Caffe LeNet: Difference between `solver.step(1)` and `solver.net.forward()`

Question

I was checking the Caffe LeNet Tutorial here and a question came to mind:

What's the difference between these 2 codes:

self.solver.step(1)

and

self.solver.net.forward()  # train net

They both seem to train the network at least according to the comment.

Personally I think the first one trains the network on the training data and updates the weights of both net and test_net but the second one seems to only forward a batch of data and apply the learned weights from the previous step.

If what I think is right, then what is the purpose of the second code in the tutorial? why did the code do a net.forward ? can't solver.step(1) do this itself?

Thanks for your time

Answer 1

step does one full iteration, covering all three phases: forward evaluation, backward propagation, and update. The call to forward does only the first of these. There are also differences in the signature (parameter list).