Second dlnd project submission: after aplying changes suggested

decoding_layer_revised.py

def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,
                   num_layers, target_vocab_to_int, keep_prob):
    """
    Create decoding layer
    :param dec_embed_input: Decoder embedded input
    :param dec_embeddings: Decoder embeddings
    :param encoder_state: The encoded state
    :param vocab_size: Size of vocabulary
    :param sequence_length: Sequence Length
    :param rnn_size: RNN Size
    :param num_layers: Number of layers
    :param target_vocab_to_int: Dictionary to go from the target words to an id
    :param keep_prob: Dropout keep probability
    :return: Tuple of (Training Logits, Inference Logits)
    """
    
    lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
    dropout = tf.contrib.rnn.DropoutWrapper(lstm, keep_prob)
    cell = tf.contrib.rnn.MultiRNNCell([dropout] * num_layers)
    
    with tf.variable_scope("decoding") as decoding_scope:
        #Output layer
        output = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)
        
    
        # Training decoder
        training_logits = decoding_layer_train(encoder_state, cell, dec_embed_input, sequence_length, decoding_scope,
                                              output, keep_prob)
        
        decoding_scope.reuse_variables()
        # Inference decoder
        inference_logits = decoding_layer_infer(encoder_state, cell, dec_embeddings, target_vocab_to_int['<GO>'],
                                                   target_vocab_to_int["<EOS>"], sequence_length, vocab_size,
                                                   decoding_scope, output, keep_prob)
        
    
    
    
    return (training_logits, inference_logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_decoding_layer(decoding_layer)