TensorFlow:如何将张量行与具有相同第一个元素的张量的第二个元素相加? [英] TensorFlow: How to combine rows of tensor with summing the 2nd element of tensor which has the same 1st element?

问题描述

例如，我想add这个张量的第二个元素，其中第一个元素是相同的.也欢迎任何基于 Numpy 的解决方案！

For example, I want to add the 2nd element of this tensor where the 1st element is same. Any Numpy based solution is also welcomed!

• 来自:

x = tf.constant([
    [1., 0.9],
    [2., 0.7],
    [1., 0.7],
    [3., 0.4],
    [4., 0.8]
], dtype=tf.float32)

• 致:

x = tf.constant([
    [1., 1.6],
    [2., 0.7],
    [3., 0.4],
    [4., 0.8]
], dtype=tf.float32)

推荐答案

感谢 FinleyGibson 的 Numpy 解决方案和一些有用的TensorFlow 指针！这是我在 TF 中使用 tf.unique_with_counts() 和 tf.segment_sum() 的解决方案:

Thanks FinleyGibson for the Numpy solution and some useful TensorFlow pointers! This is my solution in TF with using tf.unique_with_counts() and tf.segment_sum():

<打击>

x = tf.constant([
    [1., 0.9],
    [2., 0.7],
    [1., 0.7],
    [3., 0.4],
    [4., 0.8]
], dtype=tf.float32)

with tf.Session() as sess:
    y, idx, y_counts = tf.unique_with_counts(x[:, 0])
    idx_sorted = tf.sort(idx, axis=-1, direction='ASCENDING')
    score_sum = tf.segment_sum(x[:, 1], idx_sorted)
    result = tf.stack((y, score_sum), axis=1)
    print(sess.run(result))

[[1.       1.5999999]
[2.        0.7      ]
[3.        0.4      ]
[4.        0.8      ]]

• 似乎上述解决方案没有正确排序行/列对.这是固定版本.

with tf.Session() as sess:
    x = tf.constant([
        [2., 0.7],
        [1., 0.1],
        [3., 0.4],
        [4., 0.8],
        [1., 0.9]], dtype=tf.float32)

    def matrix_sort(a, col):
        return tf.gather(a, tf.nn.top_k(-a[:, col], k=a.get_shape()[0].value).indices)

    sorted_x = matrix_sort(matrix_sort(x, 1), 0)
    labels = sorted_x[:, 0]
    scores = sorted_x[:, 1]
    y, idx, y_counts = tf.unique_with_counts(labels)
    score_sum = tf.segment_sum(scores, idx)    
    result = tf.stack((y, score_sum), axis=1) 
    print(sess.run(result))

[[1.  1. ]
 [2.  0.7]
 [3.  0.4]
 [4.  0.8]]

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