Today I received a simple binary class dataset. After training some models I tried to change the labels to one-hot. There I realized that some labels where wrong.

For instance, the numpy.array has the next values (in format int64):
`

-9223372036854775808 (obviously wrong label)
0
1

`

That when converted to torch tensor using torch.from_numpy results in:

`

-9.2234e+18 (wrong label.)
0
1

`

Even more strange is that I can minimize the categorical cross entropy which normally throws error if the labels provided are not in [0,C-1].

I made some checkings. For instance if I put the tags from both classes to lets say -1 and -2 I get error:

Assertion `t >= 0 && t < n_classes` failed.

If I put the label one to 1 and the other to -1 it throws error. However If I put one label to 1 and the other to -9223372036854775808 everything works perfectly.