fix: warn when MC dropout not injected

Author: ooples

src/PredictionModelBuilder.cs

@@ -722,6 +722,7 @@ private static T FitTemperatureFromProbabilities(Tensor<T> probabilities, Vector
     {
         var eps = numOps.FromDouble(1e-12);
         var sumTolerance = numOps.FromDouble(1e-3);
+        var uniformProbability = numOps.Divide(numOps.One, numOps.FromDouble(classes));
         var logits = new Matrix<T>(batch, classes);
         var flat = probabilities.ToVector();
 
@@ -738,23 +739,22 @@ private static T FitTemperatureFromProbabilities(Tensor<T> probabilities, Vector
                 sum = numOps.Add(sum, p);
             }
 
-            if (numOps.LessThan(sum, eps))
-            {
-                throw new ArgumentException("Temperature scaling requires per-sample probabilities with a positive sum.", nameof(probabilities));
-            }
-
-            var shouldNormalize = numOps.GreaterThan(numOps.Abs(numOps.Subtract(sum, numOps.One)), sumTolerance);
+            var fallbackToUniform = numOps.LessThan(sum, eps);
+            var shouldNormalize = !fallbackToUniform && numOps.GreaterThan(numOps.Abs(numOps.Subtract(sum, numOps.One)), sumTolerance);
 
             for (int c = 0; c < classes; c++)
             {
-                var p = flat[i * classes + c];
-                if (numOps.LessThan(p, numOps.Zero))
-                {
-                    p = numOps.Zero;
-                }
-                if (shouldNormalize)
+                var p = fallbackToUniform ? uniformProbability : flat[i * classes + c];
+                if (!fallbackToUniform)
                 {
-                    p = numOps.Divide(p, sum);
+                    if (numOps.LessThan(p, numOps.Zero))
+                    {
+                        p = numOps.Zero;
+                    }
+                    if (shouldNormalize)
+                    {
+                        p = numOps.Divide(p, sum);
+                    }
                 }
                 if (numOps.LessThan(p, eps))
                 {
@@ -861,24 +861,32 @@ private static void ApplyUncertaintyQuantificationIfConfigured(
                 "ConfigureModel(new NeuralNetworkModel<T>(...)) to enable Monte Carlo Dropout uncertainty estimation.");
         }
 
-        TryInjectMonteCarloDropoutLayers(neuralNetworkModel, options);
+        var injectedCount = TryInjectMonteCarloDropoutLayers(neuralNetworkModel, options);
+        if (injectedCount == 0)
+        {
+            System.Diagnostics.Debug.WriteLine(
+                "Warning: Monte Carlo Dropout was enabled but no dropout layers were injected automatically. " +
+                "This can happen if the network has no suitable activation layers or uses a non-standard architecture. " +
+                "Consider inserting MCDropoutLayer explicitly in your network definition.");
+        }
     }
 
-    private static void TryInjectMonteCarloDropoutLayers(
+    private static int TryInjectMonteCarloDropoutLayers(
         Models.NeuralNetworkModel<T> neuralNetworkModel,
         UncertaintyQuantificationOptions options)
     {
         var layers = neuralNetworkModel.Network.LayersReadOnly;
         if (layers.OfType<MCDropoutLayer<T>>().Any())
         {
-            return;
+            return -1;
         }
 
         if (options.MonteCarloDropoutRate <= 0 || options.MonteCarloDropoutRate >= 1)
         {
             throw new ArgumentException("MonteCarloDropoutRate must be between 0 and 1.", nameof(options));
         }
 
+        var injectedCount = 0;
         for (int i = 0; i < layers.Count - 1; i++)
         {
             if (layers[i] is not ActivationLayer<T>)
@@ -898,8 +906,11 @@ private static void TryInjectMonteCarloDropoutLayers(
 
             int? seed = options.RandomSeed.HasValue ? options.RandomSeed.Value + i : (int?)null;
             neuralNetworkModel.Network.InsertLayerIntoCollection(i + 1, new MCDropoutLayer<T>(options.MonteCarloDropoutRate, mcMode: false, randomSeed: seed));
+            injectedCount++;
             i++;
         }
+
+        return injectedCount;
     }
 
     /// <summary>