Hello All,

I am using ResNet3D architecture with some changes:

```
x = self.lastConv2(x)
x = self.relu(x)
x = self.lastConv1(x)
x = self.relu(x)
x = self.lastConv0(x)
x = self.relu(x)
x = x.view(x.size(0), -1)
x = self.dropout(x) #20%
x = self.fc(x)
x = torch.tanh(x)
return x #torch.tanh(x)
```

I am facing a strange issue like the output of the FC layer is increasing in every epoch which is increasing the train MSE loss.

And the output of tanh ultimately becomes very clipped at +1 to -1 which I understand that tanh will be clipped at +1 and -1 since the inputs are very high.

I am using Xavier weight initialization and all the inputs are normalized from -1 to +1 scale.

```
def weights_init(m):
if isinstance(m, nn.modules.conv._ConvNd): #nn.Conv3d
init.xavier_uniform_(m.weight.data, gain = np.sqrt(2.0))
# m.bias.data.fill_(0) #todo: if bias = False then comment out the line
# torch.nn.init.xavier_uniform_(m.bias.data)
elif isinstance(m, nn.modules.batchnorm._BatchNorm):
# print('OK')
m.weight.data.normal_(mean=1.0, std=0.02)
# m.bias.data.fill_(0) #todo: if bias = False then comment out the line
elif isinstance(m, nn.Linear):
# m.weight.data.normal_(0.0, 0.02)
# init.xavier_uniform_(m.weight.data)
y = 1/np.sqrt(m.in_features)
m.weight.data.uniform_(-y, y)
m.bias.data.fill_(0) #0.01
```

And for the network and loss:

```
Net = ResNet(Bottleneck, [1, 1, 1, 1]).to(device)
Net.apply(weights_init)
Optimizer = optim.Adam(Net.parameters(), lr=0.0005)
Criterion = nn.MSELoss()
Criterion = Criterion.cuda()
```

And about normalizing:

```
from skimage.exposure import rescale_intensity
MRimage = rescale_intensity(MRimage, in_range=(MRimage.min(), MRimage.max()),out_range=(-1,1))
target = rescale_intensity(target, in_range=(target.min(), target.max()), out_range=(-1,1))
```

What could be happening inside??