untitled0.py

# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pK6Uua94RzuOthjwcAt95ugWmuI1WAeN
"""

# Commented out IPython magic to ensure Python compatibility.
# %reload_ext autoreload
# %autoreload 2
# %matplotlib inline
import torch
import torchvision
from fastai import*
from fastai.vision import *
from fastai.metrics import error_rate

folder = 'black'
file = 'black_cat.csv'
path = Path('data')
dest = path/folder
dest.mkdir(parents=True, exist_ok=True)

folder = 'white'
file = 'white_cat.csv'
path = Path('data')
dest = path/folder
dest.mkdir(parents=True, exist_ok=True)

folder = 'grey'
file = 'grey_cat.csv'
path = Path('data')
dest = path/folder
dest.mkdir(parents=True, exist_ok=True)

path.ls()

path = Path('data/')

classes = ['black','white','grey']

download_images(path/file, dest, max_pics=200)

for c in classes:
    print(c)
    verify_images(path/c, delete=True, max_size=500)

np.random.seed(42)
data = ImageDataBunch.from_folder(path, train=".", valid_pct=0.2,
        ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)

data.classes

data.show_batch(rows=3, figsize=(7,8))

data.classes, data.c, len(data.train_ds), len(data.valid_ds)

learn = cnn_learner(data, models.resnet34, metrics=error_rate)

learn.fit_one_cycle(4)

learn.save('stage-1')

learn.unfreeze()

learn.lr_find(start_lr=1e-5, end_lr=1e-1)

learn.fit_one_cycle(2, max_lr=slice(3e-5,3e-4))

learn.recorder.plot()

learn.save('stage-2')

learn.load('stage-2');

interp = ClassificationInterpretation.from_learner(learn)

interp.plot_confusion_matrix()