Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Changes inner progressbar from counting batches to counting samples. #6

Merged
merged 1 commit into from Mar 15, 2017
Merged

Changes inner progressbar from counting batches to counting samples. #6

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
16 changes: 9 additions & 7 deletions keras_tqdm/tqdm_callback.py
View file
Open in desktop
Expand Up @@ -45,7 +45,8 @@ def __init__(self, outer_description="Training",
self.tqdm_inner = None
self.epoch = None
self.running_logs = None
self.batch_count = None
self.sample_count = None
self.batch_size = None

def tqdm(self, desc, total, leave):
"""
Expand Down Expand Up @@ -78,9 +79,9 @@ def build_tqdm_inner(self, desc, total):
def on_epoch_begin(self, epoch, logs={}):
self.epoch = epoch
desc = self.inner_description_initial.format(epoch=self.epoch)
self.batch_count = int(ceil(self.params['nb_sample'] / self.params['batch_size']))
self.sample_count = 0
if self.show_inner:
self.tqdm_inner = self.build_tqdm_inner(desc=desc, total=self.batch_count)
self.tqdm_inner = self.build_tqdm_inner(desc=desc, total=self.params['nb_sample'])
self.running_logs = {}

def on_epoch_end(self, epoch, logs={}):
Expand All @@ -91,22 +92,23 @@ def on_epoch_end(self, epoch, logs={}):
# set miniters and mininterval to 0 so last update displays
self.tqdm_inner.miniters = 0
self.tqdm_inner.mininterval = 0
self.tqdm_inner.update(1)
self.tqdm_inner.update(self.batch_size)
self.tqdm_inner.close()
if self.show_outer:
self.tqdm_outer.update(1)

def on_batch_begin(self, batch, logs={}):
pass
self.batch_size = logs['size']
self.sample_count = self.sample_count + self.batch_size

def on_batch_end(self, batch, logs={}):
if batch < self.batch_count - 1:
if self.sample_count < self.params['nb_sample'] - 1:
self.append_logs(logs)
metrics = self.format_metrics(self.running_logs)
desc = self.inner_description_update.format(epoch=self.epoch, metrics=metrics)
if self.show_inner:
self.tqdm_inner.desc = desc
self.tqdm_inner.update(1)
self.tqdm_inner.update(self.batch_size)

def on_train_begin(self, logs={}):
if self.show_outer:
Expand Down