本文整理汇总了Python中tensorflow.python.ops.nn.fused_batch_norm方法的典型用法代码示例。如果您正苦于以下问题:Python nn.fused_batch_norm方法的具体用法?Python nn.fused_batch_norm怎么用?Python nn.fused_batch_norm使用的例子?那么, 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tensorflow.python.ops.nn
的用法示例。
在下文中一共展示了nn.fused_batch_norm方法的1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: _fused_batch_norm
# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import fused_batch_norm [as 别名]
def _fused_batch_norm(self, inputs, training):
"""Returns the output of fused batch norm."""
# TODO(reedwm): Add support for fp16 inputs.
beta = self.beta if self.center else self._beta_const
gamma = self.gamma if self.scale else self._gamma_const
def _fused_batch_norm_training():
return nn.fused_batch_norm(
inputs,
gamma,
beta,
epsilon=self.epsilon,
data_format=self._data_format)
def _fused_batch_norm_inference():
return nn.fused_batch_norm(
inputs,
gamma,
beta,
mean=self.moving_mean,
variance=self.moving_variance,
epsilon=self.epsilon,
is_training=False,
data_format=self._data_format)
output, mean, variance = utils.smart_cond(
training, _fused_batch_norm_training, _fused_batch_norm_inference)
if not self._bessels_correction_test_only:
# Remove Bessel's correction to be consistent with non-fused batch norm.
# Note that the variance computed by fused batch norm is
# with Bessel's correction.
sample_size = math_ops.cast(
array_ops.size(inputs) / array_ops.size(variance), variance.dtype)
factor = (sample_size - math_ops.cast(1.0, variance.dtype)) / sample_size
variance *= factor
training_value = utils.constant_value(training)
if training_value is None:
one_minus_decay = _smart_select(training,
lambda: self._one_minus_decay,
lambda: 0.)
else:
one_minus_decay = ops.convert_to_tensor(self._one_minus_decay)
if training_value or training_value is None:
mean_update = self._assign_moving_average(self.moving_mean, mean,
one_minus_decay)
variance_update = self._assign_moving_average(self.moving_variance,
variance, one_minus_decay)
if context.in_graph_mode():
# Note that in Eager mode, the updates are already executed when running
# assign_moving_averages. So we do not need to put them into
# collections.
self.add_update(mean_update, inputs=inputs)
self.add_update(variance_update, inputs=inputs)
return output
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:58,代码来源:normalization.py