本文整理匯總了Python中tensorflow.moving_average_variables方法的典型用法代碼示例。如果您正苦於以下問題：Python tensorflow.moving_average_variables方法的具體用法？Python tensorflow.moving_average_variables怎麽用？Python tensorflow.moving_average_variables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tensorflow的用法示例。
在下文中一共展示了tensorflow.moving_average_variables方法的7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: batch_norm
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def batch_norm(x, train, name, decay=0.99, epsilon=1e-5):
shape = x.get_shape().as_list()
with tf.variable_scope(name):
beta = tf.get_variable('beta', [shape[-1]], initializer=tf.constant_initializer(0.))
gamma = tf.get_variable('gamma', [shape[-1]], initializer=tf.random_normal_initializer(1., 0.02))
pop_mean = tf.get_variable('pop_mean', [shape[-1]], initializer=tf.constant_initializer(0.), trainable=False)
pop_var = tf.get_variable('pop_var', [shape[-1]], initializer=tf.constant_initializer(1.), trainable=False)
if pop_mean not in tf.moving_average_variables():
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_mean)
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_var)
def func1():
# Execute at training time
batch_mean, batch_var = tf.nn.moments(x, range(len(shape) - 1))
update_mean = tf.assign_sub(pop_mean, (1 - decay)*(pop_mean - batch_mean))
update_var = tf.assign_sub(pop_var, (1 - decay)*(pop_var - batch_var))
with tf.control_dependencies([update_mean, update_var]):
return tf.nn.batch_normalization(x, batch_mean, batch_var, beta, gamma, epsilon)
def func2():
# Execute at test time
return tf.nn.batch_normalization(x, pop_mean, pop_var, beta, gamma, epsilon)
return tf.cond(train, func1, func2) 示例2: testCreateVariables
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance') 示例3: testCreateVariablesWithScale
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance') 示例4: testCreateVariablesWithoutCenterWithScale
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance') 示例5: testCreateVariablesWithoutCenterWithoutScale
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance') 示例6: testMovingAverageVariables
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance') 示例7: begin
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import moving_average_variables [as 別名]
def begin(self):
""" Create restoring operations before the graph been finalized. """
ema_variables = tf.moving_average_variables()
self._restore_ops = [tf.assign(x, self._ema.average(x)) for x in ema_variables]
print("==get restore ops==")