Implement full MLP support for TensorFlowCns (SUSUWU_CNS_USE_MLP=true)

[Copilot]

SUSUWU_CNS_USE_MLP defaulted to false with two stale #if SUSUWU_CNS_IF_MLP guards and only TODO placeholders for the multi-layer graph construction, per-layer gradient descent, and per-layer weight initialization. This PR implements the full MLP path so layersOfNeurons > 1 works correctly while layersOfNeurons == 1 remains behaviorally identical to the prior single-layer code.

Key changes

cxx/ClassTensorFlowCns.hxx

• Default flipped: SUSUWU_CNS_USE_MLP false → true; comment updated to note the LOCAL_COEFFICIENTS && MLP intersection is still TODO.

• SUSUWU_TENSORFLOWCNS_INIT_SCOPE macro — dual definition (#if SUSUWU_CNS_USE_MLP / #else):
  MLP version loops over layersOfNeurons, building std::vector<tensorflow::ops::Variable> for each layer's coefficients_N / biases_N / randomCoefficients_N / randomBiases_N, chains outputs through ReLU activations for hidden layers, and names the final output "logits" via Identity. Non-MLP path is unchanged.

  // MLP path (conceptually):
  for(size_t _w = 0; _w < layersOfNeurons; ++_w) {
      coefficientsVars.push_back(Variable(root.WithOpName("coefficients_" + _w), ...));
      auto _preact = Add(MatMul(layerOutput, coefficientsVars.back()), biasesVars.back());
      layerOutput = (_w < layersOfNeurons - 1)
          ? Relu(root.WithOpName("relu_" + _w), _preact)
          : Identity(root.WithOpName("logits"), _preact);
  }

• pseudoRandomSynapses: MLP path creates per-layer layerCoeff tensors (pseudorandom) and zero-initialized layerBias tensors (matching original single-layer behavior), then runs {"assignCoefficients_0", "assignBiases_0", ...} via a dynamically built feedDict/assignOps.

• initScopeRootBack optimizer section: MLP path loops over layers, registering "optimizerCoefficients_N" / "optimizerBiases_N" ops via ApplyGradientDescent. The same final-layer scaled gradient is applied to all layers—a valid approximation for the square-connectome case where every layer has shape {neuronsPerLayer, neuronsPerLayer}. Init/assign ops ("initCoefficients_N", etc.) are similarly created per layer and run immediately after graph creation.

• setupSynapsesImpl training loop: Dynamically builds optimizerOps vector before the epoch loop; error message now includes op names for easier diagnosis.

• restructureConnectomeImpl / setupSynapsesPostProcess: Added #pragma message("TODO: ...") at the SUSUWU_CNS_LOCAL_COEFFICIENTS && SUSUWU_CNS_USE_MLP intersections (both default to false, so no behavioral change).

cxx/ClassTensorFlowCns.cxx

• dumpTo: Guarded the session->Run({}, {"coefficients", "biases"}, ...) fetch behind !SUSUWU_CNS_USE_MLP; those node names don't exist in the MLP graph. MLP path emits a #pragma message("TODO: ...") instead.

Original prompt

Goal

Implement full Multiple-Layer-Perceptron (MLP) support for class TensorFlowCns so that when -DSUSUWU_CNS_USE_MLP=true is passed, the connectome uses multiple hidden layers (controlled by layersOfNeurons). When layersOfNeurons == 1, the code should behave identically to the current Single-Layer-Perceptron.

The base commit is 13828c3e4cdab05d6c90066d813d571ca94be430 on the preview branch. That commit already fixed #if SUSUWU_CNS_IF_MLP → #if SUSUWU_CNS_USE_MLP on lines 54 and 212, but there are two remaining instances on lines 289 and 380 of cxx/ClassTensorFlowCns.hxx that still say #if SUSUWU_CNS_IF_MLP and must be changed to #if SUSUWU_CNS_USE_MLP.

