I am new to PyTorch and I’m attempting to train a transformer model on biological sequences to output a binary classification. I have a functional training regiment in the sense that it iterates without errors, however my loss function never decreases and my validation accuracy is constant at 50%. Inspecting the tensor output from one forward pass shows that it is a tensor of constant values. I’m assuming something is wrong with my forward method (perhaps my max pooling?).

My input is of shape (n, 100, 4), where n is the number of samples, 100 is the sequence length, and 4 is the one-hot encoding of DNA.

Here is my model definition

```
class NeuralNetwork(nn.Module):
'''
Build a neural network transformer for one-hot encoded DNA sequences
'''
def __init__(self):
super(NeuralNetwork, self).__init__()
self.transformer1 = nn.TransformerEncoderLayer(
d_model= 4,
nhead= 2,
batch_first= True,
dim_feedforward= 1024
)
self.transformer2 = nn.TransformerEncoderLayer(
d_model= 4,
nhead= 2,
batch_first= True,
dim_feedforward= 1024
)
self.transformer3 = nn.TransformerEncoderLayer(
d_model= 4,
nhead= 2,
batch_first= True,
dim_feedforward= 1024
)
self.linear = nn.Linear(
in_features= 4,
out_features=1
)
def forward(self, x):
x= self.transformer1(x)
x= self.transformer2(x)
x= self.transformer3(x)
x= torch.max(x, dim=1)[0]
logits = self.linear(x)
return torch.flatten(logits)
```

I am using a BCEwithLogitsLoss and the Adam optimizer. If I print the value of `output= model(input)`

I get a constant tensor.

Let me know if I need to include more details, I am attempting to not overclog the post. I posted this in NLP as I assumed you are the resident experts on transformers. Please correct me if I should move it.