def testNoGlobalStepWithDecay(self):
optimizers = [
"SGD", gradient_descent.GradientDescentOptimizer,
gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
]
for optimizer in optimizers:
with ops.Graph().as_default() as g, self.session(graph=g):
x = array_ops.placeholder(dtypes.float32, [])
var = variable_scope.get_variable(
"test", [], initializer=init_ops.constant_initializer(10))
loss = math_ops.abs(var * x)
update_var = variable_scope.get_variable(
"update", [],
initializer=init_ops.constant_initializer(10))
update_op = state_ops.assign(update_var, 20)
with self.assertRaisesRegexp(
ValueError,
"global_step is required for learning_rate_decay_fn"):
optimizers_lib.optimize_loss(
loss,
global_step=None,
learning_rate=0.1,
learning_rate_decay_fn=_no_op_learning_rate_decay_fn,
optimizer=optimizer,
update_ops=[update_op])
def testInvalidLearningRate(self):
with ops.Graph().as_default() as g, self.session(graph=g):
_, _, loss, global_step = _setup_model()
with self.assertRaises(ValueError):
optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=-0.1,
optimizer="SGD")
def testBadSummaries(self):
with ops.Graph().as_default() as g, self.session(graph=g):
_, _, loss, global_step = _setup_model()
with self.assertRaises(ValueError):
optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
summaries=["loss", "bad_summary"])
def testWrongOptimizer(self):
optimizers = ["blah", variables.Variable, object(), lambda x: None]
for optimizer in optimizers:
with ops.Graph().as_default() as g:
with self.session(graph=g):
_, _, loss, global_step = _setup_model()
with self.assertRaises(ValueError):
optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer=optimizer)
def testIgnoreVariablesWithNoGradients(self):
_, _, loss, global_step = _setup_model()
unused_variable = variable_scope.get_variable("ignore_me", [])
optimizers_lib.optimize_loss(
loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
gradient_noise_scale=10.0,
gradient_multipliers={unused_variable: 1.},
clip_gradients=10.0)
def testUpdateOpFromCollection(self):
optimizers = [
"SGD", gradient_descent.GradientDescentOptimizer,
gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
]
for optimizer in optimizers:
with ops.Graph().as_default() as g, self.session(
graph=g) as session:
x, var, loss, global_step = _setup_model()
update_var = variable_scope.get_variable(
"update", [],
initializer=init_ops.constant_initializer(10))
update_op = state_ops.assign(update_var, 20)
ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, update_op)
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer=optimizer)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, update_var_value, global_step_value = session.run(
[var, update_var, global_step])
self.assertEqual(var_value, 9.5)
self.assertEqual(update_var_value, 20)
self.assertEqual(global_step_value, 1)
def testUpdateOpWithNoOpDecay(self):
optimizers = [
"SGD", gradient_descent.GradientDescentOptimizer,
gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
]
for optimizer in optimizers:
with ops.Graph().as_default() as g, self.session(
graph=g) as session:
x, var, loss, global_step = _setup_model()
update_var = variable_scope.get_variable(
"update", [],
initializer=init_ops.constant_initializer(10))
update_op = state_ops.assign(update_var, 20)
train = optimizers_lib.optimize_loss(
loss,
global_step,
learning_rate=0.1,
learning_rate_decay_fn=_no_op_learning_rate_decay_fn,
optimizer=optimizer,
update_ops=[update_op])
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
self.assertEqual(9.5, var.eval())
self.assertEqual(20, update_var.eval())
self.assertEqual(1, global_step.eval())
def testNoGlobalStep(self):
optimizers = [
"SGD", gradient_descent.GradientDescentOptimizer,
gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
]
for optimizer in optimizers:
with ops.Graph().as_default() as g, self.session(
graph=g) as session:
x = array_ops.placeholder(dtypes.float32, [])
var = variable_scope.get_variable(
"test", [], initializer=init_ops.constant_initializer(10))
loss = math_ops.abs(var * x)
update_var = variable_scope.get_variable(
"update", [],
initializer=init_ops.constant_initializer(10))
update_op = state_ops.assign(update_var, 20)
train = optimizers_lib.optimize_loss(loss,
global_step=None,
learning_rate=0.1,
optimizer=optimizer,
update_ops=[update_op])
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
self.assertEqual(9.5, var.eval())
self.assertEqual(20, update_var.eval())
def testInvalidGlobalStep(self):
