本文整理汇总了Python中inception.slim.losses.l1_l2_regularizer方法的典型用法代码示例。如果您正苦于以下问题：Python losses.l1_l2_regularizer方法的具体用法？Python losses.l1_l2_regularizer怎么用？Python losses.l1_l2_regularizer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inception.slim.losses的用法示例。
在下文中一共展示了losses.l1_l2_regularizer方法的3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: testL1L2Regularizer
# 需要导入模块: from inception.slim import losses [as 别名]
# 或者: from inception.slim.losses import l1_l2_regularizer [as 别名]
def testL1L2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l1_l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L1L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5) 示例2: testL1L2RegularizerWithScope
# 需要导入模块: from inception.slim import losses [as 别名]
# 或者: from inception.slim.losses import l1_l2_regularizer [as 别名]
def testL1L2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l1_l2_regularizer(scope='L1L2')(tensor)
self.assertEquals(loss.op.name, 'L1L2/value')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5) 示例3: testL1L2RegularizerWithWeights
# 需要导入模块: from inception.slim import losses [as 别名]
# 或者: from inception.slim.losses import l1_l2_regularizer [as 别名]
def testL1L2RegularizerWithWeights(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight_l1 = 0.01
weight_l2 = 0.05
loss = losses.l1_l2_regularizer(weight_l1, weight_l2)(tensor)
self.assertEquals(loss.op.name, 'L1L2Regularizer/value')
self.assertAlmostEqual(loss.eval(),
num_elem * weight_l1 + num_elem * weight_l2 / 2, 5)