From 5843d2f64b3e166a07157fbb2d845d92cf7582c2 Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Fri, 31 Jan 2020 15:17:57 +0100
Subject: [PATCH] added docs
---
src/model_modules/keras_extensions.py | 56 +++++++++++++++++++++++----
1 file changed, 49 insertions(+), 7 deletions(-)
diff --git a/src/model_modules/keras_extensions.py b/src/model_modules/keras_extensions.py
index f84f33d1..e2a4b932 100644
--- a/src/model_modules/keras_extensions.py
+++ b/src/model_modules/keras_extensions.py
@@ -12,10 +12,23 @@ from keras.callbacks import History, ModelCheckpoint
class HistoryAdvanced(History):
+ """
+ This is almost an identical clone of the original History class. The only difference is that attributes epoch and
+ history are instantiated during the init phase and not during on_train_begin. This is required to resume an already
+ started but disrupted training from an saved state. This HistoryAdvanced callback needs to be added separately as
+ additional callback. To get the full history use this object for further steps instead of the default return of
+ training methods like fit_generator().
+
+ hist = HistoryAdvanced()
+ history = model.fit_generator(generator=.... , callbacks=[hist])
+ history = hist
+
+ If training was started from beginning this class is identical to the returned history class object.
+ """
- def __init__(self, old_epoch=None, old_history=None):
- self.epoch = old_epoch or []
- self.history = old_history or {}
+ def __init__(self):
+ self.epoch = []
+ self.history = {}
super().__init__()
def on_train_begin(self, logs=None):
@@ -78,20 +91,46 @@ class LearningRateDecay(History):
class ModelCheckpointAdvanced(ModelCheckpoint):
"""
- IMPORTANT: Always add the model checkpoint advanced as last callback to properly update all tracked callbacks, e.g.
- fit_generator(callbacks=[..., <last_here>])
+ Enhance the standard ModelCheckpoint class by additional saves of given callbacks. Specify this callbacks as follow:
+
+ lr = CustomLearningRate()
+ hist = CustomHistory()
+ callbacks_name = "your_custom_path_%s.pickle"
+ callbacks = [{"callback": lr, "path": callbacks_name % "lr"},
+ {"callback": hist, "path": callbacks_name % "hist"}]
+ ckpt_callbacks = ModelCheckpointAdvanced(filepath=.... , callbacks=callbacks)
+
+ Add this ckpt_callbacks as all other additional callbacks to the callback list. IMPORTANT: Always add ckpt_callbacks
+ as last callback to properly update all tracked callbacks, e.g.
+
+ fit_generator(.... , callbacks=[lr, hist, ckpt_callbacks])
+
"""
def __init__(self, *args, **kwargs):
self.callbacks = kwargs.pop("callbacks")
super().__init__(*args, **kwargs)
def update_best(self, hist):
+ """
+ Update internal best on resuming a training process. Otherwise best is set to +/- inf depending on the
+ performance metric and the first trained model (first of the resuming training process) will always saved as
+ best model because its performance will be better than infinity. To prevent this behaviour and compare the
+ performance with the best model performance, call this method before resuming the training process.
+ :param hist: The History object from the previous (interrupted) training.
+ """
self.best = hist.history.get(self.monitor)[-1]
def update_callbacks(self, callbacks):
+ """
+ Update all stored callback objects. The argument callbacks needs to follow the same convention like described
+ in the class description (list of dictionaries). Must be run before resuming a training process.
+ """
self.callbacks = callbacks
def on_epoch_end(self, epoch, logs=None):
+ """
+ Save model as usual (see ModelCheckpoint class), but also save additional callbacks.
+ """
super().on_epoch_end(epoch, logs)
for callback in self.callbacks:
@@ -100,9 +139,12 @@ class ModelCheckpointAdvanced(ModelCheckpoint):
if self.save_best_only:
current = logs.get(self.monitor)
if current == self.best:
- print('\nEpoch %05d: save to %s' % (epoch + 1, file_path))
+ if self.verbose > 0:
+ print('\nEpoch %05d: save to %s' % (epoch + 1, file_path))
with open(file_path, "wb") as f:
pickle.dump(callback["callback"], f)
else:
with open(file_path, "wb") as f:
- pickle.dump(callback["callback"], f)
\ No newline at end of file
+ if self.verbose > 0:
+ print('\nEpoch %05d: save to %s' % (epoch + 1, file_path))
+ pickle.dump(callback["callback"], f)
--
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