Hi everyone, recently I started to read about Siamese Nets and I wanted to try this type of model on a gender recognition task. My data is a .csv dataset containing ~3000 n-vectors of audio features (n=20). The loss function I’m using is the Contrastive Loss. Here is my model:

```
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance.double(), 2) +
(label) * torch.pow(torch.clamp(self.margin - euclidean_distance.double(), min=0.0), 2))
return loss_contrastive
class SiameseMLP(nn.Module):
def __init__(self):
super(SiameseMLP, self).__init__()
self.layers = nn.Sequential(
nn.Linear(20, 256),
nn.ReLU(),
nn.Dropout(p=0.1),
nn.Linear(256, 256),
nn.ReLU(),
nn.Dropout(p=0.1),
nn.Linear(256, 256),
nn.ReLU(),
nn.Dropout(p=0.1),
nn.Linear(256, 2)
)
def forward_once(self, x):
x = x.view(-1, 20)
x = self.layers(x)
return x
def forward(self, x_1, x_2):
y_1 = self.forward_once(x_1)
y_2 = self.forward_once(x_2)
return y_1, y_2
```

I started to do some tests and for now I can’t reach a good value for the training loss, keeping it around ~0.33. When I calculate the dissimilarity between pairs, I get random values so I think the model is not learning completely. Do you have any suggest? Thanks.