SeparableConv1D
Description
Returns the SeparableConv1D layer weights. Type : polymorphic.
Documentation illustration (illustration unavailable in the archive).
Input parameters
weights : cluster
index : integer, index of layer.
name : string, name of layer.
Documentation illustration (illustration unavailable in the archive). weight : variant, weight of layer.
Documentation illustration (illustration unavailable in the archive).
Output parameters
weights_info : cluster
index : integer, index of layer.
name : string, name of layer. weights : cluster
filters_depthwise : array, 3D values. filters_depthwise = [channels, 1, size]. filters_pointwise : array, 3D values. filters_pointwise = [n_filters, channels, 1]. biases : array, 1D values. biases = [n_filters].
Documentation illustration (illustration unavailable in the archive).
Dimension
- filters_depthwise = [channels, 1, size]
The size of filters_depthwise depends on the input of the SeparableConv1D layer and the parameters size.
For example if the input of the layer has a size of [batch_size = 10, channels = 5, steps = 2] and size the value 3 then filters_depthwise will have a size of [channels = 5, 1, size = 3].
- filters_pointwise = [n_filters, channels, 1]
The size of filters_pointwise depends on the input of the SeparableConv1D layer and the parameters n_filters.
For example if the input of the layer has a size of [batch_size = 10, channels = 5, steps = 2] and n_filters has the value 6 then filters_pointwise will have a size of [n_filters = 6, channels = 5, 1].
- biases = [n_filters]
The size of biases depends on the parameter n_filters of the SeparableConv1D 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).