.. _sphx_glr_beginner_examples_tensor_two_layer_net_tensor.py:


PyTorch: Tensors
----------------

A fully-connected ReLU network with one hidden layer and no biases, trained to
predict y from x by minimizing squared Euclidean distance.

This implementation uses PyTorch tensors to manually compute the forward pass,
loss, and backward pass.

A PyTorch Tensor is basically the same as a numpy array: it does not know
anything about deep learning or computational graphs or gradients, and is just
a generic n-dimensional array to be used for arbitrary numeric computation.

The biggest difference between a numpy array and a PyTorch Tensor is that
a PyTorch Tensor can run on either CPU or GPU. To run operations on the GPU,
just cast the Tensor to a cuda datatype.



.. code-block:: python


    import torch


    dtype = torch.FloatTensor
    # dtype = torch.cuda.FloatTensor # 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).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)

    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()
        print(t, loss)

        # Backprop to compute gradients of w1 and w2 with respect to loss
        grad_y_pred = 2.0 * (y_pred - y)
        grad_w2 = h_relu.t().mm(grad_y_pred)
        grad_h_relu = grad_y_pred.mm(w2.t())
        grad_h = grad_h_relu.clone()
        grad_h[h < 0] = 0
        grad_w1 = x.t().mm(grad_h)

        # Update weights using gradient descent
        w1 -= learning_rate * grad_w1
        w2 -= learning_rate * grad_w2

**Total running time of the script:** ( 0 minutes  0.000 seconds)



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