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SimpleRNN

Description

Adds the weights of the SimpleRNN layer to the weights table. Type : polymorphic.

 

Input parameters

 

Weights in : array

Β name :Β string,Β name of layer.
Β weights :Β variant,Β weights values.

Β name :Β string,Β name of layer.
Β input_weights :Β array,Β 2D values. input_weights = [features, units].
Β hidden_weights :Β array,Β 2D values. hidden_weights = [units, units].
Β biases :Β array,Β 1D values. biases = [units].

Output parameters

 

Β Weights out : array

Β name :Β string,Β name of layer.
Β weights :Β variant,Β weights values.

Dimension

  • input_weights = [features, units]

The size depends on theΒ SimpleRNNΒ 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, 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, units = 2].

 

  • hidden_weights = [units, units]

The size depends on the units parameter of theΒ SimpleRNNΒ layer.
For example, if units has a value of 6 then hidden_weights will have a size of [units = 6, units = 6].
Another example, if units has a value of 2 then hidden_weights will have a size of [units = 2, units = 2].

 

  • biases = [units]

The size depends on the units parameter of theΒ SimpleRNNΒ layer.
For example, if units has a value of 6, then biases will have a size of [units = 6].
Another example, if units has a value of 2, then biases will have a size of [units = 2].

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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