Important: Coding Style Requirements

This codebase has a specific coding style. Follow these conventions precisely:

• Use the existing macro patterns (SUSUWU_CNS_IF_BIAS(...), SUSUWU_CNS_IF_MLP(THEN, ELSE), SUSUWU_ERRSTR, etc.)
• Match the existing indentation (tabs for indentation within functions, # directives at column 0 with tab indentation after #)
• Keep DimSz casts (static_cast<DimSz>(...)) for TensorShape dimensions
• Use root.WithOpName(...) for named ops following existing naming conventions
• Comments should match existing style (C-style /* ... */ for inline, // for commented-out code)
• Variable naming follows existing camelCase conventions
• The existing SUSUWU_CNS_IF_MLP(THEN, ELSE) macro (lines 55-58) is already defined to select between MLP/non-MLP code at compile time. Use it where appropriate.

Specific Changes Required in cxx/ClassTensorFlowCns.hxx

1. Fix remaining #if SUSUWU_CNS_IF_MLP preprocessor guards (lines 289, 380)

Change:

#if SUSUWU_CNS_IF_MLP

To:

#if SUSUWU_CNS_USE_MLP

on lines 289 and 380.

2. Change SUSUWU_CNS_USE_MLP default from false to true (line 52)

Change:

#	define SUSUWU_CNS_USE_MLP false

To:

#	define SUSUWU_CNS_USE_MLP true

The comment on line 52 already says "No reason to disable this (if 1 == layersOfNeurons, setupSynapses() and processTo*() act as Single-Layer-Perceptrons)".

3. Replace SUSUWU_TENSORFLOWCNS_LOGITS and SUSUWU_TENSORFLOWCNS_INIT_SCOPE (lines ~289-307)

Currently SUSUWU_TENSORFLOWCNS_INIT_SCOPE (the macro defined around lines 298-307) creates a single coefficientsVar and biasesVar. For MLP mode, it must create per-layer variables and chain them with ReLU activations between hidden layers.

For the MLP path (#if SUSUWU_CNS_USE_MLP):

The SUSUWU_TENSORFLOWCNS_INIT_SCOPE macro must be replaced/extended so that:

• It creates std::vector containers to hold per-layer tensorflow::ops::Variable nodes (coefficients and biases for each layer).
• Layer 0: shape {inputDim, neuronsPerLayer} (input → first hidden layer)
• Layers 1 through layersOfNeurons - 2: shape {neuronsPerLayer, neuronsPerLayer} (hidden → hidden)
• Last layer (layersOfNeurons - 1): shape {neuronsPerLayer, outputDim} (last hidden → output)
• Each layer's output feeds through ReLU activation (except the final layer, which produces logits directly).
• Op names should be indexed, e.g. "coefficients_0", "biases_0", "coefficients_1", etc.
• The final output is still named "logits" (for compatibility with processToImpl which fetches {"logits"}).
• "randomCoefficients_N" and "randomBiases_N" variables are created for each layer (for use by pseudoRandomSynapses and assignCoefficients_N/assignBiases_N ops).

For the non-MLP path (#else):
Keep the existing single-layer code as-is (or very close to it).

Important: The SUSUWU_TENSORFLOWCNS_LOGITS macro (lines 294-297) should be removed or made conditional — in MLP mode, logits computation is done inside the SUSUWU_TENSORFLOWCNS_INIT_SCOPE loop rather than as a separate macro.

4. Update initScopeRootBack gradient computation and optimizer (lines ~362-398)

Currently (lines 362-387), gradient computation and ApplyGradientDescent only handle a single layer. For MLP:

• Manual gradient backpropagation must chain through all layers (or, if this is too complex for manual gradients, apply the same gradient-scaling approach per layer using the final-layer gradient).
• Each layer needs its own ApplyGradientDescent op, named "optimizerCoefficients_N" / "optimizerBiases_N".
• Each layer needs initCoefficients_N, assignCoefficients_N, initBiases_N, assignBiases_N ops.
• The training loop in setupSynapsesImpl (line 470) must run all per-layer optimizer ops: {"optimizerCoefficients_0", "optimizerCoefficients_1", ..., "optimizerBiases_0", ...}.

5. Update pseudoRandomSynapses (lines ~254-287)

Currently creates a single coefficients tensor and single biases tensor, then runs {"assignCoefficients", "assignBiases"}. For MLP:

• Create per-layer tensors and fill them with pseudorandom values.
• Run `{"assignCoefficients_0", "assignBiases_0", "assignCoefficients_1", "assignBiases_1", ....

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