基于VGG的CIFAR-10图像分类代码，resize=32

好的，以下是基于VGG的CIFAR-10图像分类代码，resize=32：

import keras
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
import numpy as np

# 超参数定义
batch_size = 128
num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20

# 数据加载和预处理
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)

# VGG网络定义
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:], activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# 模型编译和训练
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), shuffle=True)
else:
    print('Using real-time data augmentation.')
    datagen = ImageDataGenerator(
        featurewise_center=False, 
        samplewise_center=False, 
        featurewise_std_normalization=False, 
        samplewise_std_normalization=False, 
        zca_whitening=False, 
        rotation_range=0, 
        width_shift_range=0.1,
        height_shift_range=0.1, 
        horizontal_flip=True, 
        vertical_flip=False)
    datagen.fit(x_train)
    model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), 
                        steps_per_epoch=x_train.shape[0] // batch_size, 
                        epochs=epochs, 
                        validation_data=(x_test, y_test),
                        workers=4)

# 模型评估
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])

希望可以帮到你！