From 0a377f2aa4579dd22b7e96e8a7ac6f33e3142986 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Thu, 29 Nov 2018 14:26:41 +0100
Subject: [PATCH] adding BaseLogger and History callbacks
---
toolkit.py | 41 ++++++++++++++++++++---------------------
1 file changed, 20 insertions(+), 21 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index c58f005..d16bb2a 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -37,7 +37,7 @@ from sklearn.utils.extmath import stable_cumsum
from sklearn.model_selection import KFold
from keras.models import Sequential, Model, model_from_json
from keras.layers import Dense, Dropout, Input, Masking, GRU, LSTM, concatenate, SimpleRNN
-from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint, TensorBoard, CallbackList
+from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint, TensorBoard, CallbackList, BaseLogger
from keras.optimizers import SGD
import keras.optimizers
from keras.utils.vis_utils import model_to_dot
@@ -1604,7 +1604,7 @@ class ClassificationProject(object):
hist_dict[hist_key] = [float(line[hist_key_index]) for line in history_list[1:]]
return hist_dict
- def plot_loss(self, all_trainings=False, log=False, ylim=None, xlim=None):
+ def plot_loss(self, all_trainings=False, log=False, ylim=None, xlim=None, loss_key="loss"):
"""
Plot the value of the loss function for each epoch
@@ -1616,14 +1616,14 @@ class ClassificationProject(object):
else:
hist_dict = self.history.history
- if (not 'loss' in hist_dict) or (not 'val_loss' in hist_dict):
+ if (not loss_key in hist_dict) or (not 'val_'+loss_key in hist_dict):
logger.warning("No previous history found for plotting, try global history")
hist_dict = self.csv_hist
logger.info("Plot losses")
- plt.plot(hist_dict['loss'])
- plt.plot(hist_dict['val_loss'])
- plt.ylabel('loss')
+ plt.plot(hist_dict[loss_key])
+ plt.plot(hist_dict['val_'+loss_key])
+ plt.ylabel(loss_key)
plt.xlabel('epoch')
plt.legend(['training data','validation data'], loc='upper left')
if log:
@@ -2219,7 +2219,7 @@ class ClassificationProjectDecorr(ClassificationProject):
return self._model_adv
- def train(self, epochs=10):
+ def train(self, epochs=10, skip_checkpoint=False):
"""
Train classifier and adversary concurrently. Most of the garbage in this
code block is just organising stuff to get all the keras callbacks
@@ -2227,17 +2227,20 @@ class ClassificationProjectDecorr(ClassificationProject):
"""
batch_generator = self.yield_batch()
- metric_list = []
out_labels = self.model.metrics_names
+ self.model.history = History()
callback_metrics = out_labels + ['val_' + n for n in out_labels]
- callbacks = CallbackList(self.callbacks_list)
+ callbacks = CallbackList(
+ [BaseLogger()]
+ + self.callbacks_list
+ + [self.model.history])
callbacks.set_model(self.model)
callbacks.set_params({
'epochs': epochs,
'steps': self.steps_per_epoch,
'verbose': self.verbose,
#'do_validation': do_validation,
- 'do_validation': False,
+ 'do_validation': True,
'metrics': callback_metrics,
})
self.model.stop_training = False
@@ -2264,27 +2267,23 @@ class ClassificationProjectDecorr(ClassificationProject):
self.model_adv.train_on_batch(
x, y[1:], sample_weight=w[1:]
)
-
- batch_metrics = np.array(batch_metrics).reshape(1, len(batch_metrics))
- if metrics is None:
- metrics = batch_metrics
- else:
- metrics = np.concatenate([metrics, batch_metrics])
- avg_metrics = np.mean(metrics, axis=0)
outs = list(batch_metrics)
for l, o in zip(out_labels, outs):
- batch_logs[l] = o
+ batch_logs[l] = float(o)
callbacks.on_batch_end(batch_id, batch_logs)
- metric_list.append(avg_metrics)
val_metrics = self.model.test_on_batch(*self.validation_data)
val_outs = list(val_metrics)
for l, o in zip(out_labels, val_outs):
- epoch_logs['val_' + l] = o
+ epoch_logs['val_' + l] = float(o)
callbacks.on_epoch_end(epoch, epoch_logs)
if self.model.stop_training:
break
callbacks.on_train_end()
- return metric_list
+
+ if not skip_checkpoint:
+ self.checkpoint_model()
+
+ return self.model.history
if __name__ == "__main__":
--
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