thunderInfy · August 6, 2020 22:15
diff --git a/moco_loss.py b/moco_loss.py
 # get loss value
 loss = loss_function(q, k, queue)

 # put that loss value in the epoch losses list
 epoch_losses_train.append(loss.cpu().data.item())

 # perform backprop on loss value to get gradient values
 loss.backward()

 # run the optimizer
 optimizer.step()
	# get loss value
	loss = loss_function(q, k, queue)

	# put that loss value in the epoch losses list
	epoch_losses_train.append(loss.cpu().data.item())

	# perform backprop on loss value to get gradient values
	loss.backward()

	# run the optimizer
	optimizer.step()
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