# Python – tensorflow.raw_ops.Log()

• 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. Log() is used to find element wise logarithm of x.

Syntax: tf.raw_ops.Log(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 res = tf.raw_ops.Log(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.         0.69314718 1.09861229 1.38629436 1.60943791], 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 res = tf.raw_ops.Log(x = a)    # Plotting the graph plt.plot(a, res, color ='green') plt.title('tensorflow.raw_ops.Log') plt.xlabel('Input') plt.ylabel('Result') plt.show()

Output:

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