TensorFlow tf.pow() function can compute the power of one tensor to another, in this tutorial, we will use some examples to show you how to use it correctly.
Syntax
tf.pow(
x,
y,
name=None
)Computes the power of one value to another.
How to use tf.pow()?
We will write some examples to show you how to use.
(1) x and y are matrix, the shape of them are equal
import tensorflow as tf
# Initializing the input tensor
a = tf.constant([6, 8, 0, 15], dtype = tf.float64)
b = tf.constant([2, 3, 4, 0], dtype = tf.float64)
c = tf.constant(2, dtype = tf.float64)
w1 = tf.pow(a, b)
w2 = tf.pow(b, c)
w3 = tf.pow(c, b)
# Printing the result
init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
with tf.Session() as sess:
sess.run([init, init_local])
print(sess.run(w1))
print(sess.run(w2))
print(sess.run(w3))Here tensor a and b is a matrix or vector.
The result of \(a^b\) is the \({a_i}^{b_i}\)
w1 = tf.pow(a, b)
The shape of w1 is same to a and b, the value of w1 is:
[ 36. 512. 0. 1.]
(2) x is a matrix, y is a scalar
For example:
w2 = tf.pow(b, c)
Here b is a matrix, c is a scalar. The shape of w2 is same to b. The value is:
[ 4. 9. 16. 0.]
It means \(w2_i=b_i^c\)
(3) x is a scalar, y is a matrix
For example:
w3 = tf.pow(c, b)
Here c is a scalar, b is a matrix. The shape of w3 is same to b.
The value of w3 is:
[ 4. 8. 16. 1.]
It means \(w3_i=c^{b_i}\).