tf.keras.layers.experimental.preprocessing.Discretization

Buckets data into discrete ranges.

Inherits From: PreprocessingLayer, Layer, Module

Used in the notebooks

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.

Input shape:

Any tf.Tensor or tf.RaggedTensor of dimension 2 or higher.

Output shape:

Same as input shape.

Examples:

Bucketize 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 = tf.keras.layers.experimental.preprocessing.Discretization(
...          bin_boundaries=[0., 1., 2.])
>>> layer(input)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[0, 1, 3, 1],
       [0, 3, 2, 0]], dtype=int32)>

Bucketize 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 = tf.keras.layers.experimental.preprocessing.Discretization(
...          num_bins=4, epsilon=0.01)
>>> layer.adapt(input)
>>> layer(input)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[0, 2, 3, 1],
       [0, 3, 2, 0]], dtype=int32)>

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.
is_adapted Whether the layer has been fit to data already.
streaming Whether adapt can be called twice without resetting the state.

Child Classes

class DiscretizingCombiner

Methods

adapt

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Fits the state of the preprocessing layer to the data being passed.

Arguments
data The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
batch_size Integer or None. Number of samples per state update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
steps Integer or None. Total number of steps (batches of samples) When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps' is None, the epoch 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.
reset_state Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.

compile

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