bstriner
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-keras-tqdm==========Keras integration with TQDM progress bars.* 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!With ``leave_inner=False`` (default).. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_False.png   :alt: Keras TQDMWith ``leave_inner=True``.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_True.png   :alt: Keras TQDMInstallation------------Stable release::    pip install keras-tqdmDevelopment release::    git clone https://github.com/bstriner/keras-tqdm.git    cd keras-tqdm    python setup.py installKeras-----`Keras <https://github.com/fchollet/keras>`__ is an awesome machinelearning library for Theano or TensorFlow.TQDM----`TQDM <https://github.com/tqdm/tqdm>`__ is a progress bar library withgood support for nested loops and Jupyter/IPython notebooks.Basic TQDM Usage----------------Use TQDM to wrap enumerators within loops to create a progress bar.Review TQDM documentation for display options.::    from tqdm import tqdm    import time    for i in tqdm(range(10)):        time.sleep(1)Keras TQDM----------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 theminside 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()])IPython and Jupyter----------Use ``TQDMNotebookCallback`` instead of ``TQDMCallback``. Use ``tqdm_notebook`` in your own code instead of ``tqdm``.Questions?----------Please feel free to submit PRs and issues. Comments, questions, andrequests are welcome. If you need more control, subclass``TQDMCallback`` and override the ``tqdm`` function.
+keras-tqdm==========Keras integration with TQDM progress bars.* 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).. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_False.png   :alt: Keras TQDM leave_inner=False``TQDMNotebookCallback`` with ``leave_inner=True``.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_True.png   :alt: Keras TQDM leave_inner=True``TQDMCallback`` for command-line scripts.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/console.png   :alt: Keras TQDM CLI   Installation------------Stable release::    pip install keras-tqdmDevelopment release::    git clone https://github.com/bstriner/keras-tqdm.git    cd keras-tqdm    python setup.py installKeras-----`Keras <https://github.com/fchollet/keras>`__ is an awesome machinelearning library for Theano or TensorFlow.TQDM----`TQDM <https://github.com/tqdm/tqdm>`__ is a progress bar library withgood support for nested loops and Jupyter/IPython notebooks.Basic TQDM Usage----------------Use TQDM to wrap enumerators within loops to create a progress bar.Review TQDM documentation for display options.::    from tqdm import tqdm    import time    for i in tqdm(range(10)):        time.sleep(1)Keras TQDM----------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 theminside 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()])IPython and Jupyter----------Use ``TQDMNotebookCallback`` instead of ``TQDMCallback``. Use ``tqdm_notebook`` in your own code instead of ``tqdm``.Questions?----------Please feel free to submit PRs and issues. Comments, questions, andrequests are welcome. If you need more control, subclass``TQDMCallback`` and override the ``tqdm`` function.
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`1`		-keras-tqdm==========Keras integration with TQDM progress bars.* 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!With ``leave_inner=False`` (default).. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_False.png :alt: Keras TQDMWith ``leave_inner=True``.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_True.png :alt: Keras TQDMInstallation------------Stable release:: pip install keras-tqdmDevelopment release:: git clone https://github.com/bstriner/keras-tqdm.git cd keras-tqdm python setup.py installKeras-----`Keras <https://github.com/fchollet/keras>`__ is an awesome machinelearning library for Theano or TensorFlow.TQDM----`TQDM <https://github.com/tqdm/tqdm>`__ is a progress bar library withgood support for nested loops and Jupyter/IPython notebooks.Basic TQDM Usage----------------Use TQDM to wrap enumerators within loops to create a progress bar.Review TQDM documentation for display options.:: from tqdm import tqdm import time for i in tqdm(range(10)): time.sleep(1)Keras TQDM----------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 theminside 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()])IPython and Jupyter----------Use ``TQDMNotebookCallback`` instead of ``TQDMCallback``. Use ``tqdm_notebook`` in your own code instead of ``tqdm``.Questions?----------Please feel free to submit PRs and issues. Comments, questions, andrequests are welcome. If you need more control, subclass``TQDMCallback`` and override the ``tqdm`` function.
	`1`	+keras-tqdm==========Keras integration with TQDM progress bars.* 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).. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_False.png :alt: Keras TQDM leave_inner=False``TQDMNotebookCallback`` with ``leave_inner=True``.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/leave_inner_True.png :alt: Keras TQDM leave_inner=True``TQDMCallback`` for command-line scripts.. figure:: https://github.com/bstriner/keras-tqdm/raw/master/docs/images/console.png :alt: Keras TQDM CLI Installation------------Stable release:: pip install keras-tqdmDevelopment release:: git clone https://github.com/bstriner/keras-tqdm.git cd keras-tqdm python setup.py installKeras-----`Keras <https://github.com/fchollet/keras>`__ is an awesome machinelearning library for Theano or TensorFlow.TQDM----`TQDM <https://github.com/tqdm/tqdm>`__ is a progress bar library withgood support for nested loops and Jupyter/IPython notebooks.Basic TQDM Usage----------------Use TQDM to wrap enumerators within loops to create a progress bar.Review TQDM documentation for display options.:: from tqdm import tqdm import time for i in tqdm(range(10)): time.sleep(1)Keras TQDM----------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 theminside 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()])IPython and Jupyter----------Use ``TQDMNotebookCallback`` instead of ``TQDMCallback``. Use ``tqdm_notebook`` in your own code instead of ``tqdm``.Questions?----------Please feel free to submit PRs and issues. Comments, questions, andrequests are welcome. If you need more control, subclass``TQDMCallback`` and override the ``tqdm`` function.