Elmo Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import our dependencies\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import tensorflow_hub as hub\n",
    "import os\n",
    "import re\n",
    "from keras import backend as K\n",
    "import keras.layers as layers\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "# Initialize session\n",
    "sess = tf.Session()\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grieving couple move to a cabin on a mountain ...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I love this movie. It's wacky, funny, violent,...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nicole Eggert was listed as the star of this, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bell, Book and Candle was one of the great pop...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I missed the first 10 or so minutes of the mov...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                            sentence sentiment  polarity\n",
       "0  Grieving couple move to a cabin on a mountain ...         4         0\n",
       "1  I love this movie. It's wacky, funny, violent,...        10         1\n",
       "2  Nicole Eggert was listed as the star of this, ...         3         0\n",
       "3  Bell, Book and Candle was one of the great pop...         9         1\n",
       "4  I missed the first 10 or so minutes of the mov...         1         0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load all files from a directory in a DataFrame.\n",
    "def load_directory_data(directory):\n",
    "  data = {}\n",
    "  data[\"sentence\"] = []\n",
    "  data[\"sentiment\"] = []\n",
    "  for file_path in os.listdir(directory):\n",
    "    with tf.gfile.GFile(os.path.join(directory, file_path), \"r\") as f:\n",
    "      data[\"sentence\"].append(f.read())\n",
    "      data[\"sentiment\"].append(re.match(\"\\d+_(\\d+)\\.txt\", file_path).group(1))\n",
    "  return pd.DataFrame.from_dict(data)\n",
    "\n",
    "# Merge positive and negative examples, add a polarity column and shuffle.\n",
    "def load_dataset(directory):\n",
    "  pos_df = load_directory_data(os.path.join(directory, \"pos\"))\n",
    "  neg_df = load_directory_data(os.path.join(directory, \"neg\"))\n",
    "  pos_df[\"polarity\"] = 1\n",
    "  neg_df[\"polarity\"] = 0\n",
    "  return pd.concat([pos_df, neg_df]).sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# Download and process the dataset files.\n",
    "def download_and_load_datasets(force_download=False):\n",
    "  dataset = tf.keras.utils.get_file(\n",
    "      fname=\"aclImdb.tar.gz\", \n",
    "      origin=\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\", \n",
    "      extract=True)\n",
    "\n",
    "  train_df = load_dataset(os.path.join(os.path.dirname(dataset), \n",
    "                                       \"aclImdb\", \"train\"))\n",
    "  test_df = load_dataset(os.path.join(os.path.dirname(dataset), \n",
    "                                      \"aclImdb\", \"test\"))\n",
    "\n",
    "  return train_df, test_df\n",
    "\n",
    "# Reduce logging output.\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "\n",
    "train_df, test_df = download_and_load_datasets()\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now instantiate the elmo model\n",
    "elmo_model = hub.Module(\"https://tfhub.dev/google/elmo/1\", trainable=True)\n",
    "sess.run(tf.global_variables_initializer())\n",
    "sess.run(tf.tables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "lambda_1 (Lambda)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 262,657\n",
      "Trainable params: 262,657\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build our model\n",
    "\n",
    "# We create a function to integrate the tensorflow model with a Keras model\n",
    "# This requires explicitly casting the tensor to a string, because of a Keras quirk\n",
    "def ElmoEmbedding(x):\n",
    "    return elmo_model(tf.squeeze(tf.cast(x, tf.string)), signature=\"default\", as_dict=True)[\"default\"]\n",
    " \n",
    "input_text = layers.Input(shape=(1,), dtype=tf.string)\n",
    "embedding = layers.Lambda(ElmoEmbedding, output_shape=(1024,))(input_text)\n",
    "dense = layers.Dense(256, activation='relu')(embedding)\n",
    "pred = layers.Dense(1, activation='sigmoid')(dense)\n",
    "\n",
    "model = Model(inputs=[input_text], outputs=pred)\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create datasets (Only take up to 150 words for memory)\n",
    "train_text = train_df['sentence'].tolist()\n",
    "train_text = [' '.join(t.split()[0:150]) for t in train_text]\n",
    "train_text = np.array(train_text, dtype=object)[:, np.newaxis]\n",
    "train_label = train_df['polarity'].tolist()\n",
    "\n",
    "test_text = test_df['sentence'].tolist()\n",
    "test_text = [' '.join(t.split()[0:150]) for t in test_text]\n",
    "test_text = np.array(test_text, dtype=object)[:, np.newaxis]\n",
    "test_label = test_df['polarity'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/5\n",
      " 1248/25000 [>.............................] - ETA: 3:23:34 - loss: 0.6002 - acc: 0.6795"
     ]
    }
   ],
   "source": [
    "# Fit!\n",
    "model.fit(train_text, \n",
    "          train_label,\n",
    "          validation_data=(test_text, test_label),\n",
    "          epochs=5,\n",
    "          batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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