fixed example: 'Neural Network Predicts FM Params from Audio Analysis'

release-packaging/Examples/Guides/Neural Network Predicts FM Params from Audio Analysis.scd

@@ -47,10 +47,7 @@ s.waitForBoot{
 				});
 			});
 		};
-		var open_mlp = {
+		var display_mlp_params = {
-			arg path;
-			// nn.prGetParams.postln;
-			nn.read(path,{
 			var params = nn.prGetParams;
 			var n_layers = params[1];
 			var layers_string = "";
@@ -77,7 +74,6 @@ s.waitForBoot{
 				momentum_nb.value_(nn.momentum);
 				batchSize_nb.value_(nn.batchSize);*/
 			};
-			});
 		};
 
 		~in_bus = Bus.audio(s);
@@ -256,20 +252,39 @@ s.waitForBoot{
 		win.view.decorator.nextLine;
 
 		Button(win,Rect(0,0,100,20))
-		.states_([["Save MLP"]])
+		.states_([["Save"]])
 		.action_{
 			Dialog.savePanel({
 				arg path;
-				nn.write(path);
+				nn.dump{
+					arg mlp_dict;
+					scaler_params.dump{
+						arg scaler_params_dict;
+						scaler_mfcc.dump{
+							arg scaler_mfcc_dict;
+							var dict = Dictionary.new;
+							dict['mlp'] = mlp_dict;
+							dict['scaler_params'] = scaler_params_dict;
+							dict['scaler_mfcc'] = scaler_mfcc_dict;
+
+							dict.writeArchive(path);
+						};
+					};
+				};
+
 			});
 		};
 
 		Button(win,Rect(0,0,100,20))
-		.states_([["Open MLP"]])
+		.states_([["Open"]])
 		.action_{
 			Dialog.openPanel({
 				arg path;
-				open_mlp.(path);
+				var dict = Object.readArchive(path);
+
+				nn.load(dict['mlp'].postln,{display_mlp_params.value});
+				scaler_params.load(dict['scaler_params'].postln);
+				scaler_mfcc.load(dict['scaler_mfcc'].postln);
 			});
 		};
 
@@ -299,15 +314,6 @@ s.waitForBoot{
 
 		statsWinSl.valueAction_(0.0);
 
-		/*		100.do{
-		var cfreq = exprand(20,20000);
-		var mfreq = exprand(20,20000);
-		var index = rrand(0.0,20);
-		parambuf.setn(0,[cfreq,mfreq,index]);
-		0.2.wait;
-		add_point.value;
-		0.05.wait;
-		};*/
 		40.do{
 			var cfreq = exprand(100.0,1000.0);
 			var mfreq = exprand(100.0,min(cfreq,500.0));