本文整理汇总了Python中object_detection.utils.learning_schedules.cosine_decay_with_warmup方法的典型用法代码示例。如果您正苦于以下问题：Python learning_schedules.cosine_decay_with_warmup方法的具体用法？Python learning_schedules.cosine_decay_with_warmup怎么用？Python learning_schedules.cosine_decay_with_warmup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.utils.learning_schedules的用法示例。
在下文中一共展示了learning_schedules.cosine_decay_with_warmup方法的5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: testCosineDecayWithWarmup
# 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 cosine_decay_with_warmup [as 别名]
def testCosineDecayWithWarmup(self):
def graph_fn(global_step):
learning_rate_base = 1.0
total_steps = 100
warmup_learning_rate = 0.1
warmup_steps = 9
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step, learning_rate_base, total_steps,
warmup_learning_rate, warmup_steps)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
input_global_steps = [0, 4, 8, 9, 100]
output_rates = [
self.execute(graph_fn, [np.array(step).astype(np.int64)])
for step in input_global_steps
]
self.assertAllClose(output_rates, exp_rates) 示例2: testCosineDecayAfterTotalSteps
# 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 cosine_decay_with_warmup [as 别名]
def testCosineDecayAfterTotalSteps(self):
def graph_fn(global_step):
learning_rate_base = 1.0
total_steps = 100
warmup_learning_rate = 0.1
warmup_steps = 9
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step, learning_rate_base, total_steps,
warmup_learning_rate, warmup_steps)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
exp_rates = [0]
input_global_steps = [101]
output_rates = [
self.execute(graph_fn, [np.array(step).astype(np.int64)])
for step in input_global_steps
]
self.assertAllClose(output_rates, exp_rates) 示例3: testCosineDecayWithHoldBaseLearningRateSteps
# 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 cosine_decay_with_warmup [as 别名]
def testCosineDecayWithHoldBaseLearningRateSteps(self):
def graph_fn(global_step):
learning_rate_base = 1.0
total_steps = 120
warmup_learning_rate = 0.1
warmup_steps = 9
hold_base_rate_steps = 20
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step, learning_rate_base, total_steps,
warmup_learning_rate, warmup_steps, hold_base_rate_steps)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
exp_rates = [0.1, 0.5, 0.9, 1.0, 1.0, 1.0, 0.999702, 0.874255, 0.577365,
0.0]
input_global_steps = [0, 4, 8, 9, 10, 29, 30, 50, 70, 120]
output_rates = [
self.execute(graph_fn, [np.array(step).astype(np.int64)])
for step in input_global_steps
]
self.assertAllClose(output_rates, exp_rates) 示例4: testCosineDecayWithWarmup
# 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 cosine_decay_with_warmup [as 别名]
def testCosineDecayWithWarmup(self):
global_step = tf.placeholder(tf.int32, [])
learning_rate_base = 1.0
total_steps = 100
warmup_learning_rate = 0.1
warmup_steps = 9
input_global_steps = [0, 4, 8, 9, 100]
exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step, learning_rate_base, total_steps,
warmup_learning_rate, warmup_steps)
with self.test_session() as sess:
output_rates = []
for input_global_step in input_global_steps:
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5: testCosineDecayWithWarmup
# 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 cosine_decay_with_warmup [as 别名]
def testCosineDecayWithWarmup(self):
def graph_fn(global_step):
learning_rate_base = 1.0
total_steps = 100
warmup_learning_rate = 0.1
warmup_steps = 9
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step, learning_rate_base, total_steps,
warmup_learning_rate, warmup_steps)
return (learning_rate,)
exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
input_global_steps = [0, 4, 8, 9, 100]
output_rates = [
self.execute(graph_fn, [np.array(step).astype(np.int64)])
for step in input_global_steps
]
self.assertAllClose(output_rates, exp_rates)