with ops.Graph().as_default() as g, self.session(graph=g):
x = array_ops.placeholder(dtypes.float32, [])
var = variable_scope.get_variable(
"test", [], initializer=init_ops.constant_initializer(10))
loss = math_ops.abs(var * x)
with self.assertRaises(AttributeError):
optimizers_lib.optimize_loss(loss,
global_step=constant_op.constant(
43, dtype=dtypes.int64),
learning_rate=0.1,
optimizer="SGD")
with self.assertRaises(TypeError):
optimizers_lib.optimize_loss(
loss,
global_step=variable_scope.get_variable(
"global_step", [],
trainable=False,
dtype=dtypes.float64,
initializer=init_ops.constant_initializer(
0.0, dtype=dtypes.float64)),
learning_rate=0.1,
optimizer="SGD")
with self.assertRaises(ValueError):
optimizers_lib.optimize_loss(
loss,
global_step=variable_scope.get_variable(
"global_step", [1],
trainable=False,
dtype=dtypes.int64,
initializer=init_ops.constant_initializer(
[0], dtype=dtypes.int64)),
learning_rate=0.1,
optimizer="SGD")
def testGradientClip(self):
with self.cached_session() as session:
x, var, loss, global_step = _setup_model()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
clip_gradients=0.1)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run([var, global_step])
self.assertAlmostEqual(var_value, 9.98999, 4)
self.assertEqual(global_step_value, 1)
def testGradientNoiseWithClipping(self):
random_seed.set_random_seed(42)
with self.cached_session() as session:
x, var, loss, global_step = _setup_model()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
gradient_noise_scale=10.0,
clip_gradients=10.0)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run([var, global_step])
self.assertAlmostEqual(var_value, 9.801016, 4)
self.assertEqual(global_step_value, 1)
def testGradientNoise(self):
random_seed.set_random_seed(42)
with self.cached_session() as session:
x, var, loss, global_step = _setup_model()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
gradient_noise_scale=10.0)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run([var, global_step])
# Due to randomness the following number may change if graph is different.
self.assertAlmostEqual(var_value, 9.801016, 4)
self.assertEqual(global_step_value, 1)
def testNoLrCallable(self):
def optimizer_fn():
return gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
with ops.Graph().as_default() as g:
with self.session(graph=g) as session:
x, var, loss, global_step = _setup_model()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=None,
optimizer=optimizer_fn)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run([var, global_step])
self.assertEqual(var_value, 9.5)
self.assertEqual(global_step_value, 1)
def testGradientMultiplyInt64Tensor(self):
with self.cached_session() as session:
x, var, loss, global_step = _setup_model()
v = array_ops.placeholder(dtypes.float64, [])
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
gradient_multipliers={var: v})
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5, v: 7.})
var_value, global_step_value = session.run([var, global_step])
# var(0) = 10, x = 5, var(0)/dx = 5,
# var(1) = var(0) - learning_rate * gradient_multiplier * var(0)/dx
self.assertAlmostEqual(var_value, 6.5, 4)
self.assertEqual(global_step_value, 1)
def testAdaptiveGradientClip(self):
with self.cached_session() as session:
x, var, loss, global_step = _setup_model()
clip_gradients = optimizers_lib.adaptive_clipping_fn()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer="SGD",
clip_gradients=clip_gradients)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run([var, global_step])
self.assertAlmostEqual(var_value, 9.8916, 4)
self.assertEqual(global_step_value, 1)
var_count = 0
for var in variables.global_variables():
if var.name.startswith("OptimizeLoss/AdaptiveMaxNorm"):
var_count += 1
self.assertEqual(2, var_count)
def testSGDOptimizer(self):
optimizers = [
"SGD", gradient_descent.GradientDescentOptimizer,
gradient_descent.GradientDescentOptimizer(learning_rate=0.1),
lambda lr: gradient_descent.GradientDescentOptimizer(
learning_rate=lr), "Momentum"
]
for optimizer in optimizers:
with ops.Graph().as_default() as g:
with self.session(graph=g) as session:
x, var, loss, global_step = _setup_model()
train = optimizers_lib.optimize_loss(loss,
global_step,
learning_rate=0.1,
optimizer=optimizer)
variables.global_variables_initializer().run()
session.run(train, feed_dict={x: 5})
var_value, global_step_value = session.run(
[var, global_step])
self.assertEqual(var_value, 9.5)
self.assertEqual(global_step_value, 1)
