diff --git a/n2s3/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/features/logging/SimMongoLog.scala b/n2s3/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/features/logging/SimMongoLog.scala
index 0bd647cbff11db264b25207943b6279bf34706ef..b57383483bda861b30024c5dad8fe48a7441300c 100644
--- a/n2s3/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/features/logging/SimMongoLog.scala
+++ b/n2s3/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/features/logging/SimMongoLog.scala
@@ -211,7 +211,8 @@ class NeuronsFireLog(name : String,mongoCollection: MongoCollection[Document], o
}
override def destroy(): Unit = {
- mongoCollection.insertMany(documents).subscribe(observer)
+ if (documents != List.empty){
+ mongoCollection.insertMany(documents).subscribe(observer)}
}
}
@@ -249,7 +250,8 @@ class NeuronsPotentialLog(name : String,mongoCollection: MongoCollection[Documen
}
override def destroy(): Unit = {
- mongoCollection.insertMany(documents).subscribe(observer)
+ if (documents != List.empty){
+ mongoCollection.insertMany(documents).subscribe(observer)}
}
}
@@ -295,7 +297,8 @@ class LabelMonitoring(n2s3 : N2S3, outputNeuron : Seq[NeuronGroupRef],mongoColle
}
def destroy(): Unit = {
- mongoCollection.insertMany(documents).subscribe(observer)
+ if (documents != List.empty){
+ mongoCollection.insertMany(documents).subscribe(observer)}
}
}
@@ -402,7 +405,8 @@ class SynapticWeight(list : Seq[Seq[Seq[ConnectionPath]]], refreshRate : Int = 1
}
override def destroy(): Unit = {
- mongoCollection.insertMany(documents).subscribe(observer)
+ if (documents != List.empty){
+ mongoCollection.insertMany(documents).subscribe(observer)}
}
override def process(message: Message, sender : ActorRef): Unit = {
diff --git a/n2s3_examples/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/apps/ExampleMnist2.scala b/n2s3_examples/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/apps/ExampleMnist2.scala
index fa8a35e5cd2dcbd8552a77c80a744efc010c17c1..d98c98ff35a18f0b49b7bdb412c0078f0b56f1ca 100644
--- a/n2s3_examples/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/apps/ExampleMnist2.scala
+++ b/n2s3_examples/src/main/scala/fr/univ_lille/cristal/emeraude/n2s3/apps/ExampleMnist2.scala
@@ -59,8 +59,8 @@ object ExampleMnist2 extends App {
var logger: SimMongoLog = null
var log = true
var prune = false
- var globalIteration = 6
- var sizeChunk = 10000
+ var globalIteration = 1
+ var sizeChunk = 60000
for(x <- 1 to 1) {
implicit val timeout = Config.longTimeout
@@ -102,14 +102,20 @@ object ExampleMnist2 extends App {
var stream: MnistFileInputStreamP = null
//inputStream.append(stream)
- val unsupervisedLayer = n2s3.createNeuronGroup()
+ val unsupervisedLayer1 = n2s3.createNeuronGroup()
.setIdentifier("Layer1")
.setNumberOfNeurons(20)
.setNeuronModel(LIF, Seq(
(MembranePotentialThreshold, 35 millivolts)))
+
+ val unsupervisedLayer2 = n2s3.createNeuronGroup()
+ .setIdentifier("Layer2")
+ .setNumberOfNeurons(10)
+ .setNeuronModel(LIF, Seq(
+ (MembranePotentialThreshold, 35 millivolts)))
- inputLayer.connectTo(unsupervisedLayer, new FullConnection(() => new SimplifiedSTDP()))
- unsupervisedLayer.connectTo(unsupervisedLayer, new FullConnection(() => new InhibitorySynapse()))
