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)的某个列的值。我在调试时在numpy上尝试了以下值分配,并且工作得很好,但不确定如何在张量上执行相同的操作。

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]]


我知道张量不支持项目分配,但我想知道是否有一种方法不会丢失参考张量的数据。

最佳答案

我认为在这种情况下最简单的事情是:

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]]]


我为TensorFlow issue #18383中的连续切片提出了更灵活的替换操作,但是在这种情况下,这可能更简单,更快捷。

09-17 14:17
查看更多