from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.text import *
from fastai.callbacks import * 
from fastai.basic_train import * 
from fastai.train import * 
from fastai import callbacks

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
def simple_learner(): return Learner(data, simple_cnn((3,16,16,2)), metrics=[accuracy])
learn = simple_learner()

learn.lr_find()

learn.recorder.plot()

lr = 2e-2

learn.fit_one_cycle(3, lr)

learn.recorder.plot_lr(show_moms=True)

learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy]).mixup()

learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy, error_rate], callback_fns=[CSVLogger])

learn.fit(3)

learn.csv_logger.read_logged_file()

def fit_odd_shedule(learn, lr):
    n = len(learn.data.train_dl)
    phases = [TrainingPhase(n).schedule_hp('lr', lr, anneal=annealing_cos), 
              TrainingPhase(n*2).schedule_hp('lr', lr, anneal=annealing_poly(2))]
    sched = GeneralScheduler(learn, phases)
    learn.callbacks.append(sched)
    total_epochs = 3
    learn.fit(total_epochs)

learn = Learner(data, simple_cnn((3,16,16,2)), metrics=accuracy)
fit_odd_shedule(learn, 1e-3)

learn.recorder.plot_lr()

learn = Learner(data, simple_cnn((3,16,16,2)), metrics=accuracy)
learn.fit_one_cycle(3,1e-4, callbacks=[SaveModelCallback(learn, every='epoch', monitor='accuracy')])

!ls ~/.fastai/data/mnist_sample/models