BP1 - 04

BP1 - 04.py

def one_batch(xb, yb, cb):
    if not cb.begin_batch(xb,yb): return
    loss = cb.learn.loss_func(cb.learn.model(xb), yb)
    if not cb.after_loss(loss): return
    loss.backward()
    if cb.after_backward(): cb.learn.opt.step()
    if cb.after_step(): cb.learn.opt.zero_grad()

def all_batches(dl, cb):
    for xb,yb in dl:
        one_batch(xb, yb, cb)
        if cb.do_stop(): return

def fit(epochs, learn, cb):
    if not cb.begin_fit(learn): return
    for epoch in range(epochs):
        if not cb.begin_epoch(epoch): continue
        all_batches(learn.data.train_dl, cb) ###
        
        if cb.begin_validate():
            with torch.no_grad(): all_batches(learn.data.valid_dl, cb)
        if cb.do_stop() or not cb.after_epoch(): break
    cb.after_fit()