来自VGG19的Keras自定义损失

from keras.applications.vgg19 import VGG19, preprocess_input
from keras.models import Model
import keras.backend as K

# 加载预训练的VGG19模型(去掉顶层)
vgg = VGG19(weights='imagenet', include_top=False)
vgg.trainable = False  # 因为我们只用到预训练的特征提取部分

# 获取VGG19的最后一层，通常选择conv5_4作为内容特征层
content_layer = vgg.get_layer('block5_conv4').output

# 假设source_image和target_image是两个输入张量
source_image = Input(shape=(height, width, channels))
target_image = Input(shape=(height, width, channels))

# 提取内容特征
source_features = vgg(source_image)
target_features = vgg(target_image)

# 计算感知损失
def content_loss(base, combination):
    return K.mean(K.square(combination - base))

# 构建一个新模型来优化感知损失
combined_model = Model(inputs=[source_image, target_image], outputs=content_loss([source_features, target_features]))