Untitled.ipynb

{
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   "execution_count": 1,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "'''Train a Bidirectional LSTM on the IMDB sentiment classification task.\n",
    "Output after 4 epochs on CPU: ~0.8146\n",
    "Time per epoch on CPU (Core i7): ~150s.\n",
    "'''\n",
    "\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Embedding, LSTM, Input, Bidirectional\n",
    "from keras.datasets import imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "25000 train sequences\n",
      "25000 test sequences\n"
     ]
    }
   ],
   "source": [
    "max_features = 20000\n",
    "maxlen = 100  # cut texts after this number of words (among top max_features most common words)\n",
    "batch_size = 32\n",
    "\n",
    "print('Loading data...')\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)\n",
    "print(len(X_train), 'train sequences')\n",
    "print(len(X_test), 'test sequences')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000\n",
      "[ 1415    33     6    22    12   215    28    77    52     5    14   407\n",
      "    16    82 10311     8     4   107   117  5952    15   256     4     2\n",
      "     7  3766     5   723    36    71    43   530   476    26   400   317\n",
      "    46     7     4 12118  1029    13   104    88     4   381    15   297\n",
      "    98    32  2071    56    26   141     6   194  7486    18     4   226\n",
      "    22    21   134   476    26   480     5   144    30  5535    18    51\n",
      "    36    28   224    92    25   104     4   226    65    16    38  1334\n",
      "    88    12    16   283     5    16  4472   113   103    32    15    16\n",
      "  5345    19   178    32]\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(X_train[0])\n",
    "print(len(X_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pad sequences (samples x time)\n",
      "X_train shape: (25000, 100)\n",
      "X_test shape: (25000, 100)\n"
     ]
    }
   ],
   "source": [
    "print(\"Pad sequences (samples x time)\")\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=maxlen)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=maxlen)\n",
    "print('X_train shape:', X_train.shape)\n",
    "print('X_test shape:', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1415,    33,     6,    22,    12,   215,    28,    77,    52,\n",
       "           5,    14,   407,    16,    82, 10311,     8,     4,   107,\n",
       "         117,  5952,    15,   256,     4,     2,     7,  3766,     5,\n",
       "         723,    36,    71,    43,   530,   476,    26,   400,   317,\n",
       "          46,     7,     4, 12118,  1029,    13,   104,    88,     4,\n",
       "         381,    15,   297,    98,    32,  2071,    56,    26,   141,\n",
       "           6,   194,  7486,    18,     4,   226,    22,    21,   134,\n",
       "         476,    26,   480,     5,   144,    30,  5535,    18,    51,\n",
       "          36,    28,   224,    92,    25,   104,     4,   226,    65,\n",
       "          16,    38,  1334,    88,    12,    16,   283,     5,    16,\n",
       "        4472,   113,   103,    32,    15,    16,  5345,    19,   178,    32], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "Downloading data from https://s3.amazonaws.com/text-datasets/imdb_full.pkl\n",
      "65437696/65552540 [============================>.] - ETA: 0s25000 train sequences\n",
      "25000 test sequences\n",
      "Pad sequences (samples x time)\n",
      "X_train shape: (25000, 100)\n",
      "X_test shape: (25000, 100)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_features, 128, input_length=maxlen))\n",
    "model.add(Bidirectional(LSTM(64)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# try using different optimizers and different optimizer configs\n",
    "model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1415,    33,     6,    22,    12,   215,    28,    77,    52,\n",
       "           5,    14,   407,    16,    82, 10311,     8,     4,   107,\n",
       "         117,  5952,    15,   256,     4,     2,     7,  3766,     5,\n",
       "         723,    36,    71,    43,   530,   476,    26,   400,   317,\n",
       "          46,     7,     4, 12118,  1029,    13,   104,    88,     4,\n",
       "         381,    15,   297,    98,    32,  2071,    56,    26,   141,\n",
       "           6,   194,  7486,    18,     4,   226,    22,    21,   134,\n",
       "         476,    26,   480,     5,   144,    30,  5535,    18,    51,\n",
       "          36,    28,   224,    92,    25,   104,     4,   226,    65,\n",
       "          16,    38,  1334,    88,    12,    16,   283,     5,    16,\n",
       "        4472,   113,   103,    32,    15,    16,  5345,    19,   178,    32], dtype=int32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n",
      "WARNING:theano.tensor.blas:We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/4\n",
      "25000/25000 [==============================] - 238s - loss: 0.4195 - acc: 0.8099 - val_loss: 0.3387 - val_acc: 0.8520\n",
      "Epoch 2/4\n",
      "25000/25000 [==============================] - 241s - loss: 0.2256 - acc: 0.9147 - val_loss: 0.3606 - val_acc: 0.8447\n",
      "Epoch 3/4\n",
      "25000/25000 [==============================] - 243s - loss: 0.1161 - acc: 0.9585 - val_loss: 0.4971 - val_acc: 0.8377\n",
      "Epoch 4/4\n",
      "25000/25000 [==============================] - 244s - loss: 0.0627 - acc: 0.9790 - val_loss: 0.5392 - val_acc: 0.8377\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f993f303f90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Train...')\n",
    "model.fit(X_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          nb_epoch=4,\n",
    "          validation_data=[X_test, y_test])"
   ]
  }
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