Ch-4

library(keras)
library(EBImage)

# Read images
setwd("~/Desktop/image15")
temp = list.files(pattern="*.jpg")
mypic <- list()
for (i in 1:length(temp)) {mypic[[i]] <- readImage(temp[i])}

# Plot all pictures
par(mfrow = c(3,6))
for (i in 1:length(temp)) plot(mypic[[i]])
par(mfrow = c(1,1))

# Resize
for (i in 1:length(temp)) {mypic[[i]] <- resize(mypic[[i]], 28, 28)}

# Reshape
for (i in 1:length(temp)) {mypic[[i]] <- array_reshape(mypic[[i]], c(28, 28,3))}

# Row Bind
a <- c(1:4, 7:10, 13:16)
b <- c(5:6, 11:12, 17:18)
trainx <- NULL
for (i in a) {trainx <- rbind(trainx, mypic[[i]]) }
testx <- NULL
for (i in b) {testx <- rbind(testx, mypic[[i]])}
trainy <- c(0,0,0,0,1,1,1,1,2,2,2,2)
testy <- c(0,0,1,1,2,2)

# One hot encoding 
trainLabels <- to_categorical(trainy)
testLabels <- to_categorical(testy)

# Model
model <- keras_model_sequential() 
model %>%  layer_dense(units = 512, activation = 'relu', input_shape = c(2352)) %>% 
  layer_dropout(rate = 0.1) %>%
  layer_dense(units = 256, activation = 'relu') %>%
  layer_dropout(rate = 0.1) %>%
  layer_dense(units = 3, activation = 'softmax')

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

# Fit  model
history <- model %>% fit(trainx, 
                         trainLabels, 
                         epochs = 50, 
                         batch_size = 32, 
                         validation_split = 0.2)

# Evaluation & Prediction - train data 
model %>% evaluate(trainx, trainLabels)
pred <- model %>%   predict_classes(trainx)
table(Predicted=pred, Actual=trainy)
prob <- model %>%   predict_proba(trainx) 
cbind(prob, Predicted_class = pred, Actual = trainy)

# Evaluation & Prediction -  test data 
model %>% evaluate(testx, testLabels)
pred <- model %>%   predict_classes(testx)
table(Predicted=pred, Actual=testy)
prob <- model %>%   predict_proba(testx) 
cbind(prob, Predicted_class = pred, Actual = testy)