Fixing strange issues caused by the recent merge conflict

src/FitDetectors/DefaultFitDetector.cs

@@ -2,11 +2,8 @@
 
 public class DefaultFitDetector<T> : FitDetectorBase<T>
 {
-    private readonly INumericOperations<T> _numOps;
-
     public DefaultFitDetector()
     {
-        _numOps = MathHelper.GetNumericOperations<T>();
     }
 
     public override FitDetectorResult<T> DetectFit(ModelEvaluationData<T> evaluationData)

src/NeuralNetworks/ConvolutionalNeuralNetwork.cs

@@ -1,4 +1,3 @@
-
 namespace AiDotNet.NeuralNetworks;
 
 public class ConvolutionalNeuralNetwork<T> : NeuralNetworkBase<T>
@@ -8,7 +7,7 @@ public ConvolutionalNeuralNetwork(NeuralNetworkArchitecture<T> architecture) : b
         if (architecture.InputType != InputType.ThreeDimensional)
         {
             throw new ArgumentException("Convolutional Neural Network requires three-dimensional input.");
-    }
+        }
     }
 
     protected override void InitializeLayers()
@@ -23,20 +22,18 @@ protected override void InitializeLayers()
         {
             // Use default layer configuration if no layers are provided
             Layers.AddRange(LayerHelper<T>.CreateDefaultCNNLayers(Architecture));
-            }
+        }
     }
 
     public override Vector<T> Predict(Vector<T> input)
-            {
+    {
         // Convert the input Vector to a Tensor with the correct shape
         var inputShape = Architecture.GetInputShape();
         var totalSize = inputShape.Aggregate(1, (a, b) => a * b);
 
         if (input.Length != totalSize)
-                {
+        {
             throw new ArgumentException("Input vector length must match the product of input dimensions.");
-                }
-            }
         }
 
         var inputTensor = new Tensor<T>(inputShape, input);
@@ -51,7 +48,7 @@ public override Vector<T> Predict(Vector<T> input)
     public Tensor<T> Forward(Tensor<T> input)
     {
         if (!input.Shape.SequenceEqual(Architecture.GetInputShape()))
-    {
+        {
             throw new ArgumentException("Input shape does not match the expected input shape.");
         }
 
@@ -60,7 +57,8 @@ public Tensor<T> Forward(Tensor<T> input)
         {
             output = layer.Forward(output);
         }
-        return output;
+
+            return output;
         }
 
     public Tensor<T> Backward(Tensor<T> outputGradient)

src/NeuralNetworks/Layers/BatchNormalizationLayer.cs

@@ -14,7 +14,6 @@ public class BatchNormalizationLayer<T> : LayerBase<T>
     private Vector<T>? _lastVariance;
     private Vector<T>? _gammaGradient;
     private Vector<T>? _betaGradient;
-    private bool _isTraining;
 
     public override bool SupportsTraining => true;
 
@@ -27,7 +26,6 @@ public BatchNormalizationLayer(int featureSize, double epsilon = 1e-5, double mo
         _beta = new Vector<T>(featureSize);
         _runningMean = new Vector<T>(featureSize);
         _runningVariance = Vector<T>.CreateDefault(featureSize, NumOps.One);
-        _isTraining = true;
     }
 
     public override Tensor<T> Forward(Tensor<T> input)

src/NeuralNetworks/Layers/ConvLSTMLayer.cs

@@ -360,8 +360,6 @@ private Tensor<T> ApplyActivationDerivative(Tensor<T> input)
         return (dxt, dprevH, dprevC, cellGrads);
     }
 
-    private readonly SigmoidActivation<T> _sigmoidActivation = new SigmoidActivation<T>();
-
     private (Tensor<T> f, Tensor<T> i, Tensor<T> c, Tensor<T> o, Tensor<T> newC, Tensor<T> newH) ForwardStep(
     Tensor<T> xt, Tensor<T> prevH, Tensor<T> prevC)
     {

src/NeuralNetworks/Layers/DropoutLayer.cs

@@ -6,7 +6,6 @@ public class DropoutLayer<T> : LayerBase<T>
     private readonly T _scale;
     private Tensor<T>? _lastInput;
     private Tensor<T>? _dropoutMask;
-    private bool _isTraining;
 
     public override bool SupportsTraining => true;
 
@@ -18,7 +17,6 @@ public DropoutLayer(double dropoutRate = 0.5)
 
         _dropoutRate = NumOps.FromDouble(dropoutRate);
         _scale = NumOps.FromDouble(1.0 / (1.0 - dropoutRate));
-        _isTraining = true;
     }
 
     public override Tensor<T> Forward(Tensor<T> input)

src/NeuralNetworks/Layers/GaussianNoiseLayer.cs

@@ -5,8 +5,6 @@ public class GaussianNoiseLayer<T> : LayerBase<T>
     private readonly T _mean;
     private readonly T _standardDeviation;
     private Tensor<T>? _lastNoise;
-    private readonly Random _random;
-    private bool _isTraining;
 
     public override bool SupportsTraining => false;
 
@@ -18,18 +16,6 @@ public GaussianNoiseLayer(
     {
         _mean = NumOps.FromDouble(mean);
         _standardDeviation = NumOps.FromDouble(standardDeviation);
-        _random = new Random();
-        _isTraining = true;
-    }
-
-    public void SetTrainingMode(bool isTraining)
-    {
-        _isTraining = isTraining;
-    }
-
-    public bool IsTraining()
-    {
-        return _isTraining;
     }
 
     public override Tensor<T> Forward(Tensor<T> input)

src/NeuralNetworks/NeuralNetwork.cs

@@ -15,15 +15,8 @@ protected override void InitializeLayers()
             Layers.AddRange(Architecture.Layers);
             ValidateCustomLayers(Layers);
         }
-
-        for (int i = 0; i < Architecture.LayerSizes.Count - 1; i++)
+        else
         {
-            if (Architecture.CustomLayers != null && i < Architecture.CustomLayers.Count)
-            {
-                Layers.Add(Architecture.CustomLayers[i]);
-            }
-            else
-            {
             // Use default layer configuration if no layers are provided
             Layers.AddRange(LayerHelper<T>.CreateDefaultNeuralNetworkLayers(Architecture));
         }
@@ -36,6 +29,7 @@ public override Vector<T> Predict(Vector<T> input)
         {
             current = layer.Forward(Tensor<T>.FromVector(current)).ToVector();
         }
+
         return current;
     }