investigating nn with the first three instances of the test set

test-1.py

test_dl = DataLoader(test_ds, batch_size=3) # Step 1
net.eval()
with torch.no_grad():
  for testx, testy in test_dl:
    print("batch: ", testx, testy)
    linout = net(testx) # Step 2
    print("linear outputs: ", linout)
    prob = F.softmax(linout, dim=1) # Step 3
    print("softmax probabilities:", prob)
    _, pred = torch.max(prob,1) # Step 4
    print("predictions:", pred)
    break # breaking the loop after the first batch
'''
Out:
batch:  tensor([[-1.2074,  1.3340,  1.0000],
        [-0.1828,  0.8066,  1.0000],
        [-1.0836, -0.6887,  0.0000]]) tensor([1, 1, 0])
linear outputs:  tensor([[ -8.5264,   7.5463],
        [-10.7886,  10.3464],
        [  4.5804,  -5.5217]])
softmax probabilities: tensor([[1.0464e-07, 1.0000e+00],
        [6.6248e-10, 1.0000e+00],
        [9.9996e-01, 4.0994e-05]])
predictions: tensor([1, 1, 0])
'''