R interface to Keras Tuner

The kerastuneR package provides R wrappers to Keras Tuner.

Keras Tuner is a hypertuning framework made for humans. It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. Keras Tuner makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code.

Keras Tuner

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A hyperparameter tuner for Keras, specifically for tf$keras with TensorFlow 2.0.

Full documentation and tutorials available on the Keras Tuner website.



  • Python 3.9
  • TensorFlow 2.0.x

kerastuneR can be installed from CRAN:


The dev version:


Later, you need to install the python module kerastuner:


Usage: the basics

Here’s how to perform hyperparameter tuning for a single-layer dense neural network using random search.

First, we define a model-building function. It takes an argument hp from which you can sample hyperparameters, such as hp$Int('units', min_value = 32, max_value = 512, step = 32) (an integer from a certain range).

Sample data:

x_data <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5)
y_data <-  ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix()

x_data2 <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5)
y_data2 <-  ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix()

This function returns a compiled model.


build_model = function(hp) {
  model = keras_model_sequential()
  model %>% layer_dense(units = hp$Int('units',
                                     min_value = 32,
                                     max_value = 512,
                                     step=  32),input_shape = ncol(x_data),
                        activation =  'relu') %>%
    layer_dense(units = 1, activation = 'softmax') %>%
      optimizer = tf$keras$optimizers$Adam(
                  values=c(1e-2, 1e-3, 1e-4))),
      loss = 'binary_crossentropy',
      metrics = 'accuracy')

Next, instantiate a tuner. You should specify the model-building function, the name of the objective to optimize (whether to minimize or maximize is automatically inferred for built-in metrics), the total number of trials (max_trials) to test, and the number of models that should be built and fit for each trial (executions_per_trial).

Available tuners are RandomSearch and Hyperband.

Note: the purpose of having multiple executions per trial is to reduce results variance and therefore be able to more accurately assess the performance of a model. If you want to get results faster, you could set executions_per_trial=1 (single round of training for each model configuration).

tuner = RandomSearch(
    objective = 'val_accuracy',
    max_trials = 5,
    executions_per_trial = 3,
    directory = 'my_dir',
    project_name = 'helloworld')

You can print a summary of the search space:

tuner %>% search_summary()

Then, start the search for the best hyperparameter configuration. The call to search has the same signature as model %>% fit(). But here instead of fit() we call fit_tuner().

tuner %>% fit_tuner(x_data,y_data,
                    epochs = 5, 
                    validation_data = list(x_data2,y_data2))

Plot results

There is a function plot_tuner which allows user to plot the search results. For this purpose, we used the parallel coordinates plot from plotly. This function allows to get a data.frame of the results, as well.

result = kerastuneR::plot_tuner(tuner)
# the list will show the plot and the data.frame of tuning results

Keras Tuner plot

Plot Keras model

First one should extract the list of tuned models and then using function plot_keras_model to plot the model architecture.

best_5_models = tuner %>% get_best_models(5)
best_5_models[[1]] %>% plot_keras_model()

Keras model

You can easily restrict the search space to just a few parameters

If you have an existing hypermodel, and you want to search over only a few parameters (such as the learning rate), you can do so by passing a hyperparameters argument to the tuner constructor, as well as tune_new_entries=FALSE to specify that parameters that you didn’t list in hyperparameters should not be tuned. For these parameters, the default value gets used.


mnist_data = dataset_fashion_mnist()
c(mnist_train, mnist_test) %<-%  mnist_data

mnist_train$x = tf$dtypes$cast(mnist_train$x, 'float32') / 255.
mnist_test$x = tf$dtypes$cast(mnist_test$x, 'float32') / 255.

mnist_train$x = keras::k_reshape(mnist_train$x,shape = c(6e4,28,28))
mnist_test$x = keras::k_reshape(mnist_test$x,shape = c(1e4,28,28))

hp = HyperParameters()
hp$Choice('learning_rate', c(1e-1, 1e-3))
hp$Int('num_layers', 2L, 20L)

mnist_model = function(hp) {
  model = keras_model_sequential() %>% 
    layer_flatten(input_shape = c(28,28))
  for (i in 1:(hp$get('num_layers')) ) {
    model %>% layer_dense(32, activation='relu') %>% 
      layer_dense(units = 10, activation='softmax')
  } %>% 
      optimizer = tf$keras$optimizers$Adam(hp$get('learning_rate')),
      loss = 'sparse_categorical_crossentropy',
      metrics = 'accuracy') 

tuner = RandomSearch(
  hypermodel =  mnist_model,
  max_trials = 5,
  hyperparameters = hp,
  tune_new_entries = T,
  objective = 'val_accuracy',
  directory = 'dir_1',
  project_name = 'mnist_space')

tuner %>% fit_tuner(x = mnist_train$x,
                    y = mnist_train$y,
                    epochs = 5,
                    validation_data = list(mnist_test$x, mnist_test$y))