Pytorch loss is nan

I'm trying to write my first neural network with pytorch. Unfortunately, I encounter a problem when I want to get the loss. The following error message:

RuntimeError: Function 'LogSoftmaxBackward0' returned nan values in its 0th output.

So I tried debugging and found something strange. The input has no nans and infs as I verify with the following:

print(torch.any(torch.isnan(inputs)))

But if I always let the individual steps in the model x be output, I see that there will be inf at some point.

training

inputs, labels = data
print(torch.any(torch.isnan(inputs)))
optimizer.zero_grad()
outputs = model(inputs)
print(outputs)
loss = criterion(outputs, labels)
print(f"epoch: {epoch + 1} loss: {loss.item()}")
loss.backward()

optimizer.step()

model

class Net(Module):
    def __init__(self):
        super(Net, self).__init__()
        self.layer1 = Conv1d(in_channels=1, out_channels=5, kernel_size=5, stride=2, dtype=torch.float64)
        self.act1 = ReLU()
        self.pool1 = MaxPool1d(2)
        self.layer2 = Conv1d(in_channels=5, out_channels=1, kernel_size=2, dtype=torch.float64)
        self.fcl1 = Linear(1350, 16, dtype=torch.float64)

    def forward(self, x):
        print("raw", x)
        x = self.layer1(x)
        print("conv1d 1", x)
        x = self.act1(x)
        print("relu", x)
        x = self.layer2(x)
        print("conv1d 2", x)
        x = self.pool1(x)
        x = self.pool1(x)
        x = self.pool1(x)
        x = self.pool1(x)
        x = self.pool1(x)
        x = self.pool1(x)
        x = self.pool1(x)
        print("pools", x)
        x = self.fcl1(x)
        print("linear", x)
        return x

output

tensor(False)
raw tensor([[9.0616e+227, 2.4353e-152,  1.0294e-71,  ...,  0.0000e+00,
          0.0000e+00,  0.0000e+00]], dtype=torch.float64)
conv1d 1 tensor([[   -inf,    -inf,    -inf,  ..., -0.2516, -0.2516, -0.2516],
        [    inf,     inf,     inf,  ...,  0.3377,  0.3377,  0.3377],
        [   -inf,    -inf,    -inf,  ...,  0.4285,  0.4285,  0.4285],
        [   -inf,    -inf,    -inf,  ..., -0.1230, -0.1230, -0.1230],
        [    inf,     inf,     inf,  ...,  0.3793,  0.3793,  0.3793]],
       dtype=torch.float64, grad_fn=<SqueezeBackward1>)
relu tensor([[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],
        [   inf,    inf,    inf,  ..., 0.3377, 0.3377, 0.3377],
        [0.0000, 0.0000, 0.0000,  ..., 0.4285, 0.4285, 0.4285],
        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],
        [   inf,    inf,    inf,  ..., 0.3793, 0.3793, 0.3793]],
       dtype=torch.float64, grad_fn=<ReluBackward0>)
conv1d 2 tensor([[        -inf,         -inf,         -inf,  ..., -5.4167e+265,
         -5.4167e+265, -5.4167e+265]], dtype=torch.float64,
       grad_fn=<SqueezeBackward1>)
pools tensor([[        -inf, -5.4167e+265, -5.4167e+265,  ..., -5.4167e+265,
         -5.4167e+265, -5.4167e+265]], dtype=torch.float64,
       grad_fn=<SqueezeBackward1>)
linear tensor([[inf, inf, -inf, -inf, -inf, inf, inf, inf, inf, inf, inf, -inf, inf, inf, -inf, -inf]],
       dtype=torch.float64, grad_fn=<AddmmBackward0>)
tensor([[inf, inf, -inf, -inf, -inf, inf, inf, inf, inf, inf, inf, -inf, inf, inf, -inf, -inf]],
       dtype=torch.float64, grad_fn=<AddmmBackward0>)
epoch: 1 loss: nan

Thanks for helping