Dot product of two vectors in tensorflow

Question

I was wondering if there is an easy way to calculate the dot product of two vectors (i.e. 1-d tensors) and return a scalar value in tensorflow.

Given two vectors X=(

Answer 1

In addition to tf.reduce_sum(tf.multiply(x, y)), you can also do tf.matmul(x, tf.reshape(y, [-1, 1])).

Answer 2

import tensorflow as tf

x = tf.Variable([1, -2, 3], tf.float32, name='x')
y = tf.Variable([-1, 2, -3], tf.float32, name='y')

dot_product = tf.reduce_sum(tf.multiply(x, y))

sess = tf.InteractiveSession()
init_op = tf.global_variables_initializer()
sess.run(init_op)

dot_product.eval()

Out[46]: -14

Here, x and y are both vectors. We can do element wise product and then use tf.reduce_sum to sum the elements of the resulting vector. This solution is easy to read and does not require reshaping.

Interestingly, it does not seem like there is a built in dot product operator in the docs.

Note that you can easily check intermediate steps:

In [48]: tf.multiply(x, y).eval()
Out[48]: array([-1, -4, -9], dtype=int32)

Answer 3

you can use tf.matmul and tf.transpose

tf.matmul(x,tf.transpose(y))

or

tf.matmul(tf.transpose(x),y)

depending on the dimensions of x and y