+ inputLayer.connectTo(unsupervisedLayer1, new FullConnection(() => new SimplifiedSTDP()))
+ var Layer1WTAconnection = unsupervisedLayer1.connectTo(unsupervisedLayer1, new FullConnection(() => new InhibitorySynapse()))
n2s3.create()
@@ -117,34 +123,23 @@ object ExampleMnist2 extends App {
inputLayer.neurons.toList.foreach(n => {
CTotal += n.group.connections.length
})
- CTotal = CTotal * unsupervisedLayer.neurons.size
+ CTotal = CTotal * unsupervisedLayer1.neurons.size
CRmaining = CTotal
- //showHeatMap(inputLayer,unsupervisedLayer)
- SynapsesIndex(inputLayer, unsupervisedLayer)
+ //showHeatMap(inputLayer,unsupervisedLayer1)
+ SynapsesIndexInput(inputLayer, unsupervisedLayer1)
SimTime = formatter.format(new Date())
folderName = SimName.split("-")(0) + " [" + x + "] " + SimTime
new File(folderName).mkdir()
- //println("Start loading "+System.currentTimeMillis())
- //NetworkLoading.from(n2s3,"1600N/1600N-1 Arch 6 cropped")
- //println("Done loading "+System.currentTimeMillis())
- //NetworkLoading.fromWithDefaultWeights(n2s3,"100N [1] 03-02-2020 23:50:43/100N-1 Archce
- // 6 cropped","100N [1] 03-02-2020 23:50:43/100N-1 Arch 6 cropped",loadRandomWeights = false,binary = false)
- saveCroppedSynapses(unsupervisedLayer, "Initial")
- NetworkSaving.save(n2s3, croppedCnx, SynapseIndex, folderName + "/" + SimName + " Arch")
-
- // ImageSynapsesWeight.save(unsupervisedLayer.neurons.map(n => (n.getNetworkAddress, 128, 128)), 4, 1, "freeway.layer1.png")
-
- //
- val inputToClassifierIndex = new ConnectionIndex(inputLayer, unsupervisedLayer)
- var listOfConnexions = for (outputIndex <- 0 until unsupervisedLayer.shape.getNumberOfPoints) yield {
+ var inputToClassifierIndex = new ConnectionIndex(inputLayer, unsupervisedLayer1)
+ var listOfConnexions = for (outputIndex <- 0 until unsupervisedLayer1.shape.getNumberOfPoints) yield {
for (inputX <- 0 until InputMnist.shape.dimensions(0)) yield {
for {
inputY <- 0 until InputMnist.shape.dimensions(1)
input = inputLayer.getNeuronPathAt(inputX, inputY)
- output = unsupervisedLayer.getNeuronPathAt(outputIndex)
+ output = unsupervisedLayer1.getNeuronPathAt(outputIndex)
if inputToClassifierIndex.getConnectionsBetween(input, output).nonEmpty
} yield inputToClassifierIndex.getConnectionsBetween(input, output).head
}
@@ -154,57 +149,62 @@ object ExampleMnist2 extends App {
val labelTestFile = N2S3ResourceManager.getByName("mnist-test-labels").getAbsolutePath
- while (iteration <= globalIteration) {
simTime = ""
- unsupervisedLayer.unfixNeurons()
- n2s3.first = false
- stream = InputMnist.DataParts(dataFile, labelFile, sizeChunk*(iteration-1), sizeChunk)
+ // L1
+ unsupervisedLayer1.unfixNeurons()
+ stream = InputMnist.DataParts(dataFile, labelFile, sizeChunk, sizeChunk)
inputStream.append(stream)
- stream.goto()
- println("Start Training ... " + "[" + iteration + "]")
- if (iteration == 1 && log) {
+ println("Start Training L1")
+
+ if (log) {
logger = new SimMongoLog(n2s3, listOfConnexions, SimName.split("-")(0), true, true, true, true, "MNIST", SimTime, SynapseIndex)
}
duration = System.currentTimeMillis()
- simTime = "Train " + iteration
+ simTime = "Train L1"
n2s3.runAndWait()
