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# Copyright 2017-2018 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
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import tensorflow as tf
[docs]def simple_lstm(max_features, dense_out, input_length, embed_dim=256, lstm_out=140,
dropout=0.5):
"""
Simple Bi-direction LSTM Model in Keras
Single layer bi-directional lstm with recurrent dropout and a fully connected layer
Args:
max_features (int): vocabulary size
dense_out (int): size out the output dense layer, this is the number of classes
input_length (int): length of the input text
embed_dim (int): internal embedding size used in the lstm
lstm_out (int): size of the bi-directional output layer
dropout (float): value for recurrent dropout, between 0 and 1
Returns:
model (model): LSTM model
"""
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Embedding(max_features, embed_dim, input_length=input_length))
model.add(tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(lstm_out, recurrent_dropout=dropout, activation='tanh')))
model.add(tf.keras.layers.Dense(dense_out, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
[docs]def one_hot_cnn(dense_out, max_len=300, frame='small'):
"""
Temporal CNN Model
As defined in "Text Understanding from Scratch" by Zhang, LeCun 2015
https://arxiv.org/pdf/1502.01710v4.pdf
This model is a series of 1D CNNs, with a maxpooling and fully connected layers.
The frame sizes may either be large or small.
Args:
dense_out (int): size out the output dense layer, this is the number of classes
max_len (int): length of the input text
frame (str): frame size, either large or small
Returns:
model (model): temporal CNN model
"""
if frame == 'large':
cnn_size = 1024
fully_connected = [2048, 2048, dense_out]
else:
cnn_size = 256
fully_connected = [1024, 1024, dense_out]
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv1D(cnn_size, 7, padding='same', input_shape=(68, max_len)))
model.add(tf.keras.layers.MaxPooling1D(pool_size=3))
print(model.output_shape)
# Input = 22 x 256
model.add(tf.keras.layers.Conv1D(cnn_size, 7, padding='same'))
model.add(tf.keras.layers.MaxPooling1D(pool_size=3))
print(model.output_shape)
# Input = 7 x 256
model.add(tf.keras.layers.Conv1D(cnn_size, 3, padding='same'))
# Input = 7 x 256
model.add(tf.keras.layers.Conv1D(cnn_size, 3, padding='same'))
model.add(tf.keras.layers.Conv1D(cnn_size, 3, padding='same'))
# Input = 7 x 256
model.add(tf.keras.layers.Conv1D(cnn_size, 3, padding='same'))
model.add(tf.keras.layers.MaxPooling1D(pool_size=3))
model.add(tf.keras.layers.Flatten())
# Fully Connected Layers
# Input is 512 Output is 1024/2048
model.add(tf.keras.layers.Dense(fully_connected[0]))
model.add(tf.keras.layers.Dropout(0.75))
model.add(tf.keras.layers.Activation('relu'))
# Input is 1024/2048 Output is 1024/2048
model.add(tf.keras.layers.Dense(fully_connected[1]))
model.add(tf.keras.layers.Dropout(0.75))
model.add(tf.keras.layers.Activation('relu'))
# Input is 1024/2048 Output is dense_out size (number of classes)
model.add(tf.keras.layers.Dense(fully_connected[2]))
model.add(tf.keras.layers.Activation('softmax'))
# Stochastic gradient parameters as set by paper
sgd = tf.keras.optimizers.SGD(lr=0.01, decay=1e-5, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model