def _build_attention(self, encoder_outputs, encoder_sequence_length):
"""
Builds Attention part of the graph.
Currently supports "bahdanau" and "luong"
:param encoder_outputs:
:param encoder_sequence_length:
:return:
"""
with tf.variable_scope("Attention"):
attention_depth = self.model_params['attention_layer_size']
if self.model_params['attention_type'] == 'bahdanau':
bah_normalize = self.model_params['bahdanau_normalize'] if 'bahdanau_normalize' in self.model_params else False
attention_mechanism = attention_wrapper.BahdanauAttention(num_units=attention_depth,
memory=encoder_outputs, normalize = bah_normalize,
memory_sequence_length=encoder_sequence_length,
probability_fn=tf.nn.softmax)
elif self.model_params['attention_type'] == 'luong':
luong_scale = self.model_params['luong_scale'] if 'luong_scale' in self.model_params else False
attention_mechanism = attention_wrapper.LuongAttention(num_units=attention_depth,
memory=encoder_outputs, scale = luong_scale,
memory_sequence_length=encoder_sequence_length,
probability_fn=tf.nn.softmax)
else:
raise ValueError('Unknown Attention Type')
return attention_mechanism
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
if self.use_beamsearch_decode:
print ("use beamsearch decoding..")
encoder_outputs = seq2seq.tile_batch(
self.encoder_outputs, multiplier=self.beam_width)
encoder_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.beam_width), self.encoder_last_state)
encoder_inputs_length = seq2seq.tile_batch(
self.encoder_inputs_length, multiplier=self.beam_width)
# Building attention mechanism: Default Bahdanau
# 'Bahdanau' style attention: https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,)
# 'Luong' style attention: https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,)
# Building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = Dense(self.hidden_units, dtype=self.dtype,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# Note: We implement Attention mechanism only on the top decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper')
batch_size = self.batch_size if not self.use_beamsearch_decode \
else self.batch_size * self.beam_width
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def testCustomizedAttention(self):
batch_size = 2
max_time = 3
num_units = 2
memory = constant_op.constant([[[1., 1.], [2., 2.], [3., 3.]],
[[4., 4.], [5., 5.], [6., 6.]]])
memory_sequence_length = constant_op.constant([3, 2])
attention_mechanism = wrapper.BahdanauAttention(num_units, memory,
memory_sequence_length)
# Sets all returned values to be all ones.
def _customized_attention(unused_attention_mechanism, unused_cell_output,
unused_attention_state, unused_attention_layer):
"""Customized attention.
Returns:
attention: `Tensor` of shape [batch_size, num_units], attention output.
alignments: `Tensor` of shape [batch_size, max_time], sigma value for
each input memory (prob. function of input keys).
next_attention_state: A `Tensor` representing the next state for the
attention.
"""
attention = array_ops.ones([batch_size, num_units])
alignments = array_ops.ones([batch_size, max_time])
next_attention_state = alignments
return attention, alignments, next_attention_state
attention_cell = wrapper.AttentionWrapper(
rnn_cell.LSTMCell(2),
attention_mechanism,
attention_layer_size=None, # don't use attention layer.
output_attention=False,
alignment_history=(),
attention_fn=_customized_attention,
name='attention')
self.assertEqual(num_units, attention_cell.output_size)
initial_state = attention_cell.zero_state(
batch_size=2, dtype=dtypes.float32)
source_input_emb = array_ops.ones([2, 3, 2])
source_input_length = constant_op.constant([3, 2])
# 'state' is a tuple of
# (cell_state, h, attention, alignments, alignment_history, attention_state)
output, state = rnn.dynamic_rnn(
attention_cell,
inputs=source_input_emb,
sequence_length=source_input_length,
initial_state=initial_state,
dtype=dtypes.float32)
with self.session() as sess:
sess.run(variables.global_variables_initializer())
output_value, state_value = sess.run([output, state], feed_dict={})
self.assertAllEqual(np.array([2, 3, 2]), output_value.shape)
self.assertAllClose(np.array([[1., 1.], [1., 1.]]), state_value.attention)
self.assertAllClose(
np.array([[1., 1., 1.], [1., 1., 1.]]), state_value.alignments)
self.assertAllClose(
np.array([[1., 1., 1.], [1., 1., 1.]]), state_value.attention_state)
def testBahdanauNormalizedDType(self):
for dtype in [np.float16, np.float32, np.float64]:
num_units = 128
encoder_outputs = array_ops.placeholder(dtype, shape=[64, None, 256])
encoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
decoder_inputs = array_ops.placeholder(dtype, shape=[64, None, 128])
decoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
batch_size = 64
attention_mechanism = wrapper.BahdanauAttention(
num_units=num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length,
normalize=True,
dtype=dtype,
)
cell = rnn_cell.LSTMCell(num_units)
cell = wrapper.AttentionWrapper(cell, attention_mechanism)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtype, batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual(final_outputs.rnn_output.dtype, dtype)
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
def build_dec_cell(self, hidden_size):
enc_outputs = self.enc_outputs
enc_last_state = self.enc_last_state
enc_inputs_length = self.enc_inp_len
if self.use_beam_search:
self.logger.info("using beam search decoding")
enc_outputs = seq2seq.tile_batch(self.enc_outputs,
multiplier=self.p.beam_width)
enc_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.p.beam_width),
self.enc_last_state)
enc_inputs_length = seq2seq.tile_batch(self.enc_inp_len,
self.p.beam_width)
if self.p.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=hidden_size,
memory=enc_outputs,
memory_sequence_length=enc_inputs_length)
else:
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=hidden_size,
memory=enc_outputs,
memory_sequence_length=enc_inputs_length)
def attn_dec_input_fn(inputs, attention):
if not self.p.attn_input_feeding:
return inputs
else:
_input_layer = Dense(hidden_size,
dtype=self.p.dtype,
name='attn_input_feeding')
return _input_layer(tf.concat([inputs, attention], -1))
self.dec_cell_list = [
self.build_single_cell(hidden_size) for _ in range(self.p.depth)
]
if self.p.use_attn:
self.dec_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.dec_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=hidden_size,
cell_input_fn=attn_dec_input_fn,
initial_cell_state=enc_last_state[-1],
alignment_history=False,
name='attention_wrapper')
batch_size = self.p.batch_size if not self.use_beam_search else self.p.batch_size * self.p.beam_width
initial_state = [state for state in enc_last_state]
if self.p.use_attn:
initial_state[-1] = self.dec_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.p.dtype)
dec_initial_state = tuple(initial_state)
return MultiRNNCell(self.dec_cell_list), dec_initial_state
def create_decoder(self, encoded, inputs, speaker_embed, train=True):
config = self.config
attention_mech = wrapper.BahdanauAttention(
config.attention_units,
encoded,
memory_sequence_length=inputs['text_length'])
inner_cell = [GRUCell(config.decoder_units) for _ in range(3)]
decoder_cell = OutputProjectionWrapper(
InputProjectionWrapper(ResidualWrapper(MultiRNNCell(inner_cell)),
config.decoder_units),
config.mel_features * config.r)
# feed in rth frame at each time step
decoder_frame_input = \
lambda inputs, attention: tf.concat(
[self.pre_net(tf.slice(inputs,
[0, (config.r - 1)*config.mel_features], [-1, -1]),
dropout=config.audio_dropout_prob,
train=train),
attention]
, -1)
cell = wrapper.AttentionWrapper(
decoder_cell,
attention_mech,
attention_layer_size=config.attention_units,
cell_input_fn=decoder_frame_input,
alignment_history=True,
output_attention=False)
if train:
if config.scheduled_sample:
print("if train if config.scheduled_sample: %s" % str(
(inputs['mel'], inputs['speech_length'],
config.scheduled_sample)))
decoder_helper = helper.ScheduledOutputTrainingHelper(
inputs['mel'], inputs['speech_length'],
config.scheduled_sample)
else:
decoder_helper = helper.TrainingHelper(inputs['mel'],
inputs['speech_length'])
else:
decoder_helper = ops.InferenceHelper(
tf.shape(inputs['text'])[0], config.mel_features * config.r)
initial_state = cell.zero_state(dtype=tf.float32,
batch_size=tf.shape(inputs['text'])[0])
#if speaker_embed is not None:
#initial_state.attention = tf.layers.dense(speaker_embed, config.attention_units)
dec = basic_decoder.BasicDecoder(cell, decoder_helper, initial_state)
return dec
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# building attention mechanism: default Bahdanau
# 'Bahdanau': https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_size,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length)
# 'Luong': https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_size,
memory=self.encoder_outputs,
memory_sequence_length=self.encoder_inputs_length)
# building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for _ in range(self.layer_num)
]
def att_decoder_input_fn(inputs, attention):
if not self.use_att_decoding:
return inputs
_input_layer = Dense(self.hidden_size,
dtype=self.dtype,
name='att_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], axis=-1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# implement attention mechanism only on the top of decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_size,
cell_input_fn=att_decoder_input_fn,
initial_cell_state=encoder_last_state[
-1], # last hidden state of last encode layer
alignment_history=False,
name='Attention_Wrapper')
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=self.batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def build_decoder_cell(self, num_units, num_layers, keep_prob):
encoder_outputs = tf.concat(self.encoder_outputs, axis=-1)
encoder_final_state = []
encoder_fw_fs, encoder_bw_fs = self.encoder_fs
for i in range(num_layers):
final_state_c = tf.concat((encoder_fw_fs[i].c, encoder_bw_fs[i].c),
axis=1)
final_state_h = tf.concat((encoder_fw_fs[i].h, encoder_bw_fs[i].h),
axis=1)
encoder_final_state.append(
LSTMStateTuple(c=final_state_c, h=final_state_h))
encoder_fs = tuple(encoder_final_state)
# build decoder cell
decoder_cells = [
self.make_rnn_cell(num_units, keep_prob) for _ in range(num_layers)
]
attention_cell = decoder_cells.pop()
