Lesson 5 - Linear Function Error, Tensor Multiplication Confusion

In lesson 5 we were to make our own linear function can someone help me out why my loss isnt being plotted. My parameters are nan the update function is below: Why are my parameters nan?

Here is my linear function:

from torch.nn.parameter import Parameter

def linear_mul(input, weights, bias):
    return (input@weights + bias)

class Mnist_Logistic_mine(nn.Module):
    def __init__(self):
        self.device = torch.device('cuda')
        self.weights = Parameter(torch.Tensor(784, 10))
        self.bias = Parameter(torch.Tensor(10))

    def forward(self, xb):
        return linear_mul(xb, self.weights, self.bias)

here is my update function:

def update(x,y,lr):
wd = 1e-5
y_hat = model(x)
# weight decay
w2 = 0.
for p in model.parameters(): w2 += (p**2).sum()
# add to regular loss
loss = loss_func(y_hat, y) + w2*wd
with torch.no_grad():
    for p in model.parameters():
        p.sub_(lr * p.grad)
return loss.item()

my parameters somehow have nan values. idk why:

Here is the plot:

You didn’t initialize your parameters with values. You’re calling torch.tensor(…). I think that only allocates the memory for a tensor of the given size but it does not initialize its values.
So try with torch.rand(…) instead.

1 Like

Thank You @oneironaut. Yes that was the mistake. It seems to work fine Thanks.