example/image-classification/train_mnist.R

require(argparse)
require(mxnet)

download_ <- function(data_dir) {
    dir.create(data_dir, showWarnings = FALSE)
    setwd(data_dir)
    if ((!file.exists('train-images-idx3-ubyte')) ||
        (!file.exists('train-labels-idx1-ubyte')) ||
        (!file.exists('t10k-images-idx3-ubyte')) ||
        (!file.exists('t10k-labels-idx1-ubyte'))) {
        download.file(url='http://data.mxnet.io/mxnet/data/mnist.zip',
                      destfile='mnist.zip', method='wget')
        unzip("mnist.zip")
        file.remove("mnist.zip")
    }
    setwd("..")
}

# multi-layer perceptron
get_mlp <- function() {
    data <- mx.symbol.Variable('data')
    fc1  <- mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
    act1 <- mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
    fc2  <- mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
    act2 <- mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
    fc3  <- mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
    mlp  <- mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
    mlp
}

# LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
# Haffner. "Gradient-based learning applied to document recognition."
# Proceedings of the IEEE (1998)
get_lenet <- function() {
    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=10)
    # loss
    lenet <- mx.symbol.SoftmaxOutput(data=fc2, name='softmax')
    lenet
}

get_iterator <- function(data_shape) {
    get_iterator_impl <- function(args) {
        data_dir = args$data_dir
        if (!grepl('://', args$data_dir))
            download_(args$data_dir)
        flat <- TRUE
        if (length(data_shape) == 3) flat <- FALSE

        train           = mx.io.MNISTIter(
            image       = paste0(data_dir, "train-images-idx3-ubyte"),
            label       = paste0(data_dir, "train-labels-idx1-ubyte"),
            input_shape = data_shape,
            batch_size  = args$batch_size,
            shuffle     = TRUE,
            flat        = flat)

        val = mx.io.MNISTIter(
            image       = paste0(data_dir, "t10k-images-idx3-ubyte"),
            label       = paste0(data_dir, "t10k-labels-idx1-ubyte"),
            input_shape = data_shape,
            batch_size  = args$batch_size,
            flat        = flat)

        ret = list(train=train, value=val)
    }
    get_iterator_impl
}

parse_args <- function() {
    parser <- ArgumentParser(description='train an image classifer on mnist')
    parser$add_argument('--network', type='character', default='mlp',
                        choices = c('mlp', 'lenet'),
                        help = 'the cnn to use')
    parser$add_argument('--data-dir', type='character', default='mnist/',
                        help='the input data directory')
    parser$add_argument('--gpus', type='character',
                        help='the gpus will be used, e.g "0,1,2,3"')
    parser$add_argument('--batch-size', type='integer', default=128,
                        help='the batch size')
    parser$add_argument('--lr', type='double', default=.05,
                        help='the initial learning rate')
    parser$add_argument('--mom', type='double', default=.9,
                        help='momentum for sgd')
    parser$add_argument('--model-prefix', type='character',
                        help='the prefix of the model to load/save')
    parser$add_argument('--num-round', type='integer', default=10,
                        help='the number of iterations over training data to train the model')
    parser$add_argument('--kv-store', type='character', default='local',
                        help='the kvstore type')

    parser$parse_args()
}

args = parse_args()
if (args$network == 'mlp') {
    data_shape <- c(784)
    net <- get_mlp()
} else {
    data_shape <- c(28, 28, 1)
    net <- get_lenet()
}

# train
data_loader <- get_iterator(data_shape)
data <- data_loader(args)
train <- data$train
val <- data$value 

if (is.null(args$gpus)) {
  devs <- mx.cpu()  
} else {
  devs <- lapply(unlist(strsplit(args$gpus, ",")), function(i) {
    mx.gpu(as.integer(i))
  })
}

mx.set.seed(0)

model <- mx.model.FeedForward.create(
  X                  = train,
  eval.data          = val,
  ctx                = devs,
  symbol             = net,
  num.round          = args$num_round,
  array.batch.size   = args$batch_size,
  learning.rate      = args$lr,
  momentum           = args$mom,
  eval.metric        = mx.metric.accuracy,
  initializer        = mx.init.uniform(0.07),
  batch.end.callback = mx.callback.log.train.metric(100))