TypeError: 只有整数、切片 (`:`)、省略号 (`...`)、tf.newaxis (`None`) 和标量 tf.int32/tf.int64 张量是有效索引，得到 [1, 3]答案

【问题标题】：TypeError: Only integers, slices (`:`), ellipsis (`…`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got [1, 3]TypeError: 只有整数、切片 (`:`)、省略号 (`...`)、tf.newaxis (`None`) 和标量 tf.int32/tf.int64 张量是有效索引，得到 [1, 3]
【发布时间】：2020-09-01 02:22:37
【问题描述】：

我正在尝试从Github 训练一个 3D 分割网络。我的模型是由典型的 U-Net 模型 Keras (Python) 实现的。模型，总结如下，

Model: "functional_3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 128, 128, 4) 0                                            
__________________________________________________________________________________________________
gaussian_noise (GaussianNoise)  (None, 128, 128, 4)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 128, 128, 64) 1088        gaussian_noise[0][0]             
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 128, 128, 64) 256         conv2d[0][0]                     
__________________________________________________________________________________________________
p_re_lu (PReLU)                 (None, 128, 128, 64) 64          batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 128, 128, 64) 36928       p_re_lu[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 128, 128, 64) 256         conv2d_1[0][0]                   
__________________________________________________________________________________________________
p_re_lu_1 (PReLU)               (None, 128, 128, 64) 64          batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 128, 128, 64) 36928       p_re_lu_1[0][0]                  
__________________________________________________________________________________________________
add (Add)                       (None, 128, 128, 64) 0           conv2d[0][0]                     
                                                                 conv2d_2[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 64, 64, 128)  32896       add[0][0]                        
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 64, 64, 128)  512         conv2d_3[0][0]                   
__________________________________________________________________________________________________
p_re_lu_2 (PReLU)               (None, 64, 64, 128)  128         batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 64, 64, 128)  147584      p_re_lu_2[0][0]                  
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 64, 64, 128)  512         conv2d_4[0][0]                   
__________________________________________________________________________________________________
p_re_lu_3 (PReLU)               (None, 64, 64, 128)  128         batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 64, 64, 128)  147584      p_re_lu_3[0][0]                  
__________________________________________________________________________________________________
add_1 (Add)                     (None, 64, 64, 128)  0           conv2d_3[0][0]                   
                                                                 conv2d_5[0][0]                   
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 32, 32, 256)  131328      add_1[0][0]                      
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 32, 32, 256)  1024        conv2d_6[0][0]                   
__________________________________________________________________________________________________
p_re_lu_4 (PReLU)               (None, 32, 32, 256)  256         batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 32, 32, 256)  590080      p_re_lu_4[0][0]                  
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 32, 32, 256)  1024        conv2d_7[0][0]                   
__________________________________________________________________________________________________
p_re_lu_5 (PReLU)               (None, 32, 32, 256)  256         batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 32, 32, 256)  590080      p_re_lu_5[0][0]                  
__________________________________________________________________________________________________
add_2 (Add)                     (None, 32, 32, 256)  0           conv2d_6[0][0]                   
                                                                 conv2d_8[0][0]                   
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 16, 16, 512)  524800      add_2[0][0]                      
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 512)  2048        conv2d_9[0][0]                   
__________________________________________________________________________________________________
p_re_lu_6 (PReLU)               (None, 16, 16, 512)  512         batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 16, 16, 512)  2359808     p_re_lu_6[0][0]                  
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 16, 16, 512)  2048        conv2d_10[0][0]                  
__________________________________________________________________________________________________
p_re_lu_7 (PReLU)               (None, 16, 16, 512)  512         batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 16, 16, 512)  2359808     p_re_lu_7[0][0]                  
__________________________________________________________________________________________________
add_3 (Add)                     (None, 16, 16, 512)  0           conv2d_9[0][0]                   
                                                                 conv2d_11[0][0]                  
__________________________________________________________________________________________________
up_sampling2d (UpSampling2D)    (None, 32, 32, 512)  0           add_3[0][0]                      
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 32, 32, 256)  524544      up_sampling2d[0][0]              
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 32, 32, 512)  0           add_2[0][0]                      
                                                                 conv2d_12[0][0]                  
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 512)  2048        concatenate[0][0]                
__________________________________________________________________________________________________
p_re_lu_8 (PReLU)               (None, 32, 32, 512)  512         batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 32, 32, 256)  1179904     p_re_lu_8[0][0]                  
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 32, 32, 256)  1024        conv2d_13[0][0]                  
__________________________________________________________________________________________________
p_re_lu_9 (PReLU)               (None, 32, 32, 256)  256         batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 32, 32, 256)  131072      concatenate[0][0]                
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 32, 32, 256)  590080      p_re_lu_9[0][0]                  
__________________________________________________________________________________________________
add_4 (Add)                     (None, 32, 32, 256)  0           conv2d_15[0][0]                  
                                                                 conv2d_14[0][0]                  
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D)  (None, 64, 64, 256)  0           add_4[0][0]                      
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 64, 64, 128)  131200      up_sampling2d_1[0][0]            
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 64, 64, 256)  0           add_1[0][0]                      
                                                                 conv2d_16[0][0]                  
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 64, 64, 256)  1024        concatenate_1[0][0]              
__________________________________________________________________________________________________
p_re_lu_10 (PReLU)              (None, 64, 64, 256)  256         batch_normalization_10[0][0]     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 64, 64, 128)  295040      p_re_lu_10[0][0]                 
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 64, 64, 128)  512         conv2d_17[0][0]                  
__________________________________________________________________________________________________
p_re_lu_11 (PReLU)              (None, 64, 64, 128)  128         batch_normalization_11[0][0]     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 64, 64, 128)  32768       concatenate_1[0][0]              
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 64, 64, 128)  147584      p_re_lu_11[0][0]                 
__________________________________________________________________________________________________
add_5 (Add)                     (None, 64, 64, 128)  0           conv2d_19[0][0]                  
                                                                 conv2d_18[0][0]                  
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D)  (None, 128, 128, 128 0           add_5[0][0]                      
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 128, 128, 64) 32832       up_sampling2d_2[0][0]            
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 128, 128, 128 0           add[0][0]                        
                                                                 conv2d_20[0][0]                  
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 128, 128, 128 512         concatenate_2[0][0]              
__________________________________________________________________________________________________
p_re_lu_12 (PReLU)              (None, 128, 128, 128 128         batch_normalization_12[0][0]     
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 128, 128, 64) 73792       p_re_lu_12[0][0]                 
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 128, 128, 64) 256         conv2d_21[0][0]                  
__________________________________________________________________________________________________
p_re_lu_13 (PReLU)              (None, 128, 128, 64) 64          batch_normalization_13[0][0]     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 128, 128, 64) 8192        concatenate_2[0][0]              
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 128, 128, 64) 36928       p_re_lu_13[0][0]                 
__________________________________________________________________________________________________
add_6 (Add)                     (None, 128, 128, 64) 0           conv2d_23[0][0]                  
                                                                 conv2d_22[0][0]                  
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 128, 128, 64) 256         add_6[0][0]                      
__________________________________________________________________________________________________
p_re_lu_14 (PReLU)              (None, 128, 128, 64) 64          batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 128, 128, 4)  260         p_re_lu_14[0][0]                 
__________________________________________________________________________________________________
activation (Activation)         (None, 128, 128, 4)  0           conv2d_24[0][0]                  
==================================================================================================
Total params: 10,159,748
Trainable params: 10,153,092
Non-trainable params: 6,656
__________________________________________________________________________________________________

