Simple computationgraph to test the softmax activation

TestSoftmaxActivation

object TestSoftmaxActivation extends App {

  val height = 5
  val width = 5
  val depth = 3

  //var img = Nd4j.ones(1, depth, height, width)
  var array = Array(3.87,  -0.31,  -0.22,  -0.42,  -0.01,  5.79,  -0.66,  -0.20,  -0.80,  -0.03,  9.78,  -0.23,  -0.37,  -0.51,  -0.47,  5.76,  -0.09,  0.40,  -0.96,  -0.78,  -0.47, 4.05,  -0.44,  -0.37,  -0.22,  -0.14,  -0.34,  -0.62,  0.31,  9.78,  0.17,  0.50,  -0.37,  0.02,  -0.26,  -0.61,  0.18,  0.03,  0.67,  9.78,  -0.50,  -0.62,  3.95,  -0.71,  -0.51,  0.07,  -0.58,  9.78,  -0.45,  0.12,  9.78,  0.31,  0.42,  -0.27,  9.78,  -0.42,  -0.78,  0.56,  -0.06,  9.78,  -0.84,  -0.41,  -0.62, 4.03,  -0.56,  -0.29,  -0.46,  0.06,  9.78,  -0.27,  0.17,  -1.04,  -0.03,  -0.48,  -0.38)
  println(array.length)
  var img = Nd4j.create(Array(depth, height, width), array)

  val activation: IActivation = new ActivationSoftmax


  val activation_builder = new ActivationLayer.Builder()
    .activation(activation)
    .name("NormalizeLayer")
    .build

  private def convInit(name: String, in: Int, out: Int, kernel: Array[Int], stride: Array[Int], pad: Array[Int], bias: Double): ConvolutionLayer =
    return new ConvolutionLayer.Builder(kernel, stride, pad).name(name).nIn(in).nOut(out).biasInit(bias).build


  val conf: ComputationGraphConfiguration = new NeuralNetConfiguration.Builder()
    .graphBuilder()
    .addInputs("input")
    .addLayer("L1", convInit("cnn1", depth, 3, Array[Int](1, 1), Array[Int](1, 1), Array[Int](0, 0), 0), "input")
    .addLayer("L2", activation_builder, "L1")
    .setOutputs("L2", "L1")
    .build

  val net = new ComputationGraph(conf)

  net.init()

  val output = net.output(img)

  println(output(0).eq(output(1)))
  

}