DefiningDecoderRNN.py

#Defining the decoder layers RNN
def decoder_rnn(decoder_embedded_input,decoder_embeddings_matrix,encoder_state,num_words,sequence_length,rnn_size, num_layers,word2int,keep_prob,batch_size):
    with tf.variable_scope("decoding") as decoding_scope:
        lstm= tf.contrib.rnn.BasicLSTMCell(rnn_size)
        lstm_dropout= tf.contrib.rnn.DropoutWrapper(lstm, input_keep_prob=keep_prob)
        decoder_cell= tf.contrib.rnn.MultiRNNCell([lstm_dropout]*num_layers)
        weights = tf.truncated_normal_initializer(stddev = 0.1)
        biases = tf.zeros_initializer()
        output_function = lambda x: tf.contrib.layers.fully_connected(x,
                                                                      num_words,
                                                                      None,
                                                                      scope=decoding_scope,
                                                                      weights_initializer=weights,
                                                                      biases_initializer=biases)
        training_predictions=decode_training_set(encoder_state,
                                                 decoder_cell,
                                                 decoder_embedded_input,
                                                 sequence_length,
                                                 decoding_scope,
                                                 output_function,
                                                 keep_prob,
                                                 batch_size)
        decoding_scope.reuse_variables()
        test_predictions=decode_test_set(encoder_state,
                                         decoder_cell,
                                         decoder_embeddings_matrix,
                                         answersswordstoint["<SOS>"],
                                         answersswordstoint["<EOS>"],
                                         sequence_length-1,
                                         num_words,
                                         decoding_scope,
                                         output_function,
                                         keep_prob,
                                         batch_size)
    return training_predictions,test_predictions