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All outputs layers shape

Description

Gets the output form of the model.

Input parameters

 

Model in : model architecture.

Output parameters

 

Model out : model architecture.

output_array : array,

Name : cluster,

node : string, name of the ONNX node producing the output (e.g., Dense_342_output).
output : string, identifier of the output tensor from this node.

index : integer, index of the node within the ONNX graph, used to perform get or set operations on a specific node.
output_order : integer, index of the output (useful to retrieve the data after execution if there are multiple outputs).
 output_shape : array, expected shape of the output tensor. This shape is only valid for models using explicit Layers, and the first dimension always corresponds to the batch size (even if shown as 1 here).
dtype : enum, data type of the output tensor (e.g., FLOAT for floating-point 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).

Using the “Get All Output Layer Shape” function

1 – Define Graph

We define two graphs with two dense layers each and an input of different size.

2 – Merge Function

We use the “Merge” function to merge the two graphs.

3 – Get Function

We use the function “Get All Output Layer Shape” to get the output(s) form of the model.

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