def __call__()

in keras/engine/base_layer.py [0:0]

• 69 lines of code
• 26 McCabe index (conditional complexity)

    def __call__(self, inputs, **kwargs):
        """Wrapper around self.call(), for handling internal references.

        If a Keras tensor is passed:
            - We call self._add_inbound_node().
            - If necessary, we `build` the layer to match
                the _keras_shape of the input(s).
            - We update the _keras_shape of every input tensor with
                its new shape (obtained via self.compute_output_shape).
                This is done as part of _add_inbound_node().
            - We update the _keras_history of the output tensor(s)
                with the current layer.
                This is done as part of _add_inbound_node().

        # Arguments
            inputs: Can be a tensor or list/tuple of tensors.
            **kwargs: Additional keyword arguments to be passed to `call()`.

        # Returns
            Output of the layer's `call` method.

        # Raises
            ValueError: in case the layer is missing shape information
                for its `build` call.
        """
        if isinstance(inputs, list):
            inputs = inputs[:]
        with K.name_scope(self.name):
            # Handle laying building (weight creating, input spec locking).
            if not self.built:
                # Raise exceptions in case the input is not compatible
                # with the input_spec specified in the layer constructor.
                self.assert_input_compatibility(inputs)

                # Collect input shapes to build layer.
                input_shapes = []
                for x_elem in to_list(inputs):
                    if hasattr(x_elem, '_keras_shape'):
                        input_shapes.append(x_elem._keras_shape)
                    elif hasattr(K, 'int_shape'):
                        input_shapes.append(K.int_shape(x_elem))
                    else:
                        raise ValueError('You tried to call layer "' +
                                         self.name +
                                         '". This layer has no information'
                                         ' about its expected input shape, '
                                         'and thus cannot be built. '
                                         'You can build it manually via: '
                                         '`layer.build(batch_input_shape)`')
                self.build(unpack_singleton(input_shapes))
                self.built = True

                # Load weights that were specified at layer instantiation.
                if self._initial_weights is not None:
                    self.set_weights(self._initial_weights)

            # Raise exceptions in case the input is not compatible
            # with the input_spec set at build time.
            self.assert_input_compatibility(inputs)

            # Handle mask propagation.
            previous_mask = _collect_previous_mask(inputs)
            user_kwargs = copy.copy(kwargs)
            if not is_all_none(previous_mask):
                # The previous layer generated a mask.
                if has_arg(self.call, 'mask'):
                    if 'mask' not in kwargs:
                        # If mask is explicitly passed to __call__,
                        # we should override the default mask.
                        kwargs['mask'] = previous_mask
            # Handle automatic shape inference (only useful for Theano).
            input_shape = _collect_input_shape(inputs)

            # Actually call the layer,
            # collecting output(s), mask(s), and shape(s).
            output = self.call(inputs, **kwargs)
            output_mask = self.compute_mask(inputs, previous_mask)

            # If the layer returns tensors from its inputs, unmodified,
            # we copy them to avoid loss of tensor metadata.
            output_ls = to_list(output)
            inputs_ls = to_list(inputs)
            output_ls_copy = []
            for x in output_ls:
                if x in inputs_ls:
                    x = K.identity(x)
                output_ls_copy.append(x)
            output = unpack_singleton(output_ls_copy)

            # Inferring the output shape is only relevant for Theano.
            if all([s is not None
                    for s in to_list(input_shape)]):
                output_shape = self.compute_output_shape(input_shape)
            else:
                if isinstance(input_shape, list):
                    output_shape = [None for _ in input_shape]
                else:
                    output_shape = None

            if (not isinstance(output_mask, (list, tuple)) and
                    len(output_ls) > 1):
                # Augment the mask to match the length of the output.
                output_mask = [output_mask] * len(output_ls)

            # Add an inbound node to the layer, so that it keeps track
            # of the call and of all new variables created during the call.
            # This also updates the layer history of the output tensor(s).
            # If the input tensor(s) had not previous Keras history,
            # this does nothing.
            self._add_inbound_node(input_tensors=inputs,
                                   output_tensors=output,
                                   input_masks=previous_mask,
                                   output_masks=output_mask,
                                   input_shapes=input_shape,
                                   output_shapes=output_shape,
                                   arguments=user_kwargs)

            # Apply activity regularizer if any:
            if (hasattr(self, 'activity_regularizer') and
                    self.activity_regularizer is not None):
                with K.name_scope('activity_regularizer'):
                    regularization_losses = [
                        self.activity_regularizer(x)
                        for x in to_list(output)]
                self.add_loss(regularization_losses,
                              inputs=to_list(inputs))
        return output