In TensorFlow 2.0 Python Tutorial, We will Learn about the TensorFlow Math Module tf.sqrt() function. We will learn how to calculate the square root of tensors in TensorFlow using tf.sqrt() function.
tf.sqrt() : Calculate Element wise square root of numpy array and TF tensor,
It can be give sqrt of list, tuple, scaler
It allow tf variable/constant/placeholder/SparceMatrix with Specific datatype
Data Type : bfloat16, half, float32, float64, complex64, complex128.
Note: Don’t give ‘int’ data type, tf.sqrt() will through error
Syntax: tf.sqrt(x, name=None)
| Args | |
|---|---|
x | A tf.Tensor of type bfloat16, half, float32, float64, complex64, complex128 |
name | A name for the operation (optional). |
| Returns |
|---|
A tf.Tensor of same size, type and sparsity as x.If x is a SparseTensor, returns SparseTensor(x.indices, tf.math.sqrt(x.values, ...), x.dense_shape) |
Calculate the Square Root using TensorFlow
import tensorflow as tf
import numpy as np
Square Root of Scaler
a = 9.0
tf.sqrt(a)
Square Root of List
l1 = [4.0,9.0,16.0]
tf.sqrt(l1)
Square Root of Tuple
t1 = (4.0,9.0,16.0)
tf.sqrt(t1)
Square Root of Numpy Array
npa1 = np.array([4,9,16], dtype = np.float64)
tf.sqrt(npa1)
y = np.ones(6).reshape(2, 1, 3, 1)*4
print("Y: " , y)
tf.sqrt(y)
Square Root of TensorFlow Tensor
tf.sqrt(tf.convert_to_tensor(4, dtype='float')) # using scaler value
tf.sqrt(tf.convert_to_tensor([4,9,16], dtype='float')) # using list
Square Root of TF Constant Tensor
tf_ct1 = tf.constant([4,9,16], dtype='float')
tf.sqrt(tf_ct1)
Square Root of TF Variable
tf_vr1 = tf.Variable([4,9,16], dtype='half')
tf.sqrt(tf_vr1)