卷积神经网络应用-训练手写体数字数据集并展示识别精度

#卷积神经网络(CNN)训练手写体数据集
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.keras as ka
import datetime
#python3.X版本显示图片还需导入此库
import pylab
np.random.seed(0)

#定义加载数据集函数
def load_data_npz(path):
    #np.load文件可以加载npz，npy格式的文件
    f = np.load(path) 
    X_train,y_train,X_test,y_test = f['x_train'], f['y_train'], f['x_test'],f['y_test']
    f.close()
    #返回加载后的地址
    return X_train, y_train, X_test, y_test

#加载自定义mnist数据集文件地址
path = 'C:/Users/yeahamen/AppData/Local/Programs/Python/Python310/Lib/site-packages/keras/datasets/mnist.npz'
X_train,y_train,X_test,y_test = load_data_npz(path)

# 查看手写体数字数据集图像
plt.imshow(X_train[1])
#python3.X版本显示图片还需加此行
pylab.show()

# 查看mnist数据集的形状
print('X_train:{}'.format(X_train.shape))
print('y_train:{}'.format(y_train.shape))
print('X_test:{}'.format(X_test.shape))
print('y_test:{}'.format(y_test.shape))

#以行为单位将二维