classifying_movie_reviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) #importing and defining train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#decode the reviews back into english words\n",
    "word_index = imdb.get_word_index() #dictionnary mapping words to an integer index\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) #reverses the dictionnary above, i.e. mapping integers to words\n",
    "def decoded(x): #decoding function for the review x\n",
    "    print(' '.join([reverse_word_index.get(i-3,'?') for i in train_data[x]])) #decodes the review, i-3 not entirely clear!\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
     ]
    }
   ],
   "source": [
    "reverse_word_index[train_data[0][0]]\n",
    "decoded(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing our data for a neural network, i.e. convert into tensors\n",
    "import numpy as np\n",
    "def vectorize_sequences(sequences, dimension=10000): #not entirely clear!\n",
    "    results = np.zeros((len(sequences), dimension)) #all zero matrix of shape(len(sequences),dimension)\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1. #set specific indices of results[i] to 1\n",
    "    return results\n",
    "\n",
    "#vectorize labels\n",
    "x_train = vectorize_sequences(train_data) #vectorized training data\n",
    "x_test = vectorize_sequences(test_data) #vectorized test data\n",
    "\n",
    "#vectorize labels\n",
    "y_train = np.asarray(train_labels).astype('float32') #vectorized training labels\n",
    "y_test = np.asarray(test_labels).astype('float32') #vectorized test labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining the model\n",
    "#not entirely clear, but will be explained later\n",
    "from tensorflow.keras import models #without writing tensorflow. an error occurs!\n",
    "from tensorflow.keras import layers #without writing tensorflow. an error occurs!\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting aside a validation set\n",
    "x_val = x_train[:10000]\n",
    "partial_x_train = x_train[10000:]\n",
    "y_val = y_train[:10000]\n",
    "partial_y_train = y_train[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "15000/15000 [==============================] - 3s 170us/sample - loss: 0.5253 - acc: 0.7852 - val_loss: 0.4025 - val_acc: 0.8633\n",
      "Epoch 2/20\n",
      "15000/15000 [==============================] - 2s 140us/sample - loss: 0.3220 - acc: 0.9015 - val_loss: 0.3149 - val_acc: 0.8852\n",
      "Epoch 3/20\n",
      "15000/15000 [==============================] - 2s 152us/sample - loss: 0.2374 - acc: 0.9225 - val_loss: 0.2821 - val_acc: 0.8913\n",
      "Epoch 4/20\n",
      "15000/15000 [==============================] - 2s 150us/sample - loss: 0.1895 - acc: 0.9379 - val_loss: 0.2783 - val_acc: 0.8902\n",
      "Epoch 5/20\n",
      "15000/15000 [==============================] - 2s 136us/sample - loss: 0.1532 - acc: 0.9510 - val_loss: 0.2854 - val_acc: 0.8872\n",
      "Epoch 6/20\n",
      "15000/15000 [==============================] - 3s 190us/sample - loss: 0.1277 - acc: 0.9601 - val_loss: 0.2835 - val_acc: 0.8877\n",
      "Epoch 7/20\n",
      "15000/15000 [==============================] - 2s 134us/sample - loss: 0.1076 - acc: 0.9663 - val_loss: 0.2987 - val_acc: 0.8846\n",
      "Epoch 8/20\n",
      "15000/15000 [==============================] - 2s 135us/sample - loss: 0.0875 - acc: 0.9753 - val_loss: 0.3162 - val_acc: 0.8836\n",
      "Epoch 9/20\n",
      "15000/15000 [==============================] - 2s 157us/sample - loss: 0.0729 - acc: 0.9802 - val_loss: 0.3430 - val_acc: 0.8816\n",
      "Epoch 10/20\n",
      "15000/15000 [==============================] - 3s 173us/sample - loss: 0.0612 - acc: 0.9840 - val_loss: 0.3602 - val_acc: 0.8791\n",
      "Epoch 11/20\n",
      "15000/15000 [==============================] - 2s 155us/sample - loss: 0.0495 - acc: 0.9881 - val_loss: 0.4118 - val_acc: 0.8705\n",
      "Epoch 12/20\n",
      "15000/15000 [==============================] - 2s 129us/sample - loss: 0.0396 - acc: 0.9908 - val_loss: 0.4111 - val_acc: 0.8756\n",
      "Epoch 13/20\n",
      "15000/15000 [==============================] - 2s 138us/sample - loss: 0.0329 - acc: 0.9927 - val_loss: 0.4430 - val_acc: 0.8747\n",
      "Epoch 14/20\n",
      "15000/15000 [==============================] - 2s 134us/sample - loss: 0.0270 - acc: 0.9946 - val_loss: 0.4756 - val_acc: 0.8716\n",
      "Epoch 15/20\n",
      "15000/15000 [==============================] - 2s 135us/sample - loss: 0.0218 - acc: 0.9966 - val_loss: 0.5051 - val_acc: 0.8727\n",
      "Epoch 16/20\n",
      "15000/15000 [==============================] - 2s 136us/sample - loss: 0.0183 - acc: 0.9967 - val_loss: 0.5417 - val_acc: 0.8731\n",
      "Epoch 17/20\n",
      "15000/15000 [==============================] - 2s 129us/sample - loss: 0.0105 - acc: 0.9995 - val_loss: 0.5783 - val_acc: 0.8673\n",
      "Epoch 18/20\n",
      "15000/15000 [==============================] - 2s 134us/sample - loss: 0.0130 - acc: 0.9976 - val_loss: 0.6145 - val_acc: 0.8668\n",
      "Epoch 19/20\n",
      "15000/15000 [==============================] - 2s 139us/sample - loss: 0.0059 - acc: 0.9999 - val_loss: 0.6491 - val_acc: 0.8680\n",
      "Epoch 20/20\n",
      "15000/15000 [==============================] - 2s 129us/sample - loss: 0.0063 - acc: 0.9996 - val_loss: 0.6846 - val_acc: 0.8663\n"
     ]
    }
   ],
   "source": [
    "#training the model\n",
    "#not entirely clear\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
    "history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(1, 21)\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"
    }
   ],
   "source": [
    "#Plotting the training and validation loss\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_dict = history.history\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "acc = history_dict['acc']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "print(epochs)\n",
    "\n",
    "plt.plot(epochs, loss_values, 'bo', label='Training loss') #bo means blue dot\n",
    "plt.plot(epochs, val_loss_values, 'b', label='Validation loss') #b means blue line\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plotting the training and validation accuracy\n",
    "plt.clf() #clearing the figure\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_values, '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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "25000/25000 [==============================] - 3s 104us/sample - loss: 0.5353 - accuracy: 0.7706\n",
      "Epoch 2/4\n",
      "25000/25000 [==============================] - 2s 100us/sample - loss: 0.3219 - accuracy: 0.9036\n",
      "Epoch 3/4\n",
      "25000/25000 [==============================] - 3s 100us/sample - loss: 0.2266 - accuracy: 0.9286\n",
      "Epoch 4/4\n",
      "25000/25000 [==============================] - 3s 101us/sample - loss: 0.1846 - accuracy: 0.9385\n",
      "25000/25000 [==============================] - 3s 103us/sample - loss: 0.2889 - accuracy: 0.8862\n"
     ]
    }
   ],
   "source": [
    "#retraining the model\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
    "results = model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.28889627073287966, 0.88616]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.19026142],\n",
       "       [0.99010956],\n",
       "       [0.9190109 ],\n",
       "       ...,\n",
       "       [0.1291691 ],\n",
       "       [0.08310819],\n",
       "       [0.53571546]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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