Merge pull request #137 from cbovar/Remove_Unused_Regularization

Remove unused regularization parameters.

Author: cbovar

Examples/Classify2DDemo/Program.cs

@@ -52,7 +52,7 @@ private static void Classify2DDemo()
             labels.Add(1);
             var n = labels.Count;
 
-            var trainer = new SgdTrainer<double>(net) { LearningRate = 0.01, L2Decay = 0.001, BatchSize = n };
+            var trainer = new SgdTrainer<double>(net) { LearningRate = 0.01, BatchSize = n };
 
             // Training
             do

Examples/FluentMnistDemo/Program.cs

@@ -45,7 +45,6 @@ private void MnistDemo()
             {
                 LearningRate = 0.01,
                 BatchSize = 20,
-                L2Decay = 0.001,
                 Momentum = 0.9
             };
 

Examples/MinimalExample/Program.cs

@@ -39,7 +39,7 @@ private static void Main()
             // prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
             Console.WriteLine("probability that x is class 0: " + prob.Get(0)); // prints e.g. 0.50101
 
-            var trainer = new SgdTrainer(net) { LearningRate = 0.01, L2Decay = 0.001 };
+            var trainer = new SgdTrainer(net) { LearningRate = 0.01 };
             trainer.Train(x, BuilderInstance.Volume.From(new[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, new Shape(1, 1, 10, 1))); // train the network, specifying that x is class zero
 
             var prob2 = net.Forward(x);

Examples/MnistDemo/Program.cs

@@ -45,7 +45,6 @@ private void MnistDemo()
             {
                 LearningRate = 0.01,
                 BatchSize = 20,
-                L2Decay = 0.001,
                 Momentum = 0.9
             };
 

src/ConvNetSharp.Core/Layers/ConvLayer.cs

@@ -13,9 +13,6 @@ public class ConvLayer<T> : LayerBase<T>, IDotProductLayer<T> where T : struct,
 
         public ConvLayer(Dictionary<string, object> data) : base(data)
         {
-            this.L1DecayMul = Ops<T>.Zero;
-            this.L2DecayMul = Ops<T>.One;
-
             this.FilterCount = Convert.ToInt32(data["FilterCount"]);
             this.Width = Convert.ToInt32(data["Width"]);
             this.Height = Convert.ToInt32(data["Height"]);
@@ -32,9 +29,6 @@ public ConvLayer(Dictionary<string, object> data) : base(data)
 
         public ConvLayer(int width, int height, int filterCount)
         {
-            this.L1DecayMul = Ops<T>.Zero;
-            this.L2DecayMul = Ops<T>.One;
-
             this.FilterCount = filterCount;
             this.Width = width;
             this.Height = height;
@@ -54,10 +48,6 @@ public ConvLayer(int width, int height, int filterCount)
 
         public int FilterCount { get; }
 
-        public T L1DecayMul { get; set; }
-
-        public T L2DecayMul { get; set; }
-
         public int Stride
         {
             get { return this._stride; }

src/ConvNetSharp.Core/Training/SgdTrainer.cs

@@ -18,10 +18,6 @@ public SgdTrainer(INet<T> net) : base(net)
         {
         }
 
-        public T L1Decay { get; set; }
-
-        public T L2Decay { get; set; }
-
         public T Momentum { get; set; }
 
         public T LearningRate { get; set; }

src/ConvNetSharp.Performance.Tests/Program.cs

@@ -117,8 +117,6 @@ private static void ExecuteNeuralNet(
             var trainer = new SgdTrainer(net);
             trainer.LearningRate = 0.01;
             trainer.Momentum = 0.5;
-            trainer.L1Decay = 0.01;
-            trainer.L2Decay = 0.01;
             trainer.BatchSize = batchSize;
 
             for (var i = 0; i < iterations; i++)