{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hussnain\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Imports\n",
    "from keras.datasets import imdb\n",
    "\n",
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "from keras import losses\n",
    "from keras import metrics,activations\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\n",
      " 1048576/17464789 [>.............................] - ETA: 53:49"
     ]
    }
   ],
   "source": [
    "#Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\n",
    "\n",
    "(xtrain,ytrain), (xtest, ytest) = imdb.load_data(num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring the dataset\n",
    "\n",
    "print('xtrain shape', xtrain.shape)\n",
    "print('ytrain shape', ytrain.shape)\n",
    "print()\n",
    "print('xtest shape', xtest.shape)\n",
    "print('ytest shape', ytest.shape)\n",
    "print()\n",
    "print('xtrain first review as dictionary index', xtrain[1])\n",
    "print()\n",
    "print()\n",
    "print('ytrain label', ytrain[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#index to words mapping\n",
    "word_index = imdb.get_word_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decode_review = ' '.join([reverse_word_index.get(i-3, reverse_word_index.get(i)) for i in xtrain[22]])\n",
    "decode_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1. \n",
    "    return results\n",
    "\n",
    "x_train = vectorize_sequences(xtrain)\n",
    "x_test = vectorize_sequences(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain = np.asarray(ytrain).astype('float32')\n",
    "ytest = np.asarray(ytest).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation=activations.relu, input_shape=(10000,)))\n",
    "model.add(layers.Dense(16, activation=activations.relu))\n",
    "model.add(layers.Dense(1, activation=activations.sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizers.RMSprop(lr=0.0001), loss=losses.mse, metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = x_train[:10000]\n",
    "y_val = ytrain[:10000]\n",
    "\n",
    "x_train_partial = x_train[10000:]\n",
    "y_train_partial = ytrain[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(x_train_partial, y_train_partial, epochs=4, batch_size=512, validation_data=(x_val,y_val))\n",
    "history_dict = history.history\n",
    "history_dict.keys()\n",
    "print(history.history['acc'][-1])\n",
    "print(history.history['val_acc'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.predict(x_train_partial[22:23]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "epochs = range(0, len(loss)+1)\n",
    "epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "# \"bo\" is for \"blue dot\"\n",
    "plt.plot(epochs, loss, 'ro', label='Training loss')\n",
    "# b is for \"solid blue line\"\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.clf()      # clear figure# clear  \n",
    "acc_values = history_dict['acc']\n",
    "val_acc_values = history_dict['val_acc']\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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