Transpose
Description
Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3).
Documentation illustration (illustration unavailable in the archive).
Input parameters
specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
data (heterogeneous) – T : object, an input tensor.
Parameters : cluster,
perm : array, a list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.
Default value “empty”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.
name (optional) : string, name of the node.
Documentation illustration (illustration unavailable in the archive).
Output parameters
transposed (heterogeneous) – T : object, transposed output.
Type Constraints
T in (tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16),
tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain input and output types to all tensor types.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).