Image recognition tutorial in R using deep convolutional neural networks (MXNet package). Part 3. Full article at https://firsttimeprogrammer.blogspot.com/2016/08/image-recognition-tutorial-in-r-using.html

convolutional_nn_tutorial_3.R

# Clean workspace
rm(list=ls())

# Load MXNet
require(mxnet)

# Loading data and set up
#-------------------------------------------------------------------------------

# Load train and test datasets
train <- read.csv("train_28.csv")
test <- read.csv("test_28.csv")

# Set up train and test datasets
train <- data.matrix(train)
train_x <- t(train[, -1])
train_y <- train[, 1]
train_array <- train_x
dim(train_array) <- c(28, 28, 1, ncol(train_x))

test_x <- t(test[, -1])
test_y <- test[, 1]
test_array <- test_x
dim(test_array) <- c(28, 28, 1, ncol(test_x))

# Set up the symbolic model
#-------------------------------------------------------------------------------

data <- mx.symbol.Variable('data')
# 1st convolutional layer
conv_1 <- mx.symbol.Convolution(data = data, kernel = c(5, 5), num_filter = 20)
tanh_1 <- mx.symbol.Activation(data = conv_1, act_type = "tanh")
pool_1 <- mx.symbol.Pooling(data = tanh_1, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
# 2nd convolutional layer
conv_2 <- mx.symbol.Convolution(data = pool_1, kernel = c(5, 5), num_filter = 50)
tanh_2 <- mx.symbol.Activation(data = conv_2, act_type = "tanh")
pool_2 <- mx.symbol.Pooling(data=tanh_2, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))
# 1st fully connected layer
flatten <- mx.symbol.Flatten(data = pool_2)
fc_1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 500)
tanh_3 <- mx.symbol.Activation(data = fc_1, act_type = "tanh")
# 2nd fully connected layer
fc_2 <- mx.symbol.FullyConnected(data = tanh_3, num_hidden = 40)
# Output. Softmax output since we'd like to get some probabilities.
NN_model <- mx.symbol.SoftmaxOutput(data = fc_2)

# Pre-training set up
#-------------------------------------------------------------------------------

# Set seed for reproducibility
mx.set.seed(100)

# Device used. CPU in my case.
devices <- mx.cpu()

# Training
#-------------------------------------------------------------------------------

# Train the model
model <- mx.model.FeedForward.create(NN_model,
                                     X = train_array,
                                     y = train_y,
                                     ctx = devices,
                                     num.round = 480,
                                     array.batch.size = 40,
                                     learning.rate = 0.01,
                                     momentum = 0.9,
                                     eval.metric = mx.metric.accuracy,
                                     epoch.end.callback = mx.callback.log.train.metric(100))

# Testing
#-------------------------------------------------------------------------------

# Predict labels
predicted <- predict(model, test_array)
# Assign labels
predicted_labels <- max.col(t(predicted)) - 1
# Get accuracy
sum(diag(table(test[, 1], predicted_labels)))/40

################################################################################
#                           OUTPUT
################################################################################
#
# 0.975
#