# use Bahdanua attention to all cell layers.
self.attention_machenism = attention_wrapper.BahdanauAttention(
num_units=num_units,
memory=encoder_outputs,
normalize=False,
memory_sequence_length=self.encoder_length)
attention_cell = attention_wrapper.AttentionWrapper(
attention_cell,
self.attention_machenism,
attention_layer_size=None,
initial_cell_state=None,
output_attention=False,
alignment_history=False,
)
decoder_cells.append(attention_cell)
decoder_cells = tf.nn.rnn_cell.MultiRNNCell(decoder_cells)
batch = self.batch
decoder_init_state = tuple(
zs.clone(cell_state=es) if isinstance(
zs, tf.contrib.seq2seq.AttentionWrapperState) else es
for zs, es in zip(
decoder_cells.zero_state(batch, dtype=tf.float32), encoder_fs))
# why the last layers' zero state different with
# init_state = [state for state in encoder_fs]
return decoder_cells, decoder_init_state
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# Building Attention Mechanism: Default Bahdanau
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.decoder_hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length)
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)
]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
_input_layer = Dense(self.decoder_hidden_units,
dtype=tf.float32,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.decoder_hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper')
batch_size = self.batch_size
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=tf.float32)
decoder_initial_state = tuple(initial_state)
return tf.contrib.rnn.MultiRNNCell(
self.decoder_cell_list), decoder_initial_state
def build_decoder_cell(self):
self.decoder_cell_list = \
[self.build_single_cell() for i in range(self.para.num_layers)]
if self.para.mode == 'train':
encoder_outputs = self.encoder_outputs
encoder_inputs_len = self.encoder_inputs_len
encoder_states = self.encoder_states
batch_size = self.para.batch_size
else:
encoder_outputs = seq2seq.tile_batch(
self.encoder_outputs, multiplier=self.para.beam_width)
encoder_inputs_len = seq2seq.tile_batch(
self.encoder_inputs_len, multiplier=self.para.beam_width)
encoder_states = seq2seq.tile_batch(
self.encoder_states, multiplier=self.para.beam_width)
batch_size = self.para.batch_size * self.para.beam_width
if self.para.attention_mode == 'luong':
# scaled luong: recommended by authors of NMT
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.para.num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_len,
scale=True)
output_attention = True
else:
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.para.num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_len)
output_attention = False
cell = tf.contrib.rnn.MultiRNNCell(self.decoder_cell_list)
cell = attention_wrapper.AttentionWrapper(
cell=cell,
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.para.num_units,
name='attention')
decoder_initial_state = cell.zero_state(
batch_size, self.dtype).clone(cell_state=encoder_states)
return cell, decoder_initial_state
def build_attention_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length, )
if self.attention_type.lower()=='luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length, )
# # Building decoder_cell
self.decoder_cell_list = [self.build_single_cell() for i in range(self.num_layers)]
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = tf.layers.dense(tf.concat([inputs, attention], axis=-1), self.hidden_units,
name='attn_input_feeding')
return _input_layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper'
)
batch_size = self.config['batch_size']
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def _testDynamicDecodeRNN(self, time_major, has_attention):
encoder_sequence_length = np.array([3, 2, 3, 1, 1])
decoder_sequence_length = np.array([2, 0, 1, 2, 3])
batch_size = 5
decoder_max_time = 4
input_depth = 7
cell_depth = 9
attention_depth = 6
vocab_size = 20
end_token = vocab_size - 1
start_token = 0
embedding_dim = 50
max_out = max(decoder_sequence_length)
output_layer = layers_core.Dense(vocab_size,
use_bias=True,
activation=None)
beam_width = 3
with self.test_session() as sess:
batch_size_tensor = constant_op.constant(batch_size)
embedding = np.random.randn(vocab_size,
embedding_dim).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
initial_state = cell.zero_state(batch_size, dtypes.float32)
if has_attention:
inputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32),
shape=(None, None, input_depth))
tiled_inputs = beam_search_decoder.tile_batch(
inputs, multiplier=beam_width)
tiled_sequence_length = beam_search_decoder.tile_batch(
