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Define the max pooling 1D layer according to its parameters. To be used for the TimeDistributed layer. Type : polymorphic.


Input parameters


Β parameters : layer parameters.

Β sizeΒ :Β integer,Β size of the average pooling windows.
Default value β€œ2”.
strideΒ : integer, specifies how much the pooling window moves for each pooling step.
Default value “0”.
Β paddingΒ :Β boolean,Β β€œvalidβ€œorΒ β€œsameβ€œ.Β β€œvalid” means no padding. β€œsame” results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
Default value β€œFalse”.
Β data_format :Β enum, one ofΒ channels_lastΒ orΒ channels_firstΒ (default) . The ordering of the dimensions in the inputs.Β channel_lastΒ corresponds to inputs with shapeΒ (batch, steps, features)Β whileΒ channels_firstΒ corresponds to inputs with shapeΒ (batch, features, steps).
Default value β€œchannels_first”.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
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


MaxPool1DΒ out : layer max pooling 1D 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).

MaxPool1D 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, features = 7, steps = 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, features = 7, steps = 5].
Then, we add to the graph the TimeDistributed layer which we setup with a MaxPool1D layer using the define method.

3 – Run graph

We call the forward method and retrieve the result with the β€œPrediction 4D” 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, features, downsampled_steps].


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