keras——经典模型之LeNet5 实现手写字识别

经典论文：Gradient-Based Learning Applied to Document Recognition

参考博文：https://blog.csdn.net/weixin_44344462/article/details/89212507

构建LeNet-5模型

#定义LeNet5网络  深度为1的灰度图像
def LeNet5(x_train, y_train, x_test, y_test):
    ########搭建网络###########
    #直接调用顺序模型
    model = Sequential()

    #卷积和池化部分
    model.add(Conv2D(input_shape=(28, 28, 1),
                     filters=32, kernel_size=5, strides=1,
                     activation=\'sigmoid\', padding=\'same\'
                     ))
    model.add(MaxPool2D(pool_size=2, strides=2, padding=\'same\'))
    model.add(Conv2D(filters=64, kernel_size=5, strides=2,
                     activation=\'sigmoid\', padding=\'same\'
                     ))
    model.add(MaxPool2D(pool_size=2, strides=2, padding=\'same\'))
    model.add(Dropout(0.25))

    #扁平化
    model.add(Flatten())

    #全连接层
    model.add(Dense(128, activation=\'sigmoid\'))
    model.add(Dropout(0.5))
    model.add(Dense(128, activation=\'sigmoid\'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation=\'softmax\'))

    ##  定义loss function
    #整合模型
    model.compile(optimizer=\'adam\',
                  loss=\'categorical_crossentropy\',
                  metrics=[\'accuracy\']
                  )
    ##开始训练
    print("\n开始训练：")
    model.fit(x_train, y_train, batch_size=100, epochs=20)

    #评价模型
    print("\n评价模型：")
    final_loss, final_accuracy = model.evaluate(x_test, y_test)
    print("loss= ", final_loss)
    print("accuracy= ", final_accuracy)

    return model

 

结果