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'''Functional Keras is a more functional replacement for the Graph API. | |
''' | |
################### | |
# 2 LSTM branches # | |
################### | |
a = Input(input_shape=(10, 32)) # output is a TF/TH placeholder, augmented with Keras attributes | |
b = Input(input_shape=(10, 32)) | |
encoded_a = LSTM(32)(a) # output is a TF/TH tensor | |
encoded_b = LSTM(32)(b) | |
merged = merge([encoded_a, encoded_b], mode='concat') | |
decoded = RepeatVector(10)(merged) | |
decoded = LSTM(32, return_sequences=True)(decoded) | |
# this is a fully-featured Keras model, will all the goodies that come with those. | |
# this is made possible by Keras topology information stored in the tensors. | |
model = Model(input=[a, b], output=[decoded]) | |
model.compile(optimizer=Adam(), loss='mse') | |
model.fit([x1, x2], y) | |
################ | |
# Shared layer # | |
################ | |
shared_lstm = LSTM(32) | |
a = Input(input_shape=(10, 32)) | |
b = Input(input_shape=(10, 32)) | |
encoded_a = shared_lstm(a) | |
encoded_b = shared_lstm(b) | |
merged = merge([encoded_a, encoded_b], mode='concat') | |
decoded = RepeatVector(10)(merged) | |
decoded = LSTM(32, return_sequences=True)(decoded) | |
############################## | |
# Insertion of arbitrary ops # | |
############################## | |
# NOTE: cannot do a = tf.sigmoid(a), because although 'a' is a valid tf tensor, | |
# it is 'augmented' with data that allows Keras to keep track of previous operations | |
# (thus making it possible to train a model)... | |
a = Input(input_shape=(10, 32)) | |
a = Lambda(tf.sigmoid)(a) | |
model = Model(input=[a, b], output=[decoder]) | |
model.compile(optimizer=Adam(), loss='mse') | |
model.fit([x1, x2], y) |
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