back to softmax

Code/train.py

@@ -143,7 +143,7 @@ def cross_entropy(logits, target):
 	logprobs = torch.nn.functional.log_softmax (logits, dim = 1)
 	return  -torch.mean(torch.sum(target * logprobs, dim=1))
 
-#without softmax
+#without softmax -> gives all values 0.0 (acc. around 70)
 # def cross_entropy(logits, target):
 #     log_probs = -torch.log(torch.exp(logits) / torch.sum(torch.exp(logits), dim=1, keepdim=True))
 #     loss = -torch.mean(torch.sum(target * log_probs, dim=1))