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Issue #25 StateMachineDesigner 2nd level cluster computation, Crossva…
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…lidation element, Regression noncritical bugfix
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@okozelsk
okozelsk committed Nov 29, 2020
1 parent 333e4d6 commit fbab7d7
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Showing 19 changed files with 529 additions and 427 deletions.
8 changes: 4 additions & 4 deletions Demo/DemoConsoleApp/Examples/LibrasClassificationESNDesigner.cs
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@@ -1,7 +1,7 @@
using System;
using RCNet.Neural.Activation;
using RCNet.Neural.Data.Coders.AnalogToSpiking;
using RCNet.Neural.Data.Filter;
using RCNet.Neural.Network.NonRecurrent;
using RCNet.Neural.Network.SM;
using RCNet.Neural.Network.SM.Preprocessing;
using RCNet.Neural.Network.SM.Preprocessing.Input;
Expand Down Expand Up @@ -43,10 +43,10 @@ public void Run()
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 5, 400),
0.0825d,
1,
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, CrossvalidationSettings.AutoFolds, 1),
StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 5, 400),
"Hand movement",
new NetworkClusterSecondLevelCompSettings(new CrossvalidationSettings(0.25d, CrossvalidationSettings.AutoFolds, 2), StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 1, 5, 400)),
"curved swing",
"horizontal swing",
"vertical swing",
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7 changes: 4 additions & 3 deletions Demo/DemoConsoleApp/Examples/LibrasClassificationLSMDesigner.cs
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Expand Up @@ -2,6 +2,7 @@
using RCNet.Neural.Activation;
using RCNet.Neural.Data.Coders.AnalogToSpiking;
using RCNet.Neural.Data.Filter;
using RCNet.Neural.Network.NonRecurrent;
using RCNet.Neural.Network.SM;
using RCNet.Neural.Network.SM.Preprocessing;
using RCNet.Neural.Network.SM.Preprocessing.Input;
Expand Down Expand Up @@ -95,10 +96,10 @@ public void Run(InputEncoder.SpikingInputEncodingRegime spikesEncodingRegime)
}

//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 2, 5, 400),
0.0825d,
1,
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, 0, 1),
StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 2, 5, 400),
"Hand movement",
null,
"curved swing",
"horizontal swing",
"vertical swing",
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10 changes: 7 additions & 3 deletions Demo/DemoConsoleApp/Examples/LibrasClassificationNPBypassedDesigner.cs
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Expand Up @@ -2,6 +2,7 @@
using RCNet.Neural.Activation;
using RCNet.Neural.Data.Coders.AnalogToSpiking;
using RCNet.Neural.Data.Filter;
using RCNet.Neural.Network.NonRecurrent;
using RCNet.Neural.Network.SM;
using RCNet.Neural.Network.SM.Preprocessing;
using RCNet.Neural.Network.SM.Preprocessing.Input;
Expand Down Expand Up @@ -37,10 +38,13 @@ public void Run()
{
//Create StateMachine configuration
//Simplified readout layer configuration using FF-network having 2 hidden layers as the classifier
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 2, 5, 400),
0.0825d,
1,
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, 0, 1),
StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 2, 5, 400),
"Hand movement",
new NetworkClusterSecondLevelCompSettings(new CrossvalidationSettings(0.25d, CrossvalidationSettings.AutoFolds, 2),
StateMachineDesigner.CreateMultiLayerRegrNet(10, new LeakyReLUSettings(), 1, 5, 400),
TrainedNetworkCluster.SecondLevelCompMode.SecondLevelOutputOnly
),
"curved swing",
"horizontal swing",
"vertical swing",
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7 changes: 4 additions & 3 deletions Demo/DemoConsoleApp/Examples/TTOOForecastDesigner.cs
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Expand Up @@ -2,6 +2,7 @@
using RCNet.Neural.Activation;
using RCNet.Neural.Data.Coders.AnalogToSpiking;
using RCNet.Neural.Data.Filter;
using RCNet.Neural.Network.NonRecurrent;
using RCNet.Neural.Network.SM;
using RCNet.Neural.Network.SM.Preprocessing.Input;
using RCNet.Neural.Network.SM.Preprocessing.Neuron.Predictor;
Expand Down Expand Up @@ -33,9 +34,9 @@ public void Run()
new ExternalFieldSettings("Adj Close", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateForecastReadoutCfg(StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 2, 1000),
0.1d,
1,
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateForecastReadoutCfg(new CrossvalidationSettings(0.1d, 0, 1),
StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 2, 1000),
null,
"High",
"Low"
);
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11 changes: 5 additions & 6 deletions Demo/DemoConsoleApp/Examples/TTOOForecastFromScratch.cs
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Expand Up @@ -4,6 +4,7 @@
using RCNet.Neural.Activation;
using RCNet.Neural.Data.Coders.AnalogToSpiking;
using RCNet.Neural.Data.Filter;
using RCNet.Neural.Network.NonRecurrent;
using RCNet.Neural.Network.NonRecurrent.FF;
using RCNet.Neural.Network.SM;
using RCNet.Neural.Network.SM.Preprocessing;
Expand Down Expand Up @@ -238,10 +239,10 @@ NeuralPreprocessorSettings CreatePreprocessorCfg(string resInstName, string resS
/// <summary>
/// Creates readout layer configuration
/// </summary>
/// <param name="testDataRatio">Specifies what part of available data to be used as test data</param>
/// <param name="foldDataRatio">Specifies what part of available data to be used as the fold data</param>
/// <param name="numOfAttempts">Number of regression attempts. Each readout network will try to learn numOfAttempts times</param>
/// <param name="numOfEpochs">Number of training epochs within an attempt</param>
ReadoutLayerSettings CreateReadoutLayerCfg(double testDataRatio, int numOfAttempts, int numOfEpochs)
ReadoutLayerSettings CreateReadoutLayerCfg(double foldDataRatio, int numOfAttempts, int numOfEpochs)
{
//For each output field we will use prediction of two networks
//First network having only Identity output neuron and associated the resilient back propagation trainer
Expand All @@ -262,12 +263,10 @@ ReadoutLayerSettings CreateReadoutLayerCfg(double testDataRatio, int numOfAttemp
ReadoutUnitSettings highReadoutUnitCfg = new ReadoutUnitSettings("High", new ForecastTaskSettings(new RealFeatureFilterSettings()));
ReadoutUnitSettings lowReadoutUnitCfg = new ReadoutUnitSettings("Low", new ForecastTaskSettings(new RealFeatureFilterSettings()));
//Create readout layer configuration
ReadoutLayerSettings readoutLayerCfg = new ReadoutLayerSettings(new ReadoutUnitsSettings(highReadoutUnitCfg,
ReadoutLayerSettings readoutLayerCfg = new ReadoutLayerSettings(new CrossvalidationSettings(foldDataRatio),
new ReadoutUnitsSettings(highReadoutUnitCfg,
lowReadoutUnitCfg
),
testDataRatio,
ReadoutLayerSettings.AutoFolds,
ReadoutLayerSettings.DefaultRepetitions,
defaultNetworksCfg
);
return readoutLayerCfg;
Expand Down
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