本文整理匯總了Python中tensorflow.python.ops.gen_array_ops._unpack方法的典型用法代碼示例。如果您正苦於以下問題：Python gen_array_ops._unpack方法的具體用法？Python gen_array_ops._unpack怎麽用？Python gen_array_ops._unpack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.gen_array_ops的用法示例。
在下文中一共展示了gen_array_ops._unpack方法的2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: unstack
# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import _unpack [as 別名]
def unstack(value, num=None, axis=0, name="unstack"):
"""Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
If `num` is not specified (the default), it is inferred from `value`'s shape.
If `value.shape[axis]` is not known, `ValueError` is raised.
For example, given a tensor of shape `(A, B, C, D)`;
If `axis == 0` then the i'th tensor in `output` is the slice
`value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`.
(Note that the dimension unpacked along is gone, unlike `split`).
If `axis == 1` then the i'th tensor in `output` is the slice
`value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
Etc.
This is the opposite of pack. The numpy equivalent is
tf.unstack(x, n) = list(x)
Args:
value: A rank `R > 0` `Tensor` to be unstacked.
num: An `int`. The length of the dimension `axis`. Automatically inferred
if `None` (the default).
axis: An `int`. The axis to unstack along. Defaults to the first
dimension. Supports negative indexes.
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unstacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
ValueError: If `axis` is out of the range [-R, R).
"""
if num is None:
value = ops.convert_to_tensor(value)
value_shape = value.get_shape()
if value_shape.ndims is not None:
if axis < -value_shape.ndims or axis >= value_shape.ndims:
raise ValueError("axis = %d not in [%d, %d)" %
(axis, -value_shape.ndims, value_shape.ndims))
num = value_shape[axis].value
if num is None:
raise ValueError("Cannot infer num from shape %s" % value_shape)
return gen_array_ops._unpack(value, num=num, axis=axis, name=name) 示例2: unstack
# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import _unpack [as 別名]
def unstack(value, num=None, axis=0, name="unstack"):
"""Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
If `num` is not specified (the default), it is inferred from `value`'s shape.
If `value.shape[axis]` is not known, `ValueError` is raised.
For example, given a tensor of shape `(A, B, C, D)`;
If `axis == 0` then the i'th tensor in `output` is the slice
`value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`.
(Note that the dimension unpacked along is gone, unlike `split`).
If `axis == 1` then the i'th tensor in `output` is the slice
`value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
Etc.
This is the opposite of stack. The numpy equivalent is
tf.unstack(x, n) = np.unstack(x)
Args:
value: A rank `R > 0` `Tensor` to be unstacked.
num: An `int`. The length of the dimension `axis`. Automatically inferred
if `None` (the default).
axis: An `int`. The axis to unstack along. Defaults to the first
dimension. Negative values wrap around, so the valid range is `[-R, R)`.
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unstacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
ValueError: If `axis` is out of the range [-R, R).
"""
if num is None:
value = ops.convert_to_tensor(value)
value_shape = value.get_shape()
if value_shape.ndims is not None:
if axis < -value_shape.ndims or axis >= value_shape.ndims:
raise ValueError("axis = %d not in [%d, %d)" %
(axis, -value_shape.ndims, value_shape.ndims))
num = value_shape[axis].value
if num is None:
raise ValueError("Cannot infer num from shape %s" % value_shape)
return gen_array_ops._unpack(value, num=num, axis=axis, name=name) 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:49，代碼來源:array_ops.py