I’m trying to change the weights for a Conv2d after randomly initializing it
self.convs.weight = torch.nn.Parameter(data=w, requires_grad=False)
But this only trains weights for convs and not w (a tensor)
And convs.weight can only be assigned a Parameter if I remember correctly
How do I train w and not convs.weight?
use a functional if you don’t want to keep tracking of the weights. What are you trying to do?
you are making weights to be w, what does it mean training w but not weights
a = torch.nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float, device=device))
b = torch.nn.Parameter(torch.randn(1, requires_grad=True, dtype=torch.float, device=device))
c = a + 1
d = torch.nn.Parameter(c, requires_grad=True,)
for epoch in range(n_epochs):
yhat = d + b * x_train_tensor
error = y_train_tensor - yhat
loss = (error ** 2).mean()
Printing the gradients out I get
What I’m trying to do is learn a and not just d
If that makes sense?