Add

Description

Setup and add the add layer into the model during the definition graph step. Type : polymorphic.

Input parameters

 Models in : array, model architecture.

Output parameters

Model out : model architecture.

Dimension

Input shape

All layer used for add must have same output shape.
Refer Layer tensor output shape used.

Output shape

Same as input shape.

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).

We generate an array of data of type single and shape [batch_size = 10, input_dim = 5] (same input shape).

2 – Define graph

We first define two input layers named input_dense1 and input_dense2. This layers is setup as an input array shaped [input_dim = 5].
In order to have same output shape for added dense layers we define for both of these the same “units” parameter (units = 5) (refer Dense layer add to graph documentation for more details).
We construct an array of the two graphs generated at the input Add as an interpretation of the Dense1 + Dense2 operation.

3 – Summarize graph

Returns the summary of the model in file text.

4 – Run graph

We call the forward method and retrieve the result with the “Prediction 2D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, units] (Dense output shape).

We generate two array of data of type single and shape1 [batch_size = 10, input_dim = 5] and shape2 [batch_size = 10, input_dim = 15] (different input shape).

2 – Define graph

We first define two input layers named input_dense1 and input_dense2. This layers is setup as an input array shaped [input_dim = 5] and [input_dim = 15].
In order to have same output shape for added dense layers we define for both of these the same “units” parameter (units = 5) (refer Dense layer add to graph documentation for more details).
We construct an array of the two graphs generated at the input Add as an interpretation of the Dense1 + Dense2 operation.

3 – Summarize graph

Returns the summary of the model in file text.

4 – Run graph

We call the forward method and retrieve the result with the “Prediction 2D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, units] (Dense output shape).