a6819ac67444b66143ab8e0cad8a42cb7635730d,autokeras/blocks/basic.py,ResNetBlock,build,#ResNetBlock#Any#Any#,531

Before Change

        if pretrained is None:
            pretrained = hp.Boolean("pretrained", default=False)

        if not pretrained:
            self.input_tensor = input_node
            self.input_shape = None
            model = super().build(hp)
            return model.outputs

        // Use pretrained weights.
        // Do not use "version" as hp name, which is used in super class.
        version = hp.Choice("pretrained_version",
                            list(RESNET_PRETRAINED.keys()))

        pretrained_model = RESNET_PRETRAINED[version](weights="imagenet",

After Change

        if input_node.shape[3] == 1:
            input_node = layers.Concatenate()([input_node] * 3)
        if input_node.shape[3] != 3:
            input_node = layers.Conv2D(filters=3,
                                       kernel_size=1,
                                       padding="same")(input_node)

        if pretrained:
            model = self.models[version](
                weights="imagenet",

Instances