输出使用vgg16进行cifar10分类的python代码

下面是使用VGG16进行CIFAR10分类的Python代码：

import numpy as np
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.utils import np_utils
from keras.applications.vgg16 import VGG16

# 加载 CIFAR10 数据
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# 数据预处理
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, 10)
Y_test = np_utils.to_categorical(y_test, 10)

# 加载预训练的 VGG16 模型
model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))

# 冻结 VGG16 的卷积层
for layer in model.layers:
    layer.trainable = False

# 添加全连接层和输出层
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

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

# 训练模型
model.fit(X_train, Y_train, batch_size=32, epochs=10, validation_data=(X_test, Y_test))

# 评估模型
score = model.evaluate(X_test, Y_test, batch_size=32)
print('Test score:', score[0])
print('Test accuracy:', score[1])

这个代码利用了Keras中预训练的VGG16模型，将其作为特征提取器来提取图像特征，然后在其上添加全连接层和输出层进行分类。