深度学习实战 fashion-mnist数据集预处理技术分析

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_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)

where2 go 团队

   

微信号：算法与编程之美          

长按识别二维码关注我们!

温馨提示： 点击页面右下角 “写留言”发表评论，期待您的参与！期待您的转发！