Random search tuner.
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,
...
)Define a model-building function. It takes an argument "hp" from which you can sample hyperparameters.
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)
the total number of trials (max_trials) to test
Int. Random seed
HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space
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.
Whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters
Integer. Defaults to 0. The maximum number of times to retry a `Trial` if the trial crashed or the results are invalid.
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
a hyperparameter tuner object RandomSearch
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)
}