Sorry I have no clue, I don’t know where to find a solution. I’m using two networks to construct two embeddings，I have binary target to indicate whether embeddingA and embeddingB “match” or not(1 or -1). The dataset like this:

```
embA0 embB0 1.0
embA1 embB1 -1.0
embA2 embB2 1.0
...
```

I hope to use cosine similarity to get classification results. But I feel confused when choosing the loss function, the two networks that generate embeddings are trained separately, now I can think of two options as follows:

**Plan 1:**

Construct the 3rd network, use embeddingA and embeddingB as the input of nn.cosinesimilarity() to calculate the final result (should be probability in [-1,1] ), and then select a two-category loss function.

(Sorry, I dont know which loss function to choose.)

```
class cos_Similarity(nn.Module):
def __init__(self):
super(cos_Similarity,self).__init__()
cos=nn.CosineSimilarity(dim=2)
embA=generator_A()
embB=generator_B()
def forward(self,a,b):
output_a=embA(a)
output_b=embB(b)
return cos(output_a,output_b)
loss_func=nn.CrossEntropyLoss()
y=cos_Similarity(a,b)
loss=loss_func(y,target)
acc=np.int64(y>0)
```

**Plan 2:** The two Embeddings as the output, then use nn.CosineEmbeddingLoss() as loss function, when I calculate the accuracy, I use nn.Cosinesimilarity() to output the result(probability in [-1,1]).

```
output_a=embA(a)
output_b=embB(b)
cos=nn.CosineSimilarity(dim=2)
loss_function = torch.nn.CosineEmbeddingLoss()
loss=loss_function(output_a,output_b,target)
acc=cos(output_a,output_b)
```

I really need help. How do I make a choice? Why? Or I can only make a choice for me through experimental results. Thank you very much!