# conda create -n r-reticulate
# install.packages(tensorflow)
# library(tensorflow)
# install_tensorflow(method = 'conda', envname = 'r-reticulate')
# install.packages(keras)
# install_keras(method = 'conda', envname = 'r-reticulate')

# library(reticulate)
# use_condaenv('r-reticulate')
# library(tensorflow)
# library(keras)

data<-dataset_mnist()
#separating train and test file
train_x<-data$train$x
train_y<-data$train$y
test_x<-data$test$x
test_y<-data$test$y
rm(data)
# converting a 2D array into a 1D array for feeding into the MLP and normalising the matrix
train_x <- array(train_x, dim = c(dim(train_x)[1], prod(dim(train_x)[-1]))) / 255
test_x <- array(test_x, dim = c(dim(test_x)[1], prod(dim(test_x)[-1]))) / 255
image(matrix(train_x[2,],28,28,byrow=T), axes = FALSE,col=gray.colors(255))

train_y_cat<-to_categorical(train_y,10)
test_y_cat<-to_categorical(test_y,10)
train_y <- train_y_cat
test_y <- test_y_cat

model <- keras_model_sequential()
model %>%
  layer_dense(units = 784, input_shape = 784) %>%
  layer_dropout(rate=0.4)%>%
  layer_activation(activation = 'relu') %>%
  layer_dense(units = 10) %>%
  layer_activation(activation = 'softmax')
model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
learning_history <- model %>% fit(train_x, train_y, epochs = 30, batch_size = 1000)
loss_and_metrics <- model %>% evaluate(test_x, test_y, batch_size = 128)
summary(model)
plot(learning_history)
prediction <- model %>% predict(test_x)
image(as.matrix(prediction[1:5,]))
prediction[1:5,]