d795f811eb13a20e18aa30c435cef0ad99e830ea,examples/plot_unet_tutorial.py,,,#,127

Before Change

    ax.set_title("raw data")

    fig.tight_layout()
    pylab.show()

After Change

// or use a some convolutions to increase the number of channels 
// before the the UNet.
from inferno.extensions.layers import ConvReLU2D
model_a = torch.nn.Sequential(
    ConvReLU2D(in_channels=image_channels, out_channels=5, kernel_size=3),
    ResBlockUNet(dim=2, in_channels=5, out_channels=pred_channels,  activated=False,
        res_block_kwargs=dict(batchnorm=True,size=2)) ,
    RemoveSingletonDimension(dim=1)
    // torch.nn.Sigmoid()
)

Instances