koen-dejonghe

val m: Module = new Module() {
  val fc = Linear(100, 10)
  override def forward(x: Variable): Variable = x ~> fc ~> relu
}

koen-dejonghe

package org.apache.mxnet

import java.util.concurrent.atomic.AtomicLong

import com.typesafe.scalalogging.LazyLogging

import scala.concurrent.{Await, Future}
import scala.concurrent.duration._
import scala.language.postfixOps

koen-dejonghe

package jtorch.cpu

import java.util.concurrent.atomic.AtomicLong

import com.typesafe.scalalogging.LazyLogging

import scala.concurrent.{Await, Future}
import scala.concurrent.duration._
import scala.language.postfixOps

koen-dejonghe

package scorch.examples

import botkop.numsca.Tensor
import botkop.{numsca => ns}
import scorch.autograd._
import scorch.nn.rnn.RecurrentModule
import scorch.nn.Optimizer

import scala.annotation.tailrec
import scala.io.Source

koen-dejonghe

package scorch

import scorch.autograd._
import botkop.numsca.Tensor
import botkop.{numsca => ns}
import org.scalactic.{Equality, TolerantNumerics}
import org.scalatest.{FlatSpec, Matchers}

class AutoGradSpec extends FlatSpec with Matchers {
  

koen-dejonghe

ap Love whof wamp m rove mak waizor in! shenven wor gpear I d selve for trave sp 
----
iter 3326900, loss 40.139576923598945
----
 ouc
O vow sppa'g nour love?
FhCVaxt let ard tave wak! por uppit. onoum--prroudix.
For tomn olip toor houk on trarid
Are I!
Wort deve wa?!

koen-dejonghe

ap Love whof wamp m rove mak waizor in! shenven wor gpear I d selve for trave sp 
----
iter 3326900, loss 40.139576923598945
----
 ouc
O vow sppa'g nour love?
FhCVaxt let ard tave wak! por uppit. onoum--prroudix.
For tomn olip toor houk on trarid
Are I!
Wort deve wa?!

koen-dejonghe

      case class SomeEvent(value: Long)

      val events = Source
        .tick(0 seconds, 250 millis, "")
        .zipWithIndex
        .map { case (_, l) =>
            SomeEvent(l)
        }

      val group = Flow[SomeEvent].groupedWithin(100, 500 millis) // +/- 2 events per group

koen-dejonghe

package botkop.nn.lecture3

import scala.language.postfixOps
import scala.util.Random

object SigmoidIterativeLearning extends App {

  case class LabeledData(numFish: Double, numChips: Double, numKetchup: Double, price: Double = 0.0)
  case class Weights(fish: Double, chips: Double, ketchup: Double)
  val trueWeights = Weights(0.150, 0.050, 0.100)

koen-dejonghe

import scala.language.postfixOps
import scala.util.Random

object LinearIterativeLearning extends App {

  case class LabeledData(numFish: Int, numChips: Int, numKetchup: Int, price: Double = 0.0)

  case class Weights(fish: Double, chips: Double, ketchup: Double)