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Fix Discretization layer graph mode bug #21514

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Fix Discretization layer graph mode bug #21514

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04a876b
fix discreatization layer
divyashreepathihalli Jul 25, 2025
c6ce008
fix dscreatization error
divyashreepathihalli Jul 25, 2025
b19df6e
update test
divyashreepathihalli Jul 25, 2025
c66a616
fix tests
divyashreepathihalli Jul 25, 2025
795c3de
code reformat
divyashreepathihalli Jul 25, 2025
04c002b
c
divyashreepathihalli Jul 25, 2025
4287f11
code reformat
divyashreepathihalli Jul 25, 2025
53a5e25
default dtype
divyashreepathihalli Jul 25, 2025
23444dc
Update discretization.py
divyashreepathihalli Jul 28, 2025
3241db0
Update discretization.py
divyashreepathihalli