TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
softplus() is used to compute element wise log(exp(features) + 1).
Syntax: tensorflow.math.softplus(features, name)
Parameters:
- features: It’s a tensor. Allowed dtypes are half, bfloat16, float32, float64.
- 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, 9, 15], dtype = tf.float64) # Printing the input tensor print('a: ', a) # Calculating result res = tf.math.softplus(a) # Printing the result print('Result: ', res) |
Output:
a: tf.Tensor([ 5. 7. 9. 15.], shape=(4, ), dtype=float64) Result: tf.Tensor([ 5.00671535 7.00091147 9.0001234 15.00000031], shape=(4, ), 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([ 5, 7, 9, 15], dtype = tf.float64) # Calculating tangent res = tf.math.softplus(a) # Plotting the graph plt.plot(a, res, color ='green') plt.title('tensorflow.math.softplus') plt.xlabel('Input') plt.ylabel('Result') plt.show() |
Output:
