问题
I want to implement the loss function defined here. I use fcn-VGG16 to obtain a map x, and add a activation layer.(x is the output of the fcn vgg16 net). And then just some operations to get extracted features.
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')
The error i get is at this line model = Model(inputs=img_input, outputs= loss ,name='generator')
I think is because the way i calculate the loss makes it not an accepted output to keras models.
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)
How can i modify my loss function to make this work?
回答1:
loss = Lambda(co_attention_loss,name='name')([o_feature,b_feature])
means the args you input is a list, but you call args as a tuple
o_feature,b_feature = args
you could change the loss code to
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)
NOTICE: NOT TEST
来源:https://stackoverflow.com/questions/51510091/attributeerror-nonetype-object-has-no-attribute-inbound-nodes