API Reference

This section provides the auto-generated API documentation for the gaitmod library.

Leadfield Simulation

Data Simulation

Source Estimation

Base Model

Helper class that provides a standard way to create an ABC using inheritance.

Regression Models

Helper class that provides a standard way to create an ABC using inheritance.

Linear Regression Model

Helper class that provides a standard way to create an ABC using inheritance.

Regression LSTM Model

Helper class that provides a standard way to create an ABC using inheritance.

Classification Models

Helper class that provides a standard way to create an ABC using inheritance.

Classification LSTM Model

Helper class that provides a standard way to create an ABC using inheritance.

Feature Extractor 2

LSTM Classifier

Base class for all estimators in scikit-learn.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Custom Grid Search CV

Not used for now

Custom Training Logger

Base class used to build new callbacks.

Callbacks can be passed to keras methods such as fit(), evaluate(), and predict() in order to hook into the various stages of the model training, evaluation, and inference lifecycle.

To create a custom callback, subclass keras.callbacks.Callback and override the method associated with the stage of interest.

Example:

>>> training_finished = False
>>> class MyCallback(Callback):
...   def on_train_end(self, logs=None):
...     global training_finished
...     training_finished = True
>>> model = Sequential([
...     layers.Dense(1, input_shape=(1,))])
>>> model.compile(loss='mean_squared_error')
>>> model.fit(np.array([[1.0]]), np.array([[1.0]]),
...           callbacks=[MyCallback()])
>>> assert training_finished == True

If you want to use Callback objects in a custom training loop:

You should pack all your callbacks into a single callbacks.CallbackList so they can all be called together.
You will need to manually call all the on_* methods at the appropriate locations in your loop. Like this:

Example:

```python callbacks = keras.callbacks.CallbackList([…]) callbacks.append(…) callbacks.on_train_begin(…) for epoch in range(EPOCHS):

callbacks.on_epoch_begin(epoch) for i, data in dataset.enumerate(): callbacks.on_train_batch_begin(i) batch_logs = model.train_step(data) callbacks.on_train_batch_end(i, batch_logs) epoch_logs = … callbacks.on_epoch_end(epoch, epoch_logs)

final_logs=… callbacks.on_train_end(final_logs) ```

gaitmod.CustomTrainingLogger.params: Dict. Training parameters (eg. verbosity, batch size, number of epochs…).

gaitmod.CustomTrainingLogger.model: Instance of Model. Reference of the model being trained.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).