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GlobalMaxPool1D

Description

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

 

Input parameters

 

Graph in : model architecture.

parameters : layer parameters.

ย data_format :ย enum, one ofย channels_lastย orย channels_firstย (default) . The ordering of the dimensions in the inputs.ย channel_lastย corresponds to inputs with shapeย (batch, steps, features)ย whileย channels_firstย corresponds to inputs with shapeย (batch, features, steps).
Default value โ€œchannels_firstโ€.
ย keepdims :ย boolean, A boolean, whether to keep the spatial dimensions or not. If keepdims is โ€˜Falseโ€™ (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is โ€˜Trueโ€™, the spatial dimensions are retained with length 1.
Default value โ€œFalseโ€.
ย training?ย :ย boolean, whether the layer is in training mode (can store data for backward).
Default value โ€œTrueโ€.
ย lda_coeff :ย 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 โ€œ1โ€.

ย in/out paramย :

ย input_shapeย :ย integer array, shape (not including the batch axis). NB : To be used only if it is the first layer of the model.
ย output_behaviorย :ย enum, setup if the layer is an output layer.
Default โ€œNot Outputโ€โ€‹.

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

 

Output parameters

 

ย Graph out : model architecture.

Dimension

Input shape

3D tensor with shape

  • If data_format = โ€˜channels_lastโ€™ : (batch_size, steps, features).
  • If data_format = โ€˜channels_firstโ€™ : (batch_size, features, steps).

Output shape

  • If keepdims = โ€˜Falseโ€™ : 2D tensor with shape (batch_size, features).
  • If keepdims = โ€˜Trueโ€™ :
    • If data_format = โ€˜channels_lastโ€™ : 3D tensor with shape (batch_size, 1, features).
    • If data_format = โ€˜channels_firstโ€™ : 3D tensor with shape (batch_size, features, 1).

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).

GlobalMaxPool1D layer with explicit input layer

1 โ€“ Generate a set of data

We generate an array of data of type single and shape [batch size = 10, features = 7, steps = 5].

2 โ€“ 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 GlobalMaxPool1D layer.

3 โ€“ Run graph

We call the forward method and retrieve the result with the โ€œPrediction 2Dโ€ 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].
The output dimension depends on the parameters โ€œkeepdimsโ€ refer to the chapter โ€œDimensionโ€ of this documentation.

 

GlobalMaxPool1D layer with implicit input layer

1 โ€“ Generate a set of data

We generate an array of data of type single and shape [batch size = 10, features = 7, steps = 5].

2 โ€“ Define graph

First, we define the GlobalMaxPool1D layer as the input layer of the graph (implicit input layer method). To do this, we send in the โ€œinput_shapeโ€ variable of the โ€œin/out paramโ€ cluster an array of shape [features = 7, steps = 5].
An input layer will be implicitly created and the name of this input layer will be the same name as its parent prefixed with โ€œinput_โ€.
Then we add to the graph the GlobalMaxPool1D layer.

3 โ€“ Run graph

We call the forward method and retrieve the result with the โ€œPrediction 2Dโ€ 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].
The output dimension depends on the parameters โ€œkeepdimsโ€ refer to the chapter โ€œDimensionโ€ of this documentation.

 

GlobalMaxPool1D layer, batch and dimension

1 โ€“ 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 โ€“ 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 GlobalMaxPool1D layer.

3 โ€“ Run graph

We call the forward method and retrieve the result with the โ€œPrediction 2Dโ€ 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].
The output dimension depends on the parameters โ€œkeepdimsโ€ refer to the chapter โ€œDimensionโ€ of this documentation.

 

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