# Custom Iterator Tutorial¶

This tutorial provides a guideline on how to use and write custom iterators, which can help handle a dataset that does not fit into memory.

## Getting the data¶

The data we are going to use is the MNIST dataset in CSV format. The data can be found at this website.

if (!file.exists('mnist_train.csv')) {
destfile='mnist_train.csv', method='wget')
}
if (!file.exists('mnist_test.csv')) {
destfile='mnist_test.csv', method='wget')
}


You’ll get two files, mnist_train.csv that contains 60,000 images of handwritten numbers and mnist_test.csv that contains 10,000 such images, all of which are formatted in a comma-separated value (CSV) style. The first element of each line in the CSV is the label, which is a number between 0 and 9. The rest of the line are 784 numbers between 0 and 255, corresponding to the levels of grey of a matrix of 28x28 pixels which together comprise the image. Thus, each line of the file contains an image of 28x28 pixels of a hand written number and its true label.

Note: The above command relies on wget. If the command fails, you can manually download these data files: first navigate to the links above in your browser, and then place the downloaded files mnist_train.csv & mnist_test.csv into the curent working directory of our R session (use getwd() command to print this directory from the current R notebook).

## Custom CSV Iterator¶

We will create a custom CSV Iterator based on the C++ CSVIterator class.

For that we are going to use the R function mx.io.CSVIter as a base class. This class has as parameters data.csv, data.shape, batch.size and two main functions, iter.next() that calls the iterator in the next batch of data and value() that returns the train data and the label.

The R Custom Iterator needs to inherit from the C++ data iterator class, for that we used the class Rcpp_MXArrayDataIter extracted with RCPP. Also, it needs to have the same parameters: data.csv, data.shape, batch.size. Apart from that, we can also add the field iter, which is the CSV Iterator that we are going to expand.

require(mxnet)
CustomCSVIter <- setRefClass("CustomCSVIter",
fields=c("iter", "data.csv", "data.shape", "batch.size"),
contains = "Rcpp_MXArrayDataIter",
# , ... This is just an incomplete example, we will add more arguments later.
)


The next step is to initialize the class. For that we call the base mx.io.CSVIter and fill the rest of the fields.

CustomCSVIter <- setRefClass("CustomCSVIter",
fields=c("iter", "data.csv", "data.shape", "batch.size"),
contains = "Rcpp_MXArrayDataIter",
methods=list(
initialize=function(iter, data.csv, data.shape, batch.size){
feature_len <- data.shape*data.shape + 1
csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
.self$iter <- csv_iter .self$data.csv <- data.csv
.self$data.shape <- data.shape .self$batch.size <- batch.size
.self
})
# , ... # This is just an incomplete example, we will add more arguments later.
)


So far there is no difference between the original class and our custom class. Let’s implement the function value(). In this case, what we are going to do is transform the data that comes from the original class as an array of 785 numbers into a matrix of 28x28 and a label. We will also normalize the training data to be between 0 and 1.

CustomCSVIter <- setRefClass("CustomCSVIter",
fields=c("iter", "data.csv", "data.shape", "batch.size"),
contains = "Rcpp_MXArrayDataIter",
methods=list(
initialize=function(iter, data.csv, data.shape, batch.size){
feature_len <- data.shape*data.shape + 1
csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
.self$iter <- csv_iter .self$data.csv <- data.csv
.self$data.shape <- data.shape .self$batch.size <- batch.size
.self
},
value=function(){
val <- as.array(.self$iter$value()$data) val.x <- val[-1,] val.y <- val[1,] val.x <- val.x/255 dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x)) val.x <- mx.nd.array(val.x) val.y <- mx.nd.array(val.y) list(data=val.x, label=val.y) } # , ... This is just an incomplete example, we will add more arguments later. ) )  Finally we are going to add the rest of the functions needed for the training to work correctly. The final CustomCSVIter looks like this: CustomCSVIter <- setRefClass("CustomCSVIter", fields=c("iter", "data.csv", "data.shape", "batch.size"), contains = "Rcpp_MXArrayDataIter", methods=list( initialize=function(iter, data.csv, data.shape, batch.size){ feature_len <- data.shape*data.shape + 1 csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size) .self$iter <- csv_iter
.self$data.csv <- data.csv .self$data.shape <- data.shape
.self$batch.size <- batch.size .self }, value=function(){ val <- as.array(.self$iter$value()$data)
val.x <- val[-1,]
val.y <- val[1,]
val.x <- val.x/255
dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
val.x <- mx.nd.array(val.x)
val.y <- mx.nd.array(val.y)
list(data=val.x, label=val.y)
},
iter.next=function(){
.self$iter$iter.next()
},
reset=function(){
.self$iter$reset()
},
.self$iter$num.pad()
},
finalize=function(){
.self$iter$finalize()
}
)
)


To call the class we can just do:

batch.size <- 100
train.iter <- CustomCSVIter\$new(iter = NULL, data.csv = "mnist_train.csv", data.shape = 28, batch.size = batch.size)


## CNN Model¶

For the rest of this tutorial, we are going to use the known LeNet architecture. This is a convolutional neural network classification model with two convolution layers that use max-pooling, two fully-connected layers, and tanh-activation functions.

lenet.model <- function(){
data <- mx.symbol.Variable('data')
conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20) #first conv
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)# second conv
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=100) # first fullc
tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10) # second fullc
network <- mx.symbol.SoftmaxOutput(data=fc2) # loss
network
}
network <- lenet.model()


## Training with the Custom Iterator¶

Finally, we can directly add the custom iterator as the training data source.

In order to speed up the code below, you can switch ctx=mx.gpu(0) to perform training on a GPU instead of the CPU (assuming you have already properly installed the GPU-version of MXNet).

model <- mx.model.FeedForward.create(symbol=network,
X=train.iter,
ctx=mx.cpu(0), # To train on GPU instead, use: ctx=mx.gpu(0),
num.round=2,
array.batch.size=batch.size,
learning.rate=0.1,
momentum=0.9,
eval.metric=mx.metric.accuracy,
wd=0.00001,
batch.end.callback=mx.callback.log.speedometer(batch.size, frequency = 100)
)


## Conclusion¶

We have shown how to create a custom CSV Iterator by extending the class mx.io.CSVIter. In our class, we iteratively read from a CSV file a batch of data that will be transformed and then processed in the stochastic gradient descent optimization. That way, we are able to manage CSV files that are bigger than the memory of the machine we are using.

Based on this custom iterator, we can also create data loaders that internally transform or expand the data, allowing us to handle training data files of any size/format.