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@shubham0204
Created March 23, 2019 12:33
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import tensorflow as tf
encoder_inputs = tf.keras.layers.Input(shape=( None , ))
encoder_embedding = tf.keras.layers.Embedding( num_tokens, 200 , mask_zero=True) (encoder_inputs)
encoder_outputs , state_h , state_c = tf.keras.layers.LSTM( 200 , return_state=True )( encoder_embedding )
encoder_states = [ state_h , state_c ]
decoder_inputs = tf.keras.layers.Input(shape=( None , ))
decoder_embedding = tf.keras.layers.Embedding( num_tokens, 200 , mask_zero=True) (decoder_inputs)
decoder_lstm = tf.keras.layers.LSTM( 200 , return_state=True , return_sequences=True )
decoder_outputs , _ , _ = decoder_lstm ( decoder_embedding , initial_state=encoder_states )
decoder_dense = tf.keras.layers.Dense( num_tokens , activation=tf.keras.activations.softmax )
output = decoder_dense ( decoder_outputs )
model = tf.keras.models.Model([encoder_inputs, decoder_inputs], output )
model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='categorical_crossentropy')
model.summary()
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