kerastuneR (version 0.1.0.2)

fit_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 {
# }
# NOT RUN {
library(dplyr)
library(kerastuneR)
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 <- kerastuneR::PyClass(
  'HyperModel',
  inherit = kerastuneR::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)) 
# }