Hi, Everybody

I have a question. The title asks it all!

I have built a model using another framework (RAY), it is problem that calculates the loss (using loss.backward). So I make a Copy model using just PyTorch. How do I build a model using the below weights and biases?

Could anyone help me?

You could use the functional API to apply the parameters to an input.

The `weight`

and `bias`

arrays seem to be used in linear layers, so something like this should work:

```
x = torch.randn(10, 10) # your input
weight = nn.Parameter(torch.from_numpy(your_weight_array))
bias = nn.Parameter(torch.from_numpy(your_bias_array))
out = F.linear(x, weight, bias)
```

Additionally, in case you want to train these parameters, pass them to an optimizer and apply the standard training loop.

Hi, thank you every time @ptrblck !

if I wanna update weight and bias, Can I do this?

weight = torch.from_numpy(your_weight_array) / bias =torch.from_numpy(your_bias_array)

And i want to apply relu or tanh function, how can i do it?

out = F.linear(x, weight, bias)

out = F.relu (out)

Do this?

Pass both parameters to the optimizer and it should work:

```
x = torch.randn(10, 10) # your input
weight = nn.Parameter(torch.from_numpy(np.random.randn(10, 10)).float())
bias = nn.Parameter(torch.from_numpy(np.random.randn(10)).float())
optimizer = torch.optim.SGD([weight] + [bias], lr=1e-3)
target = torch.randn(10, 10)
criterion = nn.MSELoss()
for epoch in range(10):
optimizer.zero_grad()
out = F.relu(F.linear(x, weight, bias))
loss = criterion(out, target)
loss.backward()
optimizer.step()
print('epoch {}, loss {}'.format(epoch, loss.item()))
```

Oh, sorry, and thanks for the quick reply.

I have taken the mistake

What I meant to say is “I **don’t** wanna update weights and biases.”

really sorry to bother you

@ptrblck

Thank you really, i got solved.

Good to hear it’s working!

Just to make sure: yes, if you don’t want to update the `weight`

and `bias`

, you can just use tensors (not `nn.Parameter`

s).