手写数字识别之卷积神经网络版

参考代码如下：

1、导入相关包

2、 随机种子确保结果可再现

3、 加载数据

4、定义模型函数

5、开始编译模型

6、 训练模型

7、评估模型

8、输出Loss值和评分

9、运行结果

Train on 60000 samples, validate on 10000 samples Epoch 1/10

6s - loss: 0.2400 - acc: 0.9314 - val_loss: 0.0837 - val_acc: 0.9740

Epoch 2/10

6s - loss: 0.0779 - acc: 0.9765 - val_loss: 0.0482 - val_acc: 0.9843

Epoch 3/10

6s - loss: 0.0560 - acc: 0.9827 - val_loss: 0.0431 - val_acc: 0.9861

Epoch 4/10

6s - loss: 0.0433 - acc: 0.9866 - val_loss: 0.0423 - val_acc: 0.9857

Epoch 5/10

6s - loss: 0.0363 - acc: 0.9883 - val_loss: 0.0333 - val_acc: 0.9884

Epoch 6/10

6s - loss: 0.0301 - acc: 0.9906 - val_loss: 0.0310 - val_acc: 0.9893

Epoch 7/10

6s - loss: 0.0246 - acc: 0.9922 - val_loss: 0.0316 - val_acc: 0.9888

Epoch 8/10

6s - loss: 0.0232 - acc: 0.9927 - val_loss: 0.0296 - val_acc: 0.9896

Epoch 9/10

6s - loss: 0.0195 - acc: 0.9937 - val_loss: 0.0284 - val_acc: 0.9906

Epoch 10/10

6s - loss: 0.0164 - acc: 0.9949 - val_loss: 0.0282 - val_acc: 0.9911

10000/10000 [==============================] - 1s 85us/step

Loss: 0.0281819745783

scores: 0.9911