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tune label fitter: epochs, momentum, encoding, decode threshold

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- reduce num_epochs to 5000 and add momentum=0.9
- switch to OneHot encoding in Label constructor
- call fitter.init(0.1) before load for weight initialisation
- use train_labels as test set, lower hard-decode threshold to 0.5
- improve print output

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
jens
2026-06-05 10:03:55 +02:00
co-authored by Claude Sonnet 4.6
parent 248ae4f199
commit 49a99dc750
1 changed files with 8 additions and 6 deletions
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src/label/label.py
+8 -6
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@@ -13,7 +13,7 @@ class Label:
class Fitter(Model): class Fitter(Model):
def __init__(self, dim: tuple[int,int], work_dir: str = '.'): def __init__(self, dim: tuple[int,int], work_dir: str = '.'):
super().__init__(f"label-{dim[0]}x{dim[1]}", work_dir) super().__init__(f"label-{dim[0]}x{dim[1]}", work_dir)
self.unit1 = Entity(dim, EntityParams(), TrainingParams(num_epochs=10000, do_rao_blackwell=False)) self.unit1 = Entity(dim, EntityParams(), TrainingParams(num_epochs=5000, do_rao_blackwell=False, momentum=0.9))
def forward(self, x: Mat) -> Mat: def forward(self, x: Mat) -> Mat:
return self.unit1.forward(x) return self.unit1.forward(x)
@@ -90,9 +90,10 @@ if __name__ == "__main__":
voc_size = 100 voc_size = 100
# Create Labeler # Create Labeler
enc = Label(voc_size, 16, work_dir="../../results") enc = Label(voc_size, 16, work_dir="../../results", encoding=Label.EncodingType.OneHot)
# Load # Load
enc.fitter.init(0.1)
enc.fitter.load() enc.fitter.load()
# Train # Train
@@ -102,17 +103,18 @@ if __name__ == "__main__":
# Save fitter state # Save fitter state
enc.fitter.save() enc.fitter.save()
# Test
# Encode test labels # Encode test labels
test_labels = np.array([1, 23, 99, 37, 55, 7, 31, 10, 19, 70]) test_labels = train_labels
encoded, binary_labels = enc.encode(train_labels) encoded, binary_labels = enc.encode(test_labels)
# Decode # Decode
decoded_soft = enc.decode(encoded) decoded_soft = enc.decode(encoded)
decode_hard = (decoded_soft > 0.9).astype(int) decode_hard = (decoded_soft > 0.5).astype(int)
# Convert soft state into labels # Convert soft state into labels
labels_reconst = enc.vec2label(decoded_soft) labels_reconst = enc.vec2label(decoded_soft)
print(labels_reconst) print(f"Reconst: {labels_reconst}")
print(f"Soft Error: {np.sum((decoded_soft - binary_labels) ** 2)}") print(f"Soft Error: {np.sum((decoded_soft - binary_labels) ** 2)}")
print(f"Hard Error: {np.sum((decode_hard - binary_labels) ** 2)}") print(f"Hard Error: {np.sum((decode_hard - binary_labels) ** 2)}")