I am dealing with a Siamese Network for vectorised data and want to apply a Contrastive Loss through the MarginRankingLoss or CosineEmbeddingLoss functions.

This is my training loop, however I do not know how to properly evaluate the outputs from the model during test phase

**Training**

```
for batched_graph_1, batched_graph_2, labels in train_dataloader:
pred1, pred2 = model(batched_graph_1, batched_graph_2)
loss = loss_func(pred1, pred2, labels)
scheduler.step(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```

This is what I have so far:

**Testing**

```
for batched_graph_1, batched_graph_2, labels in test_dataloader:
pred1, pred2 = model(batched_graph_1, batched_graph_2)
pred = torch.cdist(pred1, pred2, p=2)
y_pred += pred.tolist()
y_true += labels.tolist()
```

Thank you for your help.