Tensorflow中矩阵的加权和 [英] Weighted sum of matrices in Tensorflow

问题描述

我有一个大小为(M，N，N)的3维张量A和一个大小为M的1维张量p.我想计算矩阵的加权和:

I have a 3-dimensional tensor A of size (M,N,N) and an 1 dimensional tensor p of size M. I want to compute the weighted sum of matrices:

在NumPy中，我正在实现以下代码:

In NumPy I am implementing the following code:

import numpy as np
temp=np.array([p[m]*A[m] for m in range(M)])
B=sum(temp);

我想在TensorFlow中做同样的事情，但是我似乎找不到任何内置的操作来执行同样的事情.我尝试了tf.matmul和tf.mul，但是它们似乎没有给出期望的结果.有人可以建议我在TensorFlow中执行此操作的正确方法吗?

I want to do the same in TensorFlow but I don't seem to find any inbuilt operations to carry out the same. I tried tf.matmul and tf.mul but they don't seem to be giving the desired result. Can someone suggest me a right way to do this in TensorFlow?

推荐答案

如果当您具有大小为(K,M)的P矩阵和大小为(M,N,N)的张量A时要计算大小为(K,N,N)的张量B ，您可以按照它进行操作.

If you want to compute a tensor B of size (K,N,N) when you have a P matrix of size (K,M) and a tensor A of size (M,N,N), you can follow it.

import tensorflow as tf
import numpy as np

K = 2
M = 3
N = 2

np.random.seed(0)
A = tf.constant(np.random.randint(1,5,(M,N,N)),dtype=tf.float64)

# when K.shape=(K,M)
P = tf.constant(np.random.randint(1,5,(K,M)),dtype=tf.float64)
# when K.shape=(M,)
# P = tf.constant(np.random.randint(1,5,(M,)),dtype=tf.float64)

P_new = tf.expand_dims(tf.expand_dims(P,-1),-1)

# if K.shape=(K,M) set axis=1,if K.shape=(M,) set axis=0,
B = tf.reduce_sum(tf.multiply(P_new , A),axis=1)

with tf.Session()as sess:
    print(sess.run(P))
    print(sess.run(A))
    print(sess.run(B))

[[1. 4. 3.]
 [1. 1. 1.]]
[[[1. 4.]
  [2. 1.]]

 [[4. 4.]
  [4. 4.]]

 [[2. 4.]
  [2. 3.]]]
[[[23. 32.]
  [24. 26.]]

 [[ 7. 12.]
  [ 8.  8.]]]

可以修改以上代码，以在您的问题中包含问题的解决方案.

The above code can be modified to include the solution of problem in your question.

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