A preprocessing layer which buckets continuous features by ranges.

layer_discretization(
object,
bin_boundaries = NULL,
num_bins = NULL,
epsilon = 0.01,
...
)

## Arguments

object What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned. A list of bin boundaries. The leftmost and rightmost bins will always extend to -Inf and Inf, so bin_boundaries = c(0., 1., 2.) generates bins (-Inf, 0.), [0., 1.), [1., 2.), and [2., +Inf). If this option is set, adapt should not be called. The integer number of bins to compute. If this option is set, adapt should be called to learn the bin boundaries. Error tolerance, typically a small fraction close to zero (e.g. 0.01). Higher values of epsilon increase the quantile approximation, and hence result in more unequal buckets, but could improve performance and resource consumption. standard layer arguments.

## Details

This layer will place each element of its input data into one of several contiguous ranges and output an integer index indicating which range each element was placed in.

Input shape: Any tf.Tensor or tf.RaggedTensor of dimension 2 or higher.

Output shape: Same as input shape.

Other numerical features preprocessing layers: layer_normalization()
Other preprocessing layers: layer_category_encoding(), layer_center_crop(), layer_hashing(), layer_integer_lookup(), layer_normalization(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), layer_random_translation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()