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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
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# 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 | |
# |
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