src/IMDB_Movie_reviews_sentiment_classification_keras_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "\n",
    "`numpy`      package for array handling  \n",
    "`pandas`     import to create and modify dataframes     \n",
    "`matplotlib` package for data visulization  \n",
    "`seaborn`    build on matploblib, higher level graph functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "Here we are taking the inbuilt function of keras to load the data from the server  \n",
    "The dataset file in present in the [Link to dataset in amazon server](https://s3.amazonaws.com/text-datasets/imdb.npz)  \n",
    "The inbuilt code \n",
    "```python\n",
    "def load_data(path='imdb.npz', num_words=None, skip_top=0,\n",
    "              maxlen=None, seed=113,\n",
    "              start_char=1, oov_char=2, index_from=3, **kwargs):\n",
    "    \"\"\"Loads the IMDB dataset.\n",
    "    # Arguments\n",
    "        path: where to cache the data (relative to `~/.keras/dataset`).\n",
    "        num_words: max number of words to include. Words are ranked\n",
    "            by how often they occur (in the training set) and only\n",
    "            the most frequent words are kept\n",
    "        skip_top: skip the top N most frequently occurring words\n",
    "            (which may not be informative).\n",
    "        maxlen: sequences longer than this will be filtered out.\n",
    "        seed: random seed for sample shuffling.\n",
    "        start_char: The start of a sequence will be marked with this character.\n",
    "            Set to 1 because 0 is usually the padding character.\n",
    "        oov_char: words that were cut out because of the `num_words`\n",
    "            or `skip_top` limit will be replaced with this character.\n",
    "        index_from: index actual words with this index and higher.\n",
    "    # Returns\n",
    "        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
    "    # Raises\n",
    "        ValueError: in case `maxlen` is so low\n",
    "            that no input sequence could be kept.\n",
    "    Note that the 'out of vocabulary' character is only used for\n",
    "    words that were present in the training set but are not included\n",
    "    because they're not making the `num_words` cut here.\n",
    "    Words that were not seen in the training set but are in the test set\n",
    "    have simply been skipped.\n",
    "    \"\"\"\n",
    "    # Legacy support\n",
    "    if 'nb_words' in kwargs:\n",
    "        warnings.warn('The `nb_words` argument in `load_data` '\n",
    "                      'has been renamed `num_words`.')\n",
    "        num_words = kwargs.pop('nb_words')\n",
    "    if kwargs:\n",
    "        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))\n",
    "\n",
    "    path = get_file(path,\n",
    "                    origin='https://s3.amazonaws.com/text-datasets/imdb.npz',\n",
    "                    file_hash='599dadb1135973df5b59232a0e9a887c')\n",
    "    with np.load(path) as f:\n",
    "        x_train, labels_train = f['x_train'], f['y_train']\n",
    "        x_test, labels_test = f['x_test'], f['y_test']\n",
    "\n",
    "    np.random.seed(seed)\n",
    "    indices = np.arange(len(x_train))\n",
    "    np.random.shuffle(indices)\n",
    "    x_train = x_train[indices]\n",
    "    labels_train = labels_train[indices]\n",
    "\n",
    "    indices = np.arange(len(x_test))\n",
    "    np.random.shuffle(indices)\n",
    "    x_test = x_test[indices]\n",
    "    labels_test = labels_test[indices]\n",
    "\n",
    "    xs = np.concatenate([x_train, x_test])\n",
    "    labels = np.concatenate([labels_train, labels_test])\n",
    "\n",
    "    if start_char is not None:\n",
    "        xs = [[start_char] + [w + index_from for w in x] for x in xs]\n",
    "    elif index_from:\n",
    "        xs = [[w + index_from for w in x] for x in xs]\n",
    "\n",
    "    if maxlen:\n",
    "        xs, labels = _remove_long_seq(maxlen, xs, labels)\n",
    "        if not xs:\n",
    "            raise ValueError('After filtering for sequences shorter than maxlen=' +\n",
    "                             str(maxlen) + ', no sequence was kept. '\n",
    "                             'Increase maxlen.')\n",
    "    if not num_words:\n",