println(System.currentTimeMillis() - duration)
simTime = simTime + " | " + (System.currentTimeMillis() - duration) + "\n"
- if (iteration == globalIteration && log) {
+ if (log) {
logger.storeSimInfoAndDestroy()
}
- //println("saving : " + System.currentTimeMillis())
- //NetworkSaving.save(n2s3, croppedCnx, SynapseIndex, folderName + "/" + SimName + " Arch " + iteration)
- //println("end : " + System.currentTimeMillis())
- //println("saving cropped : " + System.currentTimeMillis())
- //saveCroppedSynapses(unsupervisedLayer, "[" + iteration + "]")
- //println("end : " + System.currentTimeMillis())
-
- /*
- inputStream.append(InputMnist.DataFrom(dataTestFile, labelTestFile))
+ // L2
+ unsupervisedLayer1.fixNeurons()
+ Layer1WTAconnection.disconnect()
- println("Start Testing " + "[" + iteration + "]")
-
- unsupervisedLayer.fixNeurons()
-
- n2s3.first = false
-
- benchmarkMonitor = n2s3.createBenchmarkMonitor(unsupervisedLayer)
-
- duration = System.currentTimeMillis()
- simTime = simTime + "Test " + iteration + "\n" + duration
-
- n2s3.runAndWait()
+ unsupervisedLayer1.connectTo(unsupervisedLayer2, new FullConnection(() => new SimplifiedSTDP()))
+ var Layer2WTAconnection = unsupervisedLayer2.connectTo(unsupervisedLayer2, new FullConnection(() => new InhibitorySynapse()))
+ n2s3.first = false
+ stream = InputMnist.DataParts(dataFile, labelFile, sizeChunk, sizeChunk)
+ inputStream.append(stream)
- simTime = simTime + " | " + (System.currentTimeMillis() - duration) + "\n"
+ println("Start Training L2")
+
+ var layerTolayerIndex = new ConnectionIndex(unsupervisedLayer1, unsupervisedLayer2)
+ var listOfConnexions2 = for (outputIndex <- 0 until unsupervisedLayer2.shape.getNumberOfPoints) yield {
+ for (_ <- 0 until 1) yield {
+ for {
+ inputIndex <- 0 until unsupervisedLayer1.shape.getNumberOfPoints
+ input = unsupervisedLayer1.getNeuronPathAt(inputIndex)
+ output = unsupervisedLayer2.getNeuronPathAt(outputIndex)
+ if layerTolayerIndex.getConnectionsBetween(input, output).nonEmpty
+ } yield layerTolayerIndex.getConnectionsBetween(input, output).head
+ }}
+
+ SynapsesIndex(unsupervisedLayer1, unsupervisedLayer2)
+
+ if (log) {
+ logger = new SimMongoLog(n2s3, listOfConnexions2, SimName.split("-")(0), true, true, true, true, "MNIST", SimTime, SynapseIndex)
+ }
+ duration = System.currentTimeMillis()
+ simTime = "Train L2"
+ n2s3.runAndWait()
- benchmarkMonitor.exportToHtmlView(folderName + "/" + "test " + SimName + " [" + iteration + "]" + ".html")
- benchmarkMonitor.saveCrops(folderName + "/" + "Sim " + SimName + " info a:" + alpha + " no b:" + beta, simTime, " Ratio " + Ratio)
-*/
+ println(System.currentTimeMillis() - duration)
+ simTime = simTime + " | " + (System.currentTimeMillis() - duration) + "\n"
if (prune) {
Ratio = Ratio + (alpha * (CRmaining / CTotal))
@@ -215,34 +215,33 @@ object ExampleMnist2 extends App {
// cropInput(n2s3.inputLayerRef.get, Ratio)
}
- if (iteration == globalIteration) {
- inputStream.append(InputMnist.DataFrom(dataTestFile, labelTestFile))
+ // Testing
+ inputStream.append(InputMnist.DataFrom(dataTestFile, labelTestFile))
- println("Start Testing cropped " + "[" + iteration + "]" + " with " + Ratio)
+ println("Start Testing cropped " + "[" + iteration + "]" + " with " + Ratio)
