将keras.backend.argmax返回的张量作为索引传递给keras.backend，gather，该张量期望为“索引的整数张量”。

Question

I am trying to implement a custom loss function

def lossFunction(self,y_true,y_pred):

     maxi=K.argmax(y_true)

     return K.mean((K.max(y_true) -(K.gather(y_pred,maxi)))**2)

which give following error when training

---

InvalidArgumentError (see above for traceback): indices[5] = 51 is not in [0, 32) [[Node: loss/dense_3_loss/Gather = Gather[Tindices=DT_INT64, Tparams=DT_FLOAT, validate_indices=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](dense_3/BiasAdd, metrics/acc/ArgMax)]]

---

model summary

---

_________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 64, 50, 1)     0                                            
____________________________________________________________________________________________________
input_2 (InputLayer)             (None, 64, 50, 1)     0                                            
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 32, 25, 16)    272         input_1[0][0]                    
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 32, 25, 16)    272         input_2[0][0]                    
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 16, 12, 16)    0           conv2d_1[0][0]                   
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 16, 12, 16)    0           conv2d_2[0][0]                   
____________________________________________________________________________________________________
conv2d_3 (Conv2D)                (None, 15, 11, 32)    2080        max_pooling2d_1[0][0]            
____________________________________________________________________________________________________
conv2d_4 (Conv2D)                (None, 15, 11, 32)    2080        max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)   (None, 8, 6, 32)      0           conv2d_3[0][0]                   
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)   (None, 8, 6, 32)      0           conv2d_4[0][0]                   
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 1536)          0           max_pooling2d_3[0][0]            
____________________________________________________________________________________________________
flatten_2 (Flatten)              (None, 1536)          0           max_pooling2d_4[0][0]            
____________________________________________________________________________________________________
concatenate_1 (Concatenate)      (None, 3072)          0           flatten_1[0][0]                  
                                                                   flatten_2[0][0]                  
____________________________________________________________________________________________________
input_3 (InputLayer)             (None, 256)           0                                            
____________________________________________________________________________________________________
concatenate_2 (Concatenate)      (None, 3328)          0           concatenate_1[0][0]              
                                                                   input_3[0][0]                    
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 512)           1704448     concatenate_2[0][0]              
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 256)           131328      dense_1[0][0]                    
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 256)           65792       dense_2[0][0]                    
====================================================================================================
Total params: 1,906,272
Trainable params: 1,906,272
Non-trainable params: 0

Answer 1

Argmax is taking from the last axis, while gather is taking from the first. You don't have the same numbers of elements in both axes, so this is expected.

For working only on classes, use the last axis, so we are going to quirk around the gather method:

def lossFunction(self,y_true,y_pred):

    maxi=K.argmax(y_true) #ok

    #invert the axes
    y_pred = K.permute_dimensions(y_pred,(1,0))

    return K.mean((K.max(y_true,axis=-1) -(K.gather(y_pred,maxi)))**2)