Conv3D — Deep Learning Toolkit / Advanced / Add Weight / Index

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Convolution 3D

Description

Adds the weights of the Conv3D layer to the weights table. Type : polymorphic.

Documentation illustration (illustration unavailable in the archive).

Input parameters

Weights in : array

index : integer, index of layer.
Documentation illustration (illustration unavailable in the archive). weights : variant, weights values.

Documentation illustration (illustration unavailable in the archive).

index : integer, index of layer.
filters : array, 5D values. filters = [n_filters, channel, size[0], size[1], size[2]].
biases : array, 1D values. biases = [n_filters].

Output parameters

Weights out : array

index : integer, index of layer.
Documentation illustration (illustration unavailable in the archive). weights : variant, weights values.

Documentation illustration (illustration unavailable in the archive).

Dimension

filters = [n_filters, channel, size[0], size[1], size[2]]

The size of filters depends on the input of the Conv3D 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, conv_dim1 = 7, conv_dim2 = 5, conv_dim3 = 5], n_filters has the value 6 and size the value [3, 3, 3] then filters will have a size of [n_filters = 6, channel = 8, size[0] = 3, size[1] = 3, size[2] = 3].

biases = [n_filters].

The size of biases depends on the parameter n_filters of the Conv3D 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).