- benchmarkMonitor = n2s3.createBenchmarkMonitor(unsupervisedLayer)
+ benchmarkMonitor = n2s3.createBenchmarkMonitor(unsupervisedLayer1)
- duration = System.currentTimeMillis()
- simTime = simTime + "Test cropped " + iteration
+ duration = System.currentTimeMillis()
+ simTime = simTime + "Test cropped " + iteration
- n2s3.runAndWait()
+ n2s3.runAndWait()
- println(System.currentTimeMillis() - duration)
+ println(System.currentTimeMillis() - duration)
- simTime = simTime + " | " + (System.currentTimeMillis() - duration) + "\n"
+ simTime = simTime + " | " + (System.currentTimeMillis() - duration) + "\n"
- saveCroppedSynapses(unsupervisedLayer, "cropped " + "[" + iteration + "]" + " with " + Ratio)
- NetworkSaving.save(n2s3, croppedCnx, SynapseIndex, folderName + "/" + SimName + " Arch " + iteration + " cropped")
+ //saveCroppedSynapses(unsupervisedLayer1, "cropped " + "[" + iteration + "]" + " with " + Ratio)
+ //NetworkSaving.save(n2s3, croppedCnx, SynapseIndex, folderName + "/" + SimName + " Arch " + iteration + " cropped")
- benchmarkMonitor.exportToHtmlView(folderName + "/" + "test cropped " + SimName + " [" + iteration + "]" + ".html")
+ benchmarkMonitor.exportToHtmlView(folderName + "/" + "test cropped " + SimName + " [" + iteration + "]" + ".html")
- benchmarkMonitor.saveCrops(folderName + "/" + "Sim " + SimName + " info a:" + alpha + " b:" + beta, simTime, CropInfo + " Ratio " + Ratio)
- }
- if (iteration == globalIteration && log) {
+ //benchmarkMonitor.saveCrops(folderName + "/" + "Sim " + SimName + " info a:" + alpha + " b:" + beta, simTime, CropInfo + " Ratio " + Ratio)
+
+ if (log) {
logger.storeBenchmarkTestInfo(benchmarkMonitor)
+ logger.storeSimInfoAndDestroy()
}
- iteration += 1
- }
}
//logger.destroy()
n2s3.destroy()
@@ -288,7 +287,7 @@ object ExampleMnist2 extends App {
})
}
- def SynapsesIndex(inputL: InputNeuronGroupRef,layer : NeuronGroupRef): Unit = {
+ def SynapsesIndexInput(inputL: InputNeuronGroupRef,layer : NeuronGroupRef): Unit = {
val inputToClassifierIndex = new ConnectionIndex(inputL, layer)
var listOfConnexions = for(outputIndex <- 0 until layer.shape.getNumberOfPoints) yield {
@@ -317,6 +316,38 @@ object ExampleMnist2 extends App {
SynapseIndex = SynapseIndex.reverse
}
+ def SynapsesIndex(layer1: NeuronGroupRef,layer2 : NeuronGroupRef): Unit = {
+ val layerTolayerIndex = new ConnectionIndex(layer1, layer2)
+
+ var listOfConnexions = for (outputIndex <- 0 until layer2.shape.getNumberOfPoints) yield {
+ for (_ <- 0 until 1) yield {
+ for {
+ inputIndex <- 0 until layer1.shape.getNumberOfPoints
+ input = layer1.getNeuronPathAt(inputIndex)
+ output = layer2.getNeuronPathAt(outputIndex)
+ if layerTolayerIndex.getConnectionsBetween(input, output).nonEmpty
+ } yield layerTolayerIndex.getConnectionsBetween(input, output).head
+ }}
+
+
+ var x = -1
+ var y = 0
+ SynapseIndex = List[(Integer,Integer,Integer)]()
+
+ listOfConnexions.head.foreach(
+ cnx => {
+ cnx.toList.foreach(c => {
+ x+=1
+ SynapseIndex = SynapseIndex.::(c.connectionID,x,y)
+ })
+ y+=1
+ x = -1
+ }
+ )
+
+ SynapseIndex = SynapseIndex.reverse
+ }
+
def saveCroppedSynapses(layer : NeuronGroupRef,name : String): Unit ={