QnA.py 文件源码

def call(self, x, mask=None):
        # input shape: (nb_samples, time (padded with zeros), input_dim)
        # note that the .build() method of subclasses MUST define
        # self.input_spec with a complete input shape.
        input_shape = self.input_spec[0].shape
        if K._BACKEND == 'tensorflow':
            if not input_shape[1]:
                raise Exception('When using TensorFlow, you should define '
                                'explicitly the number of timesteps of '
                                'your sequences.\n'
                                'If your first layer is an Embedding, '
                                'make sure to pass it an "input_length" '
                                'argument. Otherwise, make sure '
                                'the first layer has '
                                'an "input_shape" or "batch_input_shape" '
                                'argument, including the time axis. '
                                'Found input shape at layer ' + self.name +
                                ': ' + str(input_shape))
        if self.layer.stateful:
            initial_states = self.layer.states
        else:
            initial_states = self.layer.get_initial_states(x)
        constants = self.get_constants(x)
        preprocessed_input = self.layer.preprocess_input(x)

        last_output, outputs, states = K.rnn(self.step, preprocessed_input,
                                             initial_states,
                                             go_backwards=self.layer.go_backwards,
                                             mask=mask,
                                             constants=constants,
                                             unroll=self.layer.unroll,
                                             input_length=input_shape[1])
        if self.layer.stateful:
            self.updates = []
            for i in range(len(states)):
                self.updates.append((self.layer.states[i], states[i]))

        if self.layer.return_sequences:
            return outputs
        else: