def transpose(a, perm=None, name="transpose"):
"""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(b, 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`.
"""
with ops.op_scope([a], name, "transpose") as name:
if perm is None:
dims = gen_math_ops._range(0, gen_array_ops.rank(a), 1)
perm = gen_array_ops.reverse(dims, [True])
ret = gen_array_ops.transpose(a, perm, name=name)
# NOTE(mrry): Setting the shape explicitly because
# reverse is not handled by the shape function.
input_shape = ret.op.inputs[0].get_shape().dims
if input_shape is not None:
ret.set_shape(input_shape[::-1])
else:
ret = gen_array_ops.transpose(a, perm, name=name)
return ret
def _ReverseGrad(op, grad):
reverse_dims = op.inputs[1]
return gen_array_ops.reverse(grad, reverse_dims), None
def _ReverseGrad(op, grad): reverse_dims = op.inputs[1] return gen_array_ops.reverse(grad, reverse_dims), None
