aa968dad320137a31c229be3094c6538fd5ce39c,tensorlayer/layers/core.py,Layer,__call__,#Layer#Any#,203

Before Change

    def __call__(self, prev_layer, **kwargs):

        if self.__class__.__name__ in tl.layers.inputs.__all__:
            // 1. for input layers
            // Input layers should use tf.convert_to_tensor to make sure the inputs is converted into tf.Tensor

            // code in tl 1.0
            // raise RuntimeError("Please use layers in `tl.layers.inputs` to convert Variable/Tensor/Placeholder/Numpy arrays to a TL layer")
            // FIXME: not sure convert_to_tensor here or ask user to do it
            self.inputs = tf.convert_to_tensor(prev_layer)
            self._input_layer = None
            self._built = True
            self.build(self._inputs_shape)
            self.outputs = self.forward(self.inputs, **kwargs)

        elif isinstance(prev_layer, Layer):
            // 2. for normal layer have only 1 input i.e. DenseLayer
            // Hint : list(), dict() is pass by value (shallow), without them,
            // it is pass by reference.

            self.inputs = prev_layer.outputs
            self._input_layer = prev_layer

            if not self._built:
                self.build(self._inputs_shape)
                self._built = True

            self.outputs = self.forward(self.inputs, **kwargs)
            // self._outputs_shape = self.outputs.get_shape().as_list()

            // TODO: need update
            // self._add_layers(prev_layer.all_layers)
            // self._add_weights(self._weights)
            // self._add_weights(prev_layer.all_weights)
            // self._add_dropout_layers(prev_layer.all_drop)

        elif isinstance(prev_layer, list):
            // 3. for layer have multiply inputs i.e. ConcatLayer

            self.inputs = [layer.outputs for layer in prev_layer]
            self._input_layer = prev_layer // FIXME: not sure how to deal with it

            // FIXME: only support concat/elementwise, where build does nothing
            if not self._built:
                self._built = True

            self.outputs = self.forward(self.inputs, **kwargs)

            // TODO: need update
            // self._add_layers(sum([l.all_layers for l in prev_layer], []))
            // self._add_weights(sum([l.all_weights for l in prev_layer], []))
            // self._add_dropout_layers(sum([list(l.all_drop.items()) for l in prev_layer], []))

        else:
            // FIXME: not sure if there is other cases
            pass
            // elif prev_layer is not None:
            //     // 4. tl.models
            //     self._add_layers(prev_layer.all_layers)
            //     self._add_weights(prev_layer.all_weights)
            //     self._add_dropout_layers(prev_layer.all_drop)
            //
            //     if hasattr(prev_layer, "outputs"):
            //         self.inputs = prev_layer.outputs

        return self

    def _release_memory(self):
        """

After Change

        return self._weights

    def __call__(self, inputs, **kwargs):
        if self.__class__.__name__ in tl.layers.inputs.__all__:
            self.inputs = tf.convert_to_tensor(inputs)
        else:
            self.inputs = inputs

        if not self._built:
            self.build(self._inputs_shape)
            self._built = True

Instances