fashion_mnist.py

from keras.datasets.cifar import load_batch
from keras.utils.data_utils import get_file
import os
import numpy as np
import keras.backend as K

import gzip
import os

def load_data():
    """Loads the Fashion-MNIST dataset.
    # Returns
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    dirname = os.path.join('datasets', 'fashion-mnist')
    base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
    files = ['train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
             't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz']

    paths = []
    for fname in files:
        paths.append(get_file(fname,
                              origin=base + fname,
                              cache_dir='.',
                              cache_subdir=dirname))

    with gzip.open(paths[0], 'rb') as lbpath:
        y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

    with gzip.open(paths[1], 'rb') as imgpath:
        x_train = np.frombuffer(imgpath.read(), np.uint8,
                                offset=16).reshape(len(y_train), 28, 28)

    with gzip.open(paths[2], 'rb') as lbpath:
        y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

    with gzip.open(paths[3], 'rb') as imgpath:
        x_test = np.frombuffer(imgpath.read(), np.uint8,
                               offset=16).reshape(len(y_test), 28, 28)

    return (x_train, y_train), (x_test, y_test)