jml

Yet Another Java ML Lib

Java ML library made for educational purposes mostly(no optimization, threads, gpu, etc) to study and practise ml theory things for people with strong java background - for those who find it difficult to study ml concepts(f.e. lin algebra) and python concepts(f.e. numpy) simultaneously - like me.

Made with pure java math without any libs except lombok, slf4j and junit, code organization is more common for java project with builders, interfaces, hierarchy and so on - so can be easily extended with new optimization or regularization technics.

MNIST

MNIST - is a database of hand-written digits. With 10 simple classes in java we can learn to determine digit on photo with 90% acccuracy within 15 minutes learning on one CPU. Kaggle competiton https://www.kaggle.com/c/digit-recognizer/leaderboard

Content

• Math
• Train
• Weight initializations
• Loss functions
• Layers
• Activation Functions
• Training Methods
• Optimizations
• Regularizations

With default settings:

From MNISTest.learnWithDefaultSettings

DataSet mnist = DataSets.MNIST(Paths.get("/tmp/mnist"));

Model model = Models.linear(784, 30, 10)
    .resultFunction(ResultFunctions.MAX_INDEX)
    .metrics(Metrics.ACCURACY)
    .build();

model.train(mnist);

Path path = Paths.get("/tmp/mnist_model");

model.save(path);
Model modelLoaded = Models.load(path);
        
logger.debug("X1: " + M.asPixels(M.to(mnist.train.x[1], 28, 28)));
logger.debug("Expected answer: " + modelLoaded.resultFunction.apply(mnist.train.y[1]));
logger.debug("Model answer: " + modelLoaded.evaluate(mnist.train.x[1]));

Output

2018-12-19 14:12:05:926 +0300 [main] INFO Epoch 0 train accuracy 26,893 % test accuracy 26,810 % MSE: 12,620
...
2018-12-19 14:41:55:975 +0300 [main] INFO Epoch 88 train accuracy 88,543 % test accuracy 88,940 % MSE: 1,954

2018-12-19 14:47:27:645 +0300 [main] DEBUG X1:                             
              |                            |
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              |                            |
              |               .xXx.        |
              |              .XXXXX        |
              |             .XXXXXX..      |
              |           ..XXXXX.XXx      |
              |           xXXXXXXxXXx      |
              |          .XXXXxXX..Xx      |
              |         .XXXX..x.  XX.     |
              |        .xXXX.      XXx     |
              |       .XXX...      XXX     |
              |       .XX.         XXX     |
              |       XXX          XXX     |
              |      .XXx          XXx     |
              |      .XX.        .xXX.     |
              |      .XX        .xXX.      |
              |      .Xx       .xXX        |
              |      xXX      xXXx         |
              |      .XXx...XXXXx.         |
              |      .XXXXXXXXXx           |
              |      .XXXXXXXx             |
              |       .xXXXx.              |
              |                            |
              |                            |
              |                            |
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2018-12-19 14:47:27:646 +0300 [main] DEBUG Expected answer: 0.0
2018-12-19 14:47:27:662 +0300 [main] DEBUG Model answer: 0.0