The DFA (Direct Feedback Alignment) option was never a correct implementation:
it deterministically flipped the sign of half of all gradients based on
connection index parity, causing permanent gradient ascent for those weights
and guaranteed divergence. The backward pass was also incompatible with the
OpenCL/DirectML neuron model (layer.Total() == 1). This change removes all DFA
logic, including the enum value and `DfaFeedbackSignal` method, and replaces it
with plain gradient descent in all momentum update kernels. The `optimizer`
kernel argument is retained for binary compatibility but is no longer used.
Introduce an `optimizer` parameter to UpdateWeightsMomentum, UpdateWeightsConvMomentum, and UpdateWeightsAdam kernels. When set to a non-zero value, the gradient used for weight updates is multiplied by ±1 based on the parity of the weight index, implementing a basic feedback alignment signal for experimentation. When zero, the standard gradient is used unchanged. This allows A/B testing of alternative learning signals without modifying the rest of the training pipeline.
BlendWeightsFrom now uses CObject::Type() to correctly identify neuron classes, avoiding undefined behavior when neurons are from the plain-CPU hierarchy (CNeuron, CNeuronConv, CNeuronPool, CNeuronLSTM). Weight blending for those legacy classes is also implemented.
AI/Network.mqh was 5,805 lines / 18 classes in one file. Investigation
found method implementations for several classes (CNeuronBase/Pool/Conv,
CNeuronBaseOCL) hand-interleaved across thousands of lines - not safe to
split without risky manual reassembly. But 8 classes turned out to be
genuinely self-contained (declaration + every method body physically
contiguous, and only ever depended upon, never depending on anything
declared later): CConnection/CArrayCon, CNeuron, CDirectMLMy (+ its
WarriorDML.dll/WarriorCPU.dll #import blocks), CArrayLayer,
CLayerDescription, CBufferDouble, and CNeuronConvOCL/CNeuronPoolOCL.
Extracted each verbatim, via exact line-range extraction (not manual
retyping) to eliminate transcription risk, into its own AI/*.mqh file,
included from Network.mqh at the exact point each class used to sit -
preserving original declaration order exactly. Mathematically verified
byte-for-byte: reconstructing the original file from the 7 new files'
bodies + Network.mqh's remaining segments is line-for-line identical to
the pre-split git history. Compiled clean (MetaEditor, 0 errors/0
warnings) both before and after.
The remaining tangled classes (CNeuronBase, CNeuronPool, CNeuronConv,
CNet, CNeuronLSTM, CNeuronBaseOCL, CNeuronConvOCL/PoolOCL's shared base,
CNeuronLSTMOCL, COpenCLMy) stay in Network.mqh (now ~4,450 lines) -
splitting those safely needs deliberate per-method surgery, deferred to
a future dedicated pass rather than rushed into this one.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
