类型错误: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] [英] TypeError: Only integers, slices (`:`), ellipsis (`…`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got [1, 3]
本文介绍了类型错误: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]的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试从 Github 训练 3D 分割网络.我的模型是由 Keras (Python) 实现的，这是一个典型的 U-Net 模型.模型，总结如下，
I am trying to train a 3D segmentation Network from Github. My model is implemented by Keras (Python) which is a typical U-Net model. The model, summary is given below,
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读取数据.
My training file input shape in (batch, Height, Width, Channel). I saved the training image and label in two Numpy file (.npy).
Where, x_training.npy contain images (Shape: (20, 128, 128, 4))
and y_training.npy contain label of images (Shape: (20, 128, 128, 4)).Then i use custom data generator to read the data.
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，
Finally, i am trying to train my model,
#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，它抛出一个错误，像这样...
But, it throws an error, Like this...
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.我的完整模型实现在这里在 colab 中，数据位于此处在 Google 云端硬盘中. 虽然有类似的问题，但我无法解决我的问题.任何形式的帮助将不胜感激.谢谢，提前.
If you have any recommendations or ideas, it will helpful for me. My full model implementation is here in colab and data is here in Google Drive. Although similar type of question is available, I can't solve my issue. Any kind of help woill be greatly appreciated. Thanks, in advance.
推荐答案
错误直接说明了:你给出 [1,3] 是一个列表，它需要一个数字或一个切片.
The error says it directly: you give [1,3] which is a list, where it expects either a number or a slice.
也许你的意思是 [1:3] ?
Maybe you meant [1:3] ?
你似乎在那里给出了 [1,3] 所以也许应该改变:
You seem to give the [1,3] there so maybe should change:
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您的要求.
That's at least valid syntax, am not sure if it does what you want.
这篇关于类型错误: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]的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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