keras_siamese.py

# Siamese network: two input sequences through same embedding layer

sequence_one = layers.Input((10, )) # Input of arbitrary shape
sequence_two = layers.Input((10, ))

embedding_layer = layers.Embedding(input_dim=1000,
                                   output_dim=128, input_length=10)

embedded_seq_1 = embedding_layer(sequence_one)
embedded_seq_2 = embedding_layer(sequence_two)

import tensorflow as tf 
averaging_layer = layers.Lambda(lambda x: tf.reduce_mean(x, axis=1), name='averaging')

avg_emb_1 = averaging_layer(embedded_seq_1)
avg_emb_2 = averaging_layer(embedded_seq_2)

dot_product = layers.Dot(axes=-1)([avg_emb_1, avg_emb_2])

model = models.Model(inputs=[sequence_one, sequence_two], outputs=dot_product)