Hello

I have classification problem. My input is sequence of length 341 and output one of three classes {0,1,2}, I want to train linear regression model using Pytorch, I have the following class but during the training, the loss values start to have numbers then inf then NAN. I do not know how to fix that . Also I tried to initialize the weights for linear model but it is the same thing. Also I tried different epochs 5, 20 ,100 all are same . Any suggestions.

```
class regression(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.input_dim = input_dim
# One layer
self.linear = nn.Linear(input_dim, 1)
def forward(self, x):
y_pred = self.linear(x)
return y_pred
criterion = torch.nn.MSELoss()
def fit(model, data_loader, optim, epochs):
for epoch in range(epochs):
for i, (X, y) in enumerate(data_loader):
X = X.float()
y = y.unsqueeze(1).float()
X = Variable(X, requires_grad=True)
y = Variable(y, requires_grad=True)
# Make a prediction for the input X
pred = model(X)
#loss = (y-pred).pow(2).mean()
loss = criterion(y, pred)
optim.zero_grad()
loss.backward()
optim.step()
print(loss)
print(type(loss))
# Give some feedback after each 5th pass through the data
if epoch % 5 == 0:
print("Epoch", epoch, f"loss: {loss}")
return None
regnet = regression(input_dim=341)
optim = SGD(regnet.parameters(), lr=0.01)
fit(regnet, data_loader, optim=optim, epochs=5)
pred = regnet(torch.Tensor(test_set.data_info).float())
pred = pred.detach().numpy()
```