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GRU

Description

Defines the weights of the GRU layer selected by the name. Type : polymorphic.

 

Input parameters

 

Β Model in :Β model architecture.
Β name :Β string,Β name of layer.

Β gru_weight :Β cluster

Β input_weights :Β array,Β 2D values. input_weights = [features, 3*units].
Β hidden_weights :Β array,Β 2D values. hidden_weights = [units, 3*units].
Β input_biases :Β array,Β 1D values. input_biases = [3*units].
Β hidden_biases :Β array,Β 1D values. hidden_biases = [3*units].

 

Output parameters

 

Β Model out :Β model architecture.

Dimension

  • input_weights = [features, 3*units]

The size depends on theΒ GRUΒ layer input and the units parameter.
For example, if the input has a size of [batch = 10, timesteps = 8, features = 5] and units a value of 3 then input_weights will have a size of [features = 5, 3*units = 3].
Another example, if the input has a size of [batch = 15, timesteps = 8, features = 6] and units a value of 2 then input_weights will have a size of [features = 6, 3*units = 2].

 

  • hidden_weights = [units, 3*units].

The size depends on the units parameter of theΒ GRUΒ layer.
For example, if units has a value of 6 then hidden_weights will have a size of [units = 6, 3*units = 6].
Another example, if units has a value of 4 then hidden_weights will have a size of [units = 4, 3*units = 4].

 

  • input_biases = [3*units]

The size depends on the units parameter of theΒ GRUΒ layer.
For example, if units has a value of 6, then input_biases will have a size of [3*units = 6].
Another example, if units has a value of 4, then input_biases will have a size of [3*units = 4].

 

  • hidden_biases = [3*units]

The size of hidden_biases is based on the same principle as the size of input_biases.

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 HAIBAL library to run it).

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