d3b18acb8ba630f30131a940e1dfeeb88eb5b8cc,autokeras/graph.py,Graph,to_wider_model,#Graph#Any#Any#,378

Before Change

        pre_layer = self.layer_list[pre_layer_id]
        output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
        dim = layer_width(pre_layer)
        if is_layer(pre_layer, "Conv"):
            new_layer = wider_pre_conv(pre_layer, n_add, self.weighted)
            self._replace_layer(pre_layer_id, new_layer)
        else:
            new_layer = wider_pre_dense(pre_layer, n_add, self.weighted)
            self._replace_layer(pre_layer_id, new_layer)
        self._search_next(output_id, dim, dim, n_add)

    def to_dense_deeper_model(self, target_id):
        Insert a dense layer after the target layer.

After Change

        self._insert_new_layers(new_layers, output_id)

    def to_wider_model(self, pre_layer_id, n_add):
        Widen the last dimension of the output of the pre_layer.

        Args:
            pre_layer_id: A convolutional layer or dense layer.
            n_add: The number of dimensions to add.

        Returns:
            A new Keras model with the widened layers.
        
        self.operation_history.append(("to_wider_model", pre_layer_id, n_add))
        pre_layer = self.layer_list[pre_layer_id]
        output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
        dim = layer_width(pre_layer)
        self.vis = {}
        self._search(output_id, dim, dim, n_add)

    def to_dense_deeper_model(self, target_id):
        Insert a dense layer after the target layer.

Instances