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MaxPool1D

Description

Setup and add the max pooling 1D layer into the model during the definition graph step. Type : polymorphic.

 

Input parameters

 

Model in : model architecture.

Β Parameters : layer parameters.

Β sizeΒ :Β integer,Β size of the average pooling windows.
Default value β€œ2”.
Β strideΒ :Β integer, factor by which to downscale.
Default value β€œ0”.
Β explicit paddingΒ :Β array,Β specifies the number of pixels to pad at the beginning and end of each spatial axis. Batch and channel axes are not padded. Only used when padding =Β EXPLICIT.
Default value β€œempty”.
Β paddingΒ :Β enum,Β type of padding to apply.
Default value β€œVALID”.
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”.

name (optional) : string, name of the layer.

 

Output parameters

 

Model out : model architecture.

Dimension

Input shape

3D tensor with shape

  • If data_format = β€˜channels_last’ : (batch_size, steps, features).
  • If data_format = β€˜channels_first’ : (batch_size, features, steps).

Output shape

3D tensor with shape

  • If data_format = β€˜channels_last’ : 3D tensor with shape (batch_size, downsampled_steps, features).
  • If data_format = β€˜channels_first’ : 3D tensor with shape (batch_size, features, downsampled_steps).

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 Deep Learning library to run it).

MaxPool1D layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch size = 10, 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 [features = 7, steps = 5].
Then we add to the graph the MaxPool1D layer.

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, features, downsampled_steps].

 

MaxPool1D layer, batch and dimension

1 – Generate a set of data

We generate an array of data of type single and shape [number of batch = 9, batch size = 10, 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 [features = 7, steps = 5].
Then we add to the graph the MaxPool1D layer.

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, features, downsampled_steps].

 

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