原文：https://blog.csdn.net/zzulp/article/details/76358694
import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D num_classes = 10 model_name = 'cifar10.h5' # The data, shuffled and split between train and test sets: (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32')/255 x_test = x_test.astype('float32')/255 # Convert class vectors to binary class matrices. y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes)) model.add(Activation('softmax')) model.summary() # initiate RMSprop optimizer opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-6) # train the model using RMSprop model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) hist = model.fit(x_train, y_train, epochs=40, shuffle=True) model.save(model_name) # evaluate loss, accuracy = model.evaluate(x_test, y_test) print(loss, accuracy)