status.py: add CheckpointStatus(save_fn, update_interval) — prints progress
and calls save_fn at every report interval to persist model state mid-training.
model.py: Model.train() now accepts an optional Status instance; defaults to
plain Status() if none provided.
test_faces_sub_image.py: use CheckpointStatus(model.save) during training.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
train.py: add _to_gpu() helper; convert mini-batches from CPU numpy to GPU
CuPy on the fly so large datasets can stay in RAM. Use numpy permutation for
numpy batches during shuffle. Limit status err_rms to first 5000 samples to
avoid GPU OOM on status checks.
model.py: replace np.copy() with np.array() — cupy.copy() does not convert
numpy arrays to CuPy; cupy.array() does. Fixes entity.forward() crash after
training when batch was passed as numpy.
test_faces_sub_image.py: load_patches normalizes on CPU and returns numpy,
avoiding multi-copy GPU OOM for large datasets (500 images ≈ 1.4 GB).
show_reconstructions converts numpy patches to CuPy before inference.
Add --n_images CLI arg.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Regularisation terms (L1, L2, weight_decay) were being divided by mini_batch_size
in state_adjust along with the CD gradient. The CD gradient is a batch sum so
1/N normalisation is correct; regularisation penalties are per-weight and
batch-size independent. Separating them makes l1_lambda/l2_lambda directly
interpretable regardless of batch size.
Also adds --l1_lambda CLI arg to test_faces_sub_image.py and sets num_epochs=1000.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
image.py: replace repeat/reshape with keepdims=True; guard std < 1e-8 to
prevent NaN on constant patches (previously relied on call-site pre-filtering).
test_faces_sub_image.py: load_image_patches now calls normalize() directly
instead of duplicating the per-sample normalization logic.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
GRAYSCALE flag drives N_CH (1 or 3) and N_VIS (1024 or 3072) at runtime.
_img_to_chw() handles BGR→gray or BGR→RGB conversion.
All visualisation functions use cmap='gray' when GRAYSCALE is set.
Separate model name (faces_sub_image_gray) keeps weights isolated.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace non-overlapping stride=32 with stride=16 (50% overlap) throughout.
Add assemble_overlapping() which accumulates reconstructed patches into a
float buffer and averages contributions per pixel, eliminating blocking artefacts.
Update load_image_patches to return nx_steps/ny_steps for correct reassembly.
Add start parameter to load_patches for held-out test image selection.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Extracts non-overlapping 32x32 RGB patches from face images using SubImage,
trains a GB-RBM (3072 visible, 128 hidden), and visualises learned filters,
patch reconstructions, and full image reconstruction.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
jensandClaude Sonnet 4.6