如何将 AttentionMechanism 与 MultiRNNCell 和 dynamic_decode 一起使用？

Question

我想创建一个使用注意机制的基于多层动态 RNN 的解码器。为此，我首先创建一个注意力机制：

attention_mechanism = BahdanauAttention(num_units=ATTENTION_UNITS,
                                        memory=encoder_outputs,
                                        normalize=True)

然后我使用 AttentionWrapper 用注意力机制包装一个 LSTM 单元：

attention_wrapper = AttentionWrapper(cell=self._create_lstm_cell(DECODER_SIZE),
                                             attention_mechanism=attention_mechanism,
                                             output_attention=False,
                                             alignment_history=True,
                                             attention_layer_size=ATTENTION_LAYER_SIZE)

其中self._create_lstm_cell定义如下：

@staticmethod
def _create_lstm_cell(cell_size):
    return BasicLSTMCell(cell_size)

然后我会做一些簿记（例如创建我的 MultiRNNCell、创建初始状态、创建 TrainingHelper 等）

        attention_zero = attention_wrapper.zero_state(batch_size=tf.flags.FLAGS.batch_size, dtype=tf.float32)

        # define initial state
        initial_state = attention_zero.clone(cell_state=encoder_final_states[0])

        training_helper = TrainingHelper(inputs=self.y,  # feed in ground truth
                                         sequence_length=self.y_lengths)  # feed in sequence lengths

        layered_cell = MultiRNNCell(
            [attention_wrapper] + [ResidualWrapper(self._create_lstm_cell(cell_size=DECODER_SIZE))
                                   for _ in range(NUMBER_OF_DECODER_LAYERS - 1)])

        decoder = BasicDecoder(cell=layered_cell,
                               helper=training_helper,
                               initial_state=initial_state)

        decoder_outputs, decoder_final_state, decoder_final_sequence_lengths = dynamic_decode(decoder=decoder,
                                                                                              maximum_iterations=tf.flags.FLAGS.max_number_of_scans // 12,
                                                                                              impute_finished=True)

但我收到以下错误：AttributeError: 'LSTMStateTuple' object has no attribute 'attention'。

向 MultiRNNCell 动态解码器添加注意机制的正确方法是什么？

Answer 1

你试过使用tf.contrib提供的attention wrapper吗？

这是一个同时使用注意力包装器和 dropout 的示例：

cells = []
for i in range(n_layers):                   
    cell = tf.contrib.rnn.LSTMCell(n_hidden, state_is_tuple=True)

    cell = tf.contrib.rnn.AttentionCellWrapper(
        cell, attn_length=40, state_is_tuple=True)

    cell = tf.contrib.rnn.DropoutWrapper(cell,output_keep_prob=0.5)
    cells.append(cell)

cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)
init_state = cell.zero_state(batch_size, tf.float32)

Answer 2

您需要做的是创建 MultiLayer 单元格，然后用 AttentionWrapper 包装它，下面是一个示例：

def decoding_layer(dec_input, encoder_state,
               target_sequence_length, max_target_sequence_length,
               rnn_size,
               num_layers, target_vocab_to_int, target_vocab_size,
               batch_size, keep_prob, decoding_embedding_size , encoder_outputs):
"""
Create decoding layer
:param dec_input: Decoder input
:param encoder_state: Encoder state
:param target_sequence_length: The lengths of each sequence in the target batch
:param max_target_sequence_length: Maximum length of target sequences
: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 target_vocab_size: Size of target vocabulary
:param batch_size: The size of the batch
:param keep_prob: Dropout keep probability
:param decoding_embedding_size: Decoding embedding size
:return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)
"""
# 1. Decoder Embedding
dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))
dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)

# 2. Construct the decoder cell
def create_cell(rnn_size):
    lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size,
                                        initializer=tf.random_uniform_initializer(-0.1,0.1,seed=2))
    drop = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob)
    return drop


dec_cell = tf.contrib.rnn.MultiRNNCell([create_cell(rnn_size) for _ in range(num_layers)])
#dec_cell = tf.contrib.rnn.MultiRNNCell(cells_a)  

#attention details 
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units=rnn_size, memory=encoder_outputs) 

attn_cell = tf.contrib.seq2seq.AttentionWrapper(dec_cell, attention_mechanism , attention_layer_size=rnn_size/2)

attn_zero = attn_cell.zero_state(batch_size , tf.float32 )

attn_zero = attn_zero.clone(cell_state = encoder_state)

#new_state = tf.contrib.seq2seq.AttentionWrapperState(cell_state = encoder_state, attention = attn_zero  , time = 0 ,alignments=None , alignment_history=())

"""out_cell = tf.contrib.rnn.OutputProjectionWrapper(
            attn_cell, target_vocab_size, reuse=True
        )"""
#end of attention 
#tensor_util.make_tensor_proto(attn_cell)
output_layer = Dense(target_vocab_size,
                     kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))

with tf.variable_scope("decode"):
    train_decoder_out = decoding_layer_train(attn_zero, attn_cell, dec_embed_input, 
                     target_sequence_length, max_target_sequence_length, output_layer, keep_prob)

with tf.variable_scope("decode", reuse=True):
    infer_decoder_out = decoding_layer_infer(attn_zero, attn_cell, dec_embeddings, 
                         target_vocab_to_int['<GO>'], target_vocab_to_int['<EOS>'], max_target_sequence_length, 
                         target_vocab_size, output_layer, batch_size, keep_prob)

return (train_decoder_out, infer_decoder_out)

如何将 AttentionMechanism 与 MultiRNNCell 和 dynamic_decode 一起使用？

How to use AttentionMechanism with MultiRNNCell and dynamic_decode?

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