How To Calculate Power Of Tensors In TensorFlow?
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)