沿具有给定索引的维度对张量进行切片 | 沿具有给定索引的维度对张量进行切片

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问题描述

假设我有一个张量:

tensor = tf.constant(
  [[[0.05340263, 0.27248233, 0.49127685, 0.07926575, 0.96054204],
    [0.50013988, 0.05903472, 0.43025479, 0.41379231, 0.86508251],
    [0.02033722, 0.11996034, 0.57675261, 0.12049974, 0.65760677],
    [0.71859089, 0.22825203, 0.64064407, 0.47443116, 0.64108334]],

   [[0.18813498, 0.29462021, 0.09433628, 0.97393446, 0.33451445],
    [0.01657461, 0.28126666, 0.64016929, 0.48365073, 0.26672697],
    [0.9379696 , 0.44648103, 0.39463243, 0.51797975, 0.4173626 ],
    [0.89788558, 0.31063058, 0.05492096, 0.86904097, 0.21696292]],

   [[0.07279436, 0.94773635, 0.34173115, 0.7228713 , 0.46553334],
    [0.61199848, 0.88508141, 0.97019517, 0.61465985, 0.48971128],
    [0.53037002, 0.70782324, 0.32158754, 0.2793538 , 0.62661128],
    [0.52787814, 0.17085317, 0.83711126, 0.40567032, 0.71386498]]])

形状为 (3, 4, 5)

which is of shape (3, 4, 5)

我想对它进行切片以返回形状为 (3,5) 的新张量，使用给定的一维张量，其值指示要检索的位置，例如:

I want to slice it to return a new tensor of shape (3,5), with a given 1D tensor whose value indicates which position to retrieve, for example:

index_tensor = tf.constant([2,1,3])

这会产生一个新的张量，如下所示:

which results in a new tensor which looks like this:

[[0.02033722, 0.11996034, 0.57675261, 0.12049974, 0.65760677],
 [0.01657461, 0.28126666, 0.64016929, 0.48365073, 0.26672697],
 [0.52787814, 0.17085317, 0.83711126, 0.40567032, 0.71386498]]

也就是说，沿着第二个维度，从索引 2、1 和 3 中获取项目.它类似于:

that is , along the second dimension, take items from index 2, 1, and 3.It is similar to do:

tensor[:,x,:]

除了这只会给我沿维度索引x"处的项目，我希望它是灵活的.

except this will only give me item at index 'x' along the dimension, and I want it to be flexible.

这能做到吗?

推荐答案

您可以使用 tf.one_hot() 来屏蔽 index_tensor.

You can use tf.one_hot() to mask index_tensor.

index = tf.one_hot(index_tensor,tensor.shape[1])

[[0. 0. 1. 0.]
 [0. 1. 0. 0.]
 [0. 0. 0. 1.]]

然后通过 tf.boolean_mask() 得到你的结果.

Then get your result by tf.boolean_mask().

result = tf.boolean_mask(tensor,index)

[[0.02033722 0.11996034 0.57675261 0.12049974 0.65760677]
 [0.01657461 0.28126666 0.64016929 0.48365073 0.26672697]
 [0.52787814 0.17085317 0.83711126 0.40567032 0.71386498]]

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