@mratsim
So, I am classifying images using the code below:
Code:
def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).float()
    image = Variable(image, requires_grad=True)
    image = image.unsqueeze(0)  #this is for VGG, may not be needed for ResNet
    return image.cuda()  #assumes that you're using GPU

image = image_loader('/var/www/html/' + filename)

net = torch.load('resnet50_trained3.pth')
print(net)

net.eval()
output = net(image)
print(output)
_, predicted = torch.max(output, 1)


classification1 = predicted.data[0][0]
index = int(classification1)
names = net.classes
print(names[index])

I printed out the output of the net, and got the following-what exactly is this?
I am trying to get the probability distribution for each of the classes.
Output:
Columns 0 to 9
 3.6295 -3.4569 -6.6588 -3.6976 -3.2954 -4.6076 -3.3301 -4.4151 -8.7112 -3.3557

Columns 10 to 19
-4.3437 -3.2967 -3.6236 -6.1517 -2.8511 -0.3418 -2.8497 -6.0070 -6.8882 -1.3023
[torch.cuda.FloatTensor of size 1x20 (GPU 0)]

Thanks