Tensorflow，如何将2D张量(矩阵)与1D向量中的相应元素相乘 [英] Tensorflow, how to multiply a 2D tensor (matrix) by corresponding elements in a 1D vector

问题描述

我有一个形状为[batch x dim]的2D矩阵M，我有一个形状为[batch]的矢量V.如何将矩阵中的每一列乘以V中的对应元素?那就是:

I have a 2D matrix M of shape [batch x dim], I have a vector V of shape [batch]. How can I multiply each of the columns in the matrix by the corresponding element in the V? That is:

我知道低效的numpy实现看起来像这样:

I know an inefficient numpy implementation would look like this:

import numpy as np
M = np.random.uniform(size=(4, 10))
V = np.random.randint(4)
def tst(M, V):
  rows = []
  for i in range(len(M)):
    col = []
    for j in range(len(M[i])):
      col.append(M[i][j] * V[i])
    rows.append(col)
  return np.array(rows)

在张量流中，给定两个张量，最有效的方法是什么?

In tensorflow, given two tensors, what is the most efficient way to achieve this?

import tensorflow as tf
sess = tf.InteractiveSession()
M = tf.constant(np.random.normal(size=(4,10)), dtype=tf.float32)
V = tf.constant([1,2,3,4], dtype=tf.float32)

推荐答案

在NumPy中，我们需要制作V 2D，然后让广播进行逐元素乘法(即Hadamard乘积).我猜想，在tensorflow上应该是相同的.因此，要在tensorflow上扩展暗淡效果，我们可以使用tf.newaxis(在较新版本中)或tf.expand_dims或使用tf.reshape-

In NumPy, we would need to make V 2D and then let broadcasting do the element-wise multiplication (i.e. Hadamard product). I am guessing, it should be the same on tensorflow. So, for expanding dims on tensorflow, we can use tf.newaxis (on newer versions) or tf.expand_dims or a reshape with tf.reshape -

tf.multiply(M, V[:,tf.newaxis])
tf.multiply(M, tf.expand_dims(V,1))
tf.multiply(M, tf.reshape(V, (-1, 1)))

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