notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dataset\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn import metrics as sk_mtr\n",
    "from model import build_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts, sentiments = dataset.load()\n",
    "test_texts, test_sentiments = dataset.load_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(' I`d have responded, if I were going', 'neutral')"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "texts[0], sentiments[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "('Last session of the day  http://twitpic.com/67ezh', 'neutral')"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "test_texts[0], test_sentiments[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = tf.keras.preprocessing.text.Tokenizer(2000)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "\n",
    "vocab_size = tokenizer.num_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_train = dataset.getDataset(tokenizer, batch_size=256)\n",
    "ds_test = dataset.getDataset(tokenizer, batch_size=256, test=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model(vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
    "              metrics=['accuracy'])\n",
    "              \n",
    "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True, verbose=1)"
   ]
  },
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Train for 107 steps, validate for 13 steps\nEpoch 1/50\n106/107 [============================>.] - ETA: 0s - loss: 1.1350 - accuracy: 0.3980\nEpoch 00001: val_loss improved from inf to 1.11803, saving model to best_model.h5\n107/107 [==============================] - 4s 39ms/step - loss: 1.1348 - accuracy: 0.3983 - val_loss: 1.1180 - val_accuracy: 0.4047\nEpoch 2/50\n102/107 [===========================>..] - ETA: 0s - loss: 1.1095 - accuracy: 0.4038\nEpoch 00002: val_loss improved from 1.11803 to 1.10066, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 1.1090 - accuracy: 0.4045 - val_loss: 1.1007 - val_accuracy: 0.4066\nEpoch 3/50\n101/107 [===========================>..] - ETA: 0s - loss: 1.0966 - accuracy: 0.4043\nEpoch 00003: val_loss improved from 1.10066 to 1.09090, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 1.0964 - accuracy: 0.4047 - val_loss: 1.0909 - val_accuracy: 0.4066\nEpoch 4/50\n102/107 [===========================>..] - ETA: 0s - loss: 1.0861 - accuracy: 0.4080\nEpoch 00004: val_loss improved from 1.09090 to 1.08058, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 1.0856 - accuracy: 0.4087 - val_loss: 1.0806 - val_accuracy: 0.4165\nEpoch 5/50\n106/107 [============================>.] - ETA: 0s - loss: 1.0639 - accuracy: 0.4510\nEpoch 00005: val_loss improved from 1.08058 to 1.03580, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 1.0636 - accuracy: 0.4515 - val_loss: 1.0358 - val_accuracy: 0.4868\nEpoch 6/50\n105/107 [============================>.] - ETA: 0s - loss: 0.9698 - accuracy: 0.5350\nEpoch 00006: val_loss improved from 1.03580 to 0.91742, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.9690 - accuracy: 0.5351 - val_loss: 0.9174 - val_accuracy: 0.5445\nEpoch 7/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.8787 - accuracy: 0.5794\nEpoch 00007: val_loss improved from 0.91742 to 0.86016, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.8778 - accuracy: 0.5809 - val_loss: 0.8602 - val_accuracy: 0.5682\nEpoch 8/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.8331 - accuracy: 0.6046\nEpoch 00008: val_loss improved from 0.86016 to 0.82147, saving model to best_model.h5\n107/107 [==============================] - 1s 12ms/step - loss: 0.8314 - accuracy: 0.6056 - val_loss: 0.8215 - val_accuracy: 0.5974\nEpoch 9/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.7953 - accuracy: 0.6360\nEpoch 00009: val_loss improved from 0.82147 to 0.80164, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.7966 - accuracy: 0.6352 - val_loss: 0.8016 - val_accuracy: 0.6172\nEpoch 10/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.7705 - accuracy: 0.6642\nEpoch 00010: val_loss improved from 0.80164 to 0.77954, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.7697 - accuracy: 0.6645 - val_loss: 0.7795 - val_accuracy: 0.6526\nEpoch 11/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.7471 - accuracy: 0.6877\nEpoch 00011: val_loss improved from 0.77954 to 0.75065, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.7470 - accuracy: 0.6866 - val_loss: 0.7507 - val_accuracy: 0.6743\nEpoch 12/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.7302 - accuracy: 0.7027\nEpoch 00012: val_loss did not improve from 0.75065\n107/107 [==============================] - 1s 10ms/step - loss: 0.7290 - accuracy: 0.7034 - val_loss: 0.7541 - val_accuracy: 0.6806\nEpoch 13/50\n106/107 [============================>.] - ETA: 0s - loss: 0.7170 - accuracy: 0.7115\nEpoch 00013: val_loss improved from 0.75065 to 0.73415, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.7176 - accuracy: 0.7108 - val_loss: 0.7342 - val_accuracy: 0.6935\nEpoch 14/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.7094 - accuracy: 0.7182\nEpoch 00014: val_loss improved from 0.73415 to 0.72798, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.7077 - accuracy: 0.7192 - val_loss: 0.7280 - val_accuracy: 0.6992\nEpoch 15/50\n106/107 [============================>.] - ETA: 0s - loss: 0.7026 - accuracy: 0.7246\nEpoch 00015: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.7029 - accuracy: 0.7247 - val_loss: 0.7306 - val_accuracy: 0.6947\nEpoch 16/50\n106/107 [============================>.] - ETA: 0s - loss: 0.6966 - accuracy: 0.7316\nEpoch 00016: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6966 - accuracy: 0.7312 - val_loss: 0.7423 - val_accuracy: 0.6875\nEpoch 17/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6943 - accuracy: 0.7336\nEpoch 00017: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6929 - accuracy: 0.7337 - val_loss: 0.7352 - val_accuracy: 0.6905\nEpoch 18/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6874 - accuracy: 0.7393\nEpoch 00018: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6884 - accuracy: 0.7386 - val_loss: 0.7302 - val_accuracy: 0.6947\nEpoch 19/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6851 - accuracy: 0.7405\nEpoch 00019: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6857 - accuracy: 0.7407 - val_loss: 0.7373 - val_accuracy: 0.6842\nEpoch 20/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6804 - accuracy: 0.7406\nEpoch 00020: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6806 - accuracy: 0.7399 - val_loss: 0.7415 - val_accuracy: 0.6863\nEpoch 21/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6831 - accuracy: 0.7405\nEpoch 00021: val_loss did not improve from 0.72798\n107/107 [==============================] - 1s 10ms/step - loss: 0.6831 - accuracy: 0.7405 - val_loss: 0.7340 - val_accuracy: 0.6908\nEpoch 22/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6797 - accuracy: 0.7396\nEpoch 00022: val_loss improved from 0.72798 to 0.72440, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.6797 - accuracy: 0.7398 - val_loss: 0.7244 - val_accuracy: 0.6980\nEpoch 23/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6773 - accuracy: 0.7430\nEpoch 00023: val_loss improved from 0.72440 to 0.71779, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.6778 - accuracy: 0.7426 - val_loss: 0.7178 - val_accuracy: 0.7016\nEpoch 24/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.6752 - accuracy: 0.7468\nEpoch 00024: val_loss did not improve from 0.71779\n107/107 [==============================] - 1s 10ms/step - loss: 0.6763 - accuracy: 0.7460 - val_loss: 0.7370 - val_accuracy: 0.6887\nEpoch 25/50\n105/107 [============================>.] - ETA: 0s - loss: 0.6738 - accuracy: 0.7477\nEpoch 00025: val_loss improved from 0.71779 to 0.71656, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.6737 - accuracy: 0.7477 - val_loss: 0.7166 - val_accuracy: 0.7013\nEpoch 26/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6708 - accuracy: 0.7498\nEpoch 00026: val_loss improved from 0.71656 to 0.71500, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.6716 - accuracy: 0.7487 - val_loss: 0.7150 - val_accuracy: 0.7013\nEpoch 27/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6736 - accuracy: 0.7475\nEpoch 00027: val_loss improved from 0.71500 to 0.70601, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.6736 - accuracy: 0.7468 - val_loss: 0.7060 - val_accuracy: 0.7040\nEpoch 28/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.6697 - accuracy: 0.7499\nEpoch 00028: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6698 - accuracy: 0.7499 - val_loss: 0.7143 - val_accuracy: 0.7043\nEpoch 29/50\n106/107 [============================>.] - ETA: 0s - loss: 0.6688 - accuracy: 0.7513\nEpoch 00029: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6690 - accuracy: 0.7514 - val_loss: 0.7178 - val_accuracy: 