Hi again,
This may seems as a stupid question but I have different outputs for the same network every time I train just for the forward pass.
the shape of the network looks like this :
 
  self.features = torch.nn.Sequential(
             # conv1
             torch.nn.Conv2d(3,64,3,padding=35),
             torch.nn.ReLU(),
             torch.nn.Conv2d(64, 64, 3, padding=1),
             torch.nn.ReLU(),
             torch.nn.MaxPool2d(2, stride=2),
             # conv2
             torch.nn.Conv2d(64, 128, 3, padding=1),
             torch.nn.ReLU(),
             torch.nn.Conv2d(128, 128, 3, padding=1),
             torch.nn.ReLU(),
             torch.nn.MaxPool2d(2, stride=2),
             # conv3
             torch.nn.Conv2d(128, 256, 3, padding=1),
             torch.nn.ReLU(),
             torch.nn.Conv2d(256, 256, 3, padding=1),
             torch.nn.ReLU(),
             torch.nn.Conv2d(256, 256, 3, padding=1),
             torch.nn.ReLU()
 )
 self.deconv1 = torch.nn.Sequential(
             torch.nn.ConvTranspose2d(256, 128, 4, stride=2),
             torch.nn.ReLU(),
             torch.nn.ConvTranspose2d(128, 64, 4, stride=2),
             torch.nn.ReLU(),
             torch.nn.ConvTranspose2d(64, 1, 3, padding=0,stride=1)
 )

The image go through the features and deconv blocks and then is cropped (slicing) before going through a sigmoid.
The ouput is never the same, for the same image.
Am I not seing something stupid ?
yours
Justin