本文整理汇总了Python中tensorflow.python.ops.array_ops.required_space_to_batch_paddings方法的典型用法代码示例。如果您正苦于以下问题：Python array_ops.required_space_to_batch_paddings方法的具体用法？Python array_ops.required_space_to_batch_paddings怎么用？Python array_ops.required_space_to_batch_paddings使用的例子？那么恭喜您, 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.array_ops的用法示例。
在下文中一共展示了array_ops.required_space_to_batch_paddings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: _with_space_to_batch_call
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import required_space_to_batch_paddings [as 别名]
def _with_space_to_batch_call(self, inp, filter): # pylint: disable=redefined-builtin
"""Call functionality for with_space_to_batch."""
# Handle input whose shape is unknown during graph creation.
input_spatial_shape = None
input_shape = self.input_shape
spatial_dims = self.spatial_dims
if input_shape.ndims is not None:
input_shape_list = input_shape.as_list()
input_spatial_shape = [input_shape_list[i] for i in spatial_dims]
if input_spatial_shape is None or None in input_spatial_shape:
input_shape_tensor = array_ops.shape(inp)
input_spatial_shape = array_ops.stack(
[input_shape_tensor[i] for i in spatial_dims])
base_paddings = self.base_paddings
if base_paddings is None:
# base_paddings could not be computed at build time since static filter
# shape was not fully defined.
filter_shape = array_ops.shape(filter)
base_paddings = _with_space_to_batch_base_paddings(
filter_shape,
self.num_spatial_dims,
self.rate_or_const_rate)
paddings, crops = array_ops.required_space_to_batch_paddings(
input_shape=input_spatial_shape,
base_paddings=base_paddings,
block_shape=self.dilation_rate)
dilation_rate = _with_space_to_batch_adjust(self.dilation_rate, 1,
spatial_dims)
paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims)
crops = _with_space_to_batch_adjust(crops, 0, spatial_dims)
input_converted = array_ops.space_to_batch_nd(
input=inp,
block_shape=dilation_rate,
paddings=paddings)
result = self.op(input_converted, filter)
result_converted = array_ops.batch_to_space_nd(
input=result, block_shape=dilation_rate, crops=crops)
return result_converted 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:44，代码来源:nn_ops.py