07. NLP/4.4 RNN with Keras.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> RNN with Keras\n",
    "Notebook based on https://www.kaggle.com/tharunreddy/tf-idf-0-89\n",
    "\n",
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Embedding, Flatten, Dense, Dropout, BatchNormalization\n",
    "from keras.layers import LSTM, Bidirectional, GlobalMaxPool1D\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### DOWNLOAD DATASET. ONLY IN COLAB !!!\n",
    "#!wget -P ./data \"https://raw.githubusercontent.com/SaturdaysAI-Murcia/machine-learning/master/4. NLP/data/movieReviews_trainLabel.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 2)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5814_8</th>\n",
       "      <td>1</td>\n",
       "      <td>With all this stuff going down at the moment w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sentiment                                             review\n",
       "id                                                                  \n",
       "5814_8          1  With all this stuff going down at the moment w..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/movieReviews_trainLabel.tsv\", sep=\"\\t\", index_col=\"id\")\n",
    "print(df.shape)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenize text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 10000\n",
    "max_lenght = 300\n",
    "\n",
    "t = Tokenizer(num_words=vocab_size)\n",
    "t.fit_on_texts(df.review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create sequences to feed into Neural network model\n",
    "sequences = t.texts_to_sequences(df.review)\n",
    "\n",
    "# As the average length of all reviews is around 250, \n",
    "# lets the keep the input dim to 250 and pad the sequences if it is less that 250 words\n",
    "sequences = pad_sequences(sequences, maxlen=max_lenght)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 300)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 261,   11,    6, ...,   21,    1, 1559],\n",
       "       [   0,    0,    0, ...,   27,   91, 5674],\n",
       "       [3348,   28,    4, ..., 1321,    4, 5514],\n",
       "       ...,\n",
       "       [   0,    0,    0, ...,    7,  339,  155],\n",
       "       [   0,    0,    0, ...,   16,   82,   81],\n",
       "       [   0,    0,    0, ...,   14,    3,  504]], dtype=int32)"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sequences.shape)\n",
    "sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Recurrent Neural Net (Embedding + LTSM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embed (Embedding)            (None, 300, 128)          1280000   \n",
      "_________________________________________________________________\n",
      "lstm (Bidirectional)         (None, 300, 64)           41216     \n",
      "_________________________________________________________________\n",
      "pooling (GlobalMaxPooling1D) (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 20)                1300      \n",
      "_________________________________________________________________\n",
      "droput (Dropout)             (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "softmax (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 1,322,537\n",
      "Trainable params: 1,322,537\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Network architecture\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=max_lenght, name='embed'))\n",
    "model.add(Bidirectional(LSTM(32, return_sequences=True), name='lstm'))\n",
    "model.add(GlobalMaxPool1D(name='pooling'))\n",
    "model.add(Dense(20, name='dense'))\n",
    "# model.add(Flatten(name='flatten'))\n",
    "model.add(Dropout(0.05, name='droput'))\n",
    "model.add(Dense(1, activation='sigmoid', name='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/5\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.fit(sequences, df.sentiment.values, validation_split=0.2, epochs=5, batch_size=128, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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