How to use the learn2learn.algorithms.maml.maml_update function in learn2learn
To help you get started, we’ve selected a few learn2learn examples, based on popular ways it is used in public projects.
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learnables / learn2learn / examples / rl / maml_trpo.py View on Github 
def fast_adapt_a2c(clone, train_episodes, adapt_lr, baseline, gamma, tau, first_order=False):
second_order = not first_order
loss = maml_a2c_loss(train_episodes, clone, baseline, gamma, tau)
gradients = autograd.grad(loss,
clone.parameters(),
retain_graph=second_order,
create_graph=second_order)
return l2l.algorithms.maml.maml_update(clone, adapt_lr, gradients)learn2learn
PyTorch Library for Meta-Learning Research
Package Health Score
59 / 100Popular learn2learn functions
- learn2learn.algorithms
- learn2learn.algorithms.MAML
- learn2learn.algorithms.maml.maml_update
- learn2learn.clone_module
- learn2learn.data
- learn2learn.data.MetaDataset
- learn2learn.data.TaskDataset
- learn2learn.data.TaskGenerator
- learn2learn.data.transforms
- learn2learn.data.transforms.ConsecutiveLabels
- learn2learn.data.transforms.KShots
- learn2learn.data.transforms.LoadData
- learn2learn.data.transforms.NWays
- learn2learn.data.transforms.RemapLabels
- learn2learn.gym.AsyncVectorEnv
- learn2learn.gym.envs.meta_env.MetaEnv
- learn2learn.gym.envs.meta_env.MetaEnv.__init__
- learn2learn.gym.envs.meta_env.MetaEnv.set_task
- learn2learn.vision
- learn2learn.vision.datasets.MiniImagenet