From 3c3d6c505b713d69ebe62924db3899d2c706e1ad Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Mon, 19 Nov 2018 17:59:02 +0100
Subject: [PATCH] weight individual losses
---
toolkit.py | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/toolkit.py b/toolkit.py
index 3bda85e..7199468 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -184,6 +184,8 @@ class ClassificationProject(object):
:param loss: loss function name (or list of names in case of regression targets)
+ :param loss_weights: (optional) list of weights to weight the individual losses (for multiple targets)
+
:param mask_value: value that is used for non-existent entries (e.g. 4th jet pt in events with 3 jets)
:param apply_class_weight: apply a weight that scales the events such that sumw(signal) = sumw(background)
@@ -260,6 +262,7 @@ class ClassificationProject(object):
shuffle_seed=42,
balance_dataset=False,
loss='binary_crossentropy',
+ loss_weights=None,
mask_value=None,
apply_class_weight=True,
normalize_weights=True,
@@ -346,6 +349,7 @@ class ClassificationProject(object):
self.shuffle_seed = shuffle_seed
self.balance_dataset = balance_dataset
self.loss = loss
+ self.loss_weights = loss_weights
if self.regression_branches and (not isinstance(self.loss, list)):
self.loss = [self.loss]+["mean_squared_error"]*len(self.regression_branches)
@@ -759,6 +763,7 @@ class ClassificationProject(object):
np.random.seed(self.random_seed)
self._model.compile(optimizer=optimizer,
loss=self.loss,
+ loss_weights=self.loss_weights,
weighted_metrics=['accuracy']
)
np.random.set_state(rn_state)
--
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