LSTM_IMDB.ipynb

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   "source": [
    "# Reference Link : https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/\n",
    "# LSTM for sequence classification in the IMDB dataset\n",
    "\n",
    "import numpy\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)\n"
   ],
   "outputs": [
    {
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     "name": "stderr",
     "text": [
      "/Users/I511499/anaconda3/lib/python3.6/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"
     ]
    }
   ],
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   "source": [
    "#Reference Link : https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification\n",
    "\n",
    "# load the dataset but only keep the top n words, and replace the rest with zeros\n",
    "top_words = 5000\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=top_words)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\n",
      "  278528/17464789 [..............................] - ETA: 3s"
     ]
    },
    {
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     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/datasets/imdb.py:49: UserWarning: The `nb_words` argument in `load_data` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `load_data` '\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "17465344/17464789 [==============================] - 0s 0us/step\n"
     ]
    }
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   "cell_type": "code",
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   "source": [
    "print(X_train[1])\n",
    "print(type(X_train[1]))\n",
    "print(len(X_train[1]))"
   ],
   "outputs": [
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      "<class 'list'>\n",
      "189\n"
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   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# truncate and/or pad input sequences\n",
    "max_review_length = 600\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_train[1])"
   ],
   "outputs": [
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   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Create the model\n",
    "\n",
    "embedding_vector_length = 32\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Embedding(top_words + 1, embedding_vector_length, input_length=max_review_length))\n",
    "model.add(LSTM(100))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "#Reference Link : https://datascience.stackexchange.com/questions/10615/number-of-parameters-in-an-lstm-model"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 600, 32)           160032    \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 100)               53200     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 213,333\n",
      "Trainable params: 213,333\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
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     "timestamp": 1555770421387,
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   "source": [
    "model.fit(X_train, y_train, nb_epoch=10, batch_size=64)\n",
    "\n",
    "# Final evaluation of the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/models.py:981: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 295s 12ms/step - loss: 0.4567 - acc: 0.7825\n",
      "Epoch 2/10\n",
      "20160/25000 [=======================>......] - ETA: 57s - loss: 0.3694 - acc: 0.834325000/25000 [==============================] - 295s 12ms/step - loss: 0.3647 - acc: 0.8385\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 297s 12ms/step - loss: 0.5037 - acc: 0.7639\n",
      "Epoch 4/10\n",
      " 4416/25000 [====>.........................] - ETA: 4:03 - loss: 0.3698 - acc: 0.838525000/25000 [==============================] - 297s 12ms/step - loss: 0.3554 - acc: 0.8503\n",
      "Epoch 5/10\n",
      "24832/25000 [============================>.] - ETA: 1s - loss: 0.2673 - acc: 0.896125000/25000 [==============================] - 296s 12ms/step - loss: 0.2671 - acc: 0.8961\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 297s 12ms/step - loss: 0.2477 - acc: 0.9051\n",
      "Epoch 7/10\n",
      " 5824/25000 [=====>........................] - ETA: 3:47 - loss: 0.1967 - acc: 0.926525000/25000 [==============================] - 296s 12ms/step - loss: 0.2131 - acc: 0.9180\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 296s 12ms/step - loss: 0.1861 - acc: 0.9304\n",
      "Epoch 9/10\n",
      "  256/25000 [..............................] - ETA: 4:51 - loss: 0.1830 - acc: 0.929725000/25000 [==============================] - 297s 12ms/step - loss: 0.1659 - acc: 0.9392\n",
      "Epoch 10/10\n",
      "22784/25000 [==========================>...] - ETA: 26s - loss: 0.1636 - acc: 0.938125000/25000 [==============================] - 296s 12ms/step - loss: 0.1657 - acc: 0.9374\n",
      "Accuracy: 86.24%\n"
     ]
    }
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