(batch, Height, Width, Channel) 中我的训练文件输入形状。我将训练图像和标签保存在两个 Numpy 文件 (.npy) 中。其中，x_training.npy 包含图像（形状：(20, 128, 128, 4)）和y_training.npy 包含图像标签（形状：（20、128、128、4））。然后我使用自定义数据生成器来读取数据。

def img_msk_gen(X33_train,Y_train,seed):

    '''
    a custom generator that performs data augmentation on both patches and their corresponding targets (masks)
    '''
    datagen = ImageDataGenerator(horizontal_flip=True,data_format="channels_last")
    datagen_msk = ImageDataGenerator(horizontal_flip=True,data_format="channels_last")
    image_generator = datagen.flow(X33_train,batch_size=4,seed=seed)
    y_generator = datagen_msk.flow(Y_train,batch_size=4,seed=seed)
    while True:
        yield(image_generator.next(), y_generator.next())

最后，我正在尝试训练我的模型，

#load data from disk
X_patches=np.load("./x_training.npy").astype(np.float32)
Y_labels_valid=np.load("./y_training.npy").astype(np.float32)
X33_train=X_patches
Y_train=Y_labels
train_generator=img_msk_gen(X33_train=X_patches,Y_train=Y_labels,seed= 9999)
model.fit_generator(train_generator,steps_per_epoch=len(X33_train)//batch_size,
                    verbose=1)

但是，它会抛出一个错误，像这样......

TypeError: Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got [1, 3]

如果您有任何建议或想法，将对我有所帮助。 我的完整模型实现是 colab 中的 here，数据是 Google Drive 中的 here。 虽然有类似类型的问题可用，但我无法解决我的问题。任何形式的帮助将不胜感激。提前谢谢。

【问题讨论】：

标签： python python-3.x tensorflow keras conv-neural-network

【解决方案1】：

错误直接说明：你给 [1,3] 这是一个列表，它需要一个数字或一个切片。

也许你的意思是 [1:3] ？

你似乎给了 [1,3] 所以也许应该改变：

y_core=K.sum(y_true_f[:,[1,3]],axis=1)

到

y_core=K.sum(y_true_f[1:3],axis=1)

这至少是有效的语法，我不确定它是否符合你的要求。

【讨论】：

我从你的 colab 复制粘贴了该行
是的，我发现了那个错误，那是在我的损失函数中。
但是如果我只想获得第一个和第三个 (1,3) 条目而不是第一个到第三个 (1:3) 怎么办