#%%

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# Any results you write to the current directory are saved as output.

#%%

from fastai.vision import *

#%%

doc(ImageDataBunch.from_folder)

#%%

path ="/kaggle/input/6rubbish/6rubbish"
tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)
data = ImageDataBunch.from_folder(path, train="train",valid_pct=0.2 ,ds_tfms=tfms, size=224)
data.normalize()
data.show_batch(rows=3, figsize=(6,6))


#%%

learn = cnn_learner(data, models.resnet50, metrics=accuracy,bn_final=True)
learn.model_dir = "/kaggle/working" 
learn.save("stage-1")

#%%

learn.unfreeze

#%%

# learn.lr_find()
# learn.recorder.plot()

#%%

learn.fit_one_cycle(300,max_lr=0.005)
learn.recorder.plot_losses()

#%%

learn.recorder.plot_lr()
learn.recorder.plot_lr(show_moms=True)

#%%

preds,y,losses = learn.get_preds(with_loss=True)
interp = ClassificationInterpretation(learn, preds, y, losses)
interp.plot_confusion_matrix()