Novice model

Now we are going to build a novice, NumPy-like model, not using any PyTorch-specific approach. Then, in the next session, we'll convert the same model to PyTorch's method. If you come from a NumPy background, you'll feel at home, but if you are an advanced deep learning practitioner who has used other frameworks, please take the liberty of skipping this session.

Autograd

So, now that we know which type our tensors should be, we can create PyTorch tensors from the NumPy array we got from get_numpy_data().

x = torch.from_numpy(trX).to(device=device, dtype=dtype)
y = torch.from_numpy(trY).to(device=device, dtype=dtype)
w1 = torch.randn(input_size, hidden_size, requires_grad=True, device=device, dtype=dtype)
w2 = torch.randn(hidden_size, output_size, requires_grad=True, device=device, dtype=dtype)
b1 = torch.zeros(1, hidden_size, requires_grad=True, device=device, dtype=dtype)
b2 = torch.zeros(1, output_size, requires_grad=True, device=device, dtype=dtype...