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)