A preprocessing layer which buckets continuous features by ranges.

```
layer_discretization(
object,
bin_boundaries = NULL,
num_bins = NULL,
epsilon = 0.01,
output_mode = "int",
sparse = FALSE,
...
)
```

- object
What to compose the new

`Layer`

instance with. Typically a Sequential model or a Tensor (e.g., as returned by`layer_input()`

). The return value depends on`object`

. If`object`

is:missing or

`NULL`

, the`Layer`

instance is returned.a

`Sequential`

model, the model with an additional layer is returned.a Tensor, the output tensor from

`layer_instance(object)`

is returned.

- bin_boundaries
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.- num_bins
The integer number of bins to compute. If this option is set,

`adapt`

should be called to learn the bin boundaries.- epsilon
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.

- output_mode
Specification for the output of the layer. Defaults to

`"int"`

. Values can be`"int"`

,`"one_hot"`

,`"multi_hot"`

, or`"count"`

configuring the layer as follows:`"int"`

: Return the discretized bin indices directly.`"one_hot"`

: Encodes each individual element in the input into an array the same size as`num_bins`

, containing a 1 at the input's bin index. If the last dimension is size 1, will encode on that dimension. If the last dimension is not size 1, will append a new dimension for the encoded output.`"multi_hot"`

: Encodes each sample in the input into a single array the same size as`num_bins`

, containing a 1 for each bin index index present in the sample. Treats the last dimension as the sample dimension, if input shape is`(..., sample_length)`

, output shape will be`(..., num_tokens)`

.`"count"`

: As`"multi_hot"`

, but the int array contains a count of the number of times the bin index appeared in the sample.

- sparse
Boolean. Only applicable to

`"one_hot"`

,`"multi_hot"`

, and`"count"`

output modes. If`TRUE`

, returns a`SparseTensor`

instead of a dense`Tensor`

. Defaults to`FALSE`

.- ...
standard layer arguments.

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.

`adapt()`

https://www.tensorflow.org/api_docs/python/tf/keras/layers/Discretization

https://keras.io/api/layers/preprocessing_layers/numerical/discretization

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_brightness()`

,
`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()`