2715\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtry_to_pack\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_basetuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2716\u001b[0m           \u001b[0mpacked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i, lv)\u001b[0m\n\u001b[1;32m   2703\u001b[0m         cond = lambda i, lv: (  # pylint: disable=g-long-lambda\n\u001b[1;32m   2704\u001b[0m             math_ops.logical_and(i < maximum_iterations, orig_cond(*lv)))\n\u001b[0;32m-> 2705\u001b[0;31m         \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2706\u001b[0m       \u001b[0mtry_to_pack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m_step\u001b[0;34m(time, output_ta_t, prev_output, *states)\u001b[0m\n\u001b[1;32m   4089\u001b[0m         \u001b[0mmask_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4090\u001b[0m         output, new_states = step_function(current_input,\n\u001b[0;32m-> 4091\u001b[0;31m                                            tuple(states) + tuple(constants))\n\u001b[0m\u001b[1;32m   4092\u001b[0m         \u001b[0;31m# mask output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4093\u001b[0m         \u001b[0mflat_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/recurrent_v2.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(cell_inputs, cell_states)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;31m# hidden state projected by all gate matrices at once\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m     \u001b[0mmatrix_inner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_tm1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurrent_kernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    462\u001b[0m     \u001b[0mmatrix_inner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix_inner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurrent_bias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36mdot\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1701\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse_tensor_dense_matmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1702\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1703\u001b[0;31m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1704\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py\u001b[0m in \u001b[0;36mmatmul\u001b[0;34m(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)\u001b[0m\n\u001b[1;32m   2763\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2764\u001b[0m       return gen_math_ops.mat_mul(\n\u001b[0;32m-> 2765\u001b[0;31m           a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n\u001b[0m\u001b[1;32m   2766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmat_mul\u001b[0;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[1;32m   6110\u001b[0m         \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_thread_local_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"MatMul\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6111\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_execution_callbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"transpose_a\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6112\u001b[0;31m         transpose_a, \"transpose_b\", transpose_b)\n\u001b[0m\u001b[1;32m   6113\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6114\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(start_epoch, start_epoch + EPOCHS + 1):\n",
    "    print(\"Epoch {0}\".format(epoch))\n",
    "    \n",
    "    for i, batch in enumerate(training_data()):\n",
    "        \n",
    "        val_batch = next(test_data_gen, None)\n",
    "        \n",
    "        if val_batch is None:\n",
    "            test_data_gen = test_data()\n",
    "            val_batch = next(test_data_gen, None)\n",
    "            \n",
    "        training_loss = total_model.train_on_batch(\n",
    "            [batch['document_tokens'], batch['question_input_tokens'], batch['answer_masks']]\n",
    "            , [np.expand_dims(batch['answer_labels'], axis = -1), np.expand_dims(batch['question_output_tokens'], axis = -1)]\n",
    "        )\n",
    "        \n",
    "        test_loss = total_model.test_on_batch(\n",
    "            [val_batch['document_tokens'], val_batch['question_input_tokens'], val_batch['answer_masks']]\n",
    "            , [np.expand_dims(val_batch['answer_labels'], axis = -1), np.expand_dims(val_batch['question_output_tokens'], axis = -1)]\n",
    "        )\n",
    "        \n",
    "        training_loss_history.append(training_loss)\n",
    "        test_loss_history.append(test_loss)\n",
    "        \n",
    "        print(\"{}: Train Loss: {} | Test Loss: {}\".format(i, training_loss, test_loss))\n",
    "        \n",
    "        total_model.save_weights(os.path.join(RUN_FOLDER, 'weights/weights_{}.h5'.format(epoch)))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### SHOW LOSSES ####\n",
    "\n",
    "plt.plot(np.array(training_loss_history)[:,0])\n",
    "plt.plot(np.array(test_loss_history)[:,0])\n",
    "plt.show()\n",
    "        \n",
    "pkl.dump([training_loss_history, test_loss_history], open(os.path.join(RUN_FOLDER, 'weights/histories.pkl'), 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gdl_code",
   "language": "python",
   "name": "gdl_code"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}