mrdimosthenis / scala-synapses   8.0.0

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A plug-and play library for neural networks written in Scala 3

Scala versions: 3.x

scala-synapses

A plug-and-play library for neural networks written in Scala 3!

Basic usage

Install synapses

libraryDependencies += "com.github.mrdimosthenis" %% "synapses" % "8.0.0"

Import the Net object

import synapses.lib.Net

Create a random neural network by providing its layer sizes

val randNet = Net(List(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.

Get the json of the random neural network

randNet.json()
// res0: String = """[
//   [{"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]}]
// ]"""

Create a neural network by providing its json

val net = 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]}]
]""")

Make a prediction

net.predict(List(0.2, 0.6))
// res1: List[Double] = List(0.49131100324012494)

Train a neural network

net.fit(
    learningRate = 0.1,
    inputValues = List(0.2, 0.6),
    expectedOutput = List(0.9)
)

The fit method returns the neural network with its weights adjusted to a single observation.

Advanced usage

Fully train a neural network

In practice, for a neural network to be fully trained, it should be fitted with multiple observations, usually by folding over an iterator.

Iterator(
    (List(0.2, 0.6), List(0.9)),
    (List(0.1, 0.8), List(0.2)),
    (List(0.5, 0.4), List(0.6))
).foldLeft(net){ case (acc, (xs, ys)) =>
    acc.fit(learningRate = 0.1, xs, ys)
}

Boost the performance

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).

Create a neural network for testing

Net(layerSizes = List(2, 3, 1), seed = 1000)

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

Define the activation functions and the weights

import scala.util.Random
import synapses.lib.Fun

def activationF(layerIndex: Int): Fun =
    layerIndex match
      case 0 => Fun.sigmoid
      case 1 => Fun.identity
      case 2 => Fun.leakyReLU
      case 3 => Fun.tanh

def weightInitF(layerIndex: Int): Double =
    (layerIndex + 1) * (1.0 - 2.0 * Random().nextDouble())

val customNet = Net(layerSizes = List(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.

Draw a neural network

customNet.svg()

Network Drawing

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.

Measure the difference between the expected and predicted values

import synapses.lib.Stats
def expAndPredVals() =
    Iterator(
      (List(0.0, 0.0, 1.0), List(0.0, 0.1, 0.9)),
      (List(0.0, 1.0, 0.0), List(0.8, 0.2, 0.0)),
      (List(1.0, 0.0, 0.0), List(0.7, 0.1, 0.2)),
      (List(1.0, 0.0, 0.0), List(0.3, 0.3, 0.4)),
      (List(0.0, 0.0, 1.0), List(0.2, 0.2, 0.6))
    )
  • Root-mean-square error
Stats.rmse(expAndPredVals())
// res6: Double = 0.6957010852370435
  • Classification accuracy score
Stats.score(expAndPredVals())
// res7: Double = 0.6

Import the Codec object

import synapses.lib.Codec
  • 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.
val setosa = Map(
  "petal_length" -> "1.5",
  "petal_width" -> "0.1",
  "sepal_length" -> "4.9",
  "sepal_width" -> "3.1",
  "species" -> "setosa"
)

val versicolor = Map(
  "petal_length" -> "3.8",
  "petal_width" -> "1.1",
  "sepal_length" -> "5.5",
  "sepal_width" -> "2.4",
  "species" -> "versicolor"
)

val virginica = Map(
  "petal_length" -> "6.0",
  "petal_width" -> "2.2",
  "sepal_length" -> "5.0",
  "sepal_width" -> "1.5",
  "species" -> "virginica"
)

def dataset() = Iterator(setosa,versicolor,virginica)

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

Create a Codec by providing the attributes and the data points

val codec = Codec(
    List(("petal_length", false),
         ("petal_width", false),
         ("sepal_length", false),
         ("sepal_width", false),
         ("species", true)),
    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.

Get the json of the codec

val 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"]}]}
// ]"""

Create a codec by providing its json

Codec(codecJson)

Encode a data point

val encodedSetosa = codec.encode(setosa)
// encodedSetosa: List[Double] = List(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0)

Decode a data point

codec.decode(encodedSetosa)
// res9: Map[String, String] = HashMap(
//   "species" -> "setosa",
//   "sepal_width" -> "3.1",
//   "petal_width" -> "0.1",
//   "petal_length" -> "1.5",
//   "sepal_length" -> "4.9"
// )

Statistics

4 Dependencies

No Dependent