冻结模型并进行训练

from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Model

# 加载预训练模型（不包括顶层的全连接层）
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 冻结前几层
for layer in base_model.layers[:15]:
    layer.trainable = False

# 添加新的顶层
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, 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', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels))