深度学习VGG16迁移学习模型代码

以下是使用预训练的VGG16模型进行迁移学习的代码示例：

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.applications import VGG16

# 设置数据集路径和参数
train_data_dir = 'train/'
validation_data_dir = 'validation/'
img_width, img_height = 224, 224
batch_size = 32
epochs = 10

# 加载预训练的VGG16模型
vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))

# 冻结预训练模型的所有层
for layer in vgg16_model.layers:
    layer.trainable = False

# 构建模型
model = Sequential()
model.add(vgg16_model)
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

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

# 数据增强
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_data_dir,
                                                    target_size=(img_width, img_height),
                                                    batch_size=batch_size,
                                                    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
                                                              target_size=(img_width, img_height),
                                                              batch_size=batch_size,
                                                              class_mode='binary')

# 训练模型
model.fit(train_generator,
          steps_per_epoch=train_generator.samples // batch_size,
          epochs=epochs,
          validation_data=validation_generator,
          validation_steps=validation_generator.samples // batch_size)

在这个示例中，我们使用预训练的VGG16模型作为特征提取器，并添加了几个全连接层来进行分类。我们还对预训练模型的所有层进行了冻结，并使用ImageDataGenerator进行数据增强。最后，我们使用fit()方法来训练模型。