Python | Tensorflow nn.softplus()
tensorflow.nn
nn.softplus()
math.softplus
Syntax: tf.nn.softplus(features, name=None) or tf.math.softplus(features, name=None) Parameters: features: A tensor of any of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. name (optional): The name for the operation. Return type: A tensor with the same type as that of features.
Code #1:
Python3
# Importing the Tensorflow library import tensorflow as tf # A constant vector of size 6 a = tf.constant([1.0, -0.5, 3.4, -2.1, 0.0, -6.5], dtype = tf.float32) # Applying the softplus function and # storing the result in 'b' b = tf.nn.softplus(a, name ='softplus') # Initiating a Tensorflow session with tf.Session() as sess: print('Input type:', a) print('Input:', sess.run(a)) print('Return type:', b) print('Output:', sess.run(b)) |
Output:
Input type: Tensor("Const:0", shape=(6, ), dtype=float32)
Input: [ 1. -0.5 3.4000001 -2.0999999 0. -6.5 ]
Return type: Tensor("softplus:0", shape=(6, ), dtype=float32)
Output: [ 1.31326163e+00 4.74076986e-01 3.43282866e+00 1.15519524e-01
6.93147182e-01 1.50233845e-03]
Code #2:
Python3
# Importing the Tensorflow library import tensorflow as tf # Importing the NumPy library import numpy as np # Importing the matplotlib.pyplot function import matplotlib.pyplot as plt # A vector of size 15 with values from -5 to 5 a = np.linspace(-5, 5, 15) # Applying the softplus function and # storing the result in 'b' b = tf.nn.softplus(a, name ='softplus') # Initiating a Tensorflow session with tf.Session() as sess: print('Input:', a) print('Output:', sess.run(b)) plt.plot(a, sess.run(b), color = 'red', marker = "o") plt.title("tensorflow.nn.softplus") plt.xlabel("X") plt.ylabel("Y") plt.show() |
Output:
Input: [-5. -4.28571429 -3.57142857 -2.85714286 -2.14285714 -1.42857143 -0.71428571 0. 0.71428571 1.42857143 2.14285714 2.85714286 3.57142857 4.28571429 5. ] Output: [ 0.00671535 0.01366993 0.02772767 0.05584391 0.11093221 0.21482992 0.39846846 0.69314718 1.11275418 1.64340135 2.25378936 2.91298677 3.59915624 4.29938421 5.00671535]
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