When To Use Softmax Activation Algorithm In Deep Learning

In my previous post, we have discussed how to use simple linear regression method if we have target class which is directly dependent on input class. In case of finding single value out of two, which is normally called binary distribution in that case we have to use the logistic regression method.

In today’s post, I am discussing more on the softmax activation function which is used when we have to predict the event over n number of different events. Softmax function calculates the probability of each target class over the possible target classes for the given inputs.

Let’s understand it from example that we have list of hand written digits and we want to predict out of 10 (0 - 9) digits which digit has high probability to appear.In this case, we have 10 target classes and out of 10 target classes we have to show a single class which is nothing but a single digit. In this case, we will use the softmax activation function.

I have use the below code to create softmax function graph for the numbers starting from 0 to 10 and found higher number has high probability to come. So it means we can use this activation function in deep learning or in neural network while predicting the target class out of multiple target classes.

Use the below code to see the basic functionality of softmax activation function
import numpy as np
import matplotlib.pyplot as plt

def softmax(add_inputs):
  #Softmax Equation
  return np.exp(add_inputs) / float(sum(np.exp(add_inputs)))

def line_graph(x, y, x_title, y_title):
  plt.plot(x, y)
  plt.xlabel(x_title)
  plt.ylabel(y_title)
  plt.show()

x = range(0, 10)
y = softmax(x)
line_graph(x, y, "Inputs", "Softmax Probability")

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