    "        num_words = max([max(x) for x in xs])\n",
    "\n",
    "    # by convention, use 2 as OOV word\n",
    "    # reserve 'index_from' (=3 by default) characters:\n",
    "    # 0 (padding), 1 (start), 2 (OOV)\n",
    "    if oov_char is not None:\n",
    "        xs = [[w if (skip_top <= w < num_words) else oov_char for w in x]\n",
    "              for x in xs]\n",
    "    else:\n",
    "        xs = [[w for w in x if skip_top <= w < num_words]\n",
    "              for x in xs]\n",
    "\n",
    "    idx = len(x_train)\n",
    "    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
    "    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n",
    "\n",
    "    return (x_train, y_train), (x_test, y_test)\n",
    "\n",
    "\n",
    "def get_word_index(path='imdb_word_index.json'):\n",
    "    \"\"\"Retrieves the dictionary mapping words to word indices.\n",
    "    # Arguments\n",
    "        path: where to cache the data (relative to `~/.keras/dataset`).\n",
    "    # Returns\n",
    "        The word index dictionary.\n",
    "    \"\"\"\n",
    "    path = get_file(\n",
    "        path,\n",
    "        origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json',\n",
    "        file_hash='bfafd718b763782e994055a2d397834f')\n",
    "    with open(path) as f:\n",
    "        return json.load(f)\n",
    "```\n",
    "## Regarding Dataset  \n",
    "Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer \"3\" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: \"only consider the top 10,000 most common words, but eliminate the top 20 most common words\".     \n",
    "As a convention, \"0\" does not stand for a specific word, but instead is used to encode any unknown word.   \n",
    "[official Link](http://ai.stanford.edu/~amaas/data/sentiment/)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "from keras.datasets import imdb\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000,skip_top=0,maxlen=None,seed=113,start_char=1,oov_char=2,index_from=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understand the dataset\n",
    "Let see the size and shape of test and training tuples\n",
    "\n",
    "``On Execution``  \n",
    "There will be 25000 samples list of different sized word embeddings in training set  \n",
    "There will be 25000 samples list of different sized word embeddings in testing set\n",
    "The first review would be (218,) sized word embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training samples 25000\n",
      "The number of testing samples 25000\n",
      "The shape of training samples array (25000,)\n",
      "The shape of training samples labels (25000,)\n",
      "First element in x_train ,its type : <class 'list'> it's shape : (218,)\n"
     ]
    }
   ],
   "source": [
    "#Understanding the data\n",
    "print(\"The number of training samples\",len(x_train))\n",
    "print(\"The number of testing samples\",len(x_test))\n",
    "print(\"The shape of training samples array\",np.shape(x_train))\n",
    "print(\"The shape of training samples labels\", np.shape(y_train))\n",
    "print(\"First element in x_train ,its type :\",type(x_train[0]),\"it's shape :\",np.shape(x_train[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data\n",
    "Let us try to visualise the few samples of data so, we could get an idea of how the data looks like  \n",
    "`On Execution`  \n",
    "We can see first two reviews\n",
    "> Note: on loading the dataset we load the word embedding, to convert into the actual reviews we take the help of word to index dictonary to convert the given words embedding to actual reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First Review \n",
      "\n",
      "the as you with out themselves powerful lets loves their becomes reaching had journalist of lot from anyone to have after out atmosphere never more room and it so heart shows to years of every never going and help moments or of every chest visual movie except her was several of enough more with is now current film as you of mine potentially unfortunately of you than him that with out themselves her get for was camp of you movie sometimes movie that with scary but and to story wonderful that in seeing in character to of 70s musicians with heart had shadows they of here that with her serious to have does when from why what have critics they is you that isn't one will very to as itself with other and in of seen over landed for anyone of and br show's to whether from than out themselves history he name half some br of and odd was two most of mean for 1 any an boat she he should is thought frog but of script you not while history he heart to real at barrel but when from one bit then have two of script their with her nobody most that with wasn't to with armed acting watch an for with heartfelt film want an\n",
