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)
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 testRun(self):
for sequential_update in [True, False]:
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session() as sess:
orig_val0 = [1.0, 2.0]
orig_val1 = [3.0, 4.0]
var0 = variables.Variable(orig_val0,
name='var0',
dtype=dtype)
var1 = variables.Variable(orig_val1,
name='var1',
dtype=dtype)
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])))
train_saver = opt.swapping_saver()
inference_saver = saver.Saver()
variables.global_variables_initializer().run()
# Step 1.
update.run()
val0 = var0.eval()
val1 = var1.eval()
self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
self.assertAllCloseAccordingToType([2.98, 3.98],
var1.eval())
# Test that the swapping saver save/restore operation is identity.
train_saver.save(sess, save_path)
train_saver.restore(sess, save_path)
val0 = var0.eval()
val1 = var1.eval()
self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
self.assertAllCloseAccordingToType([2.98, 3.98],
var1.eval())
# 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 _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 that 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 previous 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 _helpTestRun(self, use_resource=False, use_partitioned_vars=False):
# Partitioned variables are represented as a "collection" of partitions.
# To simplify the test and reuse as much code as possible we employ
# following test strategy for partitioned variables.
#
# In the case of non-partitioned variables test runs on variables with
# shape [2].
#
# In the case of partitioned variables we use shape [4] with two partitions,
# thus each partition has shape [2].
# For partitioned variables the test is run twice (for loop over
# variable_part_names), first time on the first partition of each variable,
# second time on the second partition of each variable.
variable_part_names = ['part_0', 'part_1'
] if use_partitioned_vars else ['']
for sequential_update in [True, False]:
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
for var_part_name in variable_part_names:
with self.session(graph=ops.Graph()) as sess:
orig_val0 = [1.0, 2.0]
orig_val1 = [3.0, 4.0]
grads0 = [0.1, 0.1]
grads1 = [0.01, 0.01]
if use_partitioned_vars:
# Use partitioned variables.
# Create partitioned and duplicate each value used as initial
# value of variables.
partitioner = partitioned_variables.fixed_size_partitioner(
num_shards=2)
orig_val0 = orig_val0 * 2
orig_val1 = orig_val1 * 2
grads0 = grads0 * 2
grads1 = grads1 * 2
else:
# Regular (non-partitioned) variables.
partitioner = None
var0 = variable_scope.get_variable(
'var0',
initializer=constant_op.constant(orig_val0,
dtype=dtype),
use_resource=use_resource,
partitioner=partitioner)
var1 = variable_scope.get_variable(
'var1',
initializer=constant_op.constant(orig_val1,
dtype=dtype),
use_resource=use_resource,
partitioner=partitioner)
# Make a fake loss, such that gradient(loss, var0) == grads0
# and gradient(loss, var1) == grads1
grads0 = constant_op.constant(grads0, dtype=dtype)
grads1 = constant_op.constant(grads1, dtype=dtype)
loss = (math_ops.reduce_sum(grads0 * var0) +
math_ops.reduce_sum(grads1 * var1))
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.minimize(loss)
# Get variables and their EMAs. In case of partitioned variables
# get proper part of each variable.
def _get_variable(var_name, part_name, ema):
"""Returns variable of it's moving average by name."""
matches = [
v for v in variables.global_variables()
if ((var_name in v.op.name) and (
part_name in v.op.name) and (
('ExponentialMovingAverage' in
v.op.name) == ema))
]
self.assertEqual(len(matches), 1)
return matches[0]
var0 = _get_variable('var0', var_part_name, ema=False)
var1 = _get_variable('var1', var_part_name, ema=False)
ema_var0 = _get_variable('var0',
var_part_name,
ema=True)
ema_var1 = _get_variable('var1',
var_part_name,
ema=True)
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 that 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 previous 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])
