Merge branch 'clients/inter_client_comms' of https://bitbucket.org/flucoma/flucoma-supercollider into clients/inter_client_comms

release-packaging/Classes/FluidMLP.sc

@@ -5,7 +5,7 @@ FluidMLPRegressor : FluidRTDataClient {
 	const <relu = 2;
 	const <tanh = 3;
 
-	*new {|server, hidden = #[3,3] , activation = 0, outputActivation = 0, inputTap = 0, outputTap = 0,maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
+	*new {|server, hidden = #[3,3] , activation = 2, outputActivation = 0, tapIn = 0, tapOut = -1,maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
 		var hiddenCtrlLabels;
 		hidden = [hidden.size]++hidden;
 
@@ -16,8 +16,8 @@ FluidMLPRegressor : FluidRTDataClient {
 			[
 			\activation,activation,
 			\outputActivation, outputActivation,
-			\inputTap, inputTap,
-			\outputTap, outputTap,
+			\tapIn, tapIn,
+			\tapOut, tapOut,
 			\maxIter, maxIter,
 			\learnRate,learnRate,
 			\momentum, momentum,

release-packaging/HelpSource/Classes/FluidMLPClassifier.schelp

@@ -34,7 +34,7 @@ The training batch size.
 ARGUMENT:: validation
 The fraction of the DataSet size to hold back during training to validate the network against.
 
-METHOD:: identity, relu, sigmoid, tanh
+METHOD:: identity, sigmoid, relu, tanh
 A set of convinience constants for the available activation functions.
 
 INSTANCEMETHODS::

release-packaging/HelpSource/Classes/FluidMLPRegressor.schelp

@@ -20,11 +20,14 @@ 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
+ARGUMENT:: outputActivation
 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.
+ARGUMENT:: tapIn
+The layer whose input is used to predict and predictPoint. It is 0 counting, where the default of 0 is the input layer, and 1 would be the first hidden layer, and so on.
+
+ARGUMENT:: tapOut
+The layer whose output to return. It is counting from 0 as the input layer, and 1 would be the first hidden layer, and so on. The default of -1 is the last layer of the whole network.
 
 ARGUMENT:: maxIter
 The maximum number of iterations to use in training.
@@ -41,7 +44,7 @@ The training batch size.
 ARGUMENT:: validation
 The fraction of the DataSet size to hold back during training to validate the network against.
 
-METHOD:: identity, relu, sigmoid, tanh
+METHOD:: identity, sigmoid, relu, tanh
 A set of convinience constants for the available activation functions.
 
 INSTANCEMETHODS::
@@ -103,7 +106,7 @@ code::
 ~test = FluidDataSet(s,\mlp_regressor_dest);
 ~output = FluidDataSet(s,\mlp_regress_out);
 ~tmpbuf = Buffer.alloc(s,1);
-~regressor = FluidMLPRegressor(s,[2], FluidMLPRegressor.tanh, FluidMLPRegressor.tanh, 0, 1000,0.1,0.1,1,0);
+~regressor = FluidMLPRegressor(s,[2], FluidMLPRegressor.tanh, FluidMLPRegressor.tanh, maxIter: 1000, learnRate: 0.1, momentum: 0.1, batchSize: 1, validation: 0);
 )
 
 //Make source, target and test data

release-packaging/HelpSource/Classes/FluidSliceCorpus.schelp

@@ -73,6 +73,10 @@ An link::Classes/IdentityDictionary:: that details labels and start-end position
 ARGUMENT:: action
 A function that runs on complettion, will be passed the link::Classes/IdentityDictionary:: from link::#index:: as an argument.
 
+ARGUMENT:: tasks
+ANCHOR::ntasks::
+The number of parallel processing tasks to run on the server. Default 4. This should probably never be greater than the number of available CPU cores.
+
 METHOD:: index
 A link::Classes/IdentityDictionary:: containing information about the position of each discovered slice, using labels based on those passed into link::#play:: (see link::#labelling::). This dictionary copies all other entries from the source dictionary on a per-key basis (so you can store arbitary stuff in there should you wish, and it will remain oassciated with its original source chunk).