Softmax Activation Function

 In neural network models that predict a multinomial probability distribution, the softmax activation function is utilised as the activation function in the output layer. Softmax is used as the activation function for multi-class classification problems requiring class membership on more than two labels.

The softmax activation will output one value for each node in the output layer by definition. The output numbers will be probabilities (or can be interpreted as such), and the values will add to 1.0.

The data must be prepared before modelling a multi-class classification problem. The class labels in the target variable are first label encoded, which means that each class label is assigned an integer from 0 to N-1, where N is the number of class labels.

The target variables that have been label encoded (or integer encoded) are then one-hot encoded. This, like the softmax output, is a probabilistic representation of the class label. Each class label and its position are given a position in a vector. All values are set to 0 (impossible), and a 1 (certain) is used to indicate the class label's position.

For example, three class labels will be integer encoded as 0, 1, and 2. Then encoded to vectors as follows:

  • Class 0: [1, 0, 0]
  • Class 1: [0, 1, 0]
  • Class 2: [0, 0, 1]

This is called a one-hot encoding.

Under supervised learning, t represents the predicted multinomial probability distribution for each class utilised to correct the model.


The softmax function returns a probability of class membership for each class label and tries to approximate the predicted target for a given input as closely as possible.


For example, if class 1 was intended for one example, the target vector would be:


[0, 1], [0, 1], [0, 1], [0, 1], [

The softmax output might look like this, with class 1 receiving the most weight and the other classes receiving less.


[0.09003057 0.66524096 0] [0.09003057 0.66524096 0] .24472847]

Cross-entropy is frequently used to calculate the error between the expected and predicted multinomial probability distributions, and this error is then utilised to update the model. The cross-entropy loss function is what it's called.

Comments

Post a Comment

Popular posts from this blog

Unleashing Creativity: The Latest Frontier of Animation AI

Image
 Introduction Animation AI is the result of the convergence of technology and creativity in the animation field. This groundbreaking fusion has not only transformed the animation landscape but also opened up exciting avenues for artists, storytellers, and tech enthusiasts alike. In this blog, we delve into the latest trends, advancements, and possibilities that Animation AI brings to the table. Lifelike Character Animation AI-driven character animation has taken realism to unprecedented levels. With machine learning algorithms studying human motion, characters move and interact in ways that mirror real-life actions. Expressions and emotions are portrayed authentically, immersing audiences in narratives that tug at heartstrings. Efficiency and Speed Automation is a hallmark of Animation AI. Inbetweening, traditionally a meticulous manual process, is now accelerated by AI, reducing production time. Creative minds can focus on refining the narrative while AI handles the technical intr
Read more

Tanh Activation Function

Image
With a difference in output range of -1 to 1, the tanh activation function is remarkably similar to the sigmoid/logistic activation function, and even has the same S-shape. The larger the input (more positive), the closer the output to 1.0, and the smaller the input (more negative), the closer the output to -1.0. Mathematically it can be represented as: The following are some of the benefits of using this activation function: We can simply map the output values as strongly negative, neutral, or very positive because the tanh activation function's output is Zero centred. Because its values range from -1 to, it's commonly utilised in hidden layers of neural networks. As a result, the hidden layer's mean is 0 or extremely close to it. It aids in data centering and makes learning the next layer much simpler. To understand the tanh activation function's limits, look at its gradient. As you can see, it, like the sigmoid activation function, has the problem of vanishing gradi
Read more

Unveiling the Hidden Gems: Exploring Data Mining Functionality

 Introduction:  In today's data-driven world, where information is abundant but insights are hidden, data mining functionality emerges as a powerful tool to unlock the hidden potential of vast datasets. By applying advanced algorithms and techniques, data mining allows us to extract valuable knowledge, patterns, and relationships from complex data. In this blog, we will delve into the functionality of data mining, uncovering its remarkable capabilities and highlighting its significance in various domains. Extracting Hidden Patterns: Data mining enables us to unearth hidden patterns and relationships within large datasets. By employing algorithms like association rule mining, we can discover interesting associations between different variables. These patterns can provide valuable insights for businesses, helping them understand customer behavior, market trends, and make informed decisions. Predictive Analytics: One of the key functionalities of data mining is predictive analytics
Read more