Hi
I checked and faced with an event in VGG network that was odd.
This is a sample code of using VGG16:
trained_model = torchvision.models.vgg16() #myModel()  #
trained_model.eval()
############ Import new image for classification #########################
img = Image.open('/home/morteza/PycharmProjects/transfer_learning/hymenoptera_data/val/ants/94999827_36895faade.jpg')
img.show()
loader = transforms.Compose([transforms.Scale(224), transforms.ToTensor()])
img = loader(img).float()
img = Variable(img)
img = img.unsqueeze(0)
pred = trained_model(img)
print(pred)

Now see outputs for each run:
First run:

The second run:

I feed one image in each run but the results are not the same. It is odd because I think all weights are constant after training and there is no change. Therefore, when we feed constant input image, we must get the same result values in the softmax layer without any change. However, it is not true here. Can you tell me what is the problem?