This function creates a conv 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
conv(
n_kernels = NULL,
kernel_size = NULL,
stride = NULL,
padding = NULL,
dilation = NULL,
bias = NULL,
activation = NULL,
normalization = NULL,
dropout = NULL
)Arguments
- n_kernels
(integer) The number of kernels (or filters) in this layer.
- kernel_size
(integer or tuple) The size of the kernels in this layer. Use a tuple if the kernel size is different in each dimension.
- stride
(integer or tuple) The stride of the kernels in this layer. If
NULL, the stride is set to the kernel size. Use a tuple if the stride is different in each dimension.- padding
(integer or tuple) The amount of zero-padding added to the input on both sides. Use a tuple if the padding is different in each dimension.
- dilation
(integer or tuple) The dilation of the kernels in this layer. Use a tuple if the dilation is different in each dimension.
- bias
(boolean) If
TRUE, a learnable bias is added to the kernels 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 "conv" "citolayer", representing a convolutional layer in the CNN architecture.
Details
This function creates a conv layer object, which is used to define a convolutional layer in a CNN architecture. Parameters that are 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 convolutional layer where all available parameters are assigned
# No value will be overwritten by 'create_architecture()'
layer1 <- conv(10, 3, 1, 0, 1, TRUE, "relu", FALSE, 0.5)
# A convolutional layer where only the activation function is assigned
# n_kernels, kernel_size, stride, padding, dilation, bias,
# normalization and dropout are filled with the defaults
# passed to the 'create_architecture()' function
layer2 <- conv(activation="selu")
}
# }