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Elmo Keras.ipynb
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Elmo Keras.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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, load_model\n",
"from keras.engine import Layer\n",
"import numpy as np\n",
"\n",
"# Initialize session\n",
"sess = tf.Session()\n",
"K.set_session(sess)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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>A demented scientist girlfriend is decapitated...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>If you were ever sad for not being able to get...</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>From watching only the trailer to Theodore Rex...</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Almost as tedious to watch as it was to read, ...</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>What I expected: A rather lame overly-stereoty...</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentence sentiment polarity\n",
"0 A demented scientist girlfriend is decapitated... 1 0\n",
"1 If you were ever sad for not being able to get... 10 1\n",
"2 From watching only the trailer to Theodore Rex... 2 0\n",
"3 Almost as tedious to watch as it was to read, ... 2 0\n",
"4 What I expected: A rather lame overly-stereoty... 9 1"
]
},
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"# Create a custom layer that allows us to update weights (lambda layers do not have trainable parameters!)\n",
"\n",
"class ElmoEmbeddingLayer(Layer):\n",
" def __init__(self, **kwargs):\n",
" self.dimensions = 1024\n",
" self.trainable=True\n",
" super(ElmoEmbeddingLayer, self).__init__(**kwargs)\n",
"\n",
" def build(self, input_shape):\n",
" self.elmo = hub.Module('https://tfhub.dev/google/elmo/2', trainable=self.trainable,\n",
" name=\"{}_module\".format(self.name))\n",
"\n",
" self.trainable_weights += K.tf.trainable_variables(scope=\"^{}_module/.*\".format(self.name))\n",
" super(ElmoEmbeddingLayer, self).build(input_shape)\n",
"\n",
" def call(self, x, mask=None):\n",
" result = self.elmo(K.squeeze(K.cast(x, tf.string), axis=1),\n",
" as_dict=True,\n",
" signature='default',\n",
" )['default']\n",
" return result\n",
"\n",
" def compute_mask(self, inputs, mask=None):\n",
" return K.not_equal(inputs, '--PAD--')\n",
"\n",
" def compute_output_shape(self, input_shape):\n",
" return (input_shape[0], self.dimensions)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Function to build model\n",
"def build_model(): \n",
" input_text = layers.Input(shape=(1,), dtype=\"string\")\n",
" embedding = ElmoEmbeddingLayer()(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()\n",
" \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": [],
"source": [
"# Build and fit\n",
"model = build_model()\n",
"model.fit(train_text, \n",
" train_label,\n",
" validation_data=(test_text, test_label),\n",
" epochs=1,\n",
" batch_size=32)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_2 (InputLayer) (None, 1) 0 \n",
"_________________________________________________________________\n",
"elmo_embedding_layer_2 (Elmo (None, 1024) 4 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 256) 262400 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 1) 257 \n",
"=================================================================\n",
"Total params: 262,661\n",
"Trainable params: 262,661\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.save('ElmoModel.h5')\n",
"pre_save_preds = model.predict(test_text[0:100]) # predictions before we clear and reload model\n",
"\n",
"# Clear and load model\n",
"model = None\n",
"model = build_model()\n",
"model.load_weights('ElmoModel.h5')\n",
"\n",
"post_save_preds = model.predict(test_text[0:100]) # predictions after we clear and reload model\n",
"all(pre_save_preds == post_save_preds) # Are they the same?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
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"nbformat": 4,
"nbformat_minor": 2
}