Hi there,

I am new to Pytorch with very little understanding of programming classes. While everything worked out so far, I don’t understand the following problem now:

In oder to allow my feed-forward network to process data with different input feature size, I was trying to implement an encoder, that

- processes each feature in tensor of shape [730, 10] seperately, so a tensor of shape [730,1] with hidden size 64 to [730,64].
- concatenates all of them together to [730,64,10]
- activates it.

Because I want to have the number of input features variable, I now apply adaptive pooling with arbitrary output size to this tensor.

While the network is working on the first view, the loss that it outputs is:

tensor(nan, grad_fn=)

Am I missing something in the class implementation, such that I can’t do proper backpropagation?

I would really appreciate any help on this!

The class I created looks like this:

class MLPmod(nn.Module):

```
def __init__(self, hidden_features, dimensions, activation):
super(MLPmod, self).__init__()
self.hidden_features = hidden_features
self.activation = activation()
self.encoder = nn.Linear(1, hidden_features)
self.avgpool = nn.AdaptiveAvgPool1d(hidden_features)
self.classifier = self.mlp(dimensions, activation)
def forward(self, x):
x = self.encode(x)
x = self.avgpool(x).view(x.shape[0],-1)
x = self.classifier(x)
return(x)
def mlp(self, dimensions, activation):
network = nn.Sequential()
network.add_module(f"hidden0", nn.Linear(self.hidden_features*self.hidden_features, dimensions[0]))
network.add_module(f'activation0', activation())
for i in range(len(dimensions)-1):
network.add_module(f'hidden{i+1}', nn.Linear(dimensions[i], dimensions[i+1]))
if i < len(dimensions)-2:
network.add_module(f'activation{i+1}', activation())
return(network)
def encode(self, x):
x = x.unsqueeze(1)
latent = torch.empty(x.shape[0], self.hidden_features, 1)
for feature in range(x.shape[-1]):
latent = torch.cat((latent, self.encoder(x[:,:,feature]).unsqueeze(2)),dim=2)
latent = self.activation(latent)
return(latent)
```