输出使用vgg16进行预训练cifar10分类权重文件的python代码

以下是使用VGG16进行预训练CIFAR10分类权重文件的Python代码：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.applications import VGG16
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical

# Load CIFAR10 dataset
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

# Convert labels to categorical one-hot encoding
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

# Create VGG16 model with pre-trained weights on ImageNet
vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))

# Freeze layers in VGG16 model to prevent further training
for layer in vgg16_model.layers:
    layer.trainable = False

# Create new model using VGG16 base and add new layers for CIFAR10 classification
model = Sequential()
model.add(vgg16_model)
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))

# Compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train model on CIFAR10 dataset
model.fit(train_images, train_labels, epochs=10, batch_size=64, validation_data=(test_images, test_labels))

# Save model weights
model.save_weights('vgg16_cifar10_weights.h5')

这个代码使用VGG16模型，对CIFAR10数据集进行预训练，并保存权重文件（vgg16_cifar10_weights.h5）。