encoder_sequence_length, multiplier=beam_width)
attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=attention_depth,
memory=tiled_inputs,
memory_sequence_length=tiled_sequence_length)
initial_state = beam_search_decoder.tile_batch(
initial_state, multiplier=beam_width)
cell = attention_wrapper.AttentionWrapper(
cell=cell,
attention_mechanism=attention_mechanism,
attention_layer_size=attention_depth,
alignment_history=False)
cell_state = cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size_tensor *
beam_width)
if has_attention:
cell_state = cell_state.clone(cell_state=initial_state)
bsd = beam_search_decoder.BeamSearchDecoder(
cell=cell,
embedding=embedding,
start_tokens=array_ops.fill([batch_size_tensor], start_token),
end_token=end_token,
initial_state=cell_state,
beam_width=beam_width,
output_layer=output_layer,
length_penalty_weight=0.0)
final_outputs, final_state, final_sequence_lengths = (
decoder.dynamic_decode(bsd,
output_time_major=time_major,
maximum_iterations=max_out))
def _t(shape):
if time_major:
return (shape[1], shape[0]) + shape[2:]
return shape
self.assertTrue(
isinstance(final_outputs,
beam_search_decoder.FinalBeamSearchDecoderOutput))
self.assertTrue(
isinstance(final_state,
beam_search_decoder.BeamSearchDecoderState))
beam_search_decoder_output = final_outputs.beam_search_decoder_output
self.assertEqual(
_t((batch_size, None, beam_width)),
tuple(beam_search_decoder_output.scores.get_shape().as_list()))
self.assertEqual(
_t((batch_size, None, beam_width)),
tuple(final_outputs.predicted_ids.get_shape().as_list()))
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
'final_outputs':
final_outputs,
'final_state':
final_state,
'final_sequence_lengths':
final_sequence_lengths
})
max_sequence_length = np.max(
sess_results['final_sequence_lengths'])
# A smoke test
self.assertEqual(
_t((batch_size, max_sequence_length, beam_width)),
sess_results['final_outputs'].beam_search_decoder_output.
scores.shape)
self.assertEqual(
_t((batch_size, max_sequence_length, beam_width)),
sess_results['final_outputs'].beam_search_decoder_output.
predicted_ids.shape)
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# To use BeamSearchDecoder, encoder_outputs, encoder_last_state, encoder_inputs_length
# needs to be tiled so that: [batch_size, .., ..] -> [batch_size x beam_width, .., ..]
if self.use_beamsearch_decode:
print("use beamsearch decoding..")
encoder_outputs = seq2seq.tile_batch(self.encoder_outputs,
multiplier=self.beam_width)
encoder_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.beam_width),
self.encoder_last_state)
encoder_inputs_length = seq2seq.tile_batch(
self.encoder_inputs_length, multiplier=self.beam_width)
# Building attention mechanism: Default Bahdanau
# 'Bahdanau' style attention: https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,
)
# 'Luong' style attention: https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,
)
# Building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)
]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = Dense(self.hidden_units,
dtype=self.dtype,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# Note: We implement Attention mechanism only on the top decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=True,
name='Attention_Wrapper')
# To be compatible with AttentionWrapper, the encoder last state
# of the top layer should be converted into the AttentionWrapperState form
# We can easily do this by calling AttentionWrapper.zero_state
# Also if beamsearch decoding is used, the batch_size argument in .zero_state
# should be ${decoder_beam_width} times to the origianl batch_size
batch_size = self.batch_size if not self.use_beamsearch_decode \
else self.batch_size * self.beam_width
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def _attention_decoder_wrapper(batch_size, num_units, memory, mutli_layer, dtype=dtypes.float32 ,\
attention_layer_size=None, cell_input_fn=None, attention_type='B',\
probability_fn=None, alignment_history=False, output_attention=True, \
initial_cell_state=None, normalization=False, sigmoid_noise=0.,
sigmoid_noise_seed=None, score_bias_init=0.):
"""
A wrapper for rnn-decoder with attention mechanism
the detail about params explanation can be found at :
blog.csdn.net/qsczse943062710/article/details/79539005
:param mutli_layer: a object returned by function _mutli_layer_rnn()
:param attention_type, string
'B' is for BahdanauAttention as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
"Neural Machine Translation by Jointly Learning to Align and Translate."
