From 4bd7de4a6f04f60f3c36d6a2f93704f6447e8402 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Wed, 16 May 2018 11:49:26 +0200
Subject: [PATCH] use ModelCheckpoint callback
---
toolkit.py | 13 ++++++++++---
1 file changed, 10 insertions(+), 3 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 01b17e7..dbec934 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -27,7 +27,7 @@ from sklearn.metrics import roc_curve, auc
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.models import model_from_json
-from keras.callbacks import History, EarlyStopping, CSVLogger
+from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint
from keras.optimizers import SGD
import keras.optimizers
@@ -157,6 +157,7 @@ class ClassificationProject(object):
optimizer_opts=None,
use_earlystopping=True,
earlystopping_opts=None,
+ use_modelcheckpoint=True,
random_seed=1234):
self.name = name
@@ -178,6 +179,7 @@ class ClassificationProject(object):
self.step_bkg = step_bkg
self.optimizer = optimizer
self.use_earlystopping = use_earlystopping
+ self.use_modelcheckpoint = use_modelcheckpoint
if optimizer_opts is None:
optimizer_opts = dict()
self.optimizer_opts = optimizer_opts
@@ -354,6 +356,10 @@ class ClassificationProject(object):
self._callbacks_list.append(self.history)
if self.use_earlystopping:
self._callbacks_list.append(EarlyStopping(**self.earlystopping_opts))
+ if self.use_modelcheckpoint:
+ self._callbacks_list.append(ModelCheckpoint(save_best_only=True,
+ verbose=True,
+ filepath=os.path.join(self.project_dir, "weights.h5")))
self._callbacks_list.append(CSVLogger(os.path.join(self.project_dir, "training.log"), append=True))
return self._callbacks_list
@@ -574,8 +580,9 @@ class ClassificationProject(object):
logger.info("Save history")
self._dump_history()
- logger.info("Save weights")
- self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
+ if not self.use_modelcheckpoint:
+ logger.info("Save weights")
+ self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
self.total_epochs += epochs
self._write_info("epochs", self.total_epochs)
--
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