Python源码示例：inception.slim.losses.l2_regularizer()
示例1
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例2
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例3
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例4
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例5
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例6
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例7
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例8
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例9
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例10
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例11
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例12
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例13
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例14
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例15
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例16
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例17
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例18
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例19
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例20
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例21
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例22
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例23
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例24
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例25
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例26
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例27
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
示例28
def testL2Regularizer(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer()(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例29
def testL2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l2_regularizer(scope='L2')(tensor)
self.assertEquals(loss.op.name, 'L2/value')
self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
示例30
def testL2RegularizerWithWeight(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight = 0.01
loss = losses.l2_regularizer(weight)(tensor)
self.assertEquals(loss.op.name, 'L2Regularizer/value')
self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)
