From e43fba934b9c561cb4fd7e77db29b2a3938ff295 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Fri, 21 Apr 2023 18:02:14 +0200
Subject: [PATCH] Adjusted setup to get better optimized BNN results.
---
pes_to_spec/bnn.py | 12 ++++++------
pes_to_spec/test/offline_analysis.py | 2 +-
2 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/pes_to_spec/bnn.py b/pes_to_spec/bnn.py
index eefa812..aaf8509 100644
--- a/pes_to_spec/bnn.py
+++ b/pes_to_spec/bnn.py
@@ -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 = 3.0
- self.beta_lambda = 6.0
+ self.alpha_lambda = 0.1
+ self.beta_lambda = 0.1
# 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 = 2.0
- self.beta_sigma = 0.15
+ self.alpha_sigma = 0.1
+ self.beta_sigma = 0.1
self.model = nn.Sequential(
bnn.BayesLinear(prior_mu=0.0,
@@ -201,7 +201,7 @@ class BNNModel(RegressorMixin, BaseEstimator):
self.model = BNN(X.shape[1], y.shape[1])
# prepare data loader
- B = 5
+ B = 100
loader = DataLoader(ds,
batch_size=B,
num_workers=5,
@@ -223,7 +223,7 @@ class BNNModel(RegressorMixin, BaseEstimator):
# train
self.model.train()
- epochs = 200
+ epochs = 1000
for epoch in range(epochs):
meter = {k: AverageMeter(k, ':6.3f')
for k in ('loss', '-log(lkl)', '-log(prior)', '-log(hyper)', 'sigma', 'w.prec.')}
diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py
index 5ede953..2da28eb 100755
--- a/pes_to_spec/test/offline_analysis.py
+++ b/pes_to_spec/test/offline_analysis.py
@@ -144,7 +144,7 @@ def main():
parser.add_argument('-o', '--offset', type=int, metavar='INT', default=0, help='Train ID offset')
parser.add_argument('-c', '--xgm_cut', type=float, metavar='INTENSITY', default=500, help='XGM intensity threshold in uJ.')
parser.add_argument('-e', '--bnn', action="store_true", default=False, help='Use BNN?')
- parser.add_argument('-w', '--weight', action="store_true", default=False, help='Whether to reweight data as a function of the pulse energy to make it invariant to that.')
+ parser.add_argument('-w', '--weight', action="store_true", default=True, help='Whether to reweight data as a function of the pulse energy to make it invariant to that.')
args = parser.parse_args()
--
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