MLP surrogate support #68
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mihajenko
reviewed
May 13, 2024
| self.fail(f'Import failed: {e}') | ||
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| def test_initialization(self): | ||
| # Check if the NNClassifier is initialized correctly |
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Was incorrect initialization an issue?
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better safe than sorry
mihajenko
reviewed
May 13, 2024
| loss_grad_fn = jax.value_and_grad(self.forward_loss) | ||
| self.ncl = len(jnp.unique(X)) | ||
| X = jax.nn.one_hot(X, num_classes=self.ncl).reshape(X.shape[0], -1) | ||
| sample_batch = jnp.ones((1, X.shape[1])) |
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Did you test perf with other initializers? Not sure how well that behaves with a hardcoded PRNGKey.
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It shouldn't and it doesn't matter for a somewhat ok space of categorical problems we're considering here.
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To go beyond linear surrogates, mlp models are supported.