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)
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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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