0.7037\nEpoch 30/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6699 - accuracy: 0.7514\nEpoch 00030: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6694 - accuracy: 0.7519 - val_loss: 0.7276 - val_accuracy: 0.6902\nEpoch 31/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6671 - accuracy: 0.7494\nEpoch 00031: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6662 - accuracy: 0.7497 - val_loss: 0.7257 - val_accuracy: 0.6941\nEpoch 32/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6660 - accuracy: 0.7516\nEpoch 00032: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6658 - accuracy: 0.7518 - val_loss: 0.7114 - val_accuracy: 0.7067\nEpoch 33/50\n106/107 [============================>.] - ETA: 0s - loss: 0.6610 - accuracy: 0.7535\nEpoch 00033: val_loss did not improve from 0.70601\n107/107 [==============================] - 1s 10ms/step - loss: 0.6615 - accuracy: 0.7535 - val_loss: 0.7157 - val_accuracy: 0.6992\nEpoch 34/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6657 - accuracy: 0.7539\nEpoch 00034: val_loss improved from 0.70601 to 0.70214, saving model to best_model.h5\n107/107 [==============================] - 1s 10ms/step - loss: 0.6656 - accuracy: 0.7537 - val_loss: 0.7021 - val_accuracy: 0.7118\nEpoch 35/50\n105/107 [============================>.] - ETA: 0s - loss: 0.6639 - accuracy: 0.7520\nEpoch 00035: val_loss did not improve from 0.70214\n107/107 [==============================] - 1s 10ms/step - loss: 0.6633 - accuracy: 0.7525 - val_loss: 0.7186 - val_accuracy: 0.7028\nEpoch 36/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6597 - accuracy: 0.7533\nEpoch 00036: val_loss did not improve from 0.70214\n107/107 [==============================] - 1s 10ms/step - loss: 0.6608 - accuracy: 0.7527 - val_loss: 0.7086 - val_accuracy: 0.7097\nEpoch 37/50\n104/107 [============================>.] - ETA: 0s - loss: 0.6590 - accuracy: 0.7541\nEpoch 00037: val_loss did not improve from 0.70214\n107/107 [==============================] - 1s 11ms/step - loss: 0.6588 - accuracy: 0.7543 - val_loss: 0.7081 - val_accuracy: 0.7067\nEpoch 38/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6561 - accuracy: 0.7555\nEpoch 00038: val_loss did not improve from 0.70214\n107/107 [==============================] - 1s 10ms/step - loss: 0.6569 - accuracy: 0.7550 - val_loss: 0.7325 - val_accuracy: 0.6914\nEpoch 39/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6548 - accuracy: 0.7581\nEpoch 00039: val_loss improved from 0.70214 to 0.70103, saving model to best_model.h5\n107/107 [==============================] - 1s 11ms/step - loss: 0.6547 - accuracy: 0.7588 - val_loss: 0.7010 - val_accuracy: 0.7136\nEpoch 40/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6545 - accuracy: 0.7564\nEpoch 00040: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 11ms/step - loss: 0.6540 - accuracy: 0.7565 - val_loss: 0.7256 - val_accuracy: 0.6968\nEpoch 41/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6525 - accuracy: 0.7592\nEpoch 00041: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6534 - accuracy: 0.7587 - val_loss: 0.7356 - val_accuracy: 0.6905\nEpoch 42/50\n106/107 [============================>.] - ETA: 0s - loss: 0.6516 - accuracy: 0.7595\nEpoch 00042: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6522 - accuracy: 0.7593 - val_loss: 0.7196 - val_accuracy: 0.7004\nEpoch 43/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.6451 - accuracy: 0.7631\nEpoch 00043: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6460 - accuracy: 0.7628 - val_loss: 0.7150 - val_accuracy: 0.7025\nEpoch 44/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6503 - accuracy: 0.7620\nEpoch 00044: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6483 - accuracy: 0.7624 - val_loss: 0.7084 - val_accuracy: 0.7097\nEpoch 45/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6496 - accuracy: 0.7621\nEpoch 00045: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6494 - accuracy: 0.7621 - val_loss: 0.7078 - val_accuracy: 0.7073\nEpoch 46/50\n102/107 [===========================>..] - ETA: 0s - loss: 0.6441 - accuracy: 0.7635\nEpoch 00046: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6439 - accuracy: 0.7629 - val_loss: 0.7064 - val_accuracy: 0.7076\nEpoch 47/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.6474 - accuracy: 0.7622\nEpoch 00047: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6464 - accuracy: 0.7624 - val_loss: 0.7047 - val_accuracy: 0.7094\nEpoch 