1.查看image的shape
print('x_train_image:',X_train_image.shape)
print('y_train_label',y_train_label.shape)
x_train_image: (60000, 28, 28)
y_train_label (60000,)
2.将image以reshape转换
下面的程序代码将原本28×28的二维数字图像以reshape转换为一维的向量,再以astype转换为float,共784个浮点数
x_Train = X_train_image.reshape(60000,784).astype('float32')
x_Test = X_test_image.reshape(10000,784).astype('float32')
3.查看转换为一维向量的shape
print('x_train:',x_Train.shape)
print('x_test',x_Test.shape)
x_train: (60000, 784)
x_test (10000, 784)
4.查看images图像的内容
print(x_Train[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.
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. 3. 18.
18. 18. 126. 136. 175. 26. 166. 255. 247. 127. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 30. 36. 94. 154. 170. 253.
253. 253. 253. 253. 225. 172. 253. 242. 195. 64. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 49. 238. 253. 253. 253. 253. 253.
253. 253. 253. 251. 93. 82. 82. 56. 39. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 18. 219. 253. 253. 253. 253. 253.
198. 182. 247. 241. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 80. 156. 107. 253. 253. 205.
11. 0. 43. 154. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 14. 1. 154. 253. 90.
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. 139. 253. 190.
2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 190. 253.
70. 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. 35. 241.
225. 160. 108. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 81.
240. 253. 253. 119. 25. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
45. 186. 253. 253. 150. 27. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 16. 93. 252. 253. 187. 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. 249. 253. 249. 64. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
46. 130. 183. 253. 253. 207. 2. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 39. 148.
229. 253. 253. 253. 250. 182. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 114. 221. 253.
253. 253. 253. 201. 78. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 23. 66. 213. 253. 253. 253.
253. 198. 81. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 18. 171. 219. 253. 253. 253. 253. 195.
80. 9. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 55. 172. 226. 253. 253. 253. 253. 244. 133. 11.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 136. 253. 253. 253. 212. 135. 132. 16. 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.]
从以上的执行结果可知,大部分都是0,少部分是数字.每一个数字都是从0到255的值,代表图形每一个灰度的深浅
5.将数字图像images的数字标准化
images的数字标准化可以提高后续训练模型的准确率,因为images的数字是从0到255的值,所以最简单的标准化方式是除以255
x_Train_normalize = x_Train/255 x_Test_normalize = x_Test/255
6.查看数字图像images数字标准化后的结果
使用下列指令查看数字图像images的数字标准化后的结果,都介于0与1之间.
[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. 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. 0.32941177 0.7254902
0.62352943 0.5921569 0.23529412 0.14117648 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.87058824 0.99607843 0.99607843 0.99607843
0.99607843 0.94509804 0.7764706 0.7764706 0.7764706 0.7764706
0.7764706 0.7764706 0.7764706 0.7764706 0.6666667 0.20392157
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.2627451 0.44705883 0.28235295 0.44705883 0.6392157 0.8901961
0.99607843 0.88235295 0.99607843 0.99607843 0.99607843 0.98039216
0.8980392 0.99607843 0.99607843 0.54901963 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.06666667 0.25882354 0.05490196
0.2627451 0.2627451 0.2627451 0.23137255 0.08235294 0.9254902
0.99607843 0.41568628 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.3254902 0.99215686 0.81960785 0.07058824
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.08627451
0.9137255 1. 0.3254902 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.5058824 0.99607843 0.93333334
0.17254902 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.23137255 0.9764706 0.99607843 0.24313726 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.52156866 0.99607843
0.73333335 0.01960784 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.03529412 0.8039216 0.972549 0.22745098 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.49411765
0.99607843 0.7137255 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.29411766 0.9843137 0.9411765 0.22352941
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.07450981
0.8666667 0.99607843 0.6509804 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.01176471 0.79607844 0.99607843 0.85882354
0.13725491 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.14901961 0.99607843 0.99607843 0.3019608 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.12156863 0.8784314 0.99607843
0.4509804 0.00392157 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.52156866 0.99607843 0.99607843 0.20392157 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.23921569 0.9490196
0.99607843 0.99607843 0.20392157 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.4745098 0.99607843 0.99607843 0.85882354
0.15686275 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.4745098 0.99607843 0.8117647 0.07058824 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. ]