Cleanup

class MAML(object):

  """Implements the Model-Agnostic Meta-Learning algorithm for low data learning.

  The algorithm is described in Finn et al., "Model-Agnostic

  Meta-Learning for Fast Adaptation of Deep Networks"

  (https://arxiv.org/abs/1703.03400).  It is used for training

  models that can perform a variety of tasks, depending on what

  data they are trained on.  It assumes you have training data

  for many tasks, but only a small amount for each one.  It

  performs "meta-learning" by looping over tasks and trying to

  minimize the loss on each one *after* one or a few steps of

  gradient descent.  That is, it does not try to create a model

  that can directly solve the tasks, but rather tries to create

  a model that is very easy to train.

  To use this class, create a subclass of MetaLearner that

  encapsulates the model and data for your learning problem.

  Pass it to a MAML object and call fit().  You can then use

  train_on_current_task() to fine tune the model for a

  particular task.

  The algorithm is described in Finn et al., "Model-Agnostic Meta-Learning for Fast

  Adaptation of Deep Networks" (https://arxiv.org/abs/1703.03400).  It is used for

  training models that can perform a variety of tasks, depending on what data they

  are trained on.  It assumes you have training data for many tasks, but only a small

  amount for each one.  It performs "meta-learning" by looping over tasks and trying

  to minimize the loss on each one *after* one or a few steps of gradient descent.

  That is, it does not try to create a model that can directly solve the tasks, but

  rather tries to create a model that is very easy to train.

  To use this class, create a subclass of MetaLearner that encapsulates the model

  and data for your learning problem.  Pass it to a MAML object and call fit().

  You can then use train_on_current_task() to fine tune the model for a particular

  task.

  """

  def __init__(self,