fashion_mnist.py

#!/usr/bin/env python
# coding: utf-8

# # Fashion MNIST 데이터셋 알아보기

from torchvision import datasets, transforms, utils
from torch.utils import data

import matplotlib.pyplot as plt
import numpy as np


# ## Fashion MNIST 데이터셋

transform = transforms.Compose([
    transforms.ToTensor()
])


trainset = datasets.FashionMNIST(
    root      = './.data/', 
    train     = True,
    download  = True,
    transform = transform
)
testset = datasets.FashionMNIST(
    root      = './.data/', 
    train     = False,
    download  = True,
    transform = transform
)


batch_size = 16

train_loader = data.DataLoader(
    dataset     = trainset,
    batch_size  = batch_size
)
test_loader = data.DataLoader(
    dataset     = testset,
    batch_size  = batch_size
)


dataiter       = iter(train_loader)
images, labels = next(dataiter)


# ## 멀리서 살펴보기
img   = utils.make_grid(images, padding=0)
npimg = img.numpy()
plt.figure(figsize=(10, 7))
plt.imshow(np.transpose(npimg, (1,2,0)))
plt.show()


print(labels)


CLASSES = {
    0: 'T-shirt/top',
    1: 'Trouser',
    2: 'Pullover',
    3: 'Dress',
    4: 'Coat',
    5: 'Sandal',
    6: 'Shirt',
    7: 'Sneaker',
    8: 'Bag',
    9: 'Ankle boot'
}


for label in labels:
    index = label.item()
    print(CLASSES[index])


# ## 가까이서 살펴보기
idx = 1

item_img = images[idx]
item_npimg = item_img.squeeze().numpy()
plt.title(CLASSES[labels[idx].item()])
print(item_npimg.shape)
plt.imshow(item_npimg, cmap='gray')
plt.show()