GAN_Compare

# GAN 
begin <- Sys.time()
#setwd("~/Desktop")

library(keras)

# MNIST data
mnist <- dataset_mnist()
c(c(trainx, trainy), c(testx, testy)) %<-% mnist
trainx <- trainx[trainy==5,,]
trainx <- array_reshape(trainx, c(nrow(trainx), 28, 28, 1))
trainx <- trainx / 255

# Generator network
h <- 28; w <- 28; c <- 1; l <- 28  
gi <- layer_input(shape = l)
go <- gi %>% layer_dense(units = 32 * 14 * 14) %>%
         layer_activation_leaky_relu() %>% 
         layer_reshape(target_shape = c(14, 14, 32)) %>% 
         layer_conv_2d(filters = 32, 
                       kernel_size = 5,
                       padding = "same") %>% 
         layer_activation_leaky_relu() %>% 
         layer_conv_2d_transpose(filters = 32, 
                                 kernel_size = 4,
                                 strides = 2, 
                                 padding = "same") %>% 
         layer_activation_leaky_relu() %>% 
         layer_conv_2d(filters = 1, 
                       kernel_size = 5,
                       activation = "tanh", 
                       padding = "same")
g <- keras_model(gi, go)

# Discriminator
di <- layer_input(shape = c(h, w, c))
do <- di %>% 
         layer_conv_2d(filters = 64, kernel_size = 4) %>% 
         layer_activation_leaky_relu() %>% 
         layer_flatten() %>%
         layer_dropout(rate = 0.3) %>%  
         layer_dense(units = 1, activation = "sigmoid")
d <- keras_model(di, do)
d %>% compile(optimizer = 'rmsprop',
         loss = "binary_crossentropy")

# Adversarial network
freeze_weights(d) 
gani <- layer_input(shape = l)
gano <- gani %>% g %>% d
gan <- keras_model(gani, gano)
gan %>% compile(optimizer = 'rmsprop', 
                loss = "binary_crossentropy")

# Train
b <- 50  
dir <- "images"
dir.create(dir)
# Start 
start <- 1; dloss <- NULL; gloss <- NULL
for (i in 1:500) {noise <- matrix(rnorm(b*l), nrow = b, ncol= l)
fake <- g %>% predict(noise)

stop <- start + b - 1 
real <- trainx[start:stop,,,]
real <- array_reshape(real, c(nrow(real), 28, 28, 1))
rows <- nrow(real)
both <- array(0, dim = c(rows * 2, dim(real)[-1]))
both[1:rows,,,] <- fake
both[(rows+1):(rows*2),,,] <- real

labels <- rbind(matrix(runif(b, 0.9,1), nrow = b, ncol = 1),
                matrix(runif(b, 0, 0.1), nrow = b, ncol = 1))

dloss[i] <- d %>% train_on_batch(both, labels) 
fakeAsReal <- array(runif(b, 0, 0.1), dim = c(b, 1))
gloss[i] <- gan %>% train_on_batch(noise, fakeAsReal)    
start <- start + b
if (start > (nrow(trainx) - b))  start <- 1

cat("Discriminator Loss:", dloss[i], "\n") 
cat("Gan Loss:", gloss[i], "\n")  
# Saves 
f <- fake[1,,,] 
dim(f) <- c(28,28,1)
image_array_save(f, path = file.path(dir, paste0("f", i+100, ".png")))}

#Time
end <- Sys.time()
begin
end
end - begin