def test_create_optimizer(self):
options_str = "adagrad{}"
options = text_format.Merge(options_str, optimizer_pb2.Optimizer())
opt = optimization.create_optimizer(options)
self.assertIsInstance(opt, tf.compat.v1.train.AdagradOptimizer)
options_str = "rmsprop{}"
options = text_format.Merge(options_str, optimizer_pb2.Optimizer())
opt = optimization.create_optimizer(options)
self.assertIsInstance(opt, tf.compat.v1.train.RMSPropOptimizer)
options_str = "adam{}"
options = text_format.Merge(options_str, optimizer_pb2.Optimizer())
opt = optimization.create_optimizer(options)
self.assertIsInstance(opt, tf.compat.v1.train.AdamOptimizer)
def testBuildEmptyOptimizer(self):
optimizer_text_proto = """
"""
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
with self.assertRaises(ValueError):
optimizer_builder.build(optimizer_proto)
def testBuildEmptyOptimizer(self):
optimizer_text_proto = """
"""
global_summaries = set([])
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
with self.assertRaises(ValueError):
optimizer_builder.build(optimizer_proto, global_summaries)
def test_build_optimizer(self):
# Gradient descent optimizer.
options_str = r"""
sgd {
}
"""
options = optimizer_pb2.Optimizer()
text_format.Merge(options_str, options)
opt = training_utils.build_optimizer(options)
self.assertIsInstance(opt, tf.train.GradientDescentOptimizer)
# Adagrad optimizer.
options_str = r"""
adagrad {
}
"""
options = optimizer_pb2.Optimizer()
text_format.Merge(options_str, options)
opt = training_utils.build_optimizer(options)
self.assertIsInstance(opt, tf.train.AdagradOptimizer)
# Adam optimizer.
options_str = r"""
adam {
}
"""
options = optimizer_pb2.Optimizer()
text_format.Merge(options_str, options)
opt = training_utils.build_optimizer(options)
self.assertIsInstance(opt, tf.train.AdamOptimizer)
# Rmsprop optimizer.
options_str = r"""
rmsprop {
}
"""
options = optimizer_pb2.Optimizer()
text_format.Merge(options_str, options)
opt = training_utils.build_optimizer(options)
self.assertIsInstance(opt, tf.train.RMSPropOptimizer)
def testBuildAdamOptimizer(self):
optimizer_text_proto = """
adam_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.002
}
}
}
use_moving_average: false
"""
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
optimizer, _ = optimizer_builder.build(optimizer_proto)
self.assertTrue(isinstance(optimizer, tf.train.AdamOptimizer))
def testBuildMomentumOptimizer(self):
optimizer_text_proto = """
momentum_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.001
}
}
momentum_optimizer_value: 0.99
}
use_moving_average: false
"""
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
optimizer, _ = optimizer_builder.build(optimizer_proto)
self.assertTrue(isinstance(optimizer, tf.train.MomentumOptimizer))
def testBuildMovingAverageOptimizer(self):
optimizer_text_proto = """
adam_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.002
}
}
}
use_moving_average: True
"""
global_summaries = set([])
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
optimizer = optimizer_builder.build(optimizer_proto, global_summaries)
self.assertTrue(
isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer))
def testBuildMovingAverageOptimizerWithNonDefaultDecay(self):
optimizer_text_proto = """
adam_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.002
}
}
}
use_moving_average: True
moving_average_decay: 0.2
"""
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
optimizer, _ = optimizer_builder.build(optimizer_proto)
self.assertTrue(
isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer))
# TODO: Find a way to not depend on the private members.
self.assertAlmostEqual(optimizer._ema._decay, 0.2)
def testBuildRMSPropOptimizer(self):
optimizer_text_proto = """
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
use_moving_average: false
"""
optimizer_proto = optimizer_pb2.Optimizer()
text_format.Merge(optimizer_text_proto, optimizer_proto)
optimizer, _ = optimizer_builder.build(optimizer_proto)
self.assertTrue(isinstance(optimizer, tf.train.RMSPropOptimizer))
