diff --git a/MLP_equivalent/README.md b/MLP_equivalent/README.md
index 8b13789..cfbbb1f 100644
--- a/MLP_equivalent/README.md
+++ b/MLP_equivalent/README.md
@@ -1 +1,51 @@
+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)
+)
+```