Tile
Description
Constructs a tensor by tiling a given tensor. This is the same as function tile in Numpy, but no broadcast.
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.
Graphs in : cluster, ONNX model architecture.
input (heterogeneous) – T : object, input tensor of any shape. repeats (heterogeneous) – T1 : object, 1D int64 tensor of the same length as input’s dimension number, includes numbers of repeated copies along input’s dimensions.
Documentation illustration (illustration unavailable in the archive). Parameters : cluster,
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.
Output parameters
output (heterogeneous) – T : object, output tensor of the same dimensions and type as tensor input. output_dim = input_dim * repeats.
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.
T1 in (tensor(int64)) : Constrain repeat’s type to int64 tensors.
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).