      "\n",
      "Its label : 1\n",
      "\n",
      "Second Review \n",
      "\n",
      "the thought solid thought senator do making to is spot nomination assumed while he of jack in where picked as getting on was did hands fact characters to always life thrillers not as me can't in at are br of sure your way of little it strongly random to view of love it so principles of guy it used producer of where it of here icon film of outside to don't all unique some like of direction it if out her imagination below keep of queen he diverse to makes this stretch and of solid it thought begins br senator and budget worthwhile though ok and awaiting for ever better were and diverse for budget look kicked any to of making it out and follows for effects show to show cast this family us scenes more it severe making senator to and finds tv tend to of emerged these thing wants but and an beckinsale cult as it is video do you david see scenery it in few those are of ship for with of wild to one is very work dark they don't do dvd with those them\n",
      "\n",
      "Its label : 0\n"
     ]
    }
   ],
   "source": [
    "# Data Visulization\n",
    "\n",
    "# A dictionary mapping words to an integer index\n",
    "word_index = imdb.get_word_index()\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) # Reversed dictionary\n",
    "\n",
    "def decode_review(text):\n",
    "    return ' '.join([reverse_word_index.get(i, '?') for i in text])\n",
    "\n",
    "## Viewing the review's\n",
    "print('\\nFirst Review \\n')\n",
    "print(decode_review(x_train[0]))\n",
    "print('\\nIts label :',y_train[0])\n",
    "\n",
    "print('\\nSecond Review \\n')\n",
    "print(decode_review(x_train[1]))\n",
    "print('\\nIts label :',y_train[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the data\n",
    "\n",
    "## Padding\n",
    "Each review in the dataset would have a different size but, in a neural network model would like to have all its inputs of same size for that we all padding to the reviews.  \n",
    "We make sure all the embeddings have the same length of max lenght i.e. 256 (for this dataset).   \n",
    "For embeddings longer than 256 the enddings will be chopped off.   \n",
    "For embeddings shorter than 256 '0' (zeros) would be \n",
    "> Note: On adding 0 to the embeddings we are not creating a new word in the embedding. The dataset doesn't assign any word to the value 0 . 0 is some kind of unknown word which we use as padding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preprocessing\n",
    "## Padding\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "x_train_padded = pad_sequences(x_train,value=0,padding='post',maxlen=256)\n",
    "x_test_padded =  pad_sequences(x_test,value=0,padding='post',maxlen=256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training varibles\n",
    "These Training varbles are hyper parameters for neural network training.   \n",
    "`epochs` : each epoch is forward propagation + backward propagation over the whole dataset once is called one epoch.  \n",
    "`learning_rate` : the magnitude in which the weights are modified one the acquired loss.   \n",
    "`learning_rate_decay` : there can be high leanring rate at the beining of the training when the loss is high. Over a period of time the learning rate can reduce for fine training of network.  \n",
    "`batch_size` : the data is fed to the network in batches of 32 samples at each time. This batch feeding is done all over the whole dataset.  \n",
    "\n",
    "`vocab_size` : vocab size is the total number of words present in the dictonary. This size would represent the input size to the nural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training varibles\n",
    "learning_rate = 0.00001\n",
    "learning_rate_decay = 0.00001\n",
    "batch_size = 512\n",
    "epochs = 60\n",
    "classes = 2\n",
    "## input shape is the vocabulary count used for the movie reviews (10,000 words)\n",
    "vocab_size = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Netowork Model\n",
    "`Line 6` : we are building a keras sequential model  \n",
    "`Line 13` : we are using Adam optimizer  \n",
    "`Line 16` : compiling the model to check if the model is build properly.  \n",
    "\n",
    "The loss function being used is `binary_crossentropy` since its a two class classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding,GlobalAveragePooling1D,Dense\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size, 16))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dense(16, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "# optimizer\n",
    "optimizer = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=learning_rate_decay, amsgrad=False)\n",
    "\n",
    "# Model Compilation\n",
    "model.compile(optimizer=optimizer,loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training \n",
    "Training is the process of feeding the data to neural network and modifiying the weights of the model using the the backpropagation algorithm. The backpropagation using loss the function acquires the loss over batch size of data and does a backpropagation to modify the weights in such a way the in the next epoch the loss would be less when compared to the current epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/60\n",
      "25000/25000 [==============================] - 5s 184us/step - loss: 0.6931 - acc: 0.5153 - val_loss: 0.6931 - val_acc: 0.5144\n",
      "Epoch 2/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6931 - acc: 0.5226 - val_loss: 0.6931 - val_acc: 0.5198\n",
      "Epoch 3/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6930 - acc: 0.5287 - val_loss: 0.6931 - val_acc: 0.5246\n",
      "Epoch 4/60\n",
      "25000/25000 [==============================] - 1s 28us/step - loss: 0.6930 - acc: 0.5352 - val_loss: 0.6930 - val_acc: 0.5308\n",
      "Epoch 5/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6929 - acc: 0.5398 - val_loss: 0.6930 - val_acc: 0.5353\n",
      "Epoch 6/60\n",
      "25000/25000 [==============================] - 1s 34us/step - loss: 0.6929 - acc: 0.5453 - val_loss: 0.6929 - val_acc: 0.5401\n",
      "Epoch 7/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6928 - acc: 0.5484 - val_loss: 0.6928 - val_acc: 0.5450\n",
      "Epoch 8/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6927 - acc: 0.5536 - val_loss: 0.6928 - val_acc: 0.5490\n",
      "Epoch 9/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6927 - acc: 0.5575 - val_loss: 0.6927 - val_acc: 0.5528\n",
      "Epoch 10/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6926 - acc: 0.5626 - val_loss: 0.6926 - val_acc: 0.5579\n",
      "Epoch 11/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6925 - acc: 0.5684 - val_loss: 0.6926 - val_acc: 0.5618\n",
      "Epoch 12/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6924 - acc: 0.5724 - val_loss: 0.6925 - val_acc: 0.5658\n",
      "Epoch 13/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6924 - acc: 0.5771 - val_loss: 0.6924 - val_acc: 0.5703\n",
      "Epoch 14/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6923 - acc: 0.5817 - val_loss: 0.6924 - val_acc: 0.5742\n",
      "Epoch 15/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6922 - acc: 0.5857 - val_loss: 0.6923 - val_acc: 0.5779\n",
      "Epoch 16/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6921 - acc: 0.5900 - val_loss: 0.6922 - val_acc: 0.5804\n",
      "Epoch 17/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6921 - acc: 0.5945 - val_loss: 0.6921 - val_acc: 0.5848\n",
      "Epoch 18/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6920 - acc: 0.5975 - val_loss: 0.6921 - val_acc: 0.5880\n",
      "Epoch 19/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6919 - acc: 0.6018 - val_loss: 0.6920 - val_acc: 0.5912\n",
      "Epoch 20/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6918 - acc: 0.6057 - val_loss: 0.6919 - val_acc: 0.5941\n",
      "Epoch 21/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6917 - acc: 0.6094 - val_loss: 0.6919 - val_acc: 0.5969\n",
      "Epoch 22/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6917 - acc: 0.6119 - val_loss: 0.6918 - val_acc: 0.5989\n",
      "Epoch 23/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6916 - acc: 0.6150 - val_loss: 0.6917 - val_acc: 0.6022\n",
      "Epoch 24/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6915 - acc: 0.6194 - val_loss: 0.6916 - val_acc: 0.6050\n",
      "Epoch 25/60\n",
      "25000/25000 [==============================] - 1s 34us/step - loss: 0.6914 - acc: 0.6242 - val_loss: 0.6916 - val_acc: 0.6090\n",
      "Epoch 26/60\n",
      "25000/25000 [==============================] - 1s 35us/step - loss: 0.6914 - acc: 0.6276 - val_loss: 0.6915 - val_acc: 0.6141\n",
      "Epoch 27/60\n",
      "25000/25000 [==============================] - 1s 33us/step - loss: 0.6913 - acc: 0.6320 - val_loss: 0.6914 - val_acc: 0.6154\n",
      "Epoch 28/60\n",
      "25000/25000 [==============================] - 1s 36us/step - loss: 0.6912 - acc: 0.6344 - val_loss: 0.6914 - val_acc: 0.6184\n",
      "Epoch 29/60\n",
      "25000/25000 [==============================] - 1s 36us/step - loss: 0.6911 - acc: 0.6392 - val_loss: 0.6913 - val_acc: 0.6233\n",
      "Epoch 30/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6910 - acc: 0.6434 - val_loss: 0.6912 - val_acc: 0.6264\n",
      "Epoch 31/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6910 - acc: 0.6488 - val_loss: 0.6911 - val_acc: 0.6330\n",
      "Epoch 32/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6909 - acc: 0.6510 - val_loss: 0.6910 - val_acc: 0.6357\n",
      "Epoch 33/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6908 - acc: 0.6535 - val_loss: 0.6910 - val_acc: 0.6392\n",
      "Epoch 34/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6907 - acc: 0.6589 - val_loss: 0.6909 - val_acc: 0.6454\n",
      "Epoch 35/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6906 - acc: 0.6629 - val_loss: 0.6908 - val_acc: 0.6492\n",
      "Epoch 36/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6905 - acc: 0.6684 - val_loss: 0.6907 - val_acc: 0.6547\n",
      "Epoch 37/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6904 - acc: 0.6729 - val_loss: 0.6906 - val_acc: 0.6591\n",
      "Epoch 38/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6903 - acc: 0.6785 - val_loss: 0.6906 - val_acc: 0.6636\n",
      "Epoch 39/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6902 - acc: 0.6825 - val_loss: 0.6905 - val_acc: 0.6651\n",
      "Epoch 40/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6902 - acc: 0.6853 - val_loss: 0.6904 - val_acc: 0.6696\n",
      "Epoch 41/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6901 - acc: 0.6904 - val_loss: 0.6903 - val_acc: 0.6742\n",
      "Epoch 42/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6900 - acc: 0.6934 - val_loss: 0.6902 - val_acc: 0.6777\n",
      "Epoch 43/60\n",
      "25000/25000 [==============================] - 1s 32us/step - loss: 0.6899 - acc: 0.6971 - val_loss: 0.6901 - val_acc: 0.6815\n",
      "Epoch 44/60\n",
      "25000/25000 [==============================] - 1s 34us/step - loss: 0.6898 - acc: 0.7015 - val_loss: 0.6900 - val_acc: 0.6859\n",
      "Epoch 45/60\n",
      "25000/25000 [==============================] - 1s 33us/step - loss: 0.6897 - acc: 0.7086 - val_loss: 0.6899 - val_acc: 0.6924\n",
      "Epoch 46/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6896 - acc: 0.7122 - val_loss: 0.6898 - val_acc: 0.6950\n",
      "Epoch 47/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6895 - acc: 0.7149 - val_loss: 0.6897 - val_acc: 0.6990\n",
      "Epoch 48/60\n",
      "25000/25000 [==============================] - 1s 36us/step - loss: 0.6894 - acc: 0.7178 - val_loss: 0.6896 - val_acc: 0.7031\n",
      "Epoch 49/60\n",
      "25000/25000 [==============================] - 1s 31us/step - loss: 0.6892 - acc: 0.7200 - val_loss: 0.6895 - val_acc: 0.7061\n",
      "Epoch 50/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6891 - acc: 0.7235 - val_loss: 0.6894 - val_acc: 0.7092\n",
      "Epoch 51/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6890 - acc: 0.7289 - val_loss: 0.6893 - val_acc: 0.7136\n",
      "Epoch 52/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6889 - acc: 0.7360 - val_loss: 0.6892 - val_acc: 0.7207\n",
      "Epoch 53/60\n",
      "25000/25000 [==============================] - 1s 30us/step - loss: 0.6888 - acc: 0.7363 - val_loss: 0.6891 - val_acc: 0.7214\n",
