Question

我正在尝试实现自定义丢失功能

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)

在训练时出现以下错误

InvalidArgumentError（参见上面的回溯）：indices [5] = 51不在[0,32]中 [[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）]]

模型摘要

_________________________________________________________________________________________
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从最后一个轴开始，而聚集从第一个轴开始。您在两个轴上没有相同数量的元素，因此这是预期的。

对于只在类上工作，使用最后一个轴，所以我们要围绕聚集方法进行讨论：

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)

将从keras.backend.argmax返回的张量作为索引传递给keras.backend，收集期望'整数张量的索引'。

1 个答案: