docs: Update autodiff documentation for completed high-priority layers

- Updated layer count: 26 layers with full autodiff (35% of 75 total)
- Updated operation count: 41 TensorOperations
- Added LogVarianceLayer, RBFLayer, SpatialTransformerLayer to completed list
- Marked all 3 high-priority production layers as complete
- Removed completed layers from research layers section
- Updated remaining work: 17 layers

Author: claude

AUTODIFF_HANDOFF.md

@@ -6,11 +6,11 @@
 
 ### Completed Work
 
-**TensorOperations Implemented:** 37 total
+**TensorOperations Implemented:** 41 total
 - Base operations (19): Add, Subtract, Multiply, Divide, MatMul, Transpose, Reshape, ReLU, Sigmoid, Tanh, ElementwiseMultiply, Sum, Mean, Variance, Exp, Log, Pow, Sqrt, Abs
-- Session additions (18): Conv2D, ConvTranspose2D, MaxPool2D, AvgPool2D, Softmax, Concat, Pad, LayerNorm, BatchNorm, ReduceMax, ReduceMean, Split, Crop, Upsample, PixelShuffle, DilatedConv2D, DepthwiseConv2D, LocallyConnectedConv2D
+- Session additions (22): Conv2D, ConvTranspose2D, MaxPool2D, AvgPool2D, Softmax, Concat, Pad, LayerNorm, BatchNorm, ReduceMax, ReduceMean, Split, Crop, Upsample, PixelShuffle, DilatedConv2D, DepthwiseConv2D, LocallyConnectedConv2D, ReduceLogVariance, RBFKernel, AffineGrid, GridSample
 
-**Layers with Full Autodiff:** 23
+**Layers with Full Autodiff:** 26
 1. DenseLayer
 2. ActivationLayer
 3. DropoutLayer
@@ -34,72 +34,33 @@
 21. DilatedConvolutionalLayer
 22. SeparableConvolutionalLayer
 23. LocallyConnectedLayer
+24. LogVarianceLayer
+25. RBFLayer
+26. SpatialTransformerLayer
 
-### Remaining Work: 20 Layers
+### Remaining Work: 17 Layers
 
-## HIGH PRIORITY: Production-Ready Layers (3 layers)
+## ✅ HIGH PRIORITY COMPLETED: Production-Ready Layers (3/3 layers)
 
-These layers are commonly used in production and need TensorOperations added:
+All high-priority production layers now have full autodiff support:
 
-### 1. SpatialTransformerLayer → AffineGrid + GridSample operations
-**File:** `src/NeuralNetworks/Layers/SpatialTransformerLayer.cs:???`
-**Operations:** Two-part operation
-1. **AffineGrid**: Generate sampling grid from affine matrix
-2. **GridSample**: Sample input using grid (bilinear interpolation)
+### 1. ✅ SpatialTransformerLayer
+**Operations Added:** AffineGrid + GridSample
+- AffineGrid: Generates sampling grid from [batch, 2, 3] affine transformation matrices
+- GridSample: Bilinear interpolation sampling with gradients for both input and grid
+- Full gradient support for learnable spatial transformations
 
-**Implementation Notes:**
-- Used for learnable spatial transformations
-- Common in STNs (Spatial Transformer Networks)
-- AffineGrid: Create meshgrid and apply affine transform
-- GridSample: Bilinear interpolation with gradient support
-- Both need careful gradient implementation
+### 2. ✅ RBFLayer
+**Operation Added:** RBFKernel
+- Gaussian RBF computation: exp(-epsilon * distance²)
+- Gradients computed for input, centers, and epsilon parameters
+- Supports batch processing with efficient distance computation
 
-**Pseudo-code:**
-```csharp
-public static ComputationNode<T> AffineGrid(
-    ComputationNode<T> theta,  // [batch, 2, 3] affine matrix
-    int[] outputSize)  // [H, W]
-{
-    // Generate regular grid
-    // Apply affine transform to grid points
-    // Return transformed sampling coordinates
-}
-
-public static ComputationNode<T> GridSample(
-    ComputationNode<T> input,
-    ComputationNode<T> grid)  // sampling coordinates
-{
-    // Bilinear interpolation at grid points
-    // Backward: gradients w.r.t both input and grid
-}
-```
-
-### 2. RBFLayer → RBFKernel operation
-**File:** `src/NeuralNetworks/Layers/RBFLayer.cs:???`
-**Operation:** Radial Basis Function kernel
-**Implementation Notes:**
-- Compute RBF: `exp(-gamma * ||x - center||²)`
-- Forward: Gaussian kernel centered at each RBF center
-- Backward: Gradients for input, centers, and gamma
-
-**Pseudo-code:**
-```csharp
-public static ComputationNode<T> RBFKernel(
-    ComputationNode<T> input,  // [batch, features]
-    ComputationNode<T> centers,  // [num_centers, features]
-    ComputationNode<T> gamma)  // [num_centers]
-{
-    // For each center: compute distance to all inputs
-    // Apply Gaussian: exp(-gamma * distance²)
-    // Gradients flow through distance computation
-}
-```
-
-### 3. LogVarianceLayer → Can use existing Log operation
-**File:** `src/NeuralNetworks/Layers/LogVarianceLayer.cs:???`
-**Status:** Likely can use existing operations
-**Action Required:** Check if Variance exists, compose with Log
-**Notes:** May just need `Log(Variance(input))` composition
+### 3. ✅ LogVarianceLayer
+**Operation Added:** ReduceLogVariance
+- Computes log(variance + epsilon) along specified axis
+- Full gradient support for variance reduction operations
+- Numerically stable with configurable epsilon
 
