From 43b757efdbf93b7bcbef384d90ea60345b107da0 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Tue, 25 Apr 2023 15:51:32 +0200
Subject: [PATCH] BNN optimization.
---
pes_to_spec/bnn.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/pes_to_spec/bnn.py b/pes_to_spec/bnn.py
index aaf8509..cf057e6 100644
--- a/pes_to_spec/bnn.py
+++ b/pes_to_spec/bnn.py
@@ -63,7 +63,7 @@ class BNN(nn.Module):
"""
def __init__(self, input_dimension: int=1, output_dimension: int=1):
super(BNN, self).__init__()
- hidden_dimension = 100
+ hidden_dimension = 30
# controls the aleatoric uncertainty
self.log_isigma2 = nn.Parameter(-torch.ones(1)*np.log(0.1**2), requires_grad=True)
# controls the weight hyperprior
@@ -82,8 +82,8 @@ class BNN(nn.Module):
# and the only regularization is to prevent the weights from becoming > 18 + 3 sqrt(var) ~= 50, making this a very loose regularization.
# An alternative would be to set the (alpha, beta) both to very low values, whichmakes the hyper prior become closer to the non-informative Jeffrey's prior.
# Using this alternative (ie: (0.1, 0.1) for the weights' hyper prior) leads to very large lambda and numerical issues with the fit.
- self.alpha_lambda = 0.1
- self.beta_lambda = 0.1
+ self.alpha_lambda = 0.001
+ self.beta_lambda = 0.001
# Hyperprior choice on the likelihood noise level:
# The likelihood noise level is controlled by sigma in the likelihood and it should be allowed to be very broad, but different
@@ -92,8 +92,8 @@ class BNN(nn.Module):
# Making both alpha and beta small makes the gamma distribution closer to the Jeffey's prior, which makes it non-informative
# This seems to lead to a larger training time, though.
# Since, after standardization, we know to expect the variance to be of order (1), we can select also alpha and beta leading to high variance in this range
- self.alpha_sigma = 0.1
- self.beta_sigma = 0.1
+ self.alpha_sigma = 0.001
+ self.beta_sigma = 0.001
self.model = nn.Sequential(
bnn.BayesLinear(prior_mu=0.0,
@@ -201,10 +201,10 @@ class BNNModel(RegressorMixin, BaseEstimator):
self.model = BNN(X.shape[1], y.shape[1])
# prepare data loader
- B = 100
+ B = 200
loader = DataLoader(ds,
batch_size=B,
- num_workers=5,
+ num_workers=32,
shuffle=True,
#pin_memory=True,
drop_last=True,
--
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