network.py 文件源码

def __call__(self, inputs, state, scope=None):
        batch_size = tf.shape(inputs)[0]

        if self._apply_to == 'input':
            inputs = slim.flatten(inputs) if self._shape == -1 else tf.reshape(inputs, [batch_size] + self._shape)
            return self._cell(inputs, state)
        elif self._apply_to == 'output':
            output, res_state = self._cell(inputs, state)
            output = slim.flatten(output) if self._shape == -1 else tf.reshape(output, [batch_size] + self._shape)
            return output, res_state
        elif self._apply_to == 'state':
            output, res_state = self._cell(inputs, state)
            res_state = slim.flatten(res_state) if self._shape == -1 else tf.reshape(res_state, [batch_size] + self._shape)
            return output, res_state
        else:
            raise ValueError('Unknown apply_to: "{}"'.format(self._apply_to))