// Create random input and output data
x = torch.randn(N, D_in).type(dtype)
y = torch.randn(N, D_out).type(dtype)
// Randomly initialize weights
w1 = torch.randn(D_in, H).type(dtype)
w2 = torch.randn(H, D_out).type(dtype)
After Change
dtype = torch.float
device = torch.device("cpu")
// dtype = torch.device("cuda:0") // Uncomment this to run on GPU
// N is batch size; D_in is input dimension;
// H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
// Create random input and output data
x = torch.randn(N, D_in, device=device, dtype=dtype)
y = torch.randn(N, D_out, device=device, dtype=dtype)
// Randomly initialize weights
w1 = torch.randn(D_in, H, device=device, dtype=dtype)
w2 = torch.randn(H, D_out, device=device, dtype=dtype)
learning_rate = 1e-6
for t in range(500):
// Forward pass: compute predicted y
h = x.mm(w1)
h_relu = h.clamp(min=0)
y_pred = h_relu.mm(w2)
// Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
print(t, loss)
// Backprop to compute gradients of w1 and w2 with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
