This function creates a linear layer object of class citolayer for use in constructing a Convolutional Neural Network (CNN) architecture. The resulting layer object can be passed to the create_architecture function to define the structure of the network.
Usage
linear(
n_neurons = NULL,
bias = NULL,
activation = NULL,
normalization = NULL,
dropout = NULL
)Arguments
- n_neurons
(integer) The number of hidden neurons in this layer.
- bias
(boolean) If
TRUE, a learnable bias is added to the neurons of this layer.- activation
(character) The activation function applied after this layer. Supported activation functions include "relu", "leaky_relu", "tanh", "elu", "rrelu", "prelu", "softplus", "celu", "selu", "gelu", "relu6", "sigmoid", "softsign", "hardtanh", "tanhshrink", "softshrink", "hardshrink", "log_sigmoid".
- normalization
(boolean) If
TRUE, batch normalization is applied after this layer.- dropout
(numeric) The dropout rate for this layer. Set to 0 to disable dropout.
Value
An S3 object of class "linear" "citolayer", representing a linear layer in the CNN architecture.
Details
This function creates a linear layer object, which is used to define a linear layer in a CNN architecture. Parameters not specified (and thus set to NULL) will be filled with default values provided to the create_architecture function.
Examples
# \donttest{
if(torch::torch_is_installed()){
library(cito)
# A linear layer where all available parameters are assigned
# No value will be overwritten by 'create_architecture()'
layer1 <- linear(100, TRUE, "relu", FALSE, 0.5)
# A linear layer where only the activation function is assigned
# n_neurons, bias, normalization and dropout are filled with the defaults
# passed to the 'create_architecture()' function
layer2 <- linear(activation="selu")
}
# }