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UpdatedNovember 3, 2025

Conv2DTranspose

Description

Gets the weights of the Conv2DTranspose layer selected by the name. Type : polymorphic.

 

Input parameters

 

Β Model in :Β model architecture.
Β name :Β string,Β name of layer.

 

Output parameters

 

Β Model out :Β model architecture.

Β weights_info : cluster

Β index :Β integer,Β index of layer.
Β name :Β string,Β name of layer.
Β weights : cluster

Β filters :Β array,Β 4D values. filters = [n_filters, channels, size[0], size[1]].
Β biases :Β array,Β 1D values. biases = [n_filters].

Dimension

  • filters = [n_filters, channel, size[0], size[1]]

The size of filters depends on the input of theΒ Conv2DTransposeΒ layer and the parameters n_filters and size.
For example, if the input of the layer has a size of [batch_size = 10, channel = 8, row = 5, column = 5], n_filters has the value 6 and size the value [3, 3] then filters will have a size of [n_filters = 6, channel = 8, size[0] = 3, size[1] = 3].

 

  • biases = [n_filters]

The size of biases depends on the parameter n_filters of theΒ Conv2DTransposeΒ layer.

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).

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