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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0. Licensed to the Apache
# Software Foundation. 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.
# ==============================================================================
"""Test for Layer-wise Adaptive Rate Scaling optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.opt.python.training import lars_optimizer as lo
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class LARSOptimizerTest(test.TestCase):
def testLARSGradientOneStep(self):
for _ in range(10):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session() as sess:
shape = [3, 3]
var_np = np.ones(shape)
grad_np = np.ones(shape)
lr_np = 0.1
m_np = 0.9
wd_np = 0.1
ep_np = 1e-5
eeta = 0.1
vel_np = np.zeros(shape)
var = variables.Variable(var_np, dtype=dtype)
grad = variables.Variable(grad_np, dtype=dtype)
opt = lo.LARSOptimizer(
learning_rate=lr_np,
momentum=m_np,
weight_decay=wd_np,
eeta=eeta,
epsilon=ep_np)
step = opt.apply_gradients([(grad, var)])
variables.global_variables_initializer().run()
pre_var = sess.run(var)
pre_vel = sess.run(opt.get_slot(var, 'momentum'))
self.assertAllClose(var_np, pre_var)
self.assertAllClose(vel_np, pre_vel)
step.run()
post_var = sess.run(var)
post_vel = sess.run(opt.get_slot(var, 'momentum'))
w_norm = np.linalg.norm(var_np.flatten(), ord=2)
g_norm = np.linalg.norm(grad_np.flatten(), ord=2)
trust_ratio = eeta * w_norm / (g_norm + wd_np * w_norm + ep_np)
scaled_lr = lr_np * trust_ratio
vel_np = m_np * vel_np + grad_np
var_np -= scaled_lr * vel_np
self.assertAllClose(var_np, post_var)
self.assertAllClose(vel_np, post_vel)
def testLARSGradientMultiStep(self):
for _ in range(10):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session() as sess:
shape = [3, 3]
var_np = np.ones(shape)
grad_np = np.ones(shape)
lr_np = 0.1
m_np = 0.9
wd_np = 0.1
ep_np = 1e-5
eeta = 0.1
vel_np = np.zeros(shape)
var = variables.Variable(var_np, dtype=dtype)
grad = variables.Variable(grad_np, dtype=dtype)
opt = lo.LARSOptimizer(
learning_rate=lr_np,
momentum=m_np,
eeta=eeta,
weight_decay=wd_np,
epsilon=ep_np)
step = opt.apply_gradients([(grad, var)])
variables.global_variables_initializer().run()
pre_var = sess.run(var)
pre_vel = sess.run(opt.get_slot(var, 'momentum'))
self.assertAllClose(var_np, pre_var)
self.assertAllClose(vel_np, pre_vel)
for _ in range(10):
step.run()
post_var = sess.run(var)
post_vel = sess.run(opt.get_slot(var, 'momentum'))
w_norm = np.linalg.norm(var_np.flatten(), ord=2)
g_norm = np.linalg.norm(grad_np.flatten(), ord=2)
trust_ratio = eeta * w_norm / (g_norm + wd_np * w_norm + ep_np)
scaled_lr = lr_np * trust_ratio
vel_np = m_np * vel_np + grad_np
var_np -= scaled_lr * vel_np
self.assertAllClose(var_np, post_var)
self.assertAllClose(vel_np, post_vel)
if __name__ == '__main__':
test.main()
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