In TensorFlow 2.0 Python Tutorial, We will Learn about the TensorFlow Math Module tf.pow() function. We will learn how to calculate the power of tensors in TensorFlow using tf.pow() function.
tf.pow(): Calculate the power of one value to another, Element wise power calculation
It can be given the power of scaler, Numpy array
It allow tf variable/constant/placeholder/SparceMatrix with Specific datatype
Data Type : float16, float32, float64, int32, int64, complex64, or complex128.
Note: Don’t pass data in integer format, it will through error
Syntax: tf.pow(x, y, name=None)
| Args | |
|---|---|
x | A Tensor of type float16, float32, float64, int32, int64, complex64, or complex128. |
y | A Tensor of type float16, float32, float64, int32, int64, complex64, or complex128. |
name | A name for the operation (optional). |
| Returns |
|---|
A Tensor. |
Calculate the Power using TensorFlow
import tensorflow as tf
import numpy as np
Power of Scaler
a = 4
b = 2
tf.pow(a, b)
Output>>>
<tf.Tensor: shape=(), dtype=int32, numpy=16>
Power of Numpy Array
npa1 = np.array([4,9,16], dtype = np.float64)
tf.pow(npa1, 2)
npa1 = np.array([1,2,3], dtype = np.float64)
npa2 = np.array([1,2,3], dtype = np.float64)
tf.pow(npa1, npa2)
y = np.ones(6).reshape(2, 1, 3, 1)*4
print("y : ", y)
tf.pow(y, 2)
Power of Tensor
tf_ct1 = tf.constant([4,9,16], dtype='float')
tf.pow(tf_ct1, 3)
tf_ct1 = tf.constant([4,9,16], dtype='float')
tf_ct2 = tf.constant([3,2,1], dtype='float')
tf.pow(tf_ct1, tf_ct2)
Power of TF Variable
tf_vr1 = tf.Variable([4,9,16], dtype='half')
tf.pow(tf_vr1, 2)
tf_vr1 = tf.Variable([4,9,16], dtype='float')
tf_vr2 = tf.Variable([3,2,1], dtype='float')
tf.pow(tf_vr1, tf_vr2)
tf_vr1 = tf.Variable([4,9,16], dtype='float')
tf_vr2 = tf.Variable([3], dtype='float')
tf.pow(tf_vr1, tf_vr2)