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# Python – tensorflow.raw_ops.Log1p()

• Last Updated : 05 Jun, 2020

TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning  neural networks. TensorFlow raw_ops provides low level access to all TensorFlow operations. Log1p() is used to find element wise logarithm of (1+x) for input x.

Syntax: tf.raw_ops.Log1p(x, name)

Parameters:

• x: It’s the input tensor. Allowed dtype for this tensor are bfloat16, half, float32, float64, complex64, complex128.
• name(optional): It’s defines the name for the operation.

Returns:  It returns a tensor of same dtype as x.

Note: It only takes keyword arguments.

Example 1:

## Python3

 `# Importing the library``import` `tensorflow as tf`` ` `# Initializing the input tensor``a ``=` `tf.constant([``1``, ``2``, ``3``, ``4``, ``5``], dtype ``=` `tf.float64)`` ` `# Printing the input tensor``print``(``'Input: '``, a)`` ` `# Calculating logarithm(1 + x)``res ``=` `tf.raw_ops.Log1p(x ``=` `a)`` ` `# Printing the result``print``(``'Result: '``, res)`

Output:

```Input:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([0.69314718 1.09861229 1.38629436 1.60943791 1.79175947], shape=(5, ), dtype=float64)

```

Example 2: Visualization

## Python3

 `# importing the library``import` `tensorflow as tf``import` `matplotlib.pyplot as plt`` ` `# Initializing the input tensor``a ``=` `tf.constant([``1``, ``2``, ``3``, ``4``, ``5``], dtype ``=` `tf.float64)`` ` `# Calculating logarithm(1 + x)``res ``=` `tf.raw_ops.Log1p(x ``=` `a)`` ` `# Plotting the graph``plt.plot(a, res, color ``=``'green'``)``plt.title(``'tensorflow.raw_ops.Log1p'``)``plt.xlabel(``'Input'``)``plt.ylabel(``'Result'``)``plt.show()`

Output:

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