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RNN

Description

Define the rnn layer according to its parameters. To be used for the TimeDistributed layer. Type : polymorphic.

 

Input parameters

 

Β parameters :Β layer parameters.

Β cell : a rnn cell instance.
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 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 lda_coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β€œ1”.

 

Output parameters

 

RNN out : layer rnn architecture.

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).

RNN layer inside Timedistributed layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size = 10, time = 6, 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 [time = 6, timesteps = 7, features = 5].
Then, we add to the graph the TimeDistributed layer which we setup with a RNN layer using the define method.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 3D” 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, time, output_size].
The output dimension depends on the parameters “return-sequences”, refer to the chapter “Dimension” of “add to graph” documentation.

 

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