What does softmax activation do?

Introduction

Softmax activation function Neural networks can’t function without the activation function. A neural network is only a linear regression model without an activation function. That’s because the activation function makes neural networks behave in a non-linear fashion.

Please watch the video explanation below if you like to absorb information visually and aurally. If not, you may skip this.

I suggest reading this article if you’re interested in learning more.

What Are Activation Functions in Deep Learning, and When Should I Use Them?

The SoftMax activation function is the topic of this essay. Many categorization difficulties can be solved with its help. Let’s start with learning the neural network architecture for a multi-class classification problem and why different activation functions can’t be employed.

Example

Let’s pretend we have the following dataset, where there are five characteristics, FeatureX1 through FeatureX5, associated with each observation, and the target variable can take on one of three possible values.

Let’s make a basic neural network to solve this issue. As there are five different features in this dataset, the Input layer contains five neurons. Following that is a single hidden layer, comprised of four neurons. Here, the Zij value is the result of a calculation performed by each of these neurons using the inputs, weights, and biases shown.

With this notation,

Z11 represents the first neuron in the first layer. Same notation is used for the second neuron in the first layer (Z12).

We then apply the activation function over these values. For example, we could apply an activation function, tanh in this case, and then pass the resulting values or results on to the output layer.

Neurons in the output layer are scaled to the dataset’s class dimension. There will be three types of neurons in the output layer to account for the three categories in the training data. These neurons will calculate the likelihood of various categories. In other words, the likelihood that a given data point is part of class 1 will be reported by the first neuron. The likelihood that a data point is associated with class 2 will be provided by the second neuron, and so on.

Sigmoid: Why Not?

Let’s pretend that we use the weights and biases of this layer to derive the Z value, and then apply the sigmoid activation function to it. For a sigmoid activation function, the range is between zero and one, as is well known. Let’s pretend that the final results look like this

Two issues arise here:

In the first place, this network claims the input data point is a member of two classes if a thresh-hold of 0.5 is used. Second, there is no correlation between any of these probabilities. This indicates that the likelihood that the data point is part of class 1 does not account for the likelihood that it is part of class 2 or class 3.

This is why the sigmoid activation function is not favoured for use in issues involving several classes.

The Activation of Softmax

As an alternative to sigmoid, the Softmax activation function will be used in the output layer. The probabilities are determined by using the Softmax activation function. This means that Z21, Z22, and Z23 are all considered when calculating the overall likelihood.

Let’s have a look at how the softmax activation function operates in practise. The SoftMax function calculates the probabilities of the various classes in a manner analogous to the sigmoid activation function. This equation represents the SoftMax activation function.

The Z in this case stands for the numbers reported by the layer’s neurons in the output. Non-linearity is provided via the exponential function. After being transformed into probabilities, these values are normalised by being divided by the sum of exponential values.

Keep in mind that when there are just two categories

It is identical to the sigmoid activation function. Therefore, the sigmoid function is really only a special case of the more general Softmax function. Here’s a link for further information on that idea if you’re interested.

First, let’s have a look at a basic example to see how the softmax works,

 The following neural network is available to us:

Consider the outcomes of Z21 = 2.33, Z22 = -1.46, and Z23 = 0.56. Now we apply the SoftMax activation function to each of these neurons, and we get the following outputs. The input unmistakably falls under Class 1 in this instance. So, the value of the first class’s probability would shift if the likelihood of any of these classes was altered.

Final Notes

The SoftMax activation function is the focus of this piece. Here, we saw an example of how the softmax function works and why activation functions like sigmoid and tanh are inappropriate for use in multiclass classification situations.

In short, here is the place to go if you’re ready to begin your Data Science journey and you want to learn about everything there is to know about the field in one convenient location. Take a look at Analytics Vidhya’s Certified AI & ML BlackBelt Plus Course!