本文整理匯總了Python中object_detection.utils.learning_schedules.manual_stepping方法的典型用法代碼示例。如果您正苦於以下問題：Python learning_schedules.manual_stepping方法的具體用法？Python learning_schedules.manual_stepping怎麽用？Python learning_schedules.manual_stepping使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類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個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testManualStepping
# 需要導入模塊: from object_detection.utils import learning_schedules [as 別名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 別名]
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
# 需要導入模塊: from object_detection.utils import learning_schedules [as 別名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 別名]
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
# 需要導入模塊: from object_detection.utils import learning_schedules [as 別名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 別名]
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
# 需要導入模塊: from object_detection.utils import learning_schedules [as 別名]
# 或者: from object_detection.utils.learning_schedules import manual_stepping [as 別名]
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)