how do you go back from nn.Embedding to the original discrete values?

In case you have the feature vector, you could calculate the difference and select the index with a 0 (or close to 0) diff.

I’m not sure if I understand what you mean. Let’s say you have some discrete valued vector that you want to represent in the latent space, how would I get feature vector? Like the feature vectors from a given model, extracted and take differences?

```
a = torch.nn.Embedding(10, 50)
# Discreet values
a.weight
# Indexer
a(torch.LongTensor([1,1,0]))
```

that still gives a real-valued vector. Want to reverse this operation. Something akin to encoder decoder .

The feature vector would be the output of the embedding layer and you could calculate the difference afterwards to get the index back:

```
emb = torch.nn.Embedding(10, 50)
x = torch.tensor([5])
out = emb(x)
out.shape
emb.weight.shape
rev = ((out - emb.weight).abs().sum(1) < 1e-6).nonzero()
print(rev)
# > tensor([[5]])
```

1 Like

@safin_salih Understood

You can use this

```
a = torch.nn.Embedding(10, 50)
b = torch.LongTensor([2,8])
results = a(b)
def get_embedding_index(x):
results = torch.where(torch.sum((a.weight==x), axis=1))
if len(results[0])==len(x):
return None
else:
return results[0][0]
indices = torch.Tensor(list(map(get_embedding_index, results)))
indices
```

```
tensor([2., 8.])
```

1 Like