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# Copyright 2016 The TensorFlow Authors. 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 moving_average_optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tempfile
import six
from tensorflow.contrib.opt.python.training import moving_average_optimizer
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent
from tensorflow.python.training import saver
class MovingAverageOptimizerTest(test.TestCase):
def testRun(self):
self._helpTestRun(use_resource=False)
def testRunUseResource(self):
# Test that MovingAverageOptimizer works with resource variables.
self._helpTestRun(use_resource=True)
def _helpTestRun(self, use_resource=False):
for sequential_update in [True, False]:
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session(graph=ops.Graph()) as sess:
orig_val0 = [1.0, 2.0]
orig_val1 = [3.0, 4.0]
var0 = variable_scope.get_variable(
'var0',
initializer=constant_op.constant(orig_val0, dtype=dtype),
use_resource=use_resource)
var1 = variable_scope.get_variable(
'var1',
initializer=constant_op.constant(orig_val1, dtype=dtype),
use_resource=use_resource)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
opt = moving_average_optimizer.MovingAverageOptimizer(
gradient_descent.GradientDescentOptimizer(learning_rate=2.0),
average_decay=0.5,
sequential_update=sequential_update)
save_dir = tempfile.mkdtemp(
prefix=os.path.join(self.get_temp_dir(), 'run_1'))
save_path = os.path.join(save_dir, 'model')
update = opt.apply_gradients(
list(six.moves.zip([grads0, grads1], [var0, var1])))
global_vars = variables.global_variables()
ema_var0 = [
v for v in global_vars
if v.op.name == 'var0/ExponentialMovingAverage'
][0]
ema_var1 = [
v for v in global_vars
if v.op.name == 'var1/ExponentialMovingAverage'
][0]
perturb = control_flow_ops.group([
state_ops.assign_add(var0, [1.0, 1.0]),
state_ops.assign_add(var1, [2.0, 2.0]),
state_ops.assign_add(ema_var0, [3.0, 3.0]),
state_ops.assign_add(ema_var1, [4.0, 4.0])
])
# Test taht saver with missing ema variables will fail.
with self.assertRaisesRegexp(ValueError, r'Variable to swap'):
opt.swapping_saver(var_list=[var0])
train_saver = opt.swapping_saver()
train_saver_subset = opt.swapping_saver(var_list=[var0, ema_var0])
inference_saver = saver.Saver()
variables.global_variables_initializer().run()
# Step 1.
update.run()
self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
self.assertAllCloseAccordingToType([2.98, 3.98], var1.eval())
if sequential_update:
self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval())
self.assertAllCloseAccordingToType([2.99, 3.99], ema_var1.eval())
# Test that the swapping saver save/restore operation is identity.
train_saver.save(sess, save_path)
train_saver.restore(sess, save_path)
self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
self.assertAllCloseAccordingToType([2.98, 3.98], var1.eval())
if sequential_update:
self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval())
self.assertAllCloseAccordingToType([2.99, 3.99], ema_var1.eval())
# Test that the subset saver saves the EMA variable as well.
if sequential_update:
subset_save_path = save_path + '_subset'
train_saver_subset.save(sess, subset_save_path)
perturb.run()
self.assertAllCloseAccordingToType([1.8, 2.8], var0.eval())
self.assertAllCloseAccordingToType([3.9, 4.9], ema_var0.eval())
self.assertAllCloseAccordingToType([4.98, 5.98], var1.eval())
self.assertAllCloseAccordingToType([6.99, 7.99], ema_var1.eval())
# Restoring should only restore var0 and ema_var0.
train_saver_subset.restore(sess, subset_save_path)
self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
self.assertAllCloseAccordingToType([0.9, 1.9], ema_var0.eval())
self.assertAllCloseAccordingToType([4.98, 5.98], var1.eval())
self.assertAllCloseAccordingToType([6.99, 7.99], ema_var1.eval())
# Restore back to previou state.
train_saver.restore(sess, save_path)
# If updates are parallel, this is not always true after the 1st step.
if sequential_update:
# Test that the normal saver will have the averaged variables.
# We test that the average values are between the original value
# and the most recent variable values (since they are an average
# of the two).
val0 = var0.eval()
val1 = var1.eval()
train_saver.save(sess, save_path)
inference_saver.restore(sess, save_path)
avg_val0 = var0.eval()
avg_val1 = var1.eval()
for i in six.moves.range(len(val0)):
self.assertLess(val0[i], avg_val0[i])
self.assertLess(avg_val0[i], orig_val0[i])
self.assertLess(val1[i], avg_val1[i])
self.assertLess(avg_val1[i], orig_val1[i])
train_saver.restore(sess, save_path)
# Step 2.
update.run()
# Test that the normal saver will have the averaged variables.
# We test that the average values are between the original value and
# the most recent variable values (since they are an average of the
# two).
val0 = var0.eval()
val1 = var1.eval()
self.assertAllCloseAccordingToType([0.6, 1.6], val0)
self.assertAllCloseAccordingToType([2.96, 3.96], val1)
train_saver.save(sess, save_path)
inference_saver.restore(sess, save_path)
avg_val0 = var0.eval()
avg_val1 = var1.eval()
for i in six.moves.range(len(val0)):
self.assertLess(val0[i], avg_val0[i])
self.assertLess(avg_val0[i], orig_val0[i])
self.assertLess(val1[i], avg_val1[i])
self.assertLess(avg_val1[i], orig_val1[i])
def testFailWhenSaverCreatedBeforeInitialized(self):
with self.test_session():
var = variables.Variable([1.0], name='var', dtype=dtypes.float32)
opt = moving_average_optimizer.MovingAverageOptimizer(
gradient_descent.GradientDescentOptimizer(learning_rate=2.0))
# We didn't call apply_gradients yet.
# This will raise an exception.
with self.assertRaises(RuntimeError):
_ = opt.swapping_saver([var])
def testCorrectOverride(self):
class WrapperOptimizer(gradient_descent.GradientDescentOptimizer):
def compute_gradients(self, *args, **kwargs):
self.compute_gradients_called = True
return super(WrapperOptimizer, self).compute_gradients(
*args, **kwargs)
def apply_gradients(self, *args, **kwargs):
self.apply_gradients_called = True
return super(WrapperOptimizer, self).apply_gradients(*args, **kwargs)
with self.test_session() as sess:
var = variables.Variable([1.2], name='var', dtype=dtypes.float32)
loss = var ** 2
wrapper_opt = WrapperOptimizer(learning_rate=2.0)
opt = moving_average_optimizer.MovingAverageOptimizer(wrapper_opt)
train_op = opt.minimize(loss)
# Check that both methods are called on the underlying optimizer.
self.assertTrue(wrapper_opt.compute_gradients_called)
self.assertTrue(wrapper_opt.apply_gradients_called)
# Run train_op once, and verify that we've updated the variable.
variables.global_variables_initializer().run()
sess.run(train_op)
var_value = sess.run(var)
# Started at 1.2, gradient is 2*1.2=2.4, lr=2, so should now be -3.6.
self.assertNear(-3.6, var_value, 1e-6)
if __name__ == '__main__':
test.main()
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