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[faces_sub_image] - add --mini_batch_size CLI arg

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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jens
2026-05-30 21:13:25 +02:00
co-authored by Claude Sonnet 4.6
parent 9911caae06
commit 68b376657c
1 changed files with 9 additions and 5 deletions
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src/tests/test_faces_sub_image.py
+9 -5
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@@ -19,13 +19,14 @@ N_IMAGES = 50
class TestModel(Model):
def __init__(self, name: str, work_dir: str = '.', l1_lambda: float = 0.0, num_epochs: int = 1000):
def __init__(self, name: str, work_dir: str = '.', l1_lambda: float = 0.0,
num_epochs: int = 1000, mini_batch_size: int = 1000):
super().__init__(name, work_dir)
self.unit1 = Entity(
(N_VIS, N_HID),
EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=num_epochs, mini_batch_size=1000,
l1_lambda=l1_lambda)
TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=num_epochs,
mini_batch_size=mini_batch_size, l1_lambda=l1_lambda)
)
def forward(self, x: Mat) -> Mat:
@@ -178,8 +179,10 @@ if __name__ == '__main__':
help='Use single-channel grayscale patches (default: false)')
ap.add_argument('--l1_lambda', type=float, default=0.0,
help='L1 regularisation strength (default: 0.0)')
ap.add_argument('--num_epochs', type=int, default=1000,
ap.add_argument('--num_epochs', type=int, default=1000,
help='Number of training epochs (default: 1000)')
ap.add_argument('--mini_batch_size', type=int, default=1000,
help='Mini-batch size (default: 1000)')
args = ap.parse_args()
if args.grayscale:
@@ -190,7 +193,8 @@ if __name__ == '__main__':
prj_name = 'faces_sub_image_gray' if args.grayscale else 'faces_sub_image'
work_dir = 'results'
model = TestModel(prj_name, work_dir, l1_lambda=args.l1_lambda, num_epochs=args.num_epochs)
model = TestModel(prj_name, work_dir, l1_lambda=args.l1_lambda,
num_epochs=args.num_epochs, mini_batch_size=args.mini_batch_size)
model.init(0.001)
if args.load_model: