Welcome to our Support Center

# GaussianDropout

## Description

Setup and add the gaussian dropout layer into the model during the definition graph step. Type : polymorphic.

### Input parameters

Graph in : model architecture.

parameters : layer parameters.

rate : float, drop probability(as with Dropout). The multiplicative noise will have standard deviation sqrt(rate / (1 – rate)).
Default value “0”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda_coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

in/out param :

input_shape : integer array, shape (not including the batch axis). NB : To be used only if it is the first layer of the model.
output_behavior : enum, setup if the layer is an output layer.
Default “Not Output”​​.

name (optional) : string, name of the layer.

### Output parameters

Graph out : model architecture.

## Dimension

### Input shape

Input tensor (of any rank).

### Output shape

Same as input shape.

## 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 HAIBAL library to run it).

### GaussianDropout layer with explicit input layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size = 10, input_dim = 5].

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [input_dim = 5].
Then we add to the graph the GaussianDropout layer.

3 – Run graph

We call the forward method and retrieve the result with the “Prediction 2D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, units].

### GaussianDropout layer with implicit input layer

1 – Generate a set of data

We generate an array of data of type single and shape [batch_size = 10, input_dim = 5].

2 – Define graph

First, we define the GaussianDropout layer as the input layer of the graph (implicit input layer method). To do this, we send in the “input_shape” variable of the “in/out param” cluster an array of shape [input_dim = 5].
An input layer will be implicitly created and the name of this input layer will be the same name as its parent prefixed with “input_”.
Then we add to the graph the GaussianDropout layer.

3 – Run graph

We call the forward method and retrieve the result with the “Prediction 2D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, units].

### GaussianDropout layer, batch and dimension

1 – Generate a set of data

We generate an array of data of type single and shape [number of batch = 9, batch_size = 10, input_dim = 5].

2 – Define graph

First, we define the first layer of the graph which is an Input layer (explicit input layer method). This layer is setup as an input array shaped [input_dim = 5].
Then we add to the graph the GaussianDropout layer.

3 – Run graph

We call the forward method and retrieve the result with the “Prediction 2D” method.
This method returns two variables, the first one is the layer information (cluster composed of the layer name, the graph index and the shape of the output layer) and the second one is the prediction with a shape of [batch_size, units].