本文整理汇总了Python中object_detection.builders.optimizer_builder.build方法的典型用法代码示例。如果您正苦于以下问题：Python optimizer_builder.build方法的具体用法？Python optimizer_builder.build怎么用？Python optimizer_builder.build使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.builders.optimizer_builder的用法示例。
在下文中一共展示了optimizer_builder.build方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testBuildRMSPropOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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
"""
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.train.RMSPropOptimizer)) 示例2: testBuildMomentumOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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
"""
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.train.MomentumOptimizer)) 示例3: testBuildAdamOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
def testBuildAdamOptimizer(self):
optimizer_text_proto = """
adam_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.002
}
}
}
use_moving_average: false
"""
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.train.AdamOptimizer)) 示例4: testBuildMovingAverageOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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)) 示例5: testBuildMovingAverageOptimizerWithNonDefaultDecay
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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
"""
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))
# TODO: Find a way to not depend on the private members.
self.assertAlmostEqual(optimizer._ema._decay, 0.2) 示例6: testBuildRMSPropOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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)) 示例7: testBuildMomentumOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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)) 示例8: testBuildMovingAverageOptimizer
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
def testBuildMovingAverageOptimizer(self):
optimizer_text_proto = """
adam_optimizer: {
learning_rate: {
constant_learning_rate {
learning_rate: 0.002
}
}
}
use_moving_average: True
"""
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)) 示例9: testBuildMovingAverageOptimizerWithNonDefaultDecay
# 需要导入模块: from object_detection.builders import optimizer_builder [as 别名]
# 或者: from object_detection.builders.optimizer_builder import build [as 别名]
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(rathodv): Find a way to not depend on the private members.
self.assertAlmostEqual(optimizer._ema._decay, 0.2)