keras-buoy 0.1.4.1
Keras wrapper that autosaves what ModelCheckpoint cannot.
License: mit

==========
keras-buoy
==========

.. image:: https://travis-ci.com/dorukkarinca/keras-buoy.svg?branch=master
    :target: https://travis-ci.com/dorukkarinca/keras-buoy

Keras wrapper that autosaves and auto-recovers not just the model weights but also the last epoch number and training history metrics.

See it in action in `this Colab notebook <https://colab.research.google.com/drive/1uubwP4DQJPpMDKN9GY1y1SZeds83gwYV?usp=sharing>`_!

::

    pip install keras-buoy


Description
===========

When training is interrupted and you rerun the whole code, it recovers the model weights and the epoch counter to the last saved values. Then it resumes training as if nothing happened. At the end, the Keras History.history dictionaries are combined so that the training history looks like one single training run.

Example
=======

::
    
    >>> from tensorflow import keras
    >>> from keras_buoy.models import ResumableModel

    >>> model = keras.Sequential()
    ...
    >>> resumable_model = ResumableModel(model, 
                                         save_every_epochs=4, 
                                         custom_objects=None, 
                                         to_path='/path/to/save/model_weights.h5')
    >>> history = resumable_model.fit(x=x_train, 
                                      y=y_train,
                                      validation_split=0.1,
                                      batch_size=256,
                                      verbose=2,
                                      epochs=15)
    
    Recovered model from kerascheckpoint.h5 at epoch 8.

    Epoch 9/15
    1125/1125 - 5s - loss: 0.4790 - top_k_categorical_accuracy: 0.9698 - val_loss: 1.1075 - val_top_k_categorical_accuracy: 0.9206
    Epoch 10/15
    1125/1125 - 5s - loss: 0.4758 - top_k_categorical_accuracy: 0.9701 - val_loss: 1.1119 - val_top_k_categorical_accuracy: 0.9214
    Epoch 11/15
    1125/1125 - 5s - loss: 0.4753 - top_k_categorical_accuracy: 0.9702 - val_loss: 1.1000 - val_top_k_categorical_accuracy: 0.9215
    Epoch 12/15

Try it out yourself in `this Colab notebook <https://colab.research.google.com/drive/1uubwP4DQJPpMDKN9GY1y1SZeds83gwYV?usp=sharing>`_.

Docs
====

++++++++++++++++++++++++++++++++++++++++
:code:`keras_buoy.models.ResumableModel`
++++++++++++++++++++++++++++++++++++++++

Creates a resumable model.

**Parameters:**

+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Parameter name                    | Description                                                                                                                                                                     |
+===================================+=================================================================================================================================================================================+
| :code:`model (tf.keras.Model)`    | The instance of :code:`tf.keras.Model` which you want to make resumable.                                                                                                        |
+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| :code:`save_every_epochs (int)`   | Specifies how often to save the model, history, and epoch counter.  In case of a crash, recovery will happen from the last saved epoch multiple.                                |
+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| :code:`custom_objects (dict)`     | At recovery time, this is passed into :code:`tf.keras.models.load_model(...)` exactly as shown in Tensorflow docs so you can load your model with a custom loss for example.    |
+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| :code:`to_path (str)`             | Specifies the path where the model weights will be saved. If it ends with :code:`.h5`, then it saves in the Keras H5 format instead of the default TensorFlow SavedModel format.|
+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

If :code:`to_path` is :code:`mymodel.h5`, then there will be :code:`mymodel_epoch_num.pkl` and :code:`mymodel_history.pkl` in the same directory as :code:`mymodel.h5`, which hold backups for the epoch counter and the history dict, respectively.

**Returns:**

A ResumableModel instance. You can call :code:`.fit(...)` on it.

|
|
|

++++++++++++++++++++++++++++++++++++++++++++
:code:`keras.buoy.models.ResumableModel.fit` 
++++++++++++++++++++++++++++++++++++++++++++

Fits a resumable model.

**Parameters:**

The accepted parameters are the same as :code:`tf.Keras.model.fit(...)` except you cannot specify :code:`initial_epoch`.

**Returns:**

:code:`history (dict)`: The history dict of the Keras History object. Note that it does not return the :code:`Keras.History` object itself, just the dict.

Note
====

This project has been set up using PyScaffold 3.2.3. For details and usage
information on PyScaffold see https://pyscaffold.org/.