输出利用resnet18预训练本地cifar10文件并保存训练权重成.h5文件的python代码

以下是利用resnet18预训练本地cifar10文件并保存训练权重成.h5文件的python代码：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.applications.resnet import ResNet18
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint

# 加载cifar10数据集
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# 数据预处理
train_images = train_images / 255.0
test_images = test_images / 255.0

# 定义ResNet18模型
base_model = ResNet18(include_top=False, input_shape=(32, 32, 3))
x = Flatten()(base_model.output)
output = Dense(10, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=output)

# 冻结ResNet18模型的权重
for layer in base_model.layers:
    layer.trainable = False

# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 定义保存权重的回调函数
checkpoint = ModelCheckpoint('resnet18_cifar10.h5', save_best_only=True, save_weights_only=True, monitor='val_accuracy', mode='max')

# 训练模型
model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_data=(test_images, test_labels), callbacks=[checkpoint])

通过以上代码，我们可以利用resnet18预训练本地cifar10文件并保存训练权重成.h5文件。