MNIST hypertuning

knitr::opts_chunk$set(echo = TRUE,eval = F)

This tutorial shows the hyperparameter tuning for MNIST dataset.

If you have difficulty with the following topic, Keras tutorials would be very helpful.

Input shape

At first, we should determine the input shape of our model.

inputs = tf$keras$Input(shape=list(28L, 28L, 1L))

Layers

The next step is to put iterable parameters into the loop function for (i in 1:n) {} and define layers.

for (i in 1:3) {
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')
}

Functional API

Later, we should concatenate all the layers and create a model via Functional API. Please, refer to the Functional API, if you want to get more details about it.

model = tf$keras$Model(inputs, outputs)

Optimizers

Finally, we should determine our optimizer. Keras Tuner allows us to tune even optimizers.

optimizer = hp$Choice('optimizer', c('adam', 'sgd'))

As the build(hp) function is ready, we should build a Hyperband object, and start searching the best hyperparameters.

Full code

Below is the full version of a tuning process for MNIST dataset.

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()


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kerastuneR documentation built on Jan. 13, 2021, 6:40 p.m.