原文链接：https://data-flair.training/blogs/python-deep-learning-project-handwritten-digit-recognition/
原文讲得很详细，这里补充一些注释。由于直接从库导入mnist数据集需要的时间非常久，因此这里导入的是本地已下载好的mnist数据集。（但我怀疑我下了假的数据集，咋验证准确率这么低，所以这里不提供了）
import keras from keras import backend as K import numpy as np from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D batch_size = 128 #一次训练所选取的样本数 num_classes = 10 #分类个数 epochs = 10 #训练轮数 #读取已下载到本地的数据集 f=np.load('C:/Users/Administrator/.keras/datasets/mnist.npz') x_train,y_train=f['x_train'],f['y_train'] x_test,y_test=f['x_test'],f['y_test'] #print(x_train.shape, y_train.shape) #数据预处理 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) x_test = x_test.reshape(x_test.shape[0], 28, 28, 1) input_shape = (28, 28, 1) x_train = x_train.astype('float32') #转换数据类型 x_test = x_test.astype('float32') x_train /= 255 #归一化 x_test /= 255 y_train = keras.utils.to_categorical(y_train, num_classes) #将整形数组转化为二元类型矩阵 y_test = keras.utils.to_categorical(y_test, num_classes) #print('x_train shape:', x_train.shape) #print(x_train.shape[0], 'train samples') #print(x_test.shape[0], 'test samples') #创建CNN模型 model = Sequential() #这里采用顺序模型构建CNN #输入层，这里指定输入数据形状为28*28*1 卷积核数量为32 形状为3*3 model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape)) #添加中间层 model.add(Conv2D(64, (3, 3), activation='relu')) #卷积层 model.add(MaxPooling2D(pool_size=(2, 2))) #最大池化层 model.add(Dropout(0.25)) #通过Dropout防止过拟合 model.add(Flatten()) #展平层 model.add(Dense(256, activation='relu')) #全连接层 model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) #损失函数 model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy']) #训练模型 hist = model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=2,validation_data=(x_test, y_test)) print("模型训练完成") #模型评估 score = model.evaluate(x_test, y_test, verbose=0) print('test loss: ', score[0]) print('test accuracy: ', score[1])