本文整理匯總了Python中tensorflow.python.ops.array_ops.required_space_to_batch_paddings方法的典型用法代碼示例。如果您正苦於以下問題：Python array_ops.required_space_to_batch_paddings方法的具體用法？Python array_ops.required_space_to_batch_paddings怎麽用？Python array_ops.required_space_to_batch_paddings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.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: _with_space_to_batch_call
# 需要導入模塊: from tensorflow.python.ops import array_ops [as 別名]
# 或者: from tensorflow.python.ops.array_ops import required_space_to_batch_paddings [as 別名]
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 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:44，代碼來源:nn_ops.py