d0dc33d074f371a6f10d7b2354173a7edca7e23b,dataset/models/tf/unet.py,UNet,default_config,#Any#,31

Before Change

        filters = 64   // number of filters in the first block
        config["input_block"].update(dict(layout="cna cna", filters=filters, kernel_size=3, strides=1))
        config["body/num_blocks"] = 4
        config["body/filters"] = 2 ** np.arange(config["body"]["num_blocks"]) * filters * 2
        config["body/upsample"] = dict(layout="tna", factor=2)
        config["head"].update(dict(layout="cna cna", filters=filters, kernel_size=3, strides=1))

After Change

        config["body/encoder"] = dict(layout="cnacna", kernel_size=3)
        config["body/upsample"] = dict(layout="tna", kernel_size=2, strides=2)
        config["body/decoder"] = dict(layout="cnacna", kernel_size=3)
        config["head"] = dict(layout="c", kernel_size=1, strides=1)

        config["loss"] = "ce"
        // The article does not specify the initial learning rate. 1e-4 was chosen arbitrarily.
        config["optimizer"] = ("Momentum", dict(learning_rate=1e-4, momentum=.99))

Instances