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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 | # 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._layouts."""
from absl.testing import absltest
import chex
import jax.numpy as jnp
from optax._src import alias
from optax._src import update
from optax.transforms import _layouts
class LayoutsTest(absltest.TestCase):
def test_flatten(self):
def init_params():
return (jnp.array(2.), jnp.array([1., 2.]), jnp.array([3., 4.]))
per_step_updates = (
jnp.array(1.0),
jnp.array([500.0, 5.0]),
jnp.array([300.0, 3.0]),
)
# First calculate new params without flattening
optax_sgd_params = init_params()
sgd = alias.sgd(1e-2, 0.0)
state_sgd = sgd.init(optax_sgd_params)
updates_sgd, _ = sgd.update(per_step_updates, state_sgd)
sgd_params_no_flatten = update.apply_updates(optax_sgd_params, updates_sgd)
# And now calculate new params with flattening
optax_sgd_params = init_params()
sgd = _layouts.flatten(sgd)
state_sgd = sgd.init(optax_sgd_params)
updates_sgd, _ = sgd.update(per_step_updates, state_sgd)
sgd_params_flatten = update.apply_updates(optax_sgd_params, updates_sgd)
# Test that both give the same result
chex.assert_trees_all_close(
sgd_params_no_flatten, sgd_params_flatten, atol=1e-7, rtol=1e-7)
if __name__ == "__main__":
absltest.main()
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