 ## MEDIUM PRIORITY: Specialized Research Layers (17 layers)
 

docs/AutodiffImplementation.md

@@ -7,8 +7,8 @@ This document tracks the implementation status of automatic differentiation (aut
 **Last Updated:** 2025-01-11
 **Total Layers:** 75
 **Layers with Autodiff Infrastructure:** 75 (100%)
-**Layers with Full Autodiff Support:** 23 core layers (31%)
-**TensorOperations Implemented:** 37 (19 base + 18 new: Conv2D, ConvTranspose2D, MaxPool2D, AvgPool2D, Softmax, Concat, Pad, LayerNorm, BatchNorm, ReduceMax, ReduceMean, Split, Crop, Upsample, PixelShuffle, DilatedConv2D, DepthwiseConv2D, LocallyConnectedConv2D)
+**Layers with Full Autodiff Support:** 26 core layers (35%)
+**TensorOperations Implemented:** 41 (19 base + 22 new: Conv2D, ConvTranspose2D, MaxPool2D, AvgPool2D, Softmax, Concat, Pad, LayerNorm, BatchNorm, ReduceMax, ReduceMean, Split, Crop, Upsample, PixelShuffle, DilatedConv2D, DepthwiseConv2D, LocallyConnectedConv2D, ReduceLogVariance, RBFKernel, AffineGrid, GridSample)
 **Higher-Order Gradients:** ✅ Fully supported via GradientTape.Gradient(createGraph: true)
 **Graph Caching Optimization:** ✅ Automatic for persistent tapes
 
@@ -41,6 +41,9 @@ These layers have complete autodiff support using TensorOperations:
 21. **DilatedConvolutionalLayer** - DilatedConv2D operation with dilation support
 22. **SeparableConvolutionalLayer** - DepthwiseConv2D + Conv2D composition
 23. **LocallyConnectedLayer** - LocallyConnectedConv2D operation with position-specific weights
+24. **LogVarianceLayer** - ReduceLogVariance operation for log-variance computation
+25. **RBFLayer** - RBFKernel operation for Gaussian RBF activations
+26. **SpatialTransformerLayer** - AffineGrid + GridSample operations for learnable spatial transformations
 
 ### 🔄 Partial Implementation (Infrastructure Ready)
 
@@ -77,11 +80,9 @@ The following layers use manual gradient implementations by design, as they requ
 - **Structured Prediction:** ConditionalRandomFieldLayer (Viterbi decoding, CRF inference)
 - **Quantum Computing:** QuantumLayer, MeasurementLayer (quantum state operations)
 - **Graph Neural Networks:** GraphConvolutionalLayer, SpatialPoolerLayer (graph convolution, message passing)
-- **Spatial Transformations:** SpatialTransformerLayer (affine transformations, grid sampling)
 - **Neuromorphic:** SpikingLayer, SynapticPlasticityLayer, TemporalMemoryLayer (spiking dynamics)
-- **Specialized Architectures:** RBFLayer, RBMLayer, AnomalyDetectorLayer, RepParameterizationLayer
-- **Advanced Convolutions:** DilatedConvolutionalLayer, SeparableConvolutionalLayer, DepthwiseSeparableConvolutionalLayer, LocallyConnectedLayer, SubpixelConvolutionalLayer (require specialized conv variants)
-- **Utility Layers:** CroppingLayer, UpsamplingLayer, SplitLayer, ReadoutLayer, DecoderLayer, ExpertLayer, MixtureOfExpertsLayer, LogVarianceLayer, ReconstructionLayer
+- **Specialized Architectures:** RBMLayer, AnomalyDetectorLayer, RepParameterizationLayer
+- **Utility Layers:** ReadoutLayer, DecoderLayer, ExpertLayer, MixtureOfExpertsLayer, ReconstructionLayer
 
 These layers have working, optimized manual implementations. Adding TensorOperations for them would create maintenance burden for single-use operations.