FashionMNIST in PyTorch

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*Memos:

• My post explains Fashion-MNIST.
• My post explains KMNIST.
• My post explains MNIST().
• My post explains EMNIST().
• My post explains QMNIST().
• My post explains KMNIST().
• My post explains MovingMNIST().

FashionMNIST() can use Fashion-MNIST dataset as shown below:

*Memos:

• The 1st argument is root(Required-Type:str or pathlib.Path). *An absolute or relative path is possible.
• The 2nd argument is train(Optional-Default:True-Type:bool). *If it's True, train data(60,000 images) is used while if it's False, test data(10,000 images) is used.
• The 3rd argument is transform(Optional-Default:None-Type:callable).
• The 4th argument is target_transform(Optional-Default:None-Type:callable).
• The 5th argument is download(Optional-Default:False-Type:bool): *Memos:
  • If it's True, the dataset is downloaded from the internet and extracted(unzipped) to root.
  • If it's True and the dataset is already downloaded, it's extracted.
  • If it's True and the dataset is already downloaded and extracted, nothing happens.
  • It should be False if the dataset is already downloaded and extracted because it's faster.
  • You can manually download and extract the dataset(t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz and train-labels-idx1-ubyte.gz) from here to data/FashionMNIST/raw/.

from torchvision.datasets import FashionMNIST

train_data = FashionMNIST(
    root="data"
)

train_data = FashionMNIST(
    root="data",
    train=True,
    transform=None,
    target_transform=None,
    download=False
)

test_data = FashionMNIST(
    root="data",
    train=False
)

len(train_data), len(test_data)
# (60000, 10000)

train_data
# Dataset FashionMNIST
#     Number of datapoints: 60000
#     Root location: data
#     Split: Train

train_data.root
# 'data'

train_data.train
# True

print(train_data.transform)
# None

print(train_data.target_transform)
# None

train_data.download
# <bound method MNIST.download of Dataset FashionMNIST
#     Number of datapoints: 60000
#     Root location: data
#     Split: Train>

len(train_data.classes), train_data.classes
# (10,
#  ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
#   'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'])

train_data[0]
# (<PIL.Image.Image image mode=L size=28x28>, 9)

train_data[1]
# (<PIL.Image.Image image mode=L size=28x28>, 0)

train_data[2]
# (<PIL.Image.Image image mode=L size=28x28>, 0)

train_data[3]
# (<PIL.Image.Image image mode=L size=28x28>, 3)

train_data[4]
# (<PIL.Image.Image image mode=L size=28x28>, 0)

import matplotlib.pyplot as plt

def show_images(data, main_title=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=1.0, fontsize=14)
    for i, (im, lab) in zip(range(1, 11), data):
        plt.subplot(2, 5, i)
        plt.imshow(X=im)
        plt.title(label=lab)
    plt.tight_layout()
    plt.show()

show_images(data=train_data, main_title="train_data")
show_images(data=test_data, main_title="test_data")