fashion-mnist数据集下载至本地并使用

导入本地fashion-mnist数据集

from tensorflow.python.keras.utils import get_file
import gzip
import numpy as np


def load_data():
    base = "file:///D:/fasionmnist/"
    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))

    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)

查看数据集图片

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras


import Load_Data

print(tf.__version__)

(train_images, train_labels), (test_images, test_labels) = Load_Data.load_data()


class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

print(train_images.shape)
print(len(train_images))
print(test_images.shape)
print(len(test_labels))

plt.figure(figsize=(5,5))
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()