explora

explora.make_data_config(data_type, class_labels, color_space=None, image_dims=None)

Helper function to generate the required data config for users running training sessions with the iOS library.

Parameters

  • data_type (str) – The type of data the model will train on. Currently, only image is supported for the iOS library

  • class_labels (list) – The list of possible labels in the dataset.

  • color_space (str, optional) – The type of image that is inputted into the model, if applicable. Must be specified if data_type is image. If specified, must be either GRAYSCALE or COLOR.

  • image_dims (tuple, optional) – The dimensions of image that is inputted into the model, if applicable. Must be specified if data_type is image. Must have a length of 2 (width x height).

Returns

Data config to be used when starting a new session.

Return type

DataConfig

async explora.start_new_session(repo_id, model, hyperparameters, percentage_averaged=0.75, max_rounds=5, library_type='PYTHON', checkpoint_frequency=1, data_config=None)

Validate arguments and then start a new session by sending a message to the server with the given configuration. Designed to be called in Explora.ipynb.

Parameters

  • repo_id (str) – The repo ID associated with the current dataset.

  • model (keras.engine.Model) – The initial Keras model to train with. The model must be compiled!

  • hyperparams (dict) – The hyperparameters to be used during training. Must include batch_size!

  • percentage_averaged (float, optional) – Percentage of nodes to be averaged before moving on to the next round. Defaults to 0.75.

  • max_rounds (int, optional) – Maximum number of rounds to train for. Defaults to 5.

  • library_type (str, optional) – The type of library to train with. Must be either PYTHON or JAVASCRIPT or IOS. Defaults to PYTHON.

  • checkpoint_frequency (int, optional) – Save the model in S3 every checkpoint_frequency rounds. Defaults to 1.

  • data_config (DataConfig, optional) – The configuration for the dataset, if applicable. If library_type is IOS, then this argument is required!

Examples

>>> start_new_session(
...     repo_id="c9bf9e57-1685-4c89-bafb-ff5af830be8a",
...     model=keras.models.load_model("model.h5"),
...     hyperparameters={"batch_size": 100},
...     percentage_averaged=0.75,
...     max_rounds=5,
...     library_type="PYTHON",
...     checkpoint_frequency=1,
... )
Starting session!
Waiting...
Session complete! Check dashboard for final model!