Python TensorFlow：使用张量索引另一个张量
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TensorFlow: using a tensor to index another tensor
提问by user200340
I have a basic question about how to do indexing in TensorFlow.
我有一个关于如何在 TensorFlow 中建立索引的基本问题。
In numpy:
在 numpy 中：
x = np.asarray([1,2,3,3,2,5,6,7,1,3])
e = np.asarray([0,1,0,1,1,1,0,1])
#numpy
print x * e[x]
I can get
我可以得到
[1 0 3 3 0 5 0 7 1 3]
How can I do this in TensorFlow?
我怎样才能在 TensorFlow 中做到这一点？
x = np.asarray([1,2,3,3,2,5,6,7,1,3])
e = np.asarray([0,1,0,1,1,1,0,1])
x_t = tf.constant(x)
e_t = tf.constant(e)
with tf.Session():
????
Thanks!
谢谢！
回答by mrry
Fortunately, the exact case you're asking about is supported in TensorFlow by tf.gather():
幸运的是，TensorFlow 通过tf.gather()以下方式支持您所询问的确切情况：
result = x_t * tf.gather(e_t, x_t)
with tf.Session() as sess:
print sess.run(result) # ==> 'array([1, 0, 3, 3, 0, 5, 0, 7, 1, 3])'
The tf.gather()op is less powerful than NumPy's advanced indexing: it only supports extracting full slices of a tensor on its 0th dimension. Support for more general indexing has been requested, and is being tracked in this GitHub issue.
该tf.gather()运算是小于强大NumPy的先进索引：它仅支持其零维提取张量的全片。已请求支持更一般的索引，并在此 GitHub 问题 中进行跟踪。
