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use stack_bidirectional_dynamic_rnn for multi-layers BiDynamicRNN #295

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Feb 1, 2018
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use stack_bidirectional_dynamic_rnn for multi-layers BiDynamicRNN #295

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71 changes: 38 additions & 33 deletions tensorlayer/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5623,53 +5623,58 @@ def __init__(
# cell_instance_fn1(),
# input_keep_prob=in_keep_prob,
# output_keep_prob=out_keep_prob)
cell_creator = lambda: DropoutWrapper_fn(rnn_creator(), input_keep_prob=in_keep_prob, output_keep_prob=1.0) # out_keep_prob)
cell_creator = lambda is_last=True: \
DropoutWrapper_fn(rnn_creator(),
input_keep_prob=in_keep_prob,
output_keep_prob=out_keep_prob if is_last else 1.0) # out_keep_prob)
else:
cell_creator = rnn_creator
self.fw_cell = cell_creator()
self.bw_cell = cell_creator()
# Apply multiple layers
if n_layer > 1:
try:
MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
except:
MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
cell_creator = lambda : rnn_creator()

# cell_instance_fn2=cell_instance_fn # HanSheng
# cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)])
self.fw_cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)])
self.bw_cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)])

if dropout:
self.fw_cell = DropoutWrapper_fn(self.fw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)
self.bw_cell = DropoutWrapper_fn(self.bw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)
# if dropout:
# self.fw_cell = DropoutWrapper_fn(self.fw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)
# self.bw_cell = DropoutWrapper_fn(self.bw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)

# self.fw_cell=cell_instance_fn()
# self.bw_cell=cell_instance_fn()
# Initial state of RNN
if fw_initial_state is None:
self.fw_initial_state = self.fw_cell.zero_state(self.batch_size, dtype=D_TYPE) # dtype=tf.float32)
else:
self.fw_initial_state = fw_initial_state
if bw_initial_state is None:
self.bw_initial_state = self.bw_cell.zero_state(self.batch_size, dtype=D_TYPE) # dtype=tf.float32)
else:
self.bw_initial_state = bw_initial_state

self.fw_initial_state = fw_initial_state
self.bw_initial_state = bw_initial_state
# Computes sequence_length
if sequence_length is None:
try: ## TF1.0
sequence_length = retrieve_seq_length_op(self.inputs if isinstance(self.inputs, tf.Tensor) else tf.stack(self.inputs))
except: ## TF0.12
sequence_length = retrieve_seq_length_op(self.inputs if isinstance(self.inputs, tf.Tensor) else tf.pack(self.inputs))

outputs, (states_fw, states_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.fw_cell,
cell_bw=self.bw_cell,
inputs=self.inputs,
sequence_length=sequence_length,
initial_state_fw=self.fw_initial_state,
initial_state_bw=self.bw_initial_state,
**dynamic_rnn_init_args)
if n_layer > 1:
self.fw_cell = [cell_creator(is_last= i == n_layer - 1) for i in range(n_layer)]
self.bw_cell = [cell_creator(is_last= i == n_layer - 1) for i in range(n_layer)]
from tensorflow.contrib.rnn import stack_bidirectional_dynamic_rnn
outputs, states_fw, states_bw = stack_bidirectional_dynamic_rnn(
cells_fw=self.fw_cell,
cells_bw=self.bw_cell,
inputs=self.inputs,
sequence_length=sequence_length,
initial_states_fw=self.fw_initial_state,
initial_states_bw=self.bw_initial_state,
dtype=D_TYPE,
**dynamic_rnn_init_args)

else:
self.fw_cell = cell_creator()
self.bw_cell = cell_creator()
outputs, (states_fw, states_bw) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=self.fw_cell,
cell_bw=self.bw_cell,
inputs=self.inputs,
sequence_length=sequence_length,
initial_state_fw=self.fw_initial_state,
initial_state_bw=self.bw_initial_state,
dtype=D_TYPE,
**dynamic_rnn_init_args)

rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)

print(" n_params : %d" % (len(rnn_variables)))
Expand Down