keras-buoy 0.1.4.1

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.

Docs

Note

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