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# from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from keras import backend as K
from keras.layers import Layer
from keras.layers.wrappers import TimeDistributed
from keras.layers.recurrent import GRU
class WrappedGRU(GRU):
def __init__(self, initial_state_provided=False, **kwargs):
kwargs['implementation'] = kwargs.get('implementation', 2)
assert(kwargs['implementation'] == 2)
super(WrappedGRU, self).__init__(**kwargs)
self.input_spec = None
self.initial_state_provided = initial_state_provided
def call(self, inputs, mask=None, training=None, initial_state=None):
if self.initial_state_provided:
initial_state = inputs[-1:]
inputs = inputs[:-1]
initial_state_mask = mask[-1:]
mask = mask[:-1] if mask is not None else None
self._non_sequences = inputs[1:]
inputs = inputs[:1]
self._mask_non_sequences = []
if mask is not None:
self._mask_non_sequences = mask[1:]
mask = mask[:1]
self._mask_non_sequences = [mask for mask in self._mask_non_sequences
if mask is not None]
if self.initial_state_provided:
assert(len(inputs) == len(initial_state))
inputs += initial_state
if len(inputs) == 1:
inputs = inputs[0]
if isinstance(mask, list) and len(mask) == 1:
mask = mask[0]
return super(WrappedGRU, self).call(inputs, mask, training)
def get_constants(self, inputs, training=None):
constants = super(WrappedGRU, self).get_constants(inputs, training=training)
constants += self._non_sequences
constants += self._mask_non_sequences
return constants
def get_config(self):
config = {'initial_state_provided': self.initial_state_provided}
base_config = super(WrappedGRU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))