mnist数据集可视化

上一篇文章已经实现了本地数据集的导入函数，现在用这个函数来导入数据并可视化：

导入数据：

def load_data(data_folder):

  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(os.path.join(data_folder,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)

这是上一篇文章实现的函数

可视化：

import os
import gzip
import numpy as np
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = load_data('/home/peterwang/Downloads/Mnist')
fig = plt.figure()
for i in range(15):
    plt.subplot(3,5,i+1)
    plt.imshow(train_images[i], cmap='Greys')       # 使用灰度图
    plt.title("Label:" + str(train_labels[i]))      # 设置标签
    # 删除x, y轴标记
    plt.xticks([])
    plt.yticks([])
plt.show()

这里取前15张图片，可视化结果如图：

参考B站教学视频：添加链接描述