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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())) |