1、数据预处理 import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D from keras import backend as K num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载MNIST数据。其中trainX就是一个60000 * 28 * 28的数组, # trainY是每一张图片对应的数字。 (trainX, trainY), (testX, testY) = mnist.load_data() # 根据对图像编码的格式要求来设置输入层的格式。 if K.image_data_format() == 'channels_first': trainX = trainX.reshape(trainX.shape[0], 1, img_rows, img_cols) testX = testX.reshape(testX.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: trainX = trainX.reshape(trainX.shape[0], img_rows, img_cols, 1) testX = testX.reshape(testX.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1)
trainX = trainX.astype('float32') testX = testX.astype('float32') trainX /= 255.0 testX /= 255.0 # 将标准答案转化为需要的格式(one-hot编码)。 trainY = keras.utils.to_categorical(trainY, num_classes) testY = keras.utils.to_categorical(testY, num_classes)
2、定义卷积神经网络
# 使用Keras API定义模型。 model = Sequential() model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=input_shape)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (5, 5), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(500, activation='relu')) model.add(Dense(num_classes, activation='softmax')) # 定义损失函数、优化函数和评测方法。 model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(), metrics=['accuracy'])
3、训练模型并测试准确率
model.fit(trainX, trainY, batch_size=128, epochs=10, validation_data=(testX, testY)) # 在测试数据上计算准确率。 score = model.evaluate(testX, testY) print('Test loss:', score[0]) print('Test accuracy:', score[1])