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Define the cell gru layer according to its parameters. To be used for the RNN layer. Type : polymorphic.


Input parameters


Β parameters :Β layer parameters.

Β units :Β integer, dimensionality of the output space.
Β activationΒ :Β enum, activation function to use.
Default value β€œtanh”.
Β recurrent_activationΒ :Β enum, activation function to use for the recurrent step.
Default value β€œsigmoid”.
Β use_bias? :Β boolean, whether the layer uses a bias vector.
Default value β€œTrue”.
Β input_weight_initializerΒ :Β enum, initializer for theΒ kernelweights matrix, used for the linear transformation of the inputs.
Default value β€œglorot_uniform”.
Β hidden_weight_initializerΒ :Β enum, initializer for theΒ recurrent_kernelweights matrix, used for the linear transformation of the recurrent state.
Default value β€œorthogonal”.
Β bias_initializerΒ :Β enum, initializer for the bias vector.
Default value β€œzeros”.
Β dropoutΒ :Β float, fraction of the units to drop for the linear transformation of the inputs.
Default value β€œ0.0”.
Β recurrent_dropout :Β float, fraction of the units to drop for the linear transformation of the recurrent state.
Default value β€œ0.0”.
Β return_sequences?Β :Β boolean, whether to return the last output in the output sequence, or the full sequence.
Default value β€œFalse”.
Β stateful?Β :Β boolean, if True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
Default value β€œFalse”.
Β optimizer :

Β algorithmΒ :Β enum, (name of optimizer) for optimizer instance.
Default value β€œadam”.
Β learning_rate :Β float, define the learning rate to use.
Default value β€œ0.001”.
Β beta_1 :Β float, define the exponential decay rate for the 1st moment estimates.
Default value β€œ0.9”.
Β beta_2 :Β float, define the exponential decay rate for the 2nd moment estimates.
Default value β€œ0.999”.

Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β store?Β :Β boolean, whether the layer stores the last iteration gradient (accessible via the β€œget_gradients” function).
Default value β€œFalse”.
Β update?Β :Β boolean, whether the layer’s variables should be updated during backward. Equivalent to freeze the layer.
Default value β€œTrue”.


Output parameters


GRUCell out : cell gru architecture.


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).

GRU cell inside RNN layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size = 10, timesteps = 7, features = 5]

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [timesteps = 7, features = 5]. Next, we configure the “cell” parameter of the RNN layer with the GRU cell.
Finally, we add to the graph the RNN layer.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 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, output_size].
The output dimension depends on the parameters “return-sequences” refer to the chapter “Dimension” of RNN add to graph documentation.


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