NLP.ipynb

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   "execution_count": 42,
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   "source": [
    "# CNN for the IMDB problem\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 Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence\n",
    "from keras.preprocessing.text import text_to_word_sequence\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# load the dataset but only keep the top n words, zero the rest\n",
    "top_words = 8000\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
    "# pad dataset to a maximum review length in words\n",
    "max_words = 300\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 300, 32)           256000    \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 300, 32)           3104      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 150, 32)           0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 4800)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 250)               1200250   \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 251       \n",
      "=================================================================\n",
      "Total params: 1,459,605\n",
      "Trainable params: 1,459,605\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# create the model\n",
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 32, input_length=max_words))\n",
    "model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(250, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/2\n",
      "27s - loss: 0.4811 - acc: 0.7296 - val_loss: 0.2931 - val_acc: 0.8772\n",
      "Epoch 2/2\n",
      "28s - loss: 0.2116 - acc: 0.9188 - val_loss: 0.2759 - val_acc: 0.8866\n",
      "Accuracy: 88.66%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=128, verbose=2)\n",
    "# Final evaluation of the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert text to IMDB Format:\n",
    "word_dict = imdb.get_word_index()\n",
    "def encode_sentence(text):\n",
    "    result = []\n",
    "    arr = text_to_word_sequence(text, lower=True, split=\" \")\n",
    "    for word in arr:\n",
    "        w = encode_word(word)\n",
    "        if w is not None:\n",
    "            result.append(w)\n",
    "    return result\n",
    "def encode_word(word):\n",
    "    if word not in word_dict:\n",
    "        return None\n",
    "    if word_dict[word] <= top_words:\n",
    "        return word_dict[word]\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SmartPredict(text):\n",
    "    txt = encode_sentence(text)\n",
    "    X_test = sequence.pad_sequences([txt], maxlen=max_words)\n",
    "    return model.predict(numpy.array(X_test))[0][0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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