kerastuneR (version 0.1.0.7)

RandomSearch: RandomSearch

Description

Random search tuner.

Usage

RandomSearch(
  hypermodel,
  objective,
  max_trials,
  seed = NULL,
  hyperparameters = NULL,
  tune_new_entries = TRUE,
  allow_new_entries = TRUE,
  max_retries_per_trial = 0,
  max_consecutive_failed_trials = 3,
  ...
)

Value

a hyperparameter tuner object RandomSearch

Arguments

hypermodel

Define a model-building function. It takes an argument "hp" from which you can sample hyperparameters.

objective

A loss metrics function for tracking the model performance e.g. "val_precision". The name of the objective to optimize (whether to minimize or maximize is automatically inferred for built-in metrics)

max_trials

the total number of trials (max_trials) to test

seed

Int. Random seed

hyperparameters

HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space

tune_new_entries

Whether hyperparameter entries that are requested by the hypermodel but that were not specified in hyperparameters should be added to the search space, or not. If not, then the default value for these parameters will be used.

allow_new_entries

Whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters

max_retries_per_trial

Integer. Defaults to 0. The maximum number of times to retry a `Trial` if the trial crashed or the results are invalid.

max_consecutive_failed_trials

Integer. Defaults to 3. The maximum number of consecutive failed `Trial`s. When this number is reached, the search will be stopped. A `Trial` is marked as failed when none of the retries succeeded. **kwargs: Keyword arguments relevant to all `Tuner` subclasses. Please see the docstring for `Tuner`.

...

Some additional arguments

Examples

if (FALSE) {

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()

build_model = function(hp) {
 model = keras_model_sequential()
  model %>% layer_dense(units=hp$Int('units',
                                    min_value=32L,
                                    max_value=512L,
                                    step=32L),
                                    input_shape = ncol(x_data),
                                    activation='relu') %>%
   layer_dense(units=1L, activation='softmax') %>%
   compile(
     optimizer= tf$keras$optimizers$Adam(
       hp$Choice('learning_rate',
                 values=c(1e-2, 1e-3, 1e-4))),
    loss='binary_crossentropy',
     metrics='accuracy')
     return(model)
 }
 tuner = RandomSearch(hypermodel = build_model,
                       objective = 'val_accuracy',
                       max_trials = 2,
                       executions_per_trial = 1)
}