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

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

## -----------------------------------------------------------------------------
#  conv_build_model = function(hp) {
#    'Builds a convolutional model.'
#    inputs = tf$keras$Input(shape=list(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$GlobalMaxPool2D()(x)
#    } else {
#      x = tf$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)
#  }
#  
#  main = function() {
#    tuner = Hyperband(
#      hypermodel = conv_build_model,
#      objective = 'val_accuracy',
#      max_epochs = 8,
#      factor = 2,
#      hyperband_iterations = 3,
#      directory = 'results_dir',
#      project_name='mnist')
#  
#    # call keras library for downloading MNIST dataset
#    library(keras)
#  
#    mnist_data = dataset_fashion_mnist()
#    c(mnist_train, mnist_test) %<-%  mnist_data
#    rm(mnist_data)
#  
#    # reshape data
#    mnist_train$x = keras::k_reshape(mnist_train$x,shape = c(6e4,28,28,1))
#    mnist_test$x = keras::k_reshape(mnist_test$x,shape = c(1e4,28,28,1))
#  
#    # call tfdatasets and slice dataset
#    # turn data type into float 32 (features, not labels/outputs)
#    library(tfdatasets)
#    mnist_train =  tensor_slices_dataset(list(tf$dtypes$cast(
#      mnist_train$x, 'float32') / 255., mnist_train$y)) %>%
#      dataset_shuffle(1e3) %>% dataset_batch(1e2) %>% dataset_repeat()
#  
#    mnist_test = tensor_slices_dataset(list(tf$dtypes$cast(
#      mnist_test$x, 'float32') / 255., mnist_test$y)) %>%
#      dataset_batch(1e2)
#  
#    # finally, begin a training with a bunch of parameters
#    tuner %>% fit_tuner(x = mnist_train,
#                 steps_per_epoch=600,
#                 validation_data=mnist_test,
#                 validation_steps=100,
#                 epochs=2,
#                 callbacks=c(tf$keras$callbacks$EarlyStopping('val_accuracy'))
#    )
#  }
#  
#  main()
#