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| library(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')
unzip("mnist.zip")
file.remove("mnist.zip")
}
setwd("..")
}
get_iterator <- function(data_shape) {
get_iterator_impl <- function() {
data_dir = 'mnist/'
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 = 128,
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 = 128,
flat = flat)
ret = list(train=train, value=val)
}
get_iterator_impl
}
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
}
get_lenet <- function() {
data <- mx.symbol.Variable('data')
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))
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))
flatten <- mx.symbol.Flatten(data=pool2)
fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=500)
tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10)
lenet <- mx.symbol.SoftmaxOutput(data=fc2, name='softmax')
lenet
}
data_loader <- get_iterator(c(28, 28, 1))
download_('mnist/')
net <- get_lenet()
data <- data_loader()
train <- data$train
val <- data$value
devs <- mx.gpu(0)
model <- mx.model.FeedForward.create(
X = train,
eval.data = val,
ctx = devs,
symbol = net,
begin.round = 0,
eval.metric = mx.metric.top_k_accuracy,
num.round = 10,
learning.rate = 0.05,
array.batch.size = 128,
optimizer = "sgd",
initializer = mx.init.Xavier(factor_type="in", magnitude=2),
batch.end.callback = mx.callback.log.train.metric(50)
)
|