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Neural network parameters

Source: R/param_network.R
dropout.Rd

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(2L, 7L), trans = transform_log2())

dropout_hidden(range = c(0, 0.5), trans = NULL)

dropout_last(range = c(0, 0.5), trans = NULL)

num_embedding(range = c(8L, 64L), trans = NULL)

dropout_embedding(range = c(0, 0.5), trans = NULL)

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.

  • dropout_hidden(): The proportion of hidden-layer units to randomly set to zero during model training.

  • dropout_last(): The proportion of final-layer units to randomly set to zero during model training.

  • num_embedding(): The dimensionality of the embedding space for features.

  • dropout_embedding(): The proportion of embedding values to randomly set to zero during model training.

Examples

dropout()
#> Dropout Rate (quantitative)
#> Range: [0, 1)