Loss Function of this model

[tsung-jui-wu]

According to your picture, the final layer of the model should decide whether the input is true or fake (0/1), but the model spatiotemporal_net.py does not have a softmax layer at the end. Instead, the output of the model is a number that ranges across -50~50 (or some other number that isn't between 0 and 1). Does this mean that there is another way to calculate the loss of a classifier when training? If there indeed is another loss function besides binary cross entropy, can you share it with us?

[ahaliassos]

The model is indeed trained with binary cross-entropy (BCE) loss. The reason why there's no sigmoid layer in the model definition is that, in PyTorch, there's a loss that combines both sigmoid and BCE.

[tsung-jui-wu]

Thanks for the reply, it works wonders