updated helpfile for MLP regressor

release-packaging/HelpSource/Classes/FluidMLPRegressor.schelp

@@ -20,10 +20,13 @@ 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
+ARGUMENT:: inputTap
+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:: outputTap
 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:: maxIter
@@ -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, 0, 0, 1000,0.1,0.1,1,0);
 )
 
 //Make source, target and test data