Cian-H/symbolic_nn_tests
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Added a garbage semantic function

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Added a semantic function where the injected "knowledge" is just random
garbage. This function was written to isolate the "knowledge" component
of the other semantic functions, basically to ensure it's the matrices
and not the rest of the process that is making the difference in
training.
This commit is contained in:
Cian Hughes
2024-05-15 19:01:35 +01:00
parent e3690b0425
commit ae14a1d7c0
2 changed files with 9 additions and 0 deletions
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1
symbolic_nn_tests/__main__.py
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@@ -31,6 +31,7 @@ def main():
run_test(semantic_loss.hasline_cross_entropy, "hasline_cross_entropy") run_test(semantic_loss.hasline_cross_entropy, "hasline_cross_entropy")
run_test(semantic_loss.hasloop_cross_entropy, "hasloop_cross_entropy") run_test(semantic_loss.hasloop_cross_entropy, "hasloop_cross_entropy")
run_test(semantic_loss.multisemantic_cross_entropy, "multisemantic_cross_entropy") run_test(semantic_loss.multisemantic_cross_entropy, "multisemantic_cross_entropy")
run_test(semantic_loss.garbage_cross_entropy, "garbage_cross_entropy")
if __name__ == "__main__": if __name__ == "__main__":

8
symbolic_nn_tests/semantic_loss.py
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@@ -73,3 +73,11 @@ MULTISEMANTIC_MATRIX = SIMILARITY_MATRIX * HASLINE_MATRIX * HASLOOP_MATRIX
MULTISEMANTIC_MATRIX /= MULTISEMANTIC_MATRIX.sum() MULTISEMANTIC_MATRIX /= MULTISEMANTIC_MATRIX.sum()
multisemantic_cross_entropy = create_semantic_cross_entropy(MULTISEMANTIC_MATRIX) multisemantic_cross_entropy = create_semantic_cross_entropy(MULTISEMANTIC_MATRIX)
# NOTE: As a final test, lets make something similar to tehse but where there's no knowledge,
# just random data. This will create a benchmark for the effects of this process wothout the
# "knowledge" component
GARBAGE_MATRIX = torch.rand(10, 10).to("cuda")
GARBAGE_MATRIX /= GARBAGE_MATRIX.sum()
garbage_cross_entropy = create_semantic_cross_entropy(GARBAGE_MATRIX)