---
title: "Bayesian Optimization"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Bayesian Optimization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = F)
```

## Subclassing Tuner for Custom Training Loops

The Tuner class at ```Tuner_class()``` can be subclassed to support advanced uses such as:

- Custom training loops (GANs, reinforement learning, etc.)
- Adding hyperparameters outside of the model builing function (preprocessing, data augmentation, test time augmentation, etc.)

## Understanding the search process.

```tuner %>% fit_tuner()``` can be passed any arguments. These arguments will be passed directly to ```Tuner$run_trial```, along with a Trial object that contains information about the current trial, including hyperparameters and the status of the trial. Typically, ```Tuner$run_trial``` is the only method that users need to override when subclassing Tuner.

Thanks to [Daniel Falbel from RStudio](https://github.com/dfalbel), the ```Bayesian Optimization``` example was successfully adapted.

```{r }

library(keras)
library(tensorflow)
library(dplyr)
library(tfdatasets)
library(kerastuneR)
library(reticulate)


conv_build_model = function(hp) {
  'Builds a convolutional model.'
  inputs = tf$keras$Input(shape=c(28L, 28L, 1L))
  
  x = inputs
  
  for (i in 1:hp$Int('conv_layers', 1L, 3L, default=3L)) {
    x = tf$keras$layers$Conv2D(filters = hp$Int(paste('filters_', i, sep = ''), 4L, 32L, step=4L, default=8L),
                               kernel_size = hp$Int(paste('kernel_size_', i, sep = ''), 3L, 5L),
                               activation ='relu',
                               padding='same')(x)
    if (hp$Choice(paste('pooling', i, sep = ''), c('max', 'avg')) == 'max') {
      x = tf$keras$layers$MaxPooling2D()(x)
    } else {
      x = tf$keras$layers$AveragePooling2D()(x)
    }
    x = tf$keras$layers$BatchNormalization()(x) 
    x =  tf$keras$layers$ReLU()(x)
    
  }
  if (hp$Choice('global_pooling', c('max', 'avg')) == 'max') {
    x =  tf$keras$layers$GlobalMaxPooling2D()(x)
  } else {
    x = tf$keras$layers$GlobalAveragePooling2D()(x)
  }
  
  outputs = tf$keras$layers$Dense(10L, activation='softmax')(x)
  model = tf$keras$Model(inputs, outputs)
  optimizer = hp$Choice('optimizer', c('adam', 'sgd'))
  model %>% compile(optimizer, loss='sparse_categorical_crossentropy', metrics='accuracy')
  return(model)
}

MyTuner = PyClass(
  'Tuner',
  inherit = Tuner_class(),
  list(
    run_trial = function(self, trial, train_ds){
      hp = trial$hyperparameters
      train_ds = train_ds$batch(hp$Int('batch_size', 32L, 128L, step=32L, default=64L))
      model = self$hypermodel$build(trial$hyperparameters)
      lr = hp$Float('learning_rate', 1e-4, 1e-2, sampling='log', default=1e-3)
      optimizer = tf$keras$optimizers$Adam(lr)
      epoch_loss_metric = tf$keras$metrics$Mean()
      
      
      run_train_step = function(data){
        images = data[[1]]
        labels = data[[2]]
        
        
        with (tf$GradientTape() %as% tape,{
          logits = model(images)
          loss = tf$keras$losses$sparse_categorical_crossentropy(labels, logits)
          if(length(model$losses) > 0){
            loss = loss + tf$math$add_n(model$losses)
          }
          gradients = tape$gradient(loss, model$trainable_variables)
        })
        optimizer$apply_gradients(purrr::transpose(list(gradients, model$trainable_variables)))
        epoch_loss_metric$update_state(loss)
        loss
      }
      
      for (epoch in 1:1) {
        print(paste('Epoch',epoch))
        self$on_epoch_begin(trial, model, epoch, logs= list())
        intializer = make_iterator_one_shot(train_ds)
        
        for (batch in 1:length(iterate(train_ds))) {
          
          init_next = iter_next(intializer)
          
          self$on_batch_begin(trial, model, batch, logs=list())
          batch_loss = as.numeric(run_train_step(init_next))
          self$on_batch_end(trial, model, batch, logs=list(paste('loss', batch_loss)))
          
          if (batch %% 100L == 0L){
            loss = epoch_loss_metric$result()$numpy()
            print(paste('Batch',batch,  'Average loss', loss))
          }
        }
        
        epoch_loss = epoch_loss_metric$result()$numpy()
        self$on_epoch_end(trial, model, epoch, logs=list('loss'= epoch_loss))
        epoch_loss_metric$reset_states()
      }
    }
  )
)


main = function () {
  tuner = MyTuner(
    oracle=BayesianOptimization(
      objective=Objective(name='loss', direction = 'min'),
      max_trials=1),
    hypermodel=conv_build_model,
    directory='results2',
    project_name='mnist_custom_training2')
  
  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_train$x = keras::k_reshape(mnist_train$x,shape = c(6e4,28,28,1))
  
  mnist_train = tensor_slices_dataset(mnist_train) %>% dataset_shuffle(1e3)
    
    tuner %>% fit_tuner(train_ds = mnist_train)
    
    best_model = tuner %>% get_best_models(1L)
  
}

main()

```