以预先训练的方式使用机器学习模型Keras，Tensorflow

pip install tensorflow

import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 数据预处理
x_train = tf.image.resize(x_train, [224, 224]) / 255.0
x_test = tf.image.resize(x_test, [224, 224]) / 255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 加载预训练的ResNet50模型，不包括顶部的全连接层
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 冻结预训练模型的权重（可选）
# base_model.trainable = False

# 添加自定义的全连接层
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)

# 构建最终模型
model = Model(inputs=base_model.input, outputs=predictions)

# 编译模型
model.compile(optimizer=Adam(lr=0.0001),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# 数据增强（可选）
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True)

datagen.fit(x_train)

# 训练模型
history = model.fit(datagen.flow(x_train, y_train, batch_size=32),
                    epochs=10,
                    validation_data=(x_test, y_test))

# 评估模型
loss, accuracy = model.evaluate(x_test, y_test)
print(f'测试集准确率: {accuracy*100:.2f}%')

# 解冻最后几层
for layer in base_model.layers[-10:]:
    layer.trainable = True

# 重新编译模型，可能需要调整学习率
model.compile(optimizer=Adam(lr=1e-5),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# 继续训练
history_finetune = model.fit(datagen.flow(x_train, y_train, batch_size=32),
                             epochs=10,
                             validation_data=(x_test, y_test))

import tensorflow_hub as hub

# 加载预训练的MobileNet V2模型
mobilenet_model = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/tensorflow/mobilenet_v2_100_224/classification/4", trainable=False),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 编译和训练模型（类似于前面的步骤）