keras-tqdm

Keras integration with TQDM progress bars.

• Keras is an awesome machine learning library for Theano or TensorFlow.
• TQDM is a progress bar library with good support for nested loops and Jupyter/IPython notebooks.

Key features

• TQDM supports nested progress bars. If you have Keras fit and predict loops within an outer TQDM loop, the nested loops will display properly.
• TQDM supports Jupyter/IPython notebooks.
• TQDM looks great!

TQDMNotebookCallback with leave_inner=False (default)

TQDMNotebookCallback with leave_inner=True

TQDMCallback for command-line scripts

Installation

Stable release

pip install keras-tqdm

Development release

pip install git+https://github.com/bstriner/keras-tqdm.git --upgrade --no-deps

Development mode (changes to source take effect without reinstalling)

git clone https://github.com/bstriner/keras-tqdm.git
cd keras-tqdm
python setup.py develop

Basic usage

It's very easy to use Keras TQDM. The only required change is to remove default messages (verbose=0) and add a callback to model.fit. The rest happens automatically! For Jupyter Notebook required code modification is as simple as:

from keras_tqdm import TQDMNotebookCallback
# keras, model definition...
model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMNotebookCallback()])

For plain text mode (e.g. for Python run from command line)

from keras_tqdm import TQDMCallback
# keras, model definition...
model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMCallback()])

Advanced usage

Use keras_tqdm to utilize TQDM progress bars for Keras fit loops. keras_tqdm loops can be nested inside TQDM loops to display nested progress bars (although you can use them inside ordinary for loops as well). Set verbose=0 to suppress the default progress bar.

from keras_tqdm import TQDMCallback
from tqdm import tqdm
for model in tqdm(models, desc="Training several models"):
    model.fit(x, y, verbose=0, callbacks=[TQDMCallback()])

For IPython and Jupyter notebook TQDMNotebookCallback instead of TQDMCallback. Use tqdm_notebook in your own code instead of tqdm. Formatting is controlled by python format strings. The default metric_format is "{name}: {value:0.3f}". For example, use TQDMCallback(metric_format="{name}: {value:0.6f}") for 6 decimal points or {name}: {value:e} for scientific notation.

Questions?

Please feel free to submit PRs and issues. Comments, questions, and requests are welcome. If you need more control, subclass TQDMCallback and override the tqdm function.