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The last 1x1 layer of the NNMF network is a problem and had to be removed.
The structure is now:
```
Sequential(
(0): ReLU()
(1): Unfold(kernel_size=(5, 5), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
(2): Fold(output_size=torch.Size([24, 24]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(3): L1NormLayer()
(4): Conv2d(75, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(6): ReLU()
(7): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
(8): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(9): ReLU()
(10): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
(11): Fold(output_size=torch.Size([12, 12]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(12): L1NormLayer()
(13): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(14): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(15): ReLU()
(16): Unfold(kernel_size=(5, 5), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
(17): Fold(output_size=torch.Size([8, 8]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(18): L1NormLayer()
(19): Conv2d(800, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(20): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(21): ReLU()
(22): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
(23): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(24): ReLU()
(25): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
(26): Fold(output_size=torch.Size([4, 4]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(27): L1NormLayer()
(28): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(29): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(30): ReLU()
(31): Unfold(kernel_size=(4, 4), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
(32): Fold(output_size=torch.Size([1, 1]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(33): L1NormLayer()
(34): Conv2d(1024, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(35): ReLU()
(36): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))
(37): ReLU()
(38): Unfold(kernel_size=(1, 1), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
(39): Fold(output_size=torch.Size([1, 1]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
(40): L1NormLayer()
(41): Conv2d(96, 10, kernel_size=(1, 1), stride=(1, 1), bias=False)
(42): Softmax(dim=1)
(43): Flatten(start_dim=1, end_dim=-1)
)
```