本文整理汇总了Python中object_detection.utils.learning_schedules.manual_stepping方法的典型用法代码示例。如果您正苦于以下问题：Python learning_schedules.manual_stepping方法的具体用法？Python learning_schedules.manual_stepping怎么用？Python learning_schedules.manual_stepping使用的例子？那么, 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.utils.learning_schedules的用法示例。
在下文中一共展示了learning_schedules.manual_stepping方法的4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: testManualStepping
# 需要导入模块: from object_detection.utils import learning_schedules [as 别名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 别名]
def testManualStepping(self):
global_step = tf.placeholder(tf.int64, [])
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
learning_rate = learning_schedules.manual_stepping(global_step, boundaries,
rates)
with self.test_session() as sess:
output_rates = []
for input_global_step in range(10):
output_rate = sess.run(learning_rate,
feed_dict={global_step: input_global_step})
output_rates.append(output_rate)
self.assertAllClose(output_rates, exp_rates) 示例2: testManualStepping
# 需要导入模块: from object_detection.utils import learning_schedules [as 别名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 别名]
def testManualStepping(self):
def graph_fn(global_step):
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
output_rates = [
self.execute(graph_fn, [np.array(i).astype(np.int64)])
for i in range(10)
]
exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
self.assertAllClose(output_rates, exp_rates) 示例3: testManualSteppingWithWarmup
# 需要导入模块: from object_detection.utils import learning_schedules [as 别名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 别名]
def testManualSteppingWithWarmup(self):
def graph_fn(global_step):
boundaries = [4, 6, 8]
rates = [0.02, 0.10, 0.01, 0.001]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates, warmup=True)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
output_rates = [
self.execute(graph_fn, [np.array(i).astype(np.int64)])
for i in range(9)
]
exp_rates = [0.02, 0.04, 0.06, 0.08, 0.10, 0.10, 0.01, 0.01, 0.001]
self.assertAllClose(output_rates, exp_rates) 示例4: testManualSteppingWithZeroBoundaries
# 需要导入模块: from object_detection.utils import learning_schedules [as 别名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 别名]
def testManualSteppingWithZeroBoundaries(self):
def graph_fn(global_step):
boundaries = []
rates = [0.01]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
return (learning_rate,)
output_rates = [
self.execute(graph_fn, [np.array(i).astype(np.int64)])
for i in range(4)
]
exp_rates = [0.01] * 4
self.assertAllClose(output_rates, exp_rates)