now with finalActivation

release-packaging/Classes/FluidMLP.sc

@@ -5,7 +5,7 @@ FluidMLPRegressor : FluidRTDataClient {
 	const <relu = 2;
 	const <tanh = 3;
 
-	*new {|server, hidden = #[3,3] , activation = 0, outputLayer = 0, maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
+	*new {|server, hidden = #[3,3] , activation = 0, finalActivation = 0, outputLayer = 0, maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
 		var hiddenCtrlLabels;
 		hidden = [hidden.size]++hidden;
 
@@ -15,6 +15,7 @@ FluidMLPRegressor : FluidRTDataClient {
 			[hiddenCtrlLabels,hidden].lace ++
 			[
 			\activation,activation,
+			\finalActivation, finalActivation,
 			\outputLayer, outputLayer,
 			\maxIter, maxIter,
 			\learnRate,learnRate,
@@ -85,9 +86,9 @@ FluidMLPClassifier : FluidRTDataClient {
 		);
 	}
 
-	predict{ |sourceDataSet, targetDataSet, action|
+	predict{ |sourceDataSet, targetLabelSet, action|
 		this.prSendMsg(\predict,
-			[sourceDataSet.asSymbol, targetDataSet.asSymbol],
+			[sourceDataSet.asSymbol, targetLabelSet.asSymbol],
 			action);
 	}
 

release-packaging/Examples/dataset/MLP-synth-control.scd

@@ -4,7 +4,7 @@ var trained = 0, entering = 0;
 var va = Array.fill(10,{0.5});
 var input = Buffer.alloc(s,2);
 var output = Buffer.alloc(s,10);
-var mlp = FluidMLPRegressor(s,[6],1,0,1000,0.1,0,1,0);
+var mlp = FluidMLPRegressor(s,[6],1,1,0,1000,0.1,0,1,0);
 var entry = 0;
 
 ~inData = FluidDataSet(s,\indata);

release-packaging/Examples/dataset/MLP-toy-example.scd

@@ -26,7 +26,7 @@ Routine{
 ~mlpHelpTarget.print
 
 // make an MLPregressor
-~mlp = FluidMLPRegressor(s, [3], FluidMLPRegressor.sigmoid, 0, 1000,0.1,0.1,1,0);//1000 epoch at a time
+~mlp = FluidMLPRegressor(s, [3], FluidMLPRegressor.sigmoid, FluidMLPRegressor.sigmoid, 0, 1000,0.1,0.1,1,0);//1000 epoch at a time
 //train on it and observe the error
 ~mlp.fit(~mlpHelpSource,~mlpHelpTarget,{|x|x.postln;});
 

release-packaging/HelpSource/Classes/FluidMLPRegressor.schelp

@@ -20,6 +20,9 @@ An link::Classes/Array:: that gives the sizes of any hidden layers in the networ
 ARGUMENT:: activation
 The activation function to use for the hidden layer units. Beware of the permitted ranges of each: relu (0->inf), sigmoid (0->1), tanh (-1,1).
 
+ARGUMENT:: finalActivation
+The activation function to use for the final layer units. Beware of the permitted ranges of each: relu (0->inf), sigmoid (0->1), tanh (-1,1).
+
 ARGUMENT:: outputLayer
 The layer whose output to return. It is negative 0 counting, where the default of 0 is the output layer, and 1 would be the last hidden layer, and so on.
 
@@ -100,7 +103,7 @@ code::
 ~test = FluidDataSet(s,\mlp_regressor_dest);
 ~output = FluidDataSet(s,\mlp_regress_out);
 ~tmpbuf = Buffer.alloc(s,1);
-~regressor = FluidMLPRegressor(s,[2], FluidMLPRegressor.tanh, 0, 1000,0.1,0.1,1,0);
+~regressor = FluidMLPRegressor(s,[2], FluidMLPRegressor.tanh, FluidMLPRegressor.tanh, 0, 1000,0.1,0.1,1,0);
 )
 
 //Make source, target and test data