movie review example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14c11211",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, Flatten, Dense\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5244b3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_dir = 'aclImdb'\n",
    "train_dir = os.path.join(imdb_dir, 'train')\n",
    "labels = []\n",
    "texts = []\n",
    "for label_type in ['neg', 'pos']:\n",
    "    dir_name = os.path.join(train_dir, label_type)\n",
    "    for fname in os.listdir(dir_name):\n",
    "        if fname[-4:] == '.txt':\n",
    "            f = open(os.path.join(dir_name, fname))\n",
    "            texts.append(f.read())\n",
    "    #    print(fname)\n",
    "            f.close()\n",
    "            if label_type == 'neg':\n",
    "                labels.append(0)\n",
    "            else:\n",
    "                labels.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9bf0f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlen = 100\n",
    "training_samples = 200\n",
    "validation_samples = 10000\n",
    "max_words = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "afb7d7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=max_words)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences = tokenizer.texts_to_sequences(texts)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f93cdd1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 88582 unique tokens.\n",
      "Shape of data tensor: (25000, 100)\n",
      "Shape of label tensor: (25000,)\n"
     ]
    }
   ],
   "source": [
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "data = pad_sequences(sequences, maxlen=maxlen)\n",
    "labels = np.asarray(labels)\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)\n",
    "indices = np.arange(data.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "data = data[indices]\n",
    "labels = labels[indices]\n",
    "x_train = data[:training_samples]\n",
    "y_train = labels[:training_samples]\n",
    "x_val = data[training_samples: training_samples + validation_samples]\n",
    "y_val = labels[training_samples: training_samples + validation_samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9119f926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 100, 32)           320000    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3200)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                102432    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 422,465\n",
      "Trainable params: 422,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(max_words, 32, input_length=maxlen))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97b42658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "7/7 [==============================] - 6s 170ms/step - loss: 0.6931 - acc: 0.5000 - val_loss: 0.6926 - val_acc: 0.5166\n",
      "Epoch 2/10\n",
      "7/7 [==============================] - 1s 95ms/step - loss: 0.6092 - acc: 0.9850 - val_loss: 0.6920 - val_acc: 0.5191\n",
      "Epoch 3/10\n",
      "7/7 [==============================] - 1s 86ms/step - loss: 0.5235 - acc: 1.0000 - val_loss: 0.6928 - val_acc: 0.5165\n",
      "Epoch 4/10\n",
      "7/7 [==============================] - 1s 100ms/step - loss: 0.4013 - acc: 1.0000 - val_loss: 0.6946 - val_acc: 0.5201\n",
      "Epoch 5/10\n",
      "7/7 [==============================] - 1s 103ms/step - loss: 0.2768 - acc: 1.0000 - val_loss: 0.6978 - val_acc: 0.5197\n",
      "Epoch 6/10\n",
      "7/7 [==============================] - 1s 83ms/step - loss: 0.1793 - acc: 1.0000 - val_loss: 0.6977 - val_acc: 0.5224\n",
      "Epoch 7/10\n",
      "7/7 [==============================] - 1s 98ms/step - loss: 0.1108 - acc: 1.0000 - val_loss: 0.6998 - val_acc: 0.5260\n",
      "Epoch 8/10\n",
      "7/7 [==============================] - 1s 101ms/step - loss: 0.0687 - acc: 1.0000 - val_loss: 0.7053 - val_acc: 0.5290\n",
      "Epoch 9/10\n",
      "7/7 [==============================] - 1s 92ms/step - loss: 0.0428 - acc: 1.0000 - val_loss: 0.7066 - val_acc: 0.5301\n",
      "Epoch 10/10\n",
      "7/7 [==============================] - 1s 94ms/step - loss: 0.0264 - acc: 1.0000 - val_loss: 0.7130 - val_acc: 0.5290\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['acc'])\n",
    "history = model.fit(x_train, y_train,\n",
    "epochs=10,\n",
    "batch_size=32,\n",
    "validation_data=(x_val, y_val))\n",
    "model.save_weights('pre_trained_glove_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a6fd50e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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   ],
   "source": [
    "\n",
    "prediction = model.predict(x_train)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9778fe10",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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