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Longtime Coder
Longtime Coder

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Minimal Neural Network in Java

#java #neural #ai

Neural Network in Java

Here is a minimal implementation of a neural network in Java. This is meant to be copy-pasted into your project, should you ever have a need to use a neural network. The code is also meant to be easy to follow and have reasonably good performance. Check it out if you're interested. Any helpful comments humbly accepted. I am not well versed in Java and would gladly like to know how to make the code more idiomatic Java.

https://github.com/dlidstrom/NeuralNetworkInAllLangs/tree/main/Java

Example usage 

var trainingData = Arrays.asList( new DataItem(new double[]{0, 0}, new double[]{Logical.xor(0, 0), Logical.xnor(0, 0), Logical.or(0, 0), Logical.and(0, 0), Logical.nor(0, 0), Logical.nand(0, 0)}), new DataItem(new double[]{0, 1}, new double[]{Logical.xor(0, 1), Logical.xnor(0, 1), Logical.or(0, 1), Logical.and(0, 1), Logical.nor(0, 1), Logical.nand(0, 1)}), new DataItem(new double[]{1, 0}, new double[]{Logical.xor(1, 0), Logical.xnor(1, 0), Logical.or(1, 0), Logical.and(1, 0), Logical.nor(1, 0), Logical.nand(1, 0)}), new DataItem(new double[]{1, 1}, new double[]{Logical.xor(1, 1), Logical.xnor(1, 1), Logical.or(1, 1), Logical.and(1, 1), Logical.nor(1, 1), Logical.nand(1, 1)}) ).toArray(new DataItem[0]); Trainer trainer = Trainer.create(2, 2, 6, rand); double lr = 1.0; int ITERS = 4000; for (int e = 0; e < ITERS; e++) { var sample = trainingData[e % trainingData.length]; trainer.train(sample.input(), sample.output(), lr); } Network network = trainer.network(); System.out.println("Result after " + ITERS + " iterations"); System.out.println(" XOR XNOR OR AND NOR NAND"); for (var sample : trainingData) { double[] pred = network.predict(sample.input()); System.out.printf( Locale.ROOT, "%d,%d = %.3f %.3f %.3f %.3f %.3f %.3f%n", (int) sample.input()[0], (int) sample.input()[1], pred[0], pred[1], pred[2], pred[3], pred[4], pred[5]); }
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This example shows how to implement a neural network that can be used to predict 6 logical functions: xor, xnor, or, and, nor, nand. It uses two input neurons, two hidden neurons, and 6 output neurons. Such a network contains 24 weights which are trained to correctly predict all 6 functions.

You can use this implementation for handwriting recognition, game playing, predictions, and more.

Here's the output of the above sample:

Result after 4000 iterations XOR XNOR OR AND NOR NAND 0,0 = 0.038 0.962 0.038 0.001 0.963 0.999 0,1 = 0.961 0.039 0.970 0.026 0.029 0.974 1,0 = 0.961 0.039 0.970 0.026 0.030 0.974 1,1 = 0.049 0.952 0.994 0.956 0.006 0.044
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You'll need to round the values to get exact answers, but this is what a neural network will output, in general.

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