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Exercise 3 Multi classification - Softmax
The goal of this exercise is to learn to a neural network architecture for multi-class data. This is an important type of problem on which to practice with neural networks because the three class values require specialized handling. A multi-classification neural network uses as output layer a softmax layer. The softmax activation function is an extension of the sigmoid as it is designed to output the probabilities to belong to each class in a multi-class problem. This output layer has to contain as much neurons as classes in the multi-classification problem. This article explains in detail how it works. https://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/softmax
Let us assume we want to classify images and we know they contain either apples, bears, candies, eggs or dogs (extension of the example in the link above).
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Create the architecture for a multi-class neural network with the following architecture and return
print(model.summary()):- 5 inputs variables
- hidden layer 1: 16 neurons and sigmoid as activation function
- hidden layer 2: 8 neurons and sigmoid as activation function
- output layer: The number of neurons and the activation function should be adapted to this multi-classification problem.