tf.keras.layers.Discretization

A preprocessing layer which buckets continuous features by ranges.

Inherits From: Layer, Operation

tf.keras.layers.Discretization(
    bin_boundaries=None,
    num_bins=None,
    epsilon=0.01,
    output_mode='int',
    sparse=False,
    dtype=None,
    name=None
)

Used in the notebooks

Used in the guide Used in the tutorials

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.

Any array of dimension 2 or higher.

Same as input shape.

bin_boundaries A list of bin boundaries. The leftmost and rightmost bins will always extend to -inf and inf, so bin_boundaries=[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. 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. Defaults to "int".
sparse Boolean. Only applicable to "one_hot", "multi_hot", and "count" output modes. Only supported with TensorFlow backend. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.

Examples:

Discretize float values based on provided buckets.

>>> input = np.array([[-1.5, 1.0, 3.4, .5], [0.0, 3.0, 1.3, 0.0]])
>>> layer = Discretization(bin_boundaries=[0., 1., 2.])
>>> layer(input)
array([[0, 2, 3, 1],
       [1, 3, 2, 1]])

Discretize float values based on a number of buckets to compute.

>>> input = np.array([[-1.5, 1.0, 3.4, .5], [0.0, 3.0, 1.3, 0.0]])
>>> layer = Discretization(num_bins=4, epsilon=0.01)
>>> layer.adapt(input)
>>> layer(input)
array([[0, 2, 3, 2],
       [1, 3, 3, 1]])

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

adapt

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adapt(
    data, steps=None
)

Computes bin boundaries from quantiles in a input dataset.

Calling adapt() on a Discretization layer is an alternative to passing in a bin_boundaries argument during construction. A Discretization layer should always be either adapted over a dataset or passed bin_boundaries.

During adapt(), the layer will estimate the quantile boundaries of the input dataset. The number of quantiles can be controlled via the num_bins argument, and the error tolerance for quantile boundaries can be controlled via the epsilon argument.

Arguments
data The data to train on. It can be passed either as a batched tf.data.Dataset, or as a NumPy array.
steps Integer or None. Total number of steps (batches of samples) to process. If data is a tf.data.Dataset, and steps is None, adapt() will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs or list inputs.

finalize_state

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finalize_state()

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

reset_state

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reset_state()

symbolic_call

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symbolic_call(
    *args, **kwargs
)

update_state

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update_state(
    data
)