CODEWITHSUNDEEP
machine-learningneural-networkdeep-learning
Student
Student

Reputation: 117

Multi output neural network

I am trying to create a neural network that outputs more than a binary value. The problem is the following: I have recently stumbled upon this problem on kaggle https://www.kaggle.com/c/poker-rule-induction Basically, the problem is getting the program to predict the hands the test set has to classify it from 0-9. I have already managed to solve this problem using RandomForest library. My question is how can I solve this problem using a neural network? I have already tried to follow some tutorials where you have 2 binary inputs and 1 binary output. Dataset looks like the following: enter image description here

Upvotes: 0

Views: 838

Answers (1)

Caspar Wylie
Caspar Wylie

Reputation: 2843

If I understand you correctly, you are asking how to structure your neural network output neurons for binary classification. Instead of having one value that outputs a non-binary (0-9) which doesn't really work for many reasons, you can design the outputs to produce a binary vector.

Where...

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

2 = [0,0,1,0,0,0,0,0,0,0]

3 = [0,0,0,1,0,0,0,0,0,0]

...etc

So, each item in the vector can be one of the 10 output neurons, and if that item is a 1, its position refers to its classification group. The best example of this, is the MNIST digit neural networks that also usually 10 neuron binary outputs.

Bare in mind the actual outputs will be decimals representing a probability / guess, that is close to either 0, or the 1.

This also means your target value has to be a vector that back propagates each item that corresponds to each neuron.

Upvotes: 1

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