v0.5.1

• Update for PyTorch 1.1.
• Transfers metric modules to GPU when appropriate.

v0.5

• Adding a new OptimizerPolicy class allowing to have Phase-based learning rate policies. The two following learning policies are also provided:
  • "Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates", Leslie N. Smith, Nicholay Topin, https://arxiv.org/abs/1708.07120
  • "SGDR: Stochastic Gradient Descent with Warm Restarts", Ilya Loshchilov, Frank Hutter, https://arxiv.org/abs/1608.0398
• Adding of "bin_acc" metric for binary classification in addition to the "accuracy" metric".
• Adding "time" in callbacks' logs.
• Various refactoring and small bug fixes.

v0.4.1

Breaking changes:

• Update for PyTorch 0.4.1 (PyTorch 0.4 not supported)
• Keyword arguments must now be passed with their keyword names in most PyToune functions.

Non-breaking changes:

• self.optimizer.zero_grad() is called instead of self.model.zero_grad().
• Support strings as input for all PyTorch loss functions, metrics and optimizers.
• Add support for generators that raise the StopIteration exception.
• Refactor of the Model class (no API break changes).
• Now using pylint as code style linter.
• Fix typos in documentation.

v0.4

• New usage example using MNIST
• New *_on_batch methods to Model
• Every Numpy array is converted into a tensor and vice-versa everywhere it applies i.e. methods return Numpy arrays and can take Numpy arrays as input.
• New convenient simple layers (Flatten, Identity and Lambda layers)
• New callbacks to save optimizers and LRSchedulers.
• New Tensorboard callback.
• Various bug fixes and improvements.

v0.3

Breaking changes:

• Update to PyTorch 0.4.0
• When one or zero metric is used, evaluate and evaluate generator do not return numpy arrays anymore.

Other changes:

• Model now offers a to() method to send the PyTorch module and its input to a specified device. (thanks PyTorch 0.4.0)
• There is now a 'accuracy' metric that can be used as string in the metrics list.
• Various bug fixes.

v0.2.2

Last release before an upgrade with breaking changes due to the update of PyTorch 0.4.0.

• Add an on_backward_end callback function
• Add a ClipNorm callback
• Fix various bugs.

v0.2.1

• Fix warning bugs and bad logic in checkpoints.
• Fix bug where we did not display metric when its value was equal to zero.

v0.2

• ModelCheckpoint now writes off the checkpoint atomically.
• New initial_epoch parameter to Model.
• Mean of losses and metrics done with batch size weighted by len(y) instead of just the mean of the losses and metrics.
• Update to the documentation.
• Model's predict and evaluate makes more sense now and have now a generator version.
• Few other bug fixes.

v0.1.1

New version 0.1.1 with all the docs updated