context('hp space')

source("utils.R")

test_succeeds("Can run hp-space", {
  library(keras3)
  library(dplyr)
  library(kerastuneR)
  
  mnist_data = dataset_fashion_mnist()
  c(mnist_train, mnist_test) %<-%  mnist_data
  rm(mnist_data)
  
  mnist_train$x = tf$dtypes$cast(mnist_train$x, 'float32') / 255.
  mnist_test$x = tf$dtypes$cast(mnist_test$x, 'float32') / 255.
  
  mnist_train$x = tf$reshape(mnist_train$x,shape = c(6e4L,28L,28L))
  mnist_test$x = tf$reshape(mnist_test$x,shape = c(1e4L,28L,28L))
  
  
  hp = HyperParameters()
  hp$Choice('learning_rate',values =c(1e-1, 1e-3))
  hp$Int('num_layers', 2L, 20L)
  
  
  mnist_model = function(hp) {
    
    model = keras_model_sequential() %>% 
      layer_flatten(input_shape = c(28,28))
    for (i in 1:(hp$get('num_layers')) ) {
      model %>% layer_dense(32, activation='relu') %>% 
        layer_dense(units = 10, activation='softmax')
    } %>% 
      compile(
        optimizer = tf$keras$optimizers$Adam(hp$get('learning_rate')),
        loss = 'sparse_categorical_crossentropy',
        metrics = 'accuracy') 
    return(model)
    
  }
  
  
  tuner = RandomSearch(
    hypermodel =  mnist_model,
    max_trials=5,
    hyperparameters=hp,
    tune_new_entries=T,
    objective='val_accuracy',
    directory='my_dir4',
    project_name = 'mnist_')
  
  tuner %>% fit_tuner(mnist_train$x, mnist_train$y,
                       validation_split=0.2,
                       epochs=1)  
  
})