fit_tuner: Search

In kerastuneR: Interface to 'Keras Tuner'

Search

Description

Start the search for the best hyperparameter configuration. The call to search has the same signature as “'model.fit()“'. 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.

Usage

fit_tuner(
  tuner,
  x = NULL,
  y = NULL,
  steps_per_epoch = NULL,
  batch_size = NULL,
  epochs = NULL,
  validation_data = NULL,
  validation_steps = NULL,
  ...
)

Arguments

tuner	A tuner object
x	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).
y	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).
steps_per_epoch	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.
batch_size	Integer or 'NULL'. Number of samples per gradient update. If unspecified, 'batch_size' will default to 32.
epochs	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.
validation_data	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
validation_steps	Only relevant if steps_per_epoch is specified. Total number of steps (batches of samples) to validate before stopping.
...	Some additional arguments

Value

performs a search for best hyperparameter configuations

Examples

## Not run: 

library(keras)
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(
  'HyperModel',
  inherit = HyperModel_class(),
  list(
    
    `__init__` = function(self, num_classes) {
      
      self$num_classes = num_classes
      NULL
    },
    build = function(self,hp) {
      model = keras_model_sequential() 
      model %>% layer_dense(units = hp$Int('units',
                                           min_value = 32,
                                           max_value = 512,
                                           step = 32),
                            input_shape = ncol(x_data),
                            activation = 'relu') %>% 
        layer_dense(as.integer(self$num_classes), activation = 'softmax') %>% 
        compile(
          optimizer = tf$keras$optimizers$Adam(
            hp$Choice('learning_rate',
                      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 Sept. 4, 2023, 1:06 a.m.