用Kersa搭建神经网络【MNIST手写数据集】

ＭNIST手写数据集的识别算得上是深度学习的”hello world“了，所以想要入门必须得掌握。新手入门可以考虑使用Keras框架达到快速实现的目的。

完整代码如下：

# 1. 导入库和模块
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D
from keras.layers import Dense, Flatten
from keras.utils import to_categorical

# 2. 加载数据
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 3. 数据预处理
img_x, img_y = 28, 28
x_train = x_train.reshape(x_train.shape[0], img_x, img_y, 1)
x_test = x_test.reshape(x_test.shape[0], img_x, img_y, 1)
#数据标准化
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
#一位有效编码
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 4. 定义模型结构
model = Sequential()
model.add(Conv2D(32, kernel_size=(5,5), activation='relu', input_shape=(img_x, img_y, 1)))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Conv2D(64, kernel_size=(5,5), activation='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(10, activation='softmax'))

# 5. 编译，声明损失函数和优化器
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])

# 6. 训练
model.fit(x_train, y_train, batch_size=128, epochs=10)

# 7. 评估模型
score = model.evaluate(x_test, y_test)
print('acc', score[1])

运行结果如下：

 

可以看出准确率达到了99%，说明神经网络在图像识别上具有巨大的优势。