# Put these at the top of every notebook, to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib inline

# This file contains all the main external libs we'll use
from fastai.imports import *

from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *

PATH = "data/dogscats/"
sz=224
arch=vgg16
bs=64

# Uncomment the below if you need to reset your precomputed activations
# !rm -rf {PATH}tmp

data = ImageClassifierData.from_paths(PATH, bs=bs, tfms=tfms_from_model(arch, sz))

learn = ConvLearner.pretrained(arch, data, precompute=True)

learn.fit(0.01, 3, cycle_len=1)



tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)

data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs, num_workers=4)
learn = ConvLearner.pretrained(arch, data, precompute=True)

learn.fit(1e-2, 2)

learn.precompute=False

learn.fit(1e-2, 1, cycle_len=1)

learn.unfreeze()

lr=np.array([1e-4,1e-3,1e-2])

learn.fit(lr, 1, cycle_len=1)

learn.fit(lr, 3, cycle_len=1, cycle_mult=2)

learn.fit(lr, 3, cycle_len=3)

log_preds,y = learn.TTA()
probs = np.mean(np.exp(log_preds),0);accuracy_np(probs,y)