One Takes All (softmax) network, cluster and builders. Preparation fo…

…r the ReadoutLayer redesign.
okozelsk · Dec 7, 2020 · aa671e1 · aa671e1
1 parent f9c4ea4
commit aa671e1
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Showing 19 changed files with 1,637 additions and 329 deletions.
diff --git a/Demo/DemoConsoleApp/Examples/TTOOForecastFromScratch.cs b/Demo/DemoConsoleApp/Examples/TTOOForecastFromScratch.cs
@@ -263,11 +263,11 @@ ReadoutLayerSettings CreateReadoutLayerCfg(double foldDataRatio, 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 CrossvalidationSettings(foldDataRatio),
+            ReadoutLayerSettings readoutLayerCfg = new ReadoutLayerSettings(new ClusterSettings(new CrossvalidationSettings(foldDataRatio), defaultNetworksCfg),
                                                                             new ReadoutUnitsSettings(highReadoutUnitCfg,
                                                                                                      lowReadoutUnitCfg
                                                                                                      ),
-                                                                            defaultNetworksCfg
+                                                                            null
                                                                             );
             return readoutLayerCfg;
         }

diff --git a/Demo/DemoConsoleApp/Playground.cs b/Demo/DemoConsoleApp/Playground.cs
@@ -13,6 +13,8 @@
 using RCNet.MathTools;
 using RCNet.Neural.Data.Filter;
 using RCNet.Neural.Data;
+using RCNet.Neural.Network.NonRecurrent;
+using RCNet.Neural.Network.NonRecurrent.FF;
 
 namespace Demo.DemoConsoleApp
 {
@@ -339,14 +341,94 @@ private void TestVectorBundleFolderization(string dataFile, int numOfClasses)
             return;
         }
 
+        /// <summary>
+        /// Displays information about the readout unit regression progress.
+        /// </summary>
+        /// <param name="buildingState">Current state of the regression process</param>
+        /// <param name="foundBetter">Indicates that the best readout unit was changed as a result of the performed epoch</param>
+        private void OnRegressionEpochDone(TrainedOneTakesAllNetworkBuilder.BuildingState buildingState, bool foundBetter)
+        {
+            int reportEpochsInterval = 5;
+            //Progress info
+            if (foundBetter ||
+                (buildingState.Epoch % reportEpochsInterval) == 0 ||
+                buildingState.Epoch == buildingState.MaxEpochs ||
+                (buildingState.Epoch == 1 && buildingState.RegrAttemptNumber == 1)
+                )
+            {
+                //Build progress report message
+                string progressText = buildingState.GetProgressInfo(4);
+                //Report the progress
+                if((buildingState.Epoch == 1 && buildingState.RegrAttemptNumber == 1))
+                {
+                    Console.WriteLine();
+                }
+                Console.Write("\x0d" + progressText + "          ");
+            }
+            return;
+        }
+
+
+        private void TestTrainedOneTakesAllClusterAndBuilder(string trainDataFile, string verifyDataFile, int numOfClasses, double foldDataRatio = 0.1d)
+        {
+            Console.BufferWidth = 320;
+            Console.WriteLine("One Takes All - Cluster and Cluster builder test");
+            //Load csv data and create vector bundle
+            Console.WriteLine($"Loading {trainDataFile}...");
+            CsvDataHolder trainCsvData = new CsvDataHolder(trainDataFile);
+            VectorBundle trainData = VectorBundle.Load(trainCsvData, numOfClasses);
+            Console.WriteLine($"Loading {verifyDataFile}...");
+            CsvDataHolder verifyCsvData = new CsvDataHolder(verifyDataFile);
+            VectorBundle verifyData = VectorBundle.Load(verifyCsvData, numOfClasses);
+            Console.WriteLine($"Cluster training on {trainDataFile}...");
+            //TRAINING
+            List<FeedForwardNetworkSettings> netCfgs = new List<FeedForwardNetworkSettings>();
+            netCfgs.Add(new FeedForwardNetworkSettings(new AFAnalogSoftMaxSettings(),
+                                                       new HiddenLayersSettings(new HiddenLayerSettings(30, new AFAnalogTanHSettings())),
+                                                       new RPropTrainerSettings(5, 750)
+                                                       )
+                        );
+
+            TrainedOneTakesAllNetworkClusterBuilder builder =
+                new TrainedOneTakesAllNetworkClusterBuilder("Test",
+                                                            netCfgs,
+                                                            null,
+                                                            null
+                                                            );
+            builder.RegressionEpochDone += OnRegressionEpochDone;
+            TrainedOneTakesAllNetworkCluster tc = builder.Build(trainData, new CrossvalidationSettings(foldDataRatio));
+
+            //VERIFICATION
+            Console.WriteLine();
+            Console.WriteLine();
+            Console.WriteLine($"Cluster verification on {verifyDataFile}...");
+            Console.WriteLine();
+            int numOfErrors = 0;
+            for(int i = 0; i < verifyData.InputVectorCollection.Count; i++)
+            {
+                double[] computed = tc.Compute(verifyData.InputVectorCollection[i]);
+                int computedWinnerIdx = computed.MaxIdx();
+                int realWinnerIdx = verifyData.OutputVectorCollection[i].MaxIdx();
+                if (computedWinnerIdx != realWinnerIdx) ++numOfErrors;
+                Console.Write("\x0d" + $"({i+1}/{verifyData.InputVectorCollection.Count}) Errors:{numOfErrors}...");
+            }
+            Console.WriteLine();
+            Console.WriteLine($"Accuracy {(1d - (double)numOfErrors/(double)verifyData.InputVectorCollection.Count).ToString(CultureInfo.InvariantCulture)}");
+            Console.WriteLine();
+
+            return;
+        }
+
         /// <summary>
         /// Playground's entry point
         /// </summary>
         public void Run()
         {
             Console.Clear();
             //TODO - place your code here
-            TestVectorBundleFolderization("./Data/ProximalPhalanxOutlineAgeGroup_train.csv", 3);
+            //TestVectorBundleFolderization("./Data/ProximalPhalanxOutlineAgeGroup_train.csv", 3);
+            TestTrainedOneTakesAllClusterAndBuilder("./Data/LibrasMovement_train.csv", "./Data/LibrasMovement_verify.csv", 15, 0.1d);
+            TestTrainedOneTakesAllClusterAndBuilder("./Data/ProximalPhalanxOutlineAgeGroup_train.csv", "./Data/ProximalPhalanxOutlineAgeGroup_verify.csv", 3, 0.1d);
             return;
         }