Python – tensorflow.raw_ops.Log1p()

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 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: