Open RylieWeaver opened 11 months ago
I think because of this for loop. It appends all the way up to the epoch hyperparameter number, which will usually be more than the early stopping number.
for epoch in range(params['epochs']):
model.train()
epoch_list.append(epoch)
train_predict = torch.zeros(0,0).cuda(CUDA_ID)
logger.info(f"== Epoch [{epoch}/{params['epochs']}] ==")
train_loss_mean = 0
t = time()
i see i'll fix this
added a fix for this as well.
@RylieWeaver9 updated the code to get the last epoch
Error in epoch array length