Python 更改张量某列的值

我想用二维张量（批量大小，序列长度）覆盖三维张量（批量大小，序列长度，类数）的某一列的值。在调试过程中，我在numpy上尝试了以下值赋值，效果非常好，但不确定如何在tensor上进行同样的赋值

Numpy Solution:

    Tensor A shape [50,4,4]
    Tensor B shape [50,4]

  * A[:,:,0]=b[:,:] 
    Tensor A shape is [50,4,4]

Example: 
    A[1]: 
        [[0.2,0.6,0.1,0.02],
        [0.3,0.4,0.5,0.12],
        [0.2,0.46,0.31,0.02],
        [0.2,0.1,0.2,0.03]]
    B[1]:
        [0,1,1,0]
    A*[1]:
        [[0,0.6,0.1,0.02],
        [1,0.4,0.5,0.12],
        [1,0.46,0.31,0.02],
        [0,0.1,0.2,0.03]]

---

我知道在张量上不支持项目分配，但我想知道是否有一种方法可以不丢失ref-tensor的数据。

我认为在这种情况下最简单的方法是：

import tensorflow as tf

a = tf.placeholder(tf.float32, [None, None, None])
b = tf.placeholder(tf.float32, [None, None])
a_star = tf.concat([b[:, :, tf.newaxis], a[:, :, 1:]], axis=-1)
# Test
with tf.Session() as sess:
    print(sess.run(a_star, feed_dict={
        a: [[[0.2 , 0.6 , 0.1 , 0.02],
             [0.3 , 0.4 , 0.5 , 0.12],
             [0.2 , 0.46, 0.31, 0.02],
             [0.2 , 0.1 , 0.2 , 0.03]]],
        b: [[0, 1, 1, 0]]
    }))

输出：

[[0.0.6 0.1 0.02]
[1.   0.4  0.5  0.12]
[1.   0.46 0.31 0.02]
[0.   0.1  0.2  0.03]]]

我在中提出了一种更灵活的连续切片替换操作，但在这种情况下，这可能更简单、更快。

对您有帮助吗？