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,
...
)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.
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.
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.
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()