Re:The State of Machine Learning Frameworks
by klintcho on Thursday October 10, 2019 @05:44PM
(#21219485)
What made me fall in love with PyTorch was also that the "neural network training process" is defined almost as it is in theory, in code in PyTorch
- loop through epochs
- loop through each batch
- run a forward pass for the batch ( model(batch) )
- calculate the loss for the batch ( criteria(y, yprim)
- compute the gradients/backprop ( loss.backward() )
- update the weights (optimizer.step())
This really enforced everything I learned and I think breaks down the problem. All this of course in addition to everything else already mentioned, and super convenient module/network building and definition.
- loop through epochs
- loop through each batch
- run a forward pass for the batch ( model(batch) )
- calculate the loss for the batch ( criteria(y, yprim)
- compute the gradients/backprop ( loss.backward() )
- update the weights (optimizer.step())
This really enforced everything I learned and I think breaks down the problem. All this of course in addition to everything else already mentioned, and super convenient module/network building and definition.