best_practice.R
In kerastuneR: Interface to 'Keras Tuner'

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

## -----------------------------------------------------------------------------
#  
#  library(keras)
#  library(tensorflow)
#  library(kerastuneR)
#  
#  if(tensorflow::tf_gpu_configured()) {
#    physical_devices = tf$config$list_physical_devices('GPU')
#    tf$config$experimental$set_memory_growth(physical_devices[[1]],TRUE)
#  }
#  
#  
#  # The data, shuffled and split between train and test sets
#  mnist <- dataset_mnist()
#  x_train <- mnist$train$x
#  y_train <- mnist$train$y
#  x_test <- mnist$test$x
#  y_test <- mnist$test$y
#  
#  augment_images = function(x, hp) {
#    use_rotation = hp$Boolean('use_rotation')
#    if(use_rotation) {
#      x = tf$keras$layers$experimental$preprocessing$RandomRotation(
#        hp$Float('rotation_factor', min_value=0.05, max_value=0.2)
#      )(x)
#    }
#    use_zoom = hp$Boolean('use_zoom')
#    if(use_zoom) {
#      x = tf$keras$layers$experimental$preprocessing$RandomZoom(
#        hp$Float('use_zoom', min_value=0.05, max_value=0.2)
#      )(x)
#    }
#    x
#  }
#  
#  make_model = function(hp) {
#    inputs = layer_input(shape=c(28, 28, 1))
#    x = tf$keras$layers$experimental$preprocessing$Rescaling(1. / 255)(inputs)
#    x = tf$keras$layers$experimental$preprocessing$Resizing(64L, 64L)(x)
#    x = augment_images(x, hp)
#    num_block = hp$Int('num_block', min_value=2, max_value=5, step=1)
#    num_filters = hp$Int('num_filters', min_value=32, max_value=128, step=32)
#    for (i in 1:length(num_block)) {
#      x = x %>% layer_conv_2d(
#        num_filters,
#        kernel_size=3,
#        activation='relu',
#        padding='same'
#      ) %>%
#        layer_conv_2d(
#          num_filters,
#          kernel_size=3,
#          activation='relu',
#          padding='same'
#        ) %>% layer_max_pooling_2d(2)
#    }
#    reduction_type = hp$Choice('reduction_type', c('flatten', 'avg'))
#  
#    if(reduction_type == 'flatten') {
#      x = x %>% layer_flatten()
#    } else {
#      x = x %>% layer_global_average_pooling_2d()
#    }
#  
#    x = x %>% layer_dense(
#      units=hp$Int('num_dense_units', min_value=32, max_value=512, step=32),
#      activation='relu'
#    ) %>% layer_dropout(
#      hp$Float('dense_dropout', min_value = 0., max_value = 0.7)
#    )
#  
#    outputs = x %>% layer_dense(10)
#    model = keras_model(inputs, outputs)
#    learning_rate = hp$Float('learning_rate', min_value = 3e-4, max_value = 3e-3)
#    optimizer = optimizer_adam(lr=1e-3)
#    model %>% compile(loss = tf$keras$losses$SparseCategoricalCrossentropy(from_logits = TRUE),
#                  optimizer = optimizer,
#                  metrics = tf$keras$metrics$SparseCategoricalAccuracy(name='acc'))
#    model %>% summary()
#    return(model)
#  }
#  
#  
#  tuner = RandomSearch(
#    make_model,
#    objective='val_acc',
#    max_trials=2,
#    overwrite=TRUE)
#  
#  
#  callbacks=callback_early_stopping(monitor = 'val_acc', mode = 'max',
#                                    patience = 3, baseline = 0.9)
#  tuner %>% fit_tuner(x_train, y_train, validation_split = 0.2,
#                      callbacks = list(callbacks), verbose=1, epochs=2)

## -----------------------------------------------------------------------------
#  best_hp = tuner %>% get_best_models(1)
#  history = model %>% fit(x_train, y_train, validation_split = 0.2, epochs = 2)