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VAEFacesLight.R
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#VAE faces
#Nacho Garcia 2019
#garcia.nacho@gmail.com
#Library loading
library(keras)
library(imager)
library(matlab)
#File loading
Path<-"/home/nacho/VAE_Faces/ImagesPGM/"
files<-list.files(Path)
setwd(Path)
#Testing purposes 10%
files<-sample(files, round(0.1*length(files)), replace = FALSE)
df.temp <- load.image(files[1])
df.temp<-as.matrix(df.temp)
df<-array(data = 0, dim = c(length(files),nrow(df.temp),ncol(df.temp),1))
for (i in 1:length(files)) {
df.temp <- load.image(files[i])
df.temp<-as.matrix(df.temp)
#Padding smaller pics
if(ncol(df.temp)<dim(df)[3]) df.temp<-padarray(df.temp,c(0,1+round((dim(df)[3]-ncol(df.temp))/2)))
if(nrow(df.temp)<dim(df)[2]) df.temp<-padarray(df.temp,c(1,1+round((dim(df)[2]-nrow(df.temp))/2)))
#Cropping bigger pics
if(ncol(df.temp)>dim(df)[3]) df.temp<-df.temp[,1:dim(df)[2]]
if(nrow(df.temp)>dim(df)[2]) df.temp<-df.temp[1:dim(df)[2],]
df[i,,,1]<-df.temp
}
df2<- array(data = 0, dim = c(dim(df)[1],300,300,1))
df2[,1:299,1:299,]<-df
df<-df2
rm(df2)
#Image visualization
image(df[1,,,],
useRaster = TRUE,
axes=FALSE,
col = gray.colors(256, start = 0, end = 1, gamma = 2.2, alpha = NULL))
#Cleaning file names
files<-gsub("_.*","",files)
files<-gsub(".pgm","",files)
files<-gsub("face.*","",files)
files<-gsub("\\d","",files)
#Model
reset_states(vae)
filters <- 4
intermediate_dim<-64
latent_dim<-2
epsilon_std <- 1.0
batch_size <- 8
epoch<-30
dimensions<-dim(df)
dimensions<-dimensions[-1]
Input <- layer_input(shape = dimensions)
faces<- Input %>%
layer_conv_2d(filters=1, kernel_size=c(10,10), activation='sigmoid', padding='same',strides=c(1,1),data_format='channels_last')%>%
layer_conv_2d(filters=filters*2, kernel_size=c(10,10), activation='sigmoid', padding='same',strides=c(2,2),data_format='channels_last')%>%
layer_conv_2d(filters=filters*4, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(1,1),data_format='channels_last')%>%
layer_conv_2d(filters=filters*8, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(2,2),data_format='channels_last')%>%
layer_flatten()
hidden <- faces %>% layer_dense( units = intermediate_dim, activation = "sigmoid") %>%
layer_dense( units = round(intermediate_dim/2), activation = "sigmoid") %>%
layer_dense( units = round(intermediate_dim/4), activation = "sigmoid")
z_mean <- hidden %>% layer_dense( units = latent_dim)
z_log_var <- hidden %>% layer_dense( units = latent_dim)
sampling <- function(args) {
z_mean <- args[, 1:(latent_dim)]
z_log_var <- args[, (latent_dim + 1):(2 * latent_dim)]
epsilon <- k_random_normal(
shape = c(k_shape(z_mean)[[1]]),
mean = 0.,
stddev = epsilon_std
)
z_mean + k_exp(z_log_var) * epsilon
}
z <- layer_concatenate(list(z_mean, z_log_var)) %>% layer_lambda(sampling)
Output<- z %>%
layer_dense( units = round(intermediate_dim/4), activation = "sigmoid") %>%
layer_dense( units = round(intermediate_dim/2), activation = "sigmoid") %>%
layer_dense(units = intermediate_dim, activation = "sigmoid") %>%
layer_dense(units = prod(75,75,filters*8), activation = "sigmoid") %>%
layer_reshape(target_shape = c(75,75,filters*8)) %>%
layer_conv_2d_transpose(filters=filters*8, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(2,2),data_format='channels_last')%>%
layer_conv_2d_transpose(filters=filters*4, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(1,1),data_format='channels_last')%>%
layer_conv_2d_transpose(filters=filters*2, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(2,2),data_format='channels_last')%>%
layer_conv_2d_transpose(filters=1, kernel_size=c(5,5), activation='sigmoid', padding='same',strides=c(1,1),data_format='channels_last')
# custom loss function
vae_loss <- function(x, x_decoded_mean_squash) {
x <- k_flatten(x)
x_decoded_mean_squash <- k_flatten(x_decoded_mean_squash)
xent_loss <- 2.0 * dimensions[1]* dimensions[1] *
loss_binary_crossentropy(x, x_decoded_mean_squash)
kl_loss <- -0.5 * k_mean(1 + z_log_var - k_square(z_mean) -
k_exp(z_log_var), axis = -1L)
k_mean(xent_loss + kl_loss)
}
## variational autoencoder
vae <- keras_model(Input, Output)
vae %>% compile(optimizer = "rmsprop", loss = vae_loss)
summary(vae)
## Split for Training
reset_states(vae)
date<-as.character(date())
logs<-gsub(" ","_",date)
logs<-gsub(":",".",logs)
logs<-paste("logs/",logs,sep = "")
history<-vae %>% fit(x= df,
y=df,
batch_size=batch_size,
epoch=epoch,
callbacks = callback_tensorboard(logs),
view_metrics=FALSE,
shuffle=TRUE)
tensorboard(logs)
Samp1.gen <- predict(vae, array(df[15,,,], dim = c(1,300,300,1)), batch_size = 10)
Samp1.gen<-as.matrix(Samp1.gen)
Samp1.gen<-matrix(Samp1.gen, ncol = 300)
image(Samp1.gen,
useRaster = TRUE,
axes=FALSE,
col = gray.colors(256, start = 0, end = 1, gamma = 2.2, alpha = NULL))
#Latent space
layer_name <- 'lambda_6'
intermediate_layer_model <- keras_model(inputs = vae$input,
outputs = get_layer(vae, layer_name)$output)
intermediate_output <- as.data.frame(predict(intermediate_layer_model, df))
plot(intermediate_output$V2,intermediate_output$V3)