Python – tensorflow.math.softplus()

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: