File size: 3,355 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 | # 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._adding."""
from absl.testing import absltest
import chex
from jax import tree_util as jtu
import jax.numpy as jnp
from optax.transforms import _adding
STEPS = 50
class AddingTest(chex.TestCase):
def setUp(self):
super().setUp()
self.init_params = (jnp.array([1., 2.]), jnp.array([3., 4.]))
self.per_step_updates = (jnp.array([500., 5.]), jnp.array([300., 3.]))
@chex.all_variants
def test_add_decayed_weights(self):
# Define a transform that add decayed weights.
# We can define a mask either as a pytree, or as a function that
# returns the pytree. Below we define the pytree directly.
mask = (True, dict(a=True, b=False))
tx = _adding.add_decayed_weights(0.1, mask=mask)
# Define input updates and weights.
updates = (
jnp.zeros((2,), dtype=jnp.float32),
dict(
a=jnp.zeros((2,), dtype=jnp.float32),
b=jnp.zeros((2,), dtype=jnp.float32),))
weights = (
jnp.ones((2,), dtype=jnp.float32),
dict(
a=jnp.ones((2,), dtype=jnp.float32),
b=jnp.ones((2,), dtype=jnp.float32),))
# This mask means that we will add decayed weights to the first two
# terms in the input updates, but not to the last element.
expected_tx_updates = (
0.1*jnp.ones((2,), dtype=jnp.float32),
dict(
a=0.1*jnp.ones((2,), dtype=jnp.float32),
b=jnp.zeros((2,), dtype=jnp.float32),))
# Apply transform
state = tx.init(weights)
transform_fn = self.variant(tx.update)
new_updates, _ = transform_fn(updates, state, weights)
# Assert output as expected.
chex.assert_trees_all_close(new_updates, expected_tx_updates)
@chex.all_variants
def test_add_noise_has_correct_variance_scaling(self):
# Prepare to compare noise with a rescaled unit-variance substitute.
eta = 0.3
gamma = 0.55
seed = 314
noise = _adding.add_noise(eta, gamma, seed)
noise_unit = _adding.add_noise(1.0, 0.0, seed)
params = self.init_params
state = noise.init(params)
state_unit = noise_unit.init(params)
# Check the noise itself by adding it to zeros.
updates = jtu.tree_map(jnp.zeros_like, params)
for i in range(1, STEPS + 1):
updates_i, state = self.variant(noise.update)(updates, state)
updates_i_unit, state_unit = noise_unit.update(updates, state_unit)
scale = jnp.sqrt(eta / i**gamma)
updates_i_rescaled = jtu.tree_map(
lambda g, s=scale: g * s, updates_i_unit)
chex.assert_trees_all_close(updates_i, updates_i_rescaled, rtol=1e-4)
if __name__ == "__main__":
absltest.main()
|