ICLR 2015. https://arxiv.org/abs/1409.0473
'L' is for LuongAttention as described in:
Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
"Effective Approaches to Attention-based Neural Machine Translation."
EMNLP 2015. https://arxiv.org/abs/1508.04025
MonotonicAttention is described in :
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
"Online and Linear-Time Attention by Enforcing Monotonic Alignments."
ICML 2017. https://arxiv.org/abs/1704.00784
'BM' : Monotonic attention mechanism with Bahadanau-style energy function
'LM' : Monotonic attention mechanism with Luong-style energy function
or maybe something user defined in the future
**warning** :
if normalization is set True,
then normalization will be applied to all types of attentions as described in:
Tim Salimans, Diederik P. Kingma.
"Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks."
https://arxiv.org/abs/1602.07868
A example usage:
att_wrapper, states = _attention_decoder_wrapper(*args)
while decoding:
output, states = att_wrapper(input, states)
...
some processing on output
...
input = processed_output
"""
if attention_type == 'B':
attention_mechanism = att_w.BahdanauAttention(
num_units=num_units,
memory=memory,
probability_fn=probability_fn,
normalize=normalization)
elif attention_type == 'BM':
attention_mechanism = att_w.BahdanauMonotonicAttention(
num_units=num_units,
memory=memory,
normalize=normalization,
sigmoid_noise=sigmoid_noise,
sigmoid_noise_seed=sigmoid_noise_seed,
score_bias_init=score_bias_init)
elif attention_type == 'L':
attention_mechanism = att_w.LuongAttention(
num_units=num_units,
memory=memory,
probability_fn=probability_fn,
scale=normalization)
elif attention_type == 'LM':
attention_mechanism = att_w.LuongMonotonicAttention(
num_units=num_units,
memory=memory,
scale=normalization,
sigmoid_noise=sigmoid_noise,
sigmoid_noise_seed=sigmoid_noise_seed,
score_bias_init=score_bias_init)
else:
raise 'Invalid attention type'
att_wrapper = att_w.AttentionWrapper(
cell=mutli_layer,
attention_mechanism=attention_mechanism,
attention_layer_size=attention_layer_size,
cell_input_fn=cell_input_fn,
alignment_history=alignment_history,
output_attention=output_attention,
initial_cell_state=initial_cell_state)
init_states = att_wrapper.zero_state(batch_size=batch_size, dtype=dtype)
return att_wrapper, init_states
from tensorflow.contrib.seq2seq.python.ops import decoder
from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
from tensorflow.contrib.seq2seq.python.ops import attention_wrapper
from tensorflow.python.ops import rnn_cell
#
# tf.enable_eager_execution()
batch_size = 5
src_len = [4, 5, 3, 5, 6]
max_times = 6
num_units = 16
enc_output = tf.random.normal((batch_size, max_times, num_units),
dtype=tf.float32)
#
# attenRNNCell
rnncell = rnn_cell.LSTMCell(num_units=16)
attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=num_units, memory=enc_output, memory_sequence_length=src_len)
attnRNNCell = attention_wrapper.AttentionWrapper(
cell=rnncell,
attention_mechanism=attention_mechanism,
alignment_history=True)
# training
tgt_len = [5, 6, 2, 7, 4]
tgt_max_times = 7
tgt_inputs = tf.random.normal((batch_size, tgt_max_times, num_units),
dtype=tf.float32)
training_helper = helper_py.TrainingHelper(tgt_inputs, tgt_len)
# train helper
train_decoder = basic_decoder.BasicDecoder(
cell=attnRNNCell,
