These functions generate parameters that are useful for neural network models.
Usage
dropout(range = c(0, 1), trans = NULL)
epochs(range = c(10L, 1000L), trans = NULL)
hidden_units(range = c(1L, 10L), trans = NULL)
hidden_units_2(range = c(1L, 10L), trans = NULL)
batch_size(range = c(unknown(), unknown()), trans = transform_log2())
Arguments
- range
A two-element vector holding the defaults for the smallest and
largest possible values, respectively. If a transformation is specified,
these values should be in the transformed units.
- trans
A trans
object from the scales
package, such as
scales::transform_log10()
or scales::transform_reciprocal()
. If not provided,
the default is used which matches the units used in range
. If no
transformation, NULL
.
Details
dropout()
: The parameter dropout rate. (See parsnip:::mlp()
).
epochs()
: The number of iterations of training. (See parsnip:::mlp()
).
hidden_units()
: The number of hidden units in a network layer.
(See parsnip:::mlp()
).
batch_size()
: The mini-batch size for neural networks.
Examples
dropout()
#> Dropout Rate (quantitative)
#> Range: [0, 1)