example/image-classification/symbol_lenet.R

library(mxnet)

get_symbol <- function(num_classes = 1000) {
  data <- mx.symbol.Variable('data')
  # first conv
  conv1 <- mx.symbol.Convolution(data = data, kernel = c(5, 5), num_filter = 20)

  tanh1 <- mx.symbol.Activation(data = conv1, act_type = "tanh")
  pool1 <- mx.symbol.Pooling(data = tanh1, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
  
  # second conv
  conv2 <- mx.symbol.Convolution(data = pool1, kernel = c(5, 5), num_filter = 50)
  tanh2 <- mx.symbol.Activation(data = conv2, act_type = "tanh")
  pool2 <- mx.symbol.Pooling(data = tanh2, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
  # first fullc
  flatten <- mx.symbol.Flatten(data = pool2)
  fc1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 500)
  tanh3 <- mx.symbol.Activation(data = fc1, act_type = "tanh")
  # second fullc
  fc2 <- mx.symbol.FullyConnected(data = tanh3, num_hidden = num_classes)
  # loss
  lenet <- mx.symbol.SoftmaxOutput(data = fc2, name = 'softmax')
  return(lenet)
}