def dynamic_rnn(cell,
inputs,
sequence_length=None,
initial_state=None,
dtype=None,
parallel_iterations=None,
swap_memory=False,
time_major=False,
scope=None):
assert not time_major # TODO : to be implemented later!
flat_inputs = flatten(inputs, 2) # [-1, J, d]
flat_len = None if sequence_length is None else tf.cast(
flatten(sequence_length, 0), 'int64')
flat_outputs, final_state = _dynamic_rnn(
cell,
flat_inputs,
sequence_length=flat_len,
initial_state=initial_state,
dtype=dtype,
parallel_iterations=parallel_iterations,
swap_memory=swap_memory,
time_major=time_major,
scope=scope)
outputs = reconstruct(flat_outputs, inputs, 2)
return outputs, final_state
def __call__(self,
inputs,
seq_len,
return_last_state=False,
keep_prob=None,
is_train=None):
with tf.variable_scope(self.scope):
if return_last_state:
_, (_, output) = _dynamic_rnn(self.cell,
inputs,
sequence_length=seq_len,
dtype=tf.float32)
else:
output, _ = _dynamic_rnn(self.cell,
inputs,
sequence_length=seq_len,
dtype=tf.float32)
output = dropout(output, keep_prob, is_train)
# print("Output", output.get_shape())
return output
def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
dtype=None, parallel_iterations=None, swap_memory=False,
time_major=False, scope=None):
assert not time_major # TODO : to be implemented later!
flat_inputs = flatten(inputs, 2) # [-1, J, d]
flat_len = None if sequence_length is None else tf.cast(flatten(sequence_length, 0), 'int64')
flat_outputs, final_state = _dynamic_rnn(cell, flat_inputs, sequence_length=flat_len,
initial_state=initial_state, dtype=dtype,
parallel_iterations=parallel_iterations, swap_memory=swap_memory,
time_major=time_major, scope=scope)
outputs = reconstruct(flat_outputs, inputs, 2)
return outputs, final_state
