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        "author": "Ben Striner",
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        "description": "keras-tqdm\r\n==========\r\n\r\nKeras integration with TQDM progress bars.\r\n\r\n* `Keras <https://github.com/fchollet/keras>`__ is an awesome machine learning library for Theano or TensorFlow.\r\n* `TQDM <https://github.com/tqdm/tqdm>`__ is a progress bar library with good support for nested loops and Jupyter/IPython notebooks.\r\n\r\nKey features\r\n------------\r\n\r\n* TQDM supports nested progress bars. If you have Keras fit and\r\n  predict loops within an outer TQDM loop, the nested loops will\r\n  display properly.\r\n\r\n* TQDM supports Jupyter/IPython notebooks.\r\n\r\n* TQDM looks great!\r\n\r\n``TQDMNotebookCallback`` with ``leave_inner=False`` (default)\r\n\r\n\r\n\r\n.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_False.png\r\n   :alt: Keras TQDM leave_inner=False\r\n\r\n``TQDMNotebookCallback`` with ``leave_inner=True``\r\n\r\n.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_True.png\r\n   :alt: Keras TQDM leave_inner=True\r\n\r\n``TQDMCallback`` for command-line scripts\r\n\r\n.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/console.png\r\n   :alt: Keras TQDM CLI\r\n\r\nInstallation\r\n------------\r\n\r\nStable release\r\n::\r\n\r\n    pip install keras-tqdm\r\n\r\n\r\nDevelopment release\r\n::\r\n\r\n   pip install git+https://github.com/bstriner/keras-tqdm.git --upgrade --no-deps\r\n\r\nDevelopment mode (changes to source take effect without reinstalling)\r\n::\r\n\r\n    git clone https://github.com/bstriner/keras-tqdm.git\r\n    cd keras-tqdm\r\n    python setup.py develop\r\n\r\nBasic usage\r\n-----------\r\n\r\nIt'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:\r\n\r\n::\r\n\r\n    from keras_tqdm import TQDMNotebookCallback\r\n    # keras, model definition...\r\n    model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMNotebookCallback()])\r\n\r\nFor plain text mode (e.g. for Python run from command line)\r\n\r\n::\r\n\r\n    from keras_tqdm import TQDMCallback\r\n    # keras, model definition...\r\n    model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMCallback()])\r\n\r\n\r\nAdvanced usage\r\n--------------\r\n\r\nUse ``keras_tqdm`` to utilize TQDM progress bars for Keras fit loops.\r\n``keras_tqdm`` loops can be nested inside TQDM loops to display nested progress bars (although you can use them\r\ninside ordinary for loops as well).\r\nSet ``verbose=0`` to suppress the default progress bar.\r\n\r\n::\r\n\r\n    from keras_tqdm import TQDMCallback\r\n    from tqdm import tqdm\r\n    for model in tqdm(models, desc=\"Training several models\"):\r\n        model.fit(x, y, verbose=0, callbacks=[TQDMCallback()])\r\n\r\nFor IPython and Jupyter notebook ``TQDMNotebookCallback`` instead of ``TQDMCallback``. Use ``tqdm_notebook`` in your own code instead of ``tqdm``.\r\nFormatting is controlled by python format strings. The default ``metric_format`` is ``\"{name}: {value:0.3f}\"``.\r\nFor example, use  ``TQDMCallback(metric_format=\"{name}: {value:0.6f}\")`` for 6 decimal points or ``{name}: {value:e}`` for scientific notation.\r\n\r\nQuestions?\r\n----------\r\n\r\nPlease feel free to submit PRs and issues. Comments, questions, and\r\nrequests are welcome. If you need more control, subclass\r\n``TQDMCallback`` and override the ``tqdm`` function.\r\n\r\n\r\n",
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