def transpose(a, perm=None, name=None):
"""
Transposes `a`. Permutes the dimensions according to `perm`.
The returned tensor's dimension i will correspond to the input dimension
`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
the rank of the input tensor. Hence by default, this operation performs a
regular matrix transpose on 2-D input Tensors.
For example:
```python
# 'x' is [[1 2 3]
# [4 5 6]]
tf.transpose(x) ==> [[1 4]
[2 5]
[3 6]]
# Equivalently
tf.transpose(x, perm=[1, 0]) ==> [[1 4]
[2 5]
[3 6]]
# 'perm' is more useful for n-dimensional tensors, for n > 2
# 'x' is [[[1 2 3]
# [4 5 6]]
# [[7 8 9]
# [10 11 12]]]
# Take the transpose of the matrices in dimension-0
tf.transpose(x, perm=[0, 2, 1]) ==> [[[1 4]
[2 5]
[3 6]]
[[7 10]
[8 11]
[9 12]]]
```
Args:
a: A `Tensor`.
perm: A permutation of the dimensions of `a`.
name: A name for the operation (optional).
Returns:
A transposed `Tensor`.
"""
return ops.Transpose(a, perm=perm, name=name)
def transpose(x, axes=None):
"""Transpose the input according to the given permutations.
Parameters
----------
x : Tensor
The input tensor.
axes : tuple, list or None
The permutations. Default is ``None`` (Reverse Dimensions).
Returns
-------
Tensor
The output tensor.
"""
return ops.Transpose(x, perms=axes)
def transpose(x, axes=None):
"""Transpose the input according to the given permutations.
Parameters
----------
x : Tensor
The input tensor.
axes : sequence of int, optional
The permutations. Default is ``None`` (Reverse Dimensions).
Returns
-------
Tensor
The output tensor.
"""
return _ops.Transpose(x, perm=axes)
def transpose(a, perm=None, name=None):
return ops.Transpose(a, perm=perm, name=name)
def LayerSetup(self, bottom):
return _ops.Transpose(bottom, **self.arguments)
def Setup(self, bottom):
super(PermuteLayer, self).Setup(bottom)
input = bottom[0] if isinstance(bottom, list) else bottom
return ops.Transpose(input, **self._param)
