本文整理汇总了Python中tensorflow.python.layers.utils.constant_value方法的典型用法代码示例。如果您正苦于以下问题:Python utils.constant_value方法的具体用法?Python utils.constant_value怎么用?Python utils.constant_value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.layers.utils
的用法示例。
在下文中一共展示了utils.constant_value方法的2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: _smart_select
# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import constant_value [as 别名]
def _smart_select(pred, fn_then, fn_else):
"""Selects fn_then() or fn_else() based on the value of pred.
The purpose of this function is the same as `utils.smart_cond`. However, at
the moment there is a bug (b/36297356) that seems to kick in only when
`smart_cond` delegates to `tf.cond`, which sometimes results in the training
hanging when using parameter servers. This function will output the result
of `fn_then` or `fn_else` if `pred` is known at graph construction time.
Otherwise, it will use `tf.where` which will result in some redundant work
(both branches will be computed but only one selected). However, the tensors
involved will usually be small (means and variances in batchnorm), so the
cost will be small and will not be incurred at all if `pred` is a constant.
Args:
pred: A boolean scalar `Tensor`.
fn_then: A callable to use when pred==True.
fn_else: A callable to use when pred==False.
Returns:
A `Tensor` whose value is fn_then() or fn_else() based on the value of pred.
"""
pred_value = utils.constant_value(pred)
if pred_value:
return fn_then()
elif pred_value is False:
return fn_else()
t_then = array_ops.expand_dims(fn_then(), 0)
t_else = array_ops.expand_dims(fn_else(), 0)
pred = array_ops.reshape(pred, [1])
result = array_ops.where(pred, t_then, t_else)
return array_ops.squeeze(result, [0])
示例2: _fused_batch_norm
# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import constant_value [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