在Keras层内实现三重态损耗

post ,他在Kears层外实现了三重损失。他得到了 anchor_out , pos_out 和 neg_out triplet_loss() 他定义的功能。

我想知道我是否可以通过定义自己的三重态损耗来计算Keras层中的三重态损耗 Lambda 层。

margin=1

anchor_input = Input((600, ), name='anchor')
positive_input = Input((600, ), name='positive_input')
negative_input = Input((600, ), name='negative_input')

# Shared embedding layer for positive and negative items
Shared_DNN = Dense(300)

encoded_anchor = Shared_DNN(anchor_input)
encoded_positive = Shared_DNN(positive_input)
encoded_negative = Shared_DNN(negative_input)

DAP = Lambda(lambda tensors:K.sum(K.square(tensors[0] - tensors[1]),axis=1,keepdims=True),name='DAP_loss') #Distance for Anchor-Positive pair
DAN = Lambda(lambda tensors:K.sum(K.square(tensors[0] - tensors[1]),axis=1,keepdims=True),name='DAN_loss') #Distance for Anchor-Negative pair
Triplet_loss = Lambda(lambda loss:K.max([(loss[0] - loss[1] + margin),0],axis=0),name='Triplet_loss') #Distance for Anchor-Negative pair

DAP_loss = DAP([encoded_anchor,encoded_positive])
DAN_loss = DAN([encoded_anchor,encoded_negative])

#call this layer on list of two input tensors.

Final_loss = Triplet_loss([DAP_loss,DAN_loss])

model = Model(inputs=[anchor_input,positive_input, negative_input], outputs=Final_loss)

但是,它给了我一个错误:

Tried to convert 'input' to a tensor and failed. Error: Shapes must be equal rank, but are 2 and 0
    From merging shape 0 with other shapes. for 'Triplet_loss_4/Max/packed' (op: 'Pack') with input shapes: [?,1], []

错误来自 Triplet_loss 图层。在 K.max() 函数,第一个数 loss[0] - loss[1] + margin 有形状 (None,1) . 但是第二个数字0的形状 (1) . 这两个数字的形状不同,因此 K.最大值() 函数发出错误。

我的问题是,如何解决这个错误? 我试过换新的 0 具有 K.constant(0,shape=(1,)) K.constant(0,shape=(None,1))