Hello, I want to analyze the changes in model weights over the training phase. For doing so, I want to use cosine similarity between model weights in two different checkpoints. I was wondering how I could do that? Thanks a lot in advance.

You can try something like this. You only need to define a variable to hold the old values and compare to the new ones and after the comparison update the βnewβ old values.

```
cos = torch.nn.CosineSimilarity(dim=0)
params = []
for param in model.parameters():
params.append(param.view(-1))
params = torch.cat(params)
with torch.no_grad():
# Same input twice --> returns 1
print(cos(params, params))
# Small deviation from the original --> close to 1, but smaller
print(cos(params, params+(torch.rand(params.shape)/100) ))
# Some random value --> close to 0
print(cos(params, torch.rand(params.shape)))
```

Hope this helps

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