I got bad values for the validation loss as the first epoch has

ep001 : loss: 4.473 val_loss : 5.635

val_loss after that became increasing and that is cause overfitting , i think the problem with the way i extract the features from the images as I got negative values like that

1.06231242e-01, -4.80975091e-01, -2.29298934e-01, -2.22610545e+00,
       -1.70528257e+00,  1.00546718e+00, -3.37958717e+00,  8.62520158e-01,
        1.09218419e+00,  1.02676439e+00, -5.19405723e-01, -1.97264120e-01,
        6.29661739e-01,  8.84239256e-01, -2.18081331e+00,  1.23091125e+00,
       -1.20287977e-01,  7.21974909e-01,  2.16648507e+00,  4.63137746e-01,
        4.28042859e-01, -7.97591209e-01, -7.74370611e-01, -4.94653493e-01,
        1.33503842e+00,  5.22300005e-01, -2.49824524e+00, -4.03152406e-01,
       -1.78791010e+00, -1.03124011e+00,  8.80928874e-01, -1.29657283e-01,
       -2.61071831e-01, -3.11868429e-01,  5.31454265e-01,  4.07434464e-01,
       -3.46822053e-01,  7.70217240e-01,  1.25887111e-01, -1.67335427e+00, ....

the code is

class Identity(nn.Module):
def init(self):
super().init()
def forward(self, x):
return x
File = ‘file.pth.tar’
model = create_model(My_model’,pretrained=False)
model.load_state_dict(torch.load(File))
model.head = Identity()
model.eval()

I found this way in this forum to freeze the last layer as I don’t need to make a classification