本文整理汇总了Python中sonnet.Conv3D方法的典型用法代码示例。如果您正苦于以下问题：Python sonnet.Conv3D方法的具体用法？Python sonnet.Conv3D怎么用？Python sonnet.Conv3D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sonnet的用法示例。
在下文中一共展示了sonnet.Conv3D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: _build
# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Conv3D [as 别名]
def _build(self, inputs, is_training):
"""Connects the module to inputs.
Args:
inputs: Inputs to the Unit3D component.
is_training: whether to use training mode for snt.BatchNorm (boolean).
Returns:
Outputs from the module.
"""
net = snt.Conv3D(output_channels=self._output_channels,
kernel_shape=self._kernel_shape,
stride=self._stride,
padding=snt.SAME,
use_bias=self._use_bias)(inputs)
if self._use_batch_norm:
bn = snt.BatchNorm()
net = bn(net, is_training=is_training, test_local_stats=False)
if self._activation_fn is not None:
net = self._activation_fn(net)
return net 示例2: _build
# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Conv3D [as 别名]
def _build(self, inputs, is_training):
"""Connects the module to inputs.
Args:
inputs: Inputs to the Unit3D component.
is_training: whether to use training mode for snt.BatchNorm (boolean).
Returns:
Outputs from the module.
"""
net = snt.Conv3D(output_channels=self._output_channels,
kernel_shape=self._kernel_shape,
stride=self._stride,
padding=snt.SAME,
use_bias=self._use_bias)(inputs)
if self._use_batch_norm:
bn = snt.BatchNorm()
#################### Warning batchnorm is hard coded to is_training=False #################
# net = bn(net, is_training=is_training, test_local_stats=False)
net = bn(net, is_training=False, test_local_stats=False)
if self._activation_fn is not None:
net = self._activation_fn(net)
return net 示例3: _build
# 需要导入模块: import sonnet [as 别名]
# 或者: from sonnet import Conv3D [as 别名]
def _build(self, inputs, is_training):
"""Connects the module to inputs.
Args:
inputs: Inputs to the Unit3Dtf component.
is_training: whether to use training mode for snt.BatchNorm (boolean).
Returns:
Outputs from the module.
"""
net = snt.Conv3D(
output_channels=self._output_channels,
kernel_shape=self._kernel_shape,
stride=self._stride,
padding=snt.SAME,
use_bias=self._use_bias)(inputs)
if self._use_batch_norm:
bn = snt.BatchNorm()
net = bn(net, is_training=is_training, test_local_stats=False)
if self._activation_fn is not None:
net = self._activation_fn(net)
return net