      "Epoch 54/60\n",
      "25000/25000 [==============================] - 1s 28us/step - loss: 0.6887 - acc: 0.7389 - val_loss: 0.6890 - val_acc: 0.7241\n",
      "Epoch 55/60\n",
      "25000/25000 [==============================] - 1s 28us/step - loss: 0.6886 - acc: 0.7416 - val_loss: 0.6889 - val_acc: 0.7287\n",
      "Epoch 56/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6884 - acc: 0.7454 - val_loss: 0.6888 - val_acc: 0.7310\n",
      "Epoch 57/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6883 - acc: 0.7462 - val_loss: 0.6887 - val_acc: 0.7329\n",
      "Epoch 58/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6882 - acc: 0.7467 - val_loss: 0.6886 - val_acc: 0.7338\n",
      "Epoch 59/60\n",
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6881 - acc: 0.7496 - val_loss: 0.6884 - val_acc: 0.7373\n",
      "Epoch 60/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 1s 29us/step - loss: 0.6879 - acc: 0.7509 - val_loss: 0.6883 - val_acc: 0.7397\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "model_history = model.fit(x_train_padded,y_train,epochs=epochs,batch_size=batch_size,validation_data=(x_test_padded, y_test),verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "using the trained model we try to predict what are the sentiments in the test set\n",
    "> here we use the thershold function to convert the output propabilites to positive or negetive with thershold value of 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Results\n",
    "y_pred = model.predict(x_test_padded)\n",
    "\n",
    "def thershold_function(x): # Function to convert sigmoid outputs to 0's and 1's\n",
    "    if x>0.5:\n",
    "        return 1\n",
    "    else :\n",
    "        return 0\n",
    "thershold = np.vectorize(thershold_function)\n",
    "\n",
    "y_pred = thershold(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verifying the results\n",
    "cheking the results how good they are with the first 10 samples.   \n",
    "Plotting the graphs of test and train set accuracies and loss values. \n",
    "> NOTE: This plot is a very curicial step. These plots would tell us how good the model converges and if there is any overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground truths of first 10 images in test set [0 1 1 0 1 1 1 0 0 1]\n",
      "Predicted values of first 10 image in test set [0 1 1 0 0 0 1 1 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Verifying the results\n",
    "print(\"Ground truths of first 10 images in test set\",np.array(y_test[0:10]))\n",
    "print(\"Predicted values of first 10 image in test set\",y_pred[0:10].ravel())\n",
    "\n",
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "plt.plot(loss,label='train')\n",
    "plt.plot(val_loss,label='test')\n",
    "plt.title('loss Graph')\n",
    "plt.ylabel('precentage')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "acc = model_history.history['acc']\n",
    "val_acc = model_history.history['val_acc']\n",
    "plt.plot(acc,label='train')\n",
    "plt.plot(val_acc,label='test')\n",
    "plt.title('Accuracy Graph')\n",
    "plt.ylabel('precentage')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visulizing the results\n",
    "checking the results by visulizing them and creating a confusion matrix. The values of precession and accuracy can be obtained by the help of confusion matrix and f1 scores to compare this architecure with other architectures of neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted     0     1\n",
      "Actual               \n",
      "0          9231  3269\n",
      "1          3238  9262\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16696e2d320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visulizing the results\n",
    "y_pred = y_pred.ravel()\n",
    "y_pred = pd.Series(y_pred, name='Predicted')\n",
    "y_test = pd.Series(y_test, name='Actual')\n",
    "df_confusion  = pd.crosstab(y_test,y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
    "print(df_confusion)\n",
    "plt.figure(figsize = (20,20))\n",
    "plt.xlabel('xlabel', fontsize=18)\n",
    "plt.ylabel('ylabel', fontsize=18)\n",
    "plt.title('Confusion Matrix',fontsize=20)\n",
    "sns.heatmap(df_confusion, annot=True,fmt=\"d\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}