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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image:
# 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 the files in the input directory

import os

# Any results you write to the current directory are saved as output.
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from fastai import *
from import *
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#Take PATH to be the parent folder of training and valid set.
PATH = "../input/art-images-drawings-painting-sculpture-engraving/art_dataset_cleaned/art_dataset"
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#do_flip=False because we do not want our paintings to be trained flipped or in any other rotation.
tfms = get_transforms(do_flip=False)

#size = the maximum size of our images, use 224 for most cases as told by Jeremy. 
#num_workers = 0, the number of CPUs to use. 0 due to lower hardware in Kaggle. 
#If training and valid set are already available, direct the function to them via the method below via arguments train and valid. 
data = ImageDataBunch.from_folder(PATH, train="training_set", valid="validation_set", ds_tfms=tfms, size=200, num_workers=0)
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data.show_batch(rows=3, figsize=(6,6))
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['drawings', 'engraving', 'iconography', 'painting', 'sculpture']
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#BEFORE THIS STEP, Click on Add Data and Search for Resnet34 from Kaggle. Because Kaggle serves read-only dirs, we cannot download pre-trained weights for Resnet. Now we have to make a dir 
#for copying those weights and hence we are making ~/.torch/models folder. 
cache_dir = os.path.expanduser(os.path.join('~','.torch'))
if not os.path.exists(cache_dir):
    os.makedirs(cache_dir) #first make ~/.torch if not already available.
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models_dir = os.path.join(cache_dir,'models')
if not os.path.exists(models_dir):
    os.makedirs(models_dir) #then make ~/.torch/models, if not already available. 
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#Copied resnet34.pth, which are pretrained weights on Resnet34 to our folder into resnet<version>-<sha-hash>.pth
!cp ../input/resnet34/resnet34.pth ~/.torch/models/resnet34-333f7ec4.pth 
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#MODEL_PATH is declared this way and glued to model_dir attr of cnn_learner.
MODEL_PATH = '/tmp/models'
learn = cnn_learner(data, models.resnet34, metrics=accuracy, model_dir=MODEL_PATH)
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#Fitting and checking for the first time. 
Total time: 07:52

epoch train_loss valid_loss accuracy time
0 0.489632 0.265858 0.901869 02:02
1 0.301034 0.202761 0.925234 01:56
2 0.233835 0.189954 0.929907 01:57
3 0.199139 0.181202 0.934579 01:55
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#Saving the model with ACCURACY = 93.4%'stage-1')
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#Initiating refit and checking LR
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
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#The lowest loss is at 1e-06 and loss increases from after 1e-04. Refitting by modulating LR
Total time: 04:03

epoch train_loss valid_loss accuracy time
0 0.186892 0.163934 0.940421 02:01
1 0.139290 0.150968 0.940421 02:01
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#Saving the model with accuracy 93.6%'stage-2')
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LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
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Total time: 04:01

epoch train_loss valid_loss accuracy time
0 0.114904 0.150118 0.947430 02:00
1 0.110616 0.144483 0.947430 02:00
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#Saving model with acc 94.7%'stage-3')
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#Uploaded a separate dataset for testing each of the above classes.
PRED_PATH = "../input/for-testing-art-images-cnn"
img_icono = open_image(f'{PRED_PATH}/icono.jpg')
img_drawing = open_image(f'{PRED_PATH}/drawing.jpg')
img_engraving = open_image(f'{PRED_PATH}/engraving.png')
img_painting = open_image(f'{PRED_PATH}/painting.jpg')
img_sculpt = open_image(f'{PRED_PATH}/sculpture.jpg')
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