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!