def __call__(self,
inputs,
seq_len,
return_last_state=False,
time_major=False):
assert not time_major, "BiRNN class cannot support time_major currently"
with tf.variable_scope(self.scope):
flat_inputs = flatten(inputs,
keep=2) # reshape to [-1, max_time, dim]
seq_len = flatten(
seq_len, keep=0) # reshape to [x] (one dimension sequence)
outputs, ((_, h_fw), (_, h_bw)) = bidirectional_dynamic_rnn(
self.cell_fw,
self.cell_bw,
flat_inputs,
sequence_length=seq_len,
dtype=tf.float32)
if return_last_state: # return last states
output = tf.concat([h_fw, h_bw],
axis=-1) # shape = [-1, 2 * num_units]
output = reconstruct(
output, ref=inputs, keep=2,
remove_shape=1) # remove the max_time shape
else:
output = tf.concat(
outputs, axis=-1) # shape = [-1, max_time, 2 * num_units]
output = reconstruct(
output, ref=inputs, keep=2
) # reshape to same as inputs, except the last two dim
return output
def __call__(self,
inputs,
seq_len,
return_last_state=False,
time_major=False):
assert not time_major, "StackBiRNN class cannot support time_major currently"
with tf.variable_scope(self.scope):
flat_inputs = flatten(inputs,
keep=2) # reshape to [-1, max_time, dim]
seq_len = flatten(
seq_len, keep=0) # reshape to [x] (one dimension sequence)
outputs, states_fw, states_bw = stack_bidirectional_dynamic_rnn(
self.cells_fw,
self.cells_fw,
flat_inputs,
sequence_length=seq_len,
dtype=tf.float32)
if return_last_state: # return last states
# since states_fw is the final states, one tensor per layer, of the forward rnn and states_bw is the
# final states, one tensor per layer, of the backward rnn, here we extract the last layer of forward
# and backward states as last state
h_fw, h_bw = states_fw[self.num_layers -
1].h, states_bw[self.num_layers - 1].h
output = tf.concat([h_fw, h_bw],
axis=-1) # shape = [-1, 2 * num_units]
output = reconstruct(
output, ref=inputs, keep=2,
remove_shape=1) # remove the max_time shape
else:
output = tf.concat(
outputs, axis=-1) # shape = [-1, max_time, 2 * num_units]
output = reconstruct(
output, ref=inputs, keep=2
) # reshape to same as inputs, except the last two dim
return output
def __call__(self, inputs, seq_len, time_major=False):
assert not time_major, "DenseConnectBiRNN class cannot support time_major currently"
# this function does not support return_last_state method currently
with tf.variable_scope(self.scope):
flat_inputs = flatten(inputs,
keep=2) # reshape to [-1, max_time, dim]
seq_len = flatten(
seq_len, keep=0) # reshape to [x] (one dimension sequence)
cur_inputs = flat_inputs
for i in range(self.num_layers):
cur_outputs = self.dense_bi_rnn[i](cur_inputs, seq_len)
if i < self.num_layers - 1:
cur_inputs = tf.concat([cur_inputs, cur_outputs], axis=-1)
else:
cur_inputs = cur_outputs
output = reconstruct(cur_inputs, ref=inputs, keep=2)
return output