AttributeError：“NoneType”对象没有特性“”_inbound_nodes“”

Question

我想实现这里定义的loss function。我使用fcn-VGG16获得一个map，并添加一个激活层。( x是fcn vgg16网的输出)。然后只需要一些操作来获得提取的特征。

co_map = Activation('sigmoid')(x)
#add mean values
img = Lambda(AddMean, name = 'addmean')(img_input)
#img map multiply
img_o = Lambda(HighLight,  name='highlightlayer1')([img, co_map])
img_b = Lambda(HighLight,  name='highlightlayer2')([img, 1-co_map])

extractor = ResNet50(weights = 'imagenet', include_top = False, pooling = 'avg')
extractor.trainable = False
extractor.summary()

o_feature = extractor(img_o)
b_feature = extractor(img_b)
loss = Lambda(co_attention_loss,name='name')([o_feature,b_feature])
model = Model(inputs=img_input, outputs= loss ,name='generator')

我在这一行得到的错误是model = Model(inputs=img_input, outputs= loss ,name='generator')，我认为是因为我计算损失的方式使得它不是keras模型的可接受输出。

def co_attention_loss(args):
loss = []
o_feature,b_feature = args
c = 2048
for i in range(5):
    for j in range(i,5):
        if i!=j:
            print("feature shape : "+str(o_feature.shape))
            d1 = K.sum(K.pow(o_feature[i] - o_feature[j],2))/c
            d2 = K.sum(K.pow(o_feature[i] - b_feature[i],2))
            d3 = K.sum(K.pow(o_feature[j] - b_feature[j],2))
            d4 = d2 + d3/(2*c)
            p = K.exp(-d1)/K.sum([K.exp(-d1),K.exp(-d4)])
            loss.append(-K.log(p)) 
return K.sum(loss)

我如何修改我的损失函数才能使其正常工作？

Answer 1

loss = Lambda(co_attention_loss,name='name')([o_feature,b_feature])

表示您输入的args是一个列表，但您将args作为元组调用

o_feature,b_feature = args

您可以将损失代码更改为

def co_attention_loss(args):
    loss = []
    o_feature = args[0]
    b_feature = args[1]
    c = 2048
    for i in range(5):
        for j in range(i,5):
            if i!=j:
                print("feature shape : "+str(o_feature.shape))
                d1 = K.sum(K.pow(o_feature[i] - o_feature[j],2))/c
                d2 = K.sum(K.pow(o_feature[i] - b_feature[i],2))
                d3 = K.sum(K.pow(o_feature[j] - b_feature[j],2))
                d4 = d2 + d3/(2*c)
                p = K.exp(-d1)/K.sum([K.exp(-d1),K.exp(-d4)])
                loss.append(-K.log(p)) 
return K.sum(loss)

注意事项:不测试