48/50\n101/107 [===========================>..] - ETA: 0s - loss: 0.6427 - accuracy: 0.7655\nEpoch 00048: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6437 - accuracy: 0.7650 - val_loss: 0.7054 - val_accuracy: 0.7142\nEpoch 49/50\n103/107 [===========================>..] - ETA: 0s - loss: 0.6396 - accuracy: 0.7649\nEpoch 00049: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6394 - accuracy: 0.7653 - val_loss: 0.7040 - val_accuracy: 0.7100\nEpoch 50/50\n105/107 [============================>.] - ETA: 0s - loss: 0.6393 - accuracy: 0.7652\nEpoch 00050: val_loss did not improve from 0.70103\n107/107 [==============================] - 1s 10ms/step - loss: 0.6390 - accuracy: 0.7656 - val_loss: 0.7030 - val_accuracy: 0.7130\n"
    }
   ],
   "source": [
    "history = model.fit(ds_train, epochs=50, validation_data=ds_test, callbacks=[mc])\n",
    "model.save_weights('best_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model(vocab_size)\n",
    "model.load_weights('best_model.h5')\n",
    "predictions = model.predict(ds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[557. 322.  49.]\n [143. 995. 201.]\n [ 26. 214. 821.]]\n"
    }
   ],
   "source": [
    "confusion = np.zeros((3,3))\n",
    "for batch in ds_test:\n",
    "    predictions = model.predict(batch[0])\n",
    "    for true, pred in zip(batch[1], predictions):\n",
    "        true_label = np.argmax(true)\n",
    "        pred_label = np.argmax(pred)\n",
    "        confusion[true_label, pred_label] +=1\n",
    "\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x20e82dea888>]"
     },
     "metadata": {},
     "execution_count": 65
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<tf.Variable 'embedding_9/embeddings:0' shape=(2000, 32) dtype=float32, numpy=\n array([[ 0.04424066, -0.01784859, -0.0432557 , ...,  0.01719034,\n          0.00955299, -0.01215024],\n        [-0.03365136,  0.01069328,  0.05879226, ..., -0.02574051,\n         -0.01190598, -0.00310444],\n        [ 0.01170665, -0.00657572, -0.04220049, ..., -0.01669901,\n         -0.00840161, -0.00816824],\n        ...,\n        [-0.00123701, -0.00586937, -0.00472165, ...,  0.00338023,\n         -0.00943799, -0.00431655],\n        [-0.00681446,  0.00374831, -0.00764997, ..., -0.00799815,\n         -0.00547371,  0.00555813],\n        [ 0.00618423, -0.00730927, -0.00237894, ...,  0.00973711,\n         -0.01399849, -0.00641342]], dtype=float32)>]"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "model.layers[0].weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1430"
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "sum([1 if sent == 'neutral' else 0 for sent in test_sentiments])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision    recall  f1-score   support\n\n           0       0.80      0.71      0.75      7781\n           1       0.72      0.81      0.76     11118\n           2       0.85      0.79      0.82      8582\n\n    accuracy                           0.78     27481\n   macro avg       0.79      0.77      0.78     27481\nweighted avg       0.78      0.78      0.78     27481\n\n"
    }
   ],
   "source": [
    "data = tf.keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences(texts), padding='post')\n",
    "print(sk_mtr.classification_report(\n",
    "    y_true=dataset.sentiments_to_number(sentiments),\n",
    "    y_pred=np.argmax(model.predict(data, batch_size=128),axis=1),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision    recall  f1-score   support\n\n           0       0.77      0.61      0.68      1001\n           1       0.65      0.74      0.69      1430\n           2       0.76      0.77      0.77      1103\n\n    accuracy                           0.71      3534\n   macro avg       0.73      0.71      0.71      3534\nweighted avg       0.72      0.71      0.71      3534\n\n"
    }
   ],
   "source": [
    "data = tf.keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences(test_texts), padding='post')\n",
    "print(sk_mtr.classification_report(\n",
    "    y_true=dataset.sentiments_to_number(test_sentiments),\n",
    "    y_pred=np.argmax(model.predict(data, batch_size=128),axis=1),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer_json = tokenizer.to_json()\n",
    "with open('tokenizer.json', 'w') as f:\n",
    "    f.write(tokenizer_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python37764bit4663b82a9d4e4504af47c5f2d9c73ebd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}