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Generate architectures for Convolutional Neural Network

Source: R/generate_cnn_architecture.R
generate_cnn_architecture.Rd

Generate architectures for Convolutional Neural Network

Usage

generate_cnn_architecture(
  number_of_features = 7,
  number_of_outputs = 1,
  sample_size = c(11, 11),
  number_of_conv_layers = 2,
  conv_layers_size = c(14, 28),
  conv_layers_kernel = 3,
  conv_layers_stride = 1,
  conv_layers_padding = 0,
  number_of_fc_layers = 1,
  fc_layers_size = c(28),
  pooling = NULL,
  batch_norm = TRUE,
  dropout = 0,
  verbose = FALSE
)

Arguments

number_of_features

numeric. Value that specifies the number of features in the dataset.

number_of_outputs

numeric. Value that specifies the number of outputs.

sample_size

vector. Specifies the size. Default c(11, 11)

number_of_conv_layers

numeric. Specifies the number of convolutional layers. Default 2.

conv_layers_size

numeric. the size the convolutional layers. Default c(14, 28).

conv_layers_kernel

numeric. Specifies the kernel size for layers. Default 3.

conv_layers_stride

numeric. Specifies the stride for the convolutional layers. Default 1.

conv_layers_padding

numeric. Specifies the padding for the convolutional layers. Default 0.

number_of_fc_layers

numeric. Specifies the number of fully connected layers. Default 1.

fc_layers_size

vector. Specifies the size of the fully connected layers. Default 14.

pooling

numeric. Specifies 2D average pooling kernel size. Default NULL

batch_norm

logical. Specifies whether batch normalization is included in the architecture. Default TRUE.

dropout

Numeric. The probability (p) of randomly zeroing elements of the input tensor during training to prevent overfitting. Must be between 0 (no dropout) and 1 (all inputs zeroed). Default is 0 (no dropout).

verbose

logical. Specifies whether the architecture should be printed. Default FALSE.

Value

A list containing:

  • net: a instantiated torch neural net.

  • arch: a string with a R expression to instantiate the neural network.

  • arch_dict: a list with a matrix describing the architecture structure.

See also

generate_arch_list, generate_dnn_architecture

Examples

if (FALSE) {
# Generate a Conclutional Neural Network with:
cnn_arch <- generate_cnn_architecture(
  number_of_features = 7, # seven input variables
  number_of_outputs = 1, # one output
  sample_size = c(11, 11), # image dimensions
  number_of_conv_layers = 2, # two convolutional layers between input and output
  conv_layers_size = c(14, 28), # of this size, respectively
  conv_layers_kernel = 3, # with a 3 pixels kernel
  conv_layers_stride = 1, # walking 1 pixel at a time
  conv_layers_padding = 1, # with 1 pixel of padding
  number_of_fc_layers = 1, # followed by one fully connected layer
  fc_layers_size = c(28), # with 28 neurons
  pooling = NULL, # without average pooling
  batch_norm = TRUE, # with batch normalization
  dropout = 0 # and without dropout
)

cnn_arch$net() # a torch net
cnn_arch$arch %>% cat() # the torch code to create it
cnn_arch$arch_dict # and a quick description of its structure
}