CaclHiddenGradient computed this layer's gradient as matrix_w[(outputs+1)*i + k]
against a buffer whose actual layout (one row per NEXT-layer neuron, stride
inputs+1) makes the correct read matrix_w[k*(inputs+1) + i]: the transpose for
square layers, and for the non-square boundaries this EA actually builds
(tapered stacks, the 3-neuron head) a mis-strided walk that ran past the buffer
end - garbage on OpenCL, zeroed reads on the CPU DLL, so the tiers did not even
agree with each other. Every gradient crossing a dense boundary on its way down
- the entire learning signal reaching the BN/conv/LSTM front ends - passed
through a fixed wrong matrix: feedback-alignment dynamics, not backprop, which
is why nets still "learned something" and this survived. The book reference
(NeuroNet_DNG) fixed this in a later article version; our kernel descended from
the earlier one. Confounds every model-based negative verdict to date.
Also in this commit, same root cause family:
- per-sample UpdateWeightsAdam (OpenCL): input for slot group j was read at
matrix_i[j] instead of matrix_i[j*4] (corrupted outer product past group 0),
and dispatch dim 1 sized on ceil(inputs/4) left the bias column unreachable
whenever inputs%4==0 - dense biases never trained on OpenCL. Rewritten as a
lane-guarded scalar loop keeping our Adam conventions (sqrt-stored v,
decoupled decay, both clamps, no sign gate). The batched accum path never had
either bug; this kernel is what SetBatchSize(1) runs - including online
continual learning on client machines, where OpenCL is the only tier.
- conv backward passed raw (int)Activation() where the kernels expect
NativeActivationCode(): NONE took the tanh branch (clamping a BN layer's
unbounded z-scores), TANH took sigmoid, PRELU took none. Dormant only because
the conv sits at layer 1 today.
- hidden-gradient dispatch over Neurons()+1 dropped to Neurons(): biases get no
backprop gradient and the extra work-item only ever read past matrix_o.
All three backends (Network.cl, WarriorCPU.cpp, WarriorDML.cpp HLSL) changed in
lockstep; DML gained an `inputs` constant to derive the row stride. New
dense_backprop_check.cpp proves the CPU kernel is central-finite-difference
consistent with the real forward kernel on 8x8, 64x3, 33x64, 5x3 (max diff
3e-9) and that all three activation branches match transcription. All 16 checks
pass. Offline math check only - the in-situ proof remains the per-layer dW/W
report on a real era.
FORCES FULL RETRAIN. Both DLLs rebuilt and redeployed to MQL5\Libraries.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
