# 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)

## 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)
```
Top