mnist_data：用于分类的MNIST数据集的子集
一个加载 MNIST 数据集到 NumPy 数组的函数。
> 从 mlxtend.data 导入 mnist_data
概述
MNIST 数据集是由美国国家标准与技术研究院 (NIST) 的两个数据集构建而成的。训练集包含来自250个不同人的手写数字，其中50%为高中生，50%为人口普查局的员工。请注意，测试集包含来自不同人的手写数字，遵循相同的拆分。
特征
每个特征向量（特征矩阵中的一行）由784个像素（强度）组成——从原始的28x28像素图像展开而来。
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样本数量：5000张图像的子集（每个类的前500个数字）
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目标变量（离散）：{500 x 0, ..., 500 x 9}
参考文献
- 来源: https://yann.lecun.com/exdb/mnist/
- Y. LeCun 和 C. Cortes. Mnist手写数字数据库. AT&T Labs [在线]. 可用: https://yann.lecun.com/exdb/mnist, 2010.
示例 1 - 数据集概述
from mlxtend.data import mnist_data
X, y = mnist_data()
print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))
print('1st row', X[0])
Dimensions: 5000 x 784
1st row [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 51. 159. 253. 159. 50.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 48. 238.
252. 252. 252. 237. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 54. 227. 253. 252. 239. 233. 252. 57. 6. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 10. 60. 224. 252. 253. 252. 202. 84. 252.
253. 122. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 163. 252. 252. 252. 253.
252. 252. 96. 189. 253. 167. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 51. 238.
253. 253. 190. 114. 253. 228. 47. 79. 255. 168. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 48. 238. 252. 252. 179. 12. 75. 121. 21. 0. 0.
253. 243. 50. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 38. 165. 253. 233. 208. 84. 0. 0.
0. 0. 0. 0. 253. 252. 165. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 7. 178. 252. 240. 71.
19. 28. 0. 0. 0. 0. 0. 0. 253. 252. 195. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 57.
252. 252. 63. 0. 0. 0. 0. 0. 0. 0. 0. 0.
253. 252. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 198. 253. 190. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 255. 253. 196. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 76. 246. 252. 112. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 253. 252. 148. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 85. 252.
230. 25. 0. 0. 0. 0. 0. 0. 0. 0. 7. 135.
253. 186. 12. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 85. 252. 223. 0. 0. 0. 0. 0. 0. 0.
0. 7. 131. 252. 225. 71. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 85. 252. 145. 0. 0. 0.
0. 0. 0. 0. 48. 165. 252. 173. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 86. 253.
225. 0. 0. 0. 0. 0. 0. 114. 238. 253. 162. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 85. 252. 249. 146. 48. 29. 85. 178. 225. 253.
223. 167. 56. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 85. 252. 252. 252. 229. 215.
252. 252. 252. 196. 130. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 28. 199.
252. 252. 253. 252. 252. 233. 145. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 25. 128. 252. 253. 252. 141. 37. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]
import numpy as np
print('Classes: Setosa, Versicolor, Virginica')
print(np.unique(y))
print('Class distribution: %s' % np.bincount(y))
Classes: Setosa, Versicolor, Virginica
[0 1 2 3 4 5 6 7 8 9]
Class distribution: [500 500 500 500 500 500 500 500 500 500]
示例 2 - 可视化 MNIST
%matplotlib inline
import matplotlib.pyplot as plt
def plot_digit(X, y, idx):
img = X[idx].reshape(28,28)
plt.imshow(img, cmap='Greys', interpolation='nearest')
plt.title('true label: %d' % y[idx])
plt.show()
plot_digit(X, y, 4)
API
mnist_data()
5000 samples from the MNIST handwritten digits dataset.
Data Source: https://yann.lecun.com/exdb/mnist/
Returns
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X, y: [n_samples, n_features], [n_class_labels]X is the feature matrix with 5000 image samples as rows, each row consists of 28x28 pixels that were unrolled into 784 pixel feature vectors. y contains the 10 unique class labels 0-9.
Examples
For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/data/mnist_data/