R_DeepLearning_Example

# YouTube link: https://youtu.be/XVfn6IpoUPU
#install.packages("keras")
library(keras)

# Data
data <- read.csv('https://raw.githubusercontent.com/bkrai/DeepLearningR/master/Cardiotocographic.csv', header=T)
data$NSP <- data$NSP -1
barplot(prop.table(table(data$NSP)),
        col = rainbow(3),
        ylim = c(0, 0.8),
        ylab = 'Proportion',
        xlab = 'NSP',
        cex.names = 1.5)

# Matrix
data <- as.matrix(data)
dimnames(data) <- NULL  

# Normalize 
data[,1:21] <- normalize(data[,1:21])

# Partition
set.seed(1234)
ind <- sample(2, nrow(data), replace = T, prob=c(.7, .3))
training <- data[ind==1, 1:21]
test <- data[ind==2, 1:21]
trainingtarget <- data[ind==1, 22]
testtarget <- data[ind==2, 22]

# One hot encoding 
trainLabels <- to_categorical(trainingtarget)
testLabels <- to_categorical(testtarget)

# Creat sequential model and add layers 
model <- keras_model_sequential()
model %>%  layer_dense(units = 8, activation = 'relu', input_shape = c(21)) %>%   
  layer_dense(units = 3, activation = 'softmax') 
summary(model)

# Compile 
model %>% compile(loss = 'categorical_crossentropy', 
  optimizer = 'adam',
  metrics = 'accuracy')

# Fit 
fit <- model %>%   
  fit(training, 
  trainLabels, 
  epochs = 25,
  batch_size = 8, 
  validation_split = 0.2)
  plot(fit)

# Evaluate 
model %>%  evaluate(test, testLabels)  

# Prediction 
pred <- model %>% 
  predict(test) %>%
   k_argmax() %>%
   as.integer()
table(Predicted=pred, Actual=testtarget)