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nmiano1111/ToyNet.scala

Last active August 29, 2015 14:26
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Revisions

  1. nmiano1111 revised this gist Aug 4, 2015. No changes.
  2. nmiano1111 revised this gist Aug 4, 2015. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion ToyNet.scala
    View file
    Original file line number Diff line number Diff line change
    @@ -6,7 +6,7 @@ import org.nd4j.linalg.ops.transforms.Transforms

    /**
    * A very simple neural network implementation using Scala and
    * ND4J.
    * ND4J. The network is trained to recognize valid xors.
    */
    object ToyNet {

  3. nmiano1111 revised this gist Aug 4, 2015. 1 changed file with 3 additions and 2 deletions.
    5 changes: 3 additions & 2 deletions ToyNet.scala
    View file
    Original file line number Diff line number Diff line change
    @@ -5,9 +5,10 @@ import org.nd4j.linalg.lossfunctions.LossFunctions
    import org.nd4j.linalg.ops.transforms.Transforms

    /**
    * Created by nmiano on 7/27/15.
    * A very simple neural network implementation using Scala and
    * ND4J.
    */
    object App {
    object ToyNet {

    def layer(rows: Int, cols: Int): INDArray = (Nd4j.rand(rows, cols) * 2) - 1

  4. nmiano1111 created this gist Aug 4, 2015.
    69 changes: 69 additions & 0 deletions ToyNet.scala
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,69 @@
    import org.nd4j.api.linalg.DSL._
    import org.nd4j.linalg.api.ndarray.INDArray
    import org.nd4j.linalg.factory.Nd4j
    import org.nd4j.linalg.lossfunctions.LossFunctions
    import org.nd4j.linalg.ops.transforms.Transforms

    /**
    * Created by nmiano on 7/27/15.
    */
    object App {

    def layer(rows: Int, cols: Int): INDArray = (Nd4j.rand(rows, cols) * 2) - 1

    def main(args: Array[String]): Unit = {

    val X = Nd4j.create(Array[Array[Float]](
    Array[Float](0, 0, 1),
    Array[Float](0, 1, 1),
    Array[Float](1, 0, 1),
    Array[Float](1, 1, 1)))

    val y = Nd4j.create(Array[Float](0, 1, 1, 0), Array[Int](4, 1))

    val syn0 = layer(3, 4)
    val syn1 = layer(4, 1)

    //train
    (0 until 60000).foreach((j) => {
    //feed forward
    val l0 = X
    val l1 = Transforms.sigmoid(l0.dot(syn0))
    val l2 = Transforms.sigmoid(l1.dot(syn1))

    //how much did we miss the target value?
    val l2Error = y - l2

    //logging
    if (j % 10000 == 0) println("Error: " + Transforms.abs(Nd4j.mean(l2Error)))

    //in what direction is the target value?
    //were we really sure? if so, don't change too much.
    val l2Delta = l2Error * (l2 * ((l2 * (-1)) + 1))

    //how much did each l1 value contribute to the l2 error (according to the weights)?
    val l1Error = l2Delta.dot(syn1.T)

    //in what direction is the target l1?
    //were we really sure? if so, don't change too much.
    val l1Delta = l1Error * (l1 * ((l1 * (-1)) + 1))

    syn1 += l1.T.dot(l2Delta)
    syn0 += l0.T.dot(l1Delta)
    })

    //playing around with it

    val n0 = Nd4j.create(Array[Float](0, 1, 1))
    val n1 = Transforms.sigmoid(n0.dot(syn0))
    val n2 = Transforms.sigmoid(n1.dot(syn1))

    println(n2)

    val z0 = Nd4j.create(Array[Float](1, 1, 1))
    val z1 = Transforms.sigmoid(z0.dot(syn0))
    val z2 = Transforms.sigmoid(z1.dot(syn1))

    println(z2)
    }
    }