inst/doc/BayesianOptimisation.R

In kerastuneR: Interface to 'Keras Tuner'

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = F)

## -----------------------------------------------------------------------------
#  
#  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()
#