From d665c504a592762c5073e4e0c882238915595774 Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 24 Jul 2018 09:46:16 +0200
Subject: [PATCH] ensure that the model weights end up in project_dir
---
toolkit.py | 15 +++++++++++----
1 file changed, 11 insertions(+), 4 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 0db064a..21dda44 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -134,9 +134,12 @@ class ClassificationProject(object):
:param earlystopping_opts: options for the keras EarlyStopping callback
- :param use_modelcheckpoint: save model weights after each epoch and don't save after no validation loss improvement
+ :param use_modelcheckpoint: save model weights after each epoch and don't save after no validation loss improvement (except if the options are set otherwise).
- :param modelcheckpoint_opts: options for the Keras ModelCheckpoint callback
+ :param modelcheckpoint_opts: options for the Keras ModelCheckpoint
+ callback. After training, the newest saved weight will be used. If
+ you change the format of the saved model weights it has to be of
+ the form "weights*.h5"
:param balance_dataset: if True, balance the dataset instead of
applying class weights. Only a fraction of the overrepresented
@@ -245,7 +248,7 @@ class ClassificationProject(object):
modelcheckpoint_opts = dict(
save_best_only=True,
verbose=True,
- filepath=os.path.join(self.project_dir, "weights.h5")
+ filepath="weights.h5"
)
self.modelcheckpoint_opts = modelcheckpoint_opts
self.random_seed = random_seed
@@ -424,7 +427,11 @@ class ClassificationProject(object):
if self.use_earlystopping:
self._callbacks_list.append(EarlyStopping(**self.earlystopping_opts))
if self.use_modelcheckpoint:
- self._callbacks_list.append(ModelCheckpoint(**self.modelcheckpoint_opts))
+ mc = ModelCheckpoint(**self.modelcheckpoint_opts)
+ self._callbacks_list.append(mc)
+ if not os.path.dirname(mc.filepath) == self.project_dir:
+ mc.filepath = os.path.join(self.project_dir, mc.filepath)
+ logger.debug("Prepending project dir to ModelCheckpoint filepath: {}".format(mc.filepath))
self._callbacks_list.append(CSVLogger(os.path.join(self.project_dir, "training.log"), append=True))
return self._callbacks_list
--
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