File size: 4,311 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # 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.
# ==============================================================================
"""Tests for optax.transforms._constraining."""
from absl.testing import absltest
import chex
import jax.numpy as jnp
from optax._src import combine
from optax._src import transform
from optax._src import update
from optax.transforms import _accumulation
from optax.transforms import _constraining
STEPS = 50
LR = 1e-2
class ConstraintsTest(chex.TestCase):
def test_keep_params_nonnegative(self):
grads = (jnp.array([500., -500., 0.]),
jnp.array([500., -500., 0.]),
jnp.array([500., -500., 0.]))
params = (jnp.array([-1., -1., -1.]),
jnp.array([1., 1., 1.]),
jnp.array([0., 0., 0.]))
# vanilla sgd
opt = combine.chain(
_accumulation.trace(decay=0, nesterov=False),
transform.scale(-LR))
opt_state = opt.init(params)
updates, _ = opt.update(grads, opt_state, params)
new_params = update.apply_updates(params, updates)
chex.assert_trees_all_close(new_params, (jnp.array([-6., 4., -1.]),
jnp.array([-4., 6., 1.]),
jnp.array([-5., 5., 0.])))
# sgd with keeping parameters non-negative
opt = combine.chain(
_accumulation.trace(decay=0, nesterov=False),
transform.scale(-LR),
_constraining.keep_params_nonnegative())
opt_state = opt.init(params)
updates, _ = opt.update(grads, opt_state, params)
new_params = update.apply_updates(params, updates)
chex.assert_trees_all_close(new_params, (jnp.array([0., 4., 0.]),
jnp.array([0., 6., 1.]),
jnp.array([0., 5., 0.])))
@chex.all_variants
def test_zero_nans(self):
params = (jnp.zeros([3]), jnp.zeros([3]), jnp.zeros([3]))
opt = _constraining.zero_nans()
opt_state = self.variant(opt.init)(params)
update_fn = self.variant(opt.update)
chex.assert_trees_all_close(
opt_state,
_constraining.ZeroNansState((jnp.array(False),) * 3))
# Check an upate with nans
grads_with_nans = (jnp.ones([3]),
jnp.array([1., float('nan'), float('nan')]),
jnp.array([float('nan'), 1., 1.]))
updates, opt_state = update_fn(grads_with_nans, opt_state)
chex.assert_trees_all_close(
opt_state,
_constraining.ZeroNansState(
(jnp.array(False), jnp.array(True), jnp.array(True))))
chex.assert_trees_all_close(
updates,
(jnp.ones([3]), jnp.array([1., 0., 0.]), jnp.array([0., 1., 1.])))
# Check an upate with nans and infs
grads_with_nans_infs = (jnp.ones([3]),
jnp.array([1., float('nan'),
float('nan')]),
jnp.array([float('inf'), 1., 1.]))
updates, opt_state = update_fn(grads_with_nans_infs, opt_state)
chex.assert_trees_all_close(
opt_state,
_constraining.ZeroNansState(
(jnp.array(False), jnp.array(True), jnp.array(False))))
chex.assert_trees_all_close(updates, (jnp.ones([3]), jnp.array(
[1., 0., 0.]), jnp.array([float('inf'), 1., 1.])))
# Check an upate with only good values
grads = (jnp.ones([3]), jnp.ones([3]), jnp.ones([3]))
updates, opt_state = update_fn(grads, opt_state)
chex.assert_trees_all_close(
opt_state,
_constraining.ZeroNansState(
(jnp.array(False), jnp.array(False), jnp.array(False))))
chex.assert_trees_all_close(updates, grads)
if __name__ == '__main__':
absltest.main()
|