# Python – tensorflow.math.xlog1py()

• Last Updated : 16 Jun, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

xlog1py() is used to compute element wise x * log1p(y).

Syntax: tensorflow.math.xlog1py(x, y, name)

Parameters:

• x: It’s a tensor. Allowed dtypes are bfloat16, half, float32, float64, complex64, complex128.
• y: It’s a tensor. ALlowed dtypes are bfloat16, half, float32, float64, complex64, complex128.
• name(optional): It defines the name for the operation.

Returns: It returns a tensor.

Example 1:

## Python3

 `# importing the library ` `import` `tensorflow as tf ` ` `  `# Initializing the input tensor ` `a ``=` `tf.constant([ ``-``5``, ``-``7``, ``2``, ``0``, ``7``], dtype ``=` `tf.float64) ` `b ``=` `tf.constant([ ``1``, ``3``, ``9``, ``4``, ``7``], dtype ``=` `tf.float64) ` ` `  `# Printing the input tensor ` `print``(``'a: '``, a) ` `print``(``'b: '``, b) ` ` `  `# Calculating result ` `res ``=` `tf.math.xlog1py(a, b) ` ` `  `# Printing the result ` `print``(``'Result: '``, res) `

Output:

```a:  tf.Tensor([-5. -7.  2.  0.  7.], shape=(5, ), dtype=float64)
b:  tf.Tensor([1. 3. 9. 4. 7.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([-3.4657359  -9.70406053  4.60517019  0.         14.55609079], shape=(5, ), dtype=float64)

```

Example 2:

## Python3

 `# importing the library ` `import` `tensorflow as tf ` `import` `numpy as np ` ` `  `# Initializing the input tensor ` `a ``=` `tf.constant([ ``-``5` `+` `2j``, ``-``7``-``5j``, ``2` `+` `2j``, ``5``-``3j``, ``7` `+` `6j``], dtype ``=` `tf.complex128) ` `b ``=` `tf.constant([ ``0` `+` `0j``, ``3``-``1j``, ``9` `+` `5j``, ``4``-``3j``, ``-``6``-``8j``], dtype ``=` `tf.complex128) ` ` `  `# Printing the input tensor ` `print``(``'a: '``, a) ` `print``(``'b: '``, b) ` ` `  `# Calculating result ` `res ``=` `tf.math.xlog1py(a, b) ` ` `  `# Printing the result ` `print``(``'Result: '``, res) `

Output:

```a:  tf.Tensor([-5.+2.j -7.-5.j  2.+2.j  5.-3.j  7.+6.j], shape=(5, ), dtype=complex128)
b:  tf.Tensor([ 0.+0.j  3.-1.j  9.+5.j  4.-3.j -6.-8.j], shape=(5, ), dtype=complex128)
Result:  tf.Tensor(
[ -0.        +0.j         -11.14114002-5.36818272j
3.90101852+5.75560896j   7.19464281-7.99163829j
28.48660115-1.43986039j], shape=(5, ), dtype=complex128)
```

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