DeepNetworkWithMNIST

#Youtube link https://www.youtube.com/watch?v=5bso_5X7Zu4&t=785s
# MNIST data
library(keras)
mnist <- dataset_mnist()
trainx <- mnist$train$x
trainy <- mnist$train$y
testx <- mnist$test$x
testy <- mnist$test$y

# Plot images
par(mfrow = c(3,3))
for (i in 1:9) plot(as.raster(trainx[i,,], max = 255))
par(mfrow= c(1,1))

# Five 
a <- c(1, 12, 36, 48, 66, 101, 133, 139, 146)
par(mfrow = c(3,3))
for (i in a) plot(as.raster(trainx[i,,], max = 255))
par(mfrow= c(1,1))

# Reshape & rescale
trainx <- array_reshape(trainx, c(nrow(trainx), 784))
testx <- array_reshape(testx, c(nrow(testx), 784))
trainx <- trainx / 255
testx <- testx /255

# One hot encoding
trainy <- to_categorical(trainy, 10)
testy <- to_categorical(testy, 10)

# Model
model <- keras_model_sequential()
model %>% 
         layer_dense(units = 512, activation = 'relu', input_shape = c(784)) %>% 
         layer_dropout(rate = 0.4) %>% 
         layer_dense(units= 256, activation = 'relu') %>% 
         layer_dropout(rate = 0.3) %>% 
         layer_dense(units = 10, activation = 'softmax')

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

# Fit model
history <- model %>% 
         fit(trainx,
             trainy,
             epochs = 30,
             batch_size = 32,
             validation_split = 0.2)

# Evaluation and Prediction - Test data
model %>% evaluate(testx, testy)
pred <- model %>% predict_classes(testx)
table(Predicted = pred, Actual = mnist$test$y)

prob <- model %>% predict_proba(testx)
cbind(prob, Predicted_class = pred, Actual = mnist$test$y)[1:5,]

# New data
library(EBImage)
setwd("~/Desktop/numbers")
temp = list.files(pattern = "*.jpg")
mypic <- list()
for (i in 1:length(temp)) {mypic[[i]] <- readImage(temp[[i]])}

par(mfrow = c(3,2))
for (i in 1:length(temp)) plot(mypic[[i]])
par(mfrow = c(1,1))

for (i in 1:length(temp)) {colorMode(mypic[[i]]) <- Grayscale}
for (i in 1:length(temp)) {mypic[[i]] <- 1-mypic[[i]]}
for (i in 1:length(temp)) {mypic[[i]] <- resize(mypic[[i]], 28, 28)}
for (i in 1:length(temp)) {mypic[[i]] <- array_reshape(mypic[[i]], c(28,28,3))}
new <- NULL
for (i in 1:length(temp)) {new <- rbind(new, mypic[[i]])}
newx <- new[,1:784]
newy <- c(7, 5,2,0, 5, 3)

# Prediction
pred <- model %>% predict_classes(newx)
table(Predicted = pred, Actual = newy)

prob <- model %>% predict_proba(newx)
cbind(prob, Predicted = pred, Actual = newy)