Ch-6: AE Dim Redn

# Load pckages
library(keras)
library(EBImage)

# Data
mnist <- dataset_fashion_mnist()
trainx <- mnist$train$x
trainy <- mnist$train$y
testx <- mnist$test$x
testy <- mnist$test$y

# Plot 9 images
par(mfrow = c(8,8), mar = rep(0, 4))
for (i in 1:64) plot(as.raster(trainx[i,,], max = 255))
par(mfrow = c(1,1))

# reshape
trainx <- array_reshape(trainx, c(nrow(trainx), 28, 28, 1))
testx <- array_reshape(testx, c(nrow(testx), 28, 28, 1))
trainx <- trainx / 255
testx <- testx / 255

# Model
input_layer <-  layer_input(shape = c(28,28,1)) 
encoder <-  input_layer %>% 
         layer_conv_2d(filters = 8, 
                       kernel_size = c(3,3), 
                       activation = 'relu', 
                       padding = 'same') %>%   
         layer_max_pooling_2d(pool_size = c(2,2),
                              padding = 'same') %>% 
         layer_conv_2d(filters = 4, 
                       kernel_size = c(3,3), 
                       activation = 'relu', 
                       padding = 'same') %>%  
         layer_max_pooling_2d(pool_size = c(2,2), 
                              padding = 'same')  
decoder <- encoder %>% 
         layer_conv_2d(filters = 4, 
                       kernel_size = c(3,3), 
                       activation = 'relu',
                       padding = 'same') %>%   
         layer_upsampling_2d(c(2,2)) %>% 
         layer_conv_2d(filters = 8, 
                       kernel_size = c(3,3), 
                       activation = 'relu',
                       padding = 'same') %>%  
         layer_upsampling_2d(c(2,2)) %>% 
         layer_conv_2d(filters = 1, 
                       kernel_size = c(3,3), 
                       activation = 'sigmoid',
                       padding = 'same')
# Compile model 
ae_model <- keras_model(inputs = input_layer, outputs = decoder)
ae_model %>% compile( loss='mean_squared_error', optimizer='adam')

# Fit  model
model_one <- ae_model %>% fit(trainx, 
                         trainx, 
                         epochs = 20, 
                         shuffle=TRUE,
                         batch_size = 32, 
                         validation_data = list(testx,testx))
plot(model_one)

rc <-   ae_model %>%    keras::predict_on_batch(x = testx)
par(mfrow = c(2,5), mar = rep(0, 4))
for (i in 1:5) plot(as.raster(rc[i,,,]))
for (i in 1:5) plot(as.raster(testx[i,,,]))