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Output int64 by default from Discretization #14841

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Jul 2, 2021
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Output int64 by default from Discretization #14841

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13 changes: 8 additions & 5 deletions keras/layers/preprocessing/discretization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,18 +151,18 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
>>> input = np.array([[-1.5, 1.0, 3.4, .5], [0.0, 3.0, 1.3, 0.0]])
>>> layer = tf.keras.layers.Discretization(bin_boundaries=[0., 1., 2.])
>>> layer(input)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
<tf.Tensor: shape=(2, 4), dtype=int64, numpy=
array([[0, 2, 3, 1],
[1, 3, 2, 1]], dtype=int32)>
[1, 3, 2, 1]], dtype=int64)>

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.Discretization(num_bins=4, epsilon=0.01)
>>> layer.adapt(input)
>>> layer(input)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
<tf.Tensor: shape=(2, 4), dtype=int64, numpy=
array([[0, 2, 3, 2],
[1, 3, 3, 1]], dtype=int32)>
[1, 3, 3, 1]], dtype=int64)>
"""

def __init__(self,
Expand Down Expand Up @@ -263,8 +263,11 @@ def compute_output_signature(self, input_spec):

def call(self, inputs):
def bucketize(inputs):
return tf.raw_ops.Bucketize(
outputs = tf.raw_ops.Bucketize(
input=inputs, boundaries=self.bin_boundaries)
# All other preprocessing layers use int64 for int output, so we conform
# here. Sadly the underlying op only supports int32, so we need to cast.
return tf.cast(outputs, tf.int64)

if tf_utils.is_ragged(inputs):
integer_buckets = tf.ragged.map_flat_values(bucketize, inputs)
Expand Down
6 changes: 6 additions & 0 deletions keras/layers/preprocessing/discretization_test.py
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Expand Up @@ -128,6 +128,12 @@ def test_output_shape(self):
output = layer(input_data)
self.assertAllEqual(output.shape.as_list(), [16, 4])

def test_output_dtype(self):
input_data = keras.Input(batch_size=16, shape=(4,), dtype=tf.int64)
layer = discretization.Discretization(bin_boundaries=[-.5, 0.5, 1.5])
output = layer(input_data)
self.assertAllEqual(output.dtype, tf.int64)

def test_num_bins_negative_fails(self):
with self.assertRaisesRegex(ValueError, "`num_bins` must be.*num_bins=-7"):
_ = discretization.Discretization(num_bins=-7)
Expand Down