[faces_sub_image] - add --load_model and --do_train CLI args

Reconstruction always runs. Default: load saved weights, skip training.

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

src/tests/test_faces_sub_image.py

@@ -146,28 +146,39 @@ def show_image_reconstruction(model: TestModel, img_path: str):
 
 
 if __name__ == '__main__':
+	from argparse import ArgumentParser
+	ap = ArgumentParser()
+	ap.add_argument('--load_model', type=lambda s: s.lower() != 'false', default=True,
+	                help='Load saved model weights (default: true)')
+	ap.add_argument('--do_train',   type=lambda s: s.lower() != 'false', default=False,
+	                help='Train the model (default: false)')
+	args = ap.parse_args()
+
 	prj_name = 'faces_sub_image'
 	work_dir = 'results'
 
 	model = TestModel(prj_name, work_dir)
 	model.init(0.001)
-	model.load()
 
-	print(f'Loading patches from {N_IMAGES} images...')
+	if args.load_model:
-	train_patches = load_patches(DATA_DIR, N_IMAGES)
+		model.load()
-	print(f'Training on {train_patches.shape[0]} patches ({N_VIS}-dim each)')
 
-	model.train(train_patches)
+	if args.do_train:
-	model.save()
+		print(f'Loading patches from {N_IMAGES} images...')
+		train_patches = load_patches(DATA_DIR, N_IMAGES)
+		print(f'Training on {train_patches.shape[0]} patches ({N_VIS}-dim each)')
+		model.train(train_patches)
+		model.save()
 
 	# Show learned weight filters
 	show_filters(model.unit1)
 
 	# Show patch-level reconstructions on held-out images
+	print('Loading test patches...')
 	test_patches = load_patches(DATA_DIR, n_images=N_IMAGES + 10)
-	test_patches = test_patches[train_patches.shape[0]:]     # use only unseen patches
+	test_patches = test_patches[N_IMAGES * 80:]     # approximate held-out offset
 	if test_patches.shape[0] < 10:
-		test_patches = train_patches
+		test_patches = load_patches(DATA_DIR, n_images=10)
 	show_reconstructions(model, test_patches)
 
 	# Show full image reconstruction