fit_tuner: Search

View source: R/search.R

fit_tunerR Documentation



Start the search for the best hyperparameter configuration. The call to search has the same signature as “'“'. Models are built iteratively by calling the model-building function, which populates the hyperparameter space (search space) tracked by the hp object. The tuner progressively explores the space, recording metrics for each configuration.


  x = NULL,
  y = NULL,
  steps_per_epoch = NULL,
  batch_size = NULL,
  epochs = NULL,
  validation_data = NULL,
  validation_steps = NULL,



A tuner object


Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. x can be NULL (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).


Vector, matrix, or array of target (label) data (or list if the model has multiple outputs). If all outputs in the model are named, you can also pass a list mapping output names to data. y can be NULL (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).


Integer. Total number of steps (batches of samples) to yield from generator before declaring one epoch finished and starting the next epoch. It should typically be equal to ceil(num_samples / batch_size). Optional for Sequence: if unspecified, will use the len(generator) as a number of steps.


Integer or 'NULL'. Number of samples per gradient update. If unspecified, 'batch_size' will default to 32.


to train the model. Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.


Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be: - tuple (x_val, y_val) of Numpy arrays or tensors - tuple (x_val, y_val, val_sample_weights) of Numpy arrays - dataset or a dataset iterator


Only relevant if steps_per_epoch is specified. Total number of steps (batches of samples) to validate before stopping.


Some additional arguments


performs a search for best hyperparameter configuations


## Not run: 

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()

HyperModel <- PyClass(
  inherit = HyperModel_class(),
    `__init__` = function(self, num_classes) {
      self$num_classes = num_classes
    build = function(self,hp) {
      model = keras_model_sequential() 
      model %>% layer_dense(units = hp$Int('units',
                                           min_value = 32,
                                           max_value = 512,
                                           step = 32),
                            input_shape = ncol(x_data),
                            activation = 'relu') %>% 
        layer_dense(as.integer(self$num_classes), activation = 'softmax') %>% 
          optimizer = tf$keras$optimizers$Adam(
                      values = c(1e-2, 1e-3, 1e-4))),
          loss = 'sparse_categorical_crossentropy',
          metrics = 'accuracy')

hypermodel = HyperModel(num_classes=10L)

tuner = RandomSearch(hypermodel = hypermodel,
                     objective = 'val_accuracy',
                     max_trials = 2,
                    executions_per_trial = 1,
                     directory = 'my_dir5',
                     project_name = 'helloworld')
tuner %>% fit_tuner(x_data, y_data, epochs = 1, validation_data = list(x_data2,y_data2)) 

## End(Not run)

kerastuneR documentation built on March 25, 2022, 9:07 a.m.