keras -- 实现cifar10分类

import kerasfrom keras.datasets import mnist,cifar10from keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flatten, MaxPooling3Dfrom keras.layers import Conv2D, MaxPooling2Dfrom keras import backend as Kbatch_size = 128num_classes = 10epochs = 12# input image dimensionsimg_rows, img_cols, img_channels = 32, 32, 3# the data, shuffled and split between train and test sets(x_train, y_train), (x_test, y_test) = cifar10.load_data()print(x_train.shape)if K.image_data_format() == 'channels_first':    x_train = x_train.reshape(x_train.shape[0], img_channels, img_rows, img_cols)    x_test = x_test.reshape(x_test.shape[0], img_channels, img_rows, img_cols)    input_shape = (img_channels, img_rows, img_cols)else:    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, img_channels)    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_channels)    input_shape = (img_rows, img_cols, img_channels)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train /= 255x_test /= 255print('x_train shape:', x_train.shape)print(x_train.shape[0], 'train samples')print(x_test.shape[0], 'test samples')print(y_train.shape)# # convert class vectors to binary class matricesy_train = keras.utils.np_utils.to_categorical(y_train, num_classes)y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)model = Sequential()model.add(Conv2D(32,kernel_size=[3,3],                 activation='relu',                 input_shape=input_shape))model.add(Conv2D(64, activation='relu',kernel_size=[3,3]))# model.add(MaxPooling2D(pool_size=(2, 2)))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())model.add(Dense(1024, activation='relu'))model.add(Dense(num_classes, activation='softmax'))model.compile(loss=keras.metrics.categorical_crossentropy,              optimizer=keras.optimizers.Adam(),              metrics=['accuracy'])model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,          verbose=1, validation_data=(x_test, y_test))score = model.evaluate(x_test, y_test, verbose=0)model.summary()print('Test loss:', score[0])print('Test accuracy:', score[1])