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Showing content from https://github.com/mrdimosthenis/synapses-java below:

mrdimosthenis/synapses-java: A neural networks library for Java

A neural networks library for Java!

<!-- https://mvnrepository.com/artifact/com.github.mrdimosthenis/synapses-java -->
<dependency>
    <groupId>com.github.mrdimosthenis</groupId>
    <artifactId>synapses-java</artifactId>
    <version>1.0.0</version>
</dependency>

import com.github.mrdimosthenis.synapses.Net;

Net randNet = new Net(new int[]{2, 3, 1});

• Input layer: the first layer of the network has 2 nodes.
• Hidden layer: the second layer has 3 neurons.
• Output layer: the third layer has 1 neuron.

randNet.json();
// """
// [[{"activationF" : "sigmoid", "weights" : [-0.5,0.1,0.8]},
//   {"activationF" : "sigmoid", "weights" : [0.7,0.6,-0.1]},
//   {"activationF" : "sigmoid", "weights" : [-0.8,-0.1,-0.7]}],
//  [{"activationF" : "sigmoid", "weights" : [0.5,-0.3,-0.4,-0.5]}]]
// """

Net net = new Net(
    """
    [[{"activationF" : "sigmoid", "weights" : [-0.5,0.1,0.8]},
      {"activationF" : "sigmoid", "weights" : [0.7,0.6,-0.1]},
      {"activationF" : "sigmoid", "weights" : [-0.8,-0.1,-0.7]}],
     [{"activationF" : "sigmoid", "weights" : [0.5,-0.3,-0.4,-0.5]}]]
    """
);

net.predict(new double[]{0.2, 0.6});
// [0.49131100324012494]

net.fit(0.1, new double[]{0.2, 0.6}, new double[]{0.9});

The fit method adjusts the weights of the neural network to a single observation.

In practice, for a neural network to be fully trained, it should be fitted with multiple observations.

import com.github.mrdimosthenis.synapses.Attribute;
import com.github.mrdimosthenis.synapses.Codec;
import com.github.mrdimosthenis.synapses.Fun;
import com.github.mrdimosthenis.synapses.Stats;

Every function is efficient because its implementation is based on lazy list and all information is obtained at a single pass.

For a neural network that has huge layers, the performance can be further improved by using the parallel counterparts of predict and fit (parPredict and parFit).

new Net(new int[]{2, 3, 1}, 1000L);

We can provide a seed to create a non-random neural network. This way, we can use it for testing.

IntFunction<Fun> activationF = layerIndex ->
        switch (layerIndex) {
            case 0 -> Fun.IDENTITY;
            case 1 -> Fun.SIGMOID;
            case 2 -> Fun.LEAKY_RE_LU;
            default -> Fun.TANH;
        };

IntFunction<Double> weightInitF = _layerIndex ->
        1.0 - 2.0 * new Random().nextDouble();

Net customNet = new Net(new int[]{4, 6, 8, 5, 3}, activationF, weightInitF);

• The activationF function accepts the index of a layer and returns an activation function for its neurons.
• The weightInitF function accepts the index of a layer and returns a weight for the synapses of its neurons.

If we don't provide these functions, the activation function of all neurons is sigmoid, and the weight distribution of the synapses is normal between -1.0 and 1.0.

With its svg drawing, we can see what a neural network looks like. The color of each neuron depends on its activation function while the transparency of the synapses depends on their weight.

Supplier<Stream<double[][]>> expAndPredVals = () -> Arrays.stream(
        new double[][][]{
            {{0.0, 0.0, 1.0}, {0.0, 0.1, 0.9}},
            {{0.0, 1.0, 0.0}, {0.8, 0.2, 0.0}},
            {{1.0, 0.0, 0.0}, {0.7, 0.1, 0.2}},
            {{1.0, 0.0, 0.0}, {0.3, 0.3, 0.4}},
            {{0.0, 0.0, 1.0}, {0.2, 0.2, 0.6}}
        }
);

• Root-mean-square error

Stats.rmse(expAndPredVals.get());
// 0.6957010852370435

• Classification accuracy score

Stats.score(expAndPredVals.get());
// 0.6

• One hot encoding is a process that turns discrete attributes into a list of 0.0 and 1.0.
• Minmax normalization scales continuous attributes into values between 0.0 and 1.0.

You can use a Codec to encode and decode a data point.

Map<String, String> setosa = Map.of(
    "petal_length", "1.5",
    "petal_width", "0.1",
    "sepal_length", "4.9",
    "sepal_width", "3.1",
    "species", "setosa"
);

Map<String, String> versicolor = Map.of(
    "petal_length", "3.8",
    "petal_width", "1.1",
    "sepal_length", "5.5",
    "sepal_width", "2.4",
    "species", "versicolor"
);

Map<String, String> virginica = Map.of(
    "petal_length", "6.0",
    "petal_width", "2.2",
    "sepal_length", "5.0",
    "sepal_width", "1.5",
    "species", "virginica"
);

Stream dataset = Arrays.stream(
    new Map[]{setosa, versicolor, virginica}
);

Attribute[] attributes = {
    new Attribute("petal_length", false),
    new Attribute("petal_width", false),
    new Attribute("sepal_length", false),
    new Attribute("sepal_width", false),
    new Attribute("species", true)
};

Codec codec = new Codec(attributes, dataset);

• The first parameter is a list of pairs that define the name and the type (discrete or not) of each attribute.
• The second parameter is an iterator that contains the data points.

String codecJson = codec.json();
// codecJson: String = """[
//   {"Case" : "SerializableContinuous",
//    "Fields" : [{"key" : "petal_length","min" : 1.5,"max" : 6.0}]},
//   {"Case" : "SerializableContinuous",
//    "Fields" : [{"key" : "petal_width","min" : 0.1,"max" : 2.2}]},
//   {"Case" : "SerializableContinuous",
//    "Fields" : [{"key" : "sepal_length","min" : 4.9,"max" : 5.5}]},
//   {"Case" : "SerializableContinuous",
//    "Fields" : [{"key" : "sepal_width","min" : 1.5,"max" : 3.1}]},
//   {"Case" : "SerializableDiscrete",
//    "Fields" : [{"key" : "species","values" : ["virginica","versicolor","setosa"]}]}
// ]"""

double[] encodedSetosa = codec.encode(setosa);
// [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]

codec.decode(encodedSetosa);
// {species=setosa, sepal_width=3.1, petal_width=0.1, petal_length=1.5, sepal_length=4.9}

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