[README] - document all bug fixes in implementation notes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/tests/README.md

@@ -51,15 +51,30 @@ These expect a trained model to already exist in `results/`.
 
 ## Implementation notes
 
-### `cd_gaussian_binary` — two fixes applied (see `rbm/train.py`)
+### Bug fixes applied (see `rbm/train.py`, `rbm/matrix.py`, `rbm/label.py`)
 
-Surfaced by `test_gaussian_autoencoder.py`. The original negative phase had two bugs:
+#### `train.py` — momentum reset every epoch (critical)
 
-1. **Spurious Gaussian noise** — `h_probs_neg` was computed from `data_neg + N(0,1)` instead of `data_neg` directly. Noise in the negative hidden activations injects variance into every gradient step.
+`grad_zero()` was called inside the epoch loop, zeroing the momentum accumulator before every gradient update. For the default full-batch case (one mini-batch per epoch), momentum had zero effect — every step was just `lr * dw`. Moved `grad_zero()` outside the loop so momentum accumulates correctly across epochs. **GB-RBM reconstruction MSE dropped from ~0.22 to ~0.014 on the Gaussian blob test.**
 
-2. **Soft hidden states for reconstruction** — `data_neg` was computed from `h_probs_pos` (soft probabilities) instead of sampled binary states. Using probabilities produces a reconstruction that averages over all hidden configurations, giving an overly smooth fantasy particle that biases the negative phase toward the mean.
+#### `train.py` — `cd_gaussian_binary` negative phase (two fixes)
 
-Both are now fixed. Reconstruction MSE on the Gaussian blob test improved from ~0.225 to ~0.217. The remaining error floor (~0.22) is a model capacity limit (32 binary hidden units for 64-dimensional continuous input), not a training algorithm issue.
+1. **Spurious Gaussian noise** — `h_probs_neg` was computed from `data_neg + N(0,1)` instead of `data_neg`. Noise injected variance into every gradient step.
+2. **Soft hidden states for reconstruction** — `data_neg` was computed from `h_probs_pos` (probabilities) instead of sampled binary states, biasing the fantasy particle toward the mean.
+
+#### `train.py` — `cd_gaussian_gaussian` negative phase (same pattern)
+
+Same two bugs as `cd_gaussian_binary`: noise was added to `data_neg` before computing `h_probs_neg`, and `h_probs_pos` (mean) was used to drive the negative reconstruction instead of a Gaussian sample. Fixed to use sampled `h` for reconstruction and clean means for weight updates.
+
+#### `matrix.py` — `rms_error` wrong denominator
+
+`d_err_squared[1]` (row 1 of the matrix) was used instead of `d_err_squared.shape[1]` (column count). Dead code but now correct.
+
+#### `label.py` — CuPy compatibility and incomplete implementation
+
+- `_dec_binary` used Python's `reversed()` on a CuPy array (unsupported) — replaced with `np.flip()`.
+- `label2vec_onehot` returned a Python list instead of a stacked array — now uses `np.stack()`.
+- `vec2label_onehot` was unimplemented (returned `None`) — now uses `np.argmax()`.
 
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