File size: 3,135 Bytes
09d8e80 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Gradient transformations used to enforce specific constraints."""
from typing import Any, NamedTuple
from jax import tree_util as jtu
import jax.numpy as jnp
from optax._src import base
NonNegativeParamsState = base.EmptyState
def keep_params_nonnegative() -> base.GradientTransformation:
"""Modifies the updates to keep parameters non-negative, i.e. >= 0.
This transformation ensures that parameters after the update will be
larger than or equal to zero.
In a chain of transformations, this should be the last one.
WARNING: the transformation expects input params to be non-negative.
When params is negative the transformed update will move them to 0.
Returns:
A `GradientTransformation` object.
"""
def init_fn(params):
del params
return NonNegativeParamsState()
def update_fn(updates, state, params):
if params is None:
raise ValueError(base.NO_PARAMS_MSG)
updates = jtu.tree_map(
lambda p, u: jnp.where((p + u) < 0., -p, u), params, updates)
return updates, state
return base.GradientTransformation(init_fn, update_fn)
class ZeroNansState(NamedTuple):
"""Contains a tree.
The entry `found_nan` has the same tree structure as that of the parameters.
Each leaf is a single boolean which contains True iff a NaN was detected in
the corresponding parameter array at the last call to `update`.
"""
found_nan: Any
def zero_nans() -> base.GradientTransformation:
"""A transformation which replaces NaNs with 0.
The state of the transformation has the same tree structure as that of the
parameters. Each leaf is a single boolean which contains True iff a NaN was
detected in the corresponding parameter array at the last call to ``update``.
This state is not used by the transformation internally, but lets users be
aware when NaNs have been zeroed out.
Returns:
A `GradientTransformation`.
"""
def init_fn(params):
return ZeroNansState(
found_nan=jtu.tree_map(
lambda p: jnp.array(False, dtype=jnp.bool_), params))
def update_fn(updates, opt_state, params=None):
del params, opt_state
opt_state = ZeroNansState(
found_nan=jtu.tree_map(lambda p: jnp.any(jnp.isnan(p)), updates))
updates = jtu.tree_map(
lambda p: jnp.where(jnp.isnan(p), jnp.zeros_like(p), p), updates)
return updates, opt_state
return base.GradientTransformation(init=init_fn, update=update_fn)
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