StreamedML

Framework for training machine learning models from streamed data

Extend your model for CL methods Inherit ContinualLearner class

class MLP(ContinualLearner):

For EWC:

# after learning a task - Estimate Fisher
model.estimate_fisher(current_task_data_set, loss_func, is_mlp = True)

# while training get EWC Loss
ewc_loss = regularizer_strength * model.ewc_loss()

For Replay-Based Methods:

# while training
reference_data = sampled_Data_from_replay_memory()
if layerwise:
    # calculating reference gradients
    model.calculate_ref_gradients_layerwise(reference_data)

    # optimization step
    model.overwrite_grad_layerwise()

# A-GEM Case
else:
    # calculating reference gradients
    model.calculate_ref_gradients(reference_data)

    # optimization step
    model.overwrite_grad()

# After Successful Task Training - append data to Replay Memory
# Examples are in ReplayTrainer.py

Episodic Memory implementation - utils/EpisodicMemory.py