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Add capability of regularization towards random weights. #27

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Add capability of regularization towards random weights. #27

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23 changes: 18 additions & 5 deletions enn/losses/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,8 @@ def noisy_loss(
def add_l2_weight_decay(
loss_fn: _LossFn,
scale: Union[float, Callable[[hk.Params], hk.Params]],
predicate: Optional[PredicateFn] = None
predicate: Optional[PredicateFn] = None,
regularize_towards_random_weights: bool = False,
) -> _LossFn:
"""Adds scale * l2 weight decay to an existing loss function."""
try: # Scale is numeric.
Expand All @@ -79,14 +80,26 @@ def add_l2_weight_decay(

def new_loss(
enn: base.EpistemicNetwork[base.Input, base.Output],
params: hk.Params, state: hk.State, batch: base.Data,
key: chex.PRNGKey) -> base.LossOutput:
loss, (state, metrics) = loss_fn(enn, params, state, batch, key)
params: hk.Params,
state: hk.State,
batch: base.Data,
key: chex.PRNGKey,
) -> base.LossOutput:
loss_key, params_key = jax.random.split(key)
loss, (state, metrics) = loss_fn(enn, params, state, batch, loss_key)
if regularize_towards_random_weights:
random_params = jax.tree_util.tree_map(
lambda x: jax.random.normal(params_key, x.shape), params
)
params = jax.tree_util.tree_map(
lambda p, rp: p - rp, params, random_params
)
decay = l2_weights_with_predicate(scale_fn(params), predicate)
total_loss = loss + decay
total_loss = loss + decay
metrics['decay'] = decay
metrics['raw_loss'] = loss
return total_loss, (state, metrics)

return new_loss


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