LayerNormalization
Description
Adds the weights of the LayerNormalization 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.
gamma : array, 1D values. gamma = [input_dim1]. beta : array, 1D values. beta = [input_dim1].
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
- gamma = [input_dim1]
The size depends on the input to the LayerNormalization layer.
For example, if the layer input has a size of [batch_size = 10, input_dim1 = 5, input_dim2 = 4, input_dim3 = 2] then gamma will have a size of [input_dim1 = 5].
Another example, if the input of the layer has a size of [batch_size = 12, input_dim1 = 8, input_dim2 = 5, input_dim3 = 3] then gamma will have a size of [input_dim1 = 8].
- beta = [input_dim1]
The beta size is based on the same principle as the gamma size.
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).