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 1 1 1 1 1 1 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 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 1 1 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 1 1 1 1 1 1 1 1 1 1 1 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 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 1 1 1 1 1 1 3 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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "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: [5.358553, 0.54447705, 4.814076] | Test Loss: [9.527245, 0.37646043, 9.1507845]\n",
      "1: Train Loss: [5.5388675, 0.36127976, 5.1775875] | Test Loss: [9.390775, 0.29728138, 9.093493]\n",
      "2: Train Loss: [5.2074175, 0.43401504, 4.7734027] | Test Loss: [9.312523, 0.2819618, 9.030561]\n",
      "3: Train Loss: [5.5489464, 0.35577145, 5.193175] | Test Loss: [9.23679, 0.28210592, 8.954684]\n",
      "4: Train Loss: [5.0804615, 0.5494464, 4.531015] | Test Loss: [9.144372, 0.28190714, 8.862465]\n",
      "5: Train Loss: [5.186718, 0.33600417, 4.8507137] | Test Loss: [9.021345, 0.28114313, 8.740202]\n",
      "6: Train Loss: [4.6811576, 0.33942044, 4.3417373] | Test Loss: [8.869452, 0.2807353, 8.588717]\n",
      "7: Train Loss: [4.819796, 0.41325742, 4.4065385] | Test Loss: [8.682507, 0.2813026, 8.401204]\n",
      "8: Train Loss: [4.5515738, 0.49589273, 4.055681] | Test Loss: [8.466556, 0.2862665, 8.180289]\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    266\u001b[0m           \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backwards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled_total_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\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--> 268\u001b[0;31m           \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_total_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainable_weights\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    269\u001b[0m           if isinstance(model.optimizer,\n\u001b[1;32m    270\u001b[0m                         loss_scale_optimizer.LossScaleOptimizer):\n",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0moutput_gradients\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \u001b[0msources_raw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflat_sources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m         unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[1;32m   1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_persistent\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/eager/imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[1;32m     74\u001b[0m       \u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m       \u001b[0msources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m       compat.as_str(unconnected_gradients.value))\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/gdl_code_2/lib/python3.7/site-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[0;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmock_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mout_grads\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    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\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_grad.py\u001b[0m in \u001b[0;36m_MaximumGrad\u001b[0;34m(op, grad)\u001b[0m\n\u001b[1;32m   1437\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_MaximumGrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\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   1438\u001b[0m   \u001b[0;34m\"\"\"Returns grad*(x > y, x <= y) with type of grad.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_MaximumMinimumGrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgreater_equal\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   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\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_grad.py\u001b[0m in \u001b[0;36m_MaximumMinimumGrad\u001b[0;34m(op, grad, selector_op)\u001b[0m\n\u001b[1;32m   1407\u001b[0m       \u001b[0;31m# When we want to get gradients for the first input only, and the second\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1408\u001b[0m       \u001b[0;31m# input tensor is a scalar, we can do a much simpler calculation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_MaximumMinimumGradInputOnly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselector_op\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   1410\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m     \u001b[0;31m# No gradient skipping, so do the full gradient computation\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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cBuCqkmwevn0lt6wq0L+XyCWwaLR6ysvL3a5duyJ+3khyzlFzupf99Z3sC4XygfpOatt7R49ZlJnMmqJMVhVmcKD+DG8eaWE46FixcAH3rQ9w7/oAZfln3ZVsXnDOUXGqnecq6nihso7W7gGyUhP56JVF3Lc+wNHmLn7wx6PUtveybnEWD9+2ks2XL1Rgi5yDme12zk26Sum8COqBoSBHmrpCodwx2lI+0zcEgBksy0/nikAWawKZrCnK5PKiTAoyksedp7Wrn5f2NvBCRR3vVLcBcGVxFvetD/CxdUUEslNn/Hubaceau9haUcfzFbVUt/aQlBDH5ssXsmVDMTevLiA5IX702IGhIM/uqeGf/nCUk209XF6UycO3reCuKwqJi1Ngi4SbV0Hd2TfIgVAQj7SUq5rOMDjsfZ8piXFcVpjJFYHM0VBeXZhBWtL0eoHqO3p5sbKeF96v4/2aDgA2leZy7/oiPnplEXkLki9whtmj+Uw/L1TW8VxFLZU1HZjBdcvy2LKhmLuuLCQzJfG8nx8aDvJ8ZR3f33aEYy3drFy4gC/dtoJ71gWIV2CLAHM0qJ1z1Hf0jbaO99V1sL++k1NtY10XeelJrAlkjmspl+WnRzwcjrd082JlHc9X1lHV1EV8nHHDinzuXVfEnWsvHGR+1N0/xCv7GthaUTfa5bOmKJM/21jMvesDFGalTPucw0HHbz+o5/vbqjjc2MWy/HS+cOsK7t8QIDFe17Vlfpv1QT04HORYczf7671ui32hcG7vGRw9piw/nTVFoVZyIJMrQl0XM9kn6pzjUOMZnq+o44X36zjV1ktSQhy3ri7g3vUBbr9sEalJ8Rc+UYwMDgd5vaqZrXvqeHV/A32DQYqzU9myMcCWDcWsXJQRka8TDDpe3d/Ao68dYX99J0tyU/nCLSv4+FWLSUpQYMv8NKuCuqt/iIP1oVZyrfd4qPEMA0NBAJIS4risMIM1RWPdF6sLM1mQ7K8BLCMX256vrOPF9+tpPtNPWlI8d6xZxH3rA9y4ssAXoeSc472T7TxXUcuL79fT1j1AdloiH7uyiC0bi7m6JCdq/cnOOV470MRj26qorOkgkJXC529ZzifLl5CS6N9faCLRMCuCenA4yJ3f3cGxlu7R97LTEr0wLhrrvliWn07CLPszeTjo2Hm8lRcq63jpgwY6egfJSk3k7rWF3Lc+wDXL8ma8r/ZocxfP7alla0UdJ9t6SE6IY/OaRd5FwVUz+0vEOceOqhYee62KXSdOsygzmYduWs5/2lTi679ARCJpVgQ1wN9v3UtBRvJoS7kwM2XODecaGAryxpFmXqis59V9DXQPDFOQkczHrizivg0BNi7Jjtr33HSmjxcq69m6p5YPajuIM7h+eT5bNhZz5xWLyIhxX7pzjreOtfLoa1W8fayN/AVJfPbGZXzq2qWk++wvJpFImzVBPd/0Dgyz7WATL1TWse1QEwNDQRbnpHLv+gD3rgtwedGlT6zp6h/ilb0NbK2o5c0jLQQdrC3OZMuGYu5bH2Bh5vQvCs6Ed6vbePS1Kl6vaiEnLZHPfLiMv7y+dFZemBWZCgX1LNDZN8jv9jXyfGUdb1zixJrB4SA7Djfz7J5afn+gkb7BIEtyU9myoZj7NwRYsTAyFwVnwp6Tp3ls2xG2HWwiMyWBT99Qxn+9oYysNAW2zC0K6lmmtauff9/bwPOVdbxb3YZz3sSae9cXcc+6wKQTa5xz7D5xmq0Vtfz2/XpO9wySk5bIPesCbNkY4KqSnFndjbS3toPHtlXxyr5GFiQn8JfXLeWvb1xGbnpSrEsTiQgF9SxW39HLb9+v5/nKySfWnO4ZYOueOp6rrOVUWy8piXHcsaaQLRsC3LSqYM6NTz7Y0Mlj247w0gf1pCTE8xfXLeWvbyxjYYY/u3BEpkpBPUdUt3TzQtjEGjNwDuIMbliRz5YNxdy5ttB3QxWj4UjTGf7pD0d5rqKWxPg4HtxUwuduXn5RE3FibWAoSEKcaVr9PKegnoMONnTy7x80kJWayD3ri+Zti/J4Szf//IcjPLunljgzPlm+mM/fspzFOWmxLg2A/qFhmjr7qe/oo76j13tsDz2GtpaufgJZKTyyeSUfv2rxrBt+KpGhoJY571RbDz/YfpSndp3COfj4VYv5wq3LWZoXvdUNR0K4rr2Xhs6+c4bwRBkpCRRlpVCUlUpRVgqLMlPYfriZilPtLCtI56t3rObutVq4ar5RUMu8Udfeyw+3H+XJd08xHHTcvz7AF29bwfKCBdM6z8QQrmvvo6Gjl7qOPhouEMKBrFQKs1IIZKdQmOmFcVF2CkVZKRRmpU7aNeWc49X9jXz7lUNUNXWxtjiTr915GTetzJ/VF4Fl6hTUMu80dfbx+I5j/GLnSfqGhrlnXYAv3bqC1YUZ9A8N09jRP9YV0TExhHtp6Ro465yZKQleKzgUukWhQA5/fqnXB4aDjq17avnu7w9Tc7qXa5fl8vW7LuOqkpxLOq/4n4Ja5q3Wrn6eeOM4P/tTNd0Dw+SlJ9HaPXkIB7LPDt5AWBjP5OzI/qFhfvXOKR7bVkVL1wCbL1/E1+5czerC2TMGXqbnkoPazKqBM8AwMHSuk41QUIvfnO4e4P+9fYL6jt7RvuGiGIXwdHT3D/GTN4/zw+3H6BoY4s82FPOVO1axJNcfF0slciIV1OXOuZapfEEFtUhktfcM8IPtR/nXN6sJOseDm0r40m0r5u1on7nofEGtcUAis0B2WhJ/d/fl7Pj6rfyH8iX8cudJbv7WH/nWywfp6B288AlkVptqi/o4cBpwwA+dc49PcsxDwEMAJSUlV584cSLCpYrIiOqWbr7zu8M8X1lHZkoCn7tlOZ++vkzLws5ikej6KHbO1ZrZQuB3wMPOuR3nOl5dHyIzY39dJ99+9RDbDjZRkJHM396+kv9YvsQXN6WQ6bnkrg/nXG3osQl4FtgUufJE5GKtCWTy47/6EE997jrK8tL5+6172fyd7WzdU0swGPkRXRIbFwxqM0s3s4yR58BHgL3RLkxEpu5Dpbn8+m+u5Sef/hALkhP48q8r+Oijr/PagUaiMQRXZtZUWtSLgDfMrBJ4B/itc+7l6JYlItNlZty6eiEvPvxhHn1wI32Dw3zmp7v4xP99i53HWmNdnlwCTXgRmaMGh4M8tauGf3ztMI2d/dy8qoCv3bmatcVZsS5NJqGZiSLzWN/gMD97q5p//uNR2nsG+di6Ir56xyqWTXP9k1gKBh11Hb1Ut/RwvLWbeDM+vCKfkry5M/FHQS0idPYN8sSOYzzxxnH6h4J88urFPLJ5JUVZZ98xKBaCQUfjmT6Ot3RT3dJDdWt36Hk3J9p6GBgKnvWZsvx0bl5VwM2rCrhmWS5pSf6cYToVCmoRGdXS1c/3tx3hlztPgsF/uW4pn79lxYzc1sw5R3NXv9cybunieEsP1S3dVLd6W9/gWBgnJcSxNDeN0vx0yvLTKc1LpzQ/jbL8dHoGhtlxuJkdh5t561grfYNBkuLj2FSWy82rCrhpVQGrFi2YVSsPKqhF5Cw1p3v43u+reOa9GtKSEvjsjcv4zI1ll7wCoHOOtu6BUIvYC+LjrV7LuLqlm+6B4dFjE+KMkrw0yvLSKc33trJQIBdlpRI/hTW5+waHebe6je2HmtlR1czhxi4ACjNTRkP7wyvyfX9DZAW1iJxTVeMZvv3qIV7Z10heehJfvHUF//naEpITzj/Lsb1nwOuaCAvkke6KM31Do8fFxxmLc1IpzRtpGY+1kouzUyN+R5u69l6vtV3VzOtVLZzpGyLOYGNJDjetLODm1QVcWZw1pV8CM0lBLSIXVHGqnf/zykHePNJKcXYqj2xeyZ1rCjnZ1jOuRXw8FMbtPWNrjJhBcXZqWBdFOmX5aZTmpbMkNy1mN1keGg5ScaqdHYeb2X64mfdrO3AOctISuXGl19q+aVW+Lxa3UlCLyJS9eaSFb718kMrQXe/DFWWleC3jgvTR7oqy/DSW5KZdsAXuB23dA7xe5YX2jsMto3fpWVOUyU2hi5JXL82JyRR8BbWITItzjt8faOJoc9doV8XS3PQ5tehTMOg40NDJ9sPNbD/UzO4TpxkKOtKT4rlueT43ry7g5pUFMzYEUEEtInIBZ/oGeetoqxfch5upOd0LzNwQQAW1iMg0OOc43tI9Gtpvz8AQQAW1iMglCB8CuP1wM1VNkR8CqKAWEYmgcw0BLF+ayy8/e81FDTk8X1DP3vmWIiIxEshO5YFNJTywqWTcEMDmrv6IjwsHBbWIyCVJiI+jvDSX8tLcqH0N3a9HRMTnFNQiIj6noBYR8TkFtYiIzymoRUR8TkEtIuJzCmoREZ9TUIuI+FxUppCbWTNw4iI/ng+0RLCcSFFd06O6pkd1Tc9crGupc65gsh1RCepLYWa7zjXfPZZU1/SorulRXdMz3+pS14eIiM8pqEVEfM6PQf14rAs4B9U1PaprelTX9MyrunzXRy0iIuP5sUUtIiJhFNQiIj7nm6A2s7vM7JCZHTGzb8S6nhFm9mMzazKzvbGuZYSZLTGzP5jZfjPbZ2aPxLqmEWaWYmbvmFllqLb/EeuaRphZvJntMbMXY11LODOrNrMPzKzCzHxzDzszyzazp83soJkdMLPrfFDT6tC/08jWaWZfjnVdAGb2ldD/+b1m9qSZpUTs3H7oozazeOAwcAdQA7wLPOic2x/TwgAzuwnoAn7mnFsb63oAzKwIKHLOvWdmGcBuYItP/r0MSHfOdZlZIvAG8Ihz7u0Yl4aZ/TegHMh0zt0T63pGmFk1UO6c89UEDjP7KfC6c+4JM0sC0pxz7bGua0QoN2qBa5xzFzvBLlK1FOP9X1/jnOs1s38DXnLO/Wskzu+XFvUm4Ihz7phzbgD4FXB/jGsCwDm3A2iLdR3hnHP1zrn3Qs/PAAeA4thW5XGertDLxNAW89aAmS0GPgY8EetaZgMzywJuAn4E4Jwb8FNIh9wOHI11SIdJAFLNLAFIA+oidWK/BHUxcCrsdQ0+CR6/M7NSYCOwM7aVjAl1MVQATcDvnHN+qO17wNeBYKwLmYQDXjWz3Wb2UKyLCSkDmoGfhLqLnjCz9FgXNcEDwJOxLgLAOVcLfBs4CdQDHc65VyN1fr8EtVwEM1sA/Ab4snOuM9b1jHDODTvnNgCLgU1mFtMuIzO7B2hyzu2OZR3n8WHn3FXA3cAXQ91tsZYAXAX8wDm3EegG/HTtKAm4D3gq1rUAmFkOXi9AGRAA0s3sU5E6v1+CuhZYEvZ6ceg9OYdQ/+9vgF84556JdT2TCf2p/AfgrhiXcgNwX6gv+FfAbWb289iWNCbUGsM51wQ8i9cVGGs1QE3YX0NP4wW3X9wNvOeca4x1ISGbgePOuWbn3CDwDHB9pE7ul6B+F1hpZmWh35QPAM/HuCbfCl2w+xFwwDn3nVjXE87MCswsO/Q8Fe8C8cFY1uSc+zvn3GLnXCne/61tzrmItXYuhZmlhy4IE+pa+AgQ8xFGzrkG4JSZrQ69dTsQ84vVYR7EJ90eISeBa80sLfTzeTvetaOISIjUiS6Fc27IzL4EvALEAz92zu2LcVkAmNmTwC1AvpnVAN90zv0otlVxA/AXwAehvmCA/+6ceymGNY0oAn4auiIfB/ybc85Xw+F8ZhHwrPezTQLwS+fcy7EtadTDwC9CjadjwKdjXA8w+gvtDuBvYl3LCOfcTjN7GngPGAL2EMHp5L4YniciIufml64PERE5BwW1iIjPKahFRHxOQS0i4nMKahERn1NQi4j4nIJaRMTn/j8mna8FiTdpogAAAABJRU5ErkJggg==\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": []
  }
 ],
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