Convolution 2D

Description

Defines the weights of the Conv2D layer selected by the index. Type : polymorphic.

Input parameters

 Model in : model architecture.
 index : integer, index of layer.
filters : array, 4D values. filters = [n_filters, channels, size[0], size[1]].
biases : array, 1D values. biases = [n_filters].

Output parameters

 Model out : model architecture.

Dimension

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

The size of filters depends on the input of the Conv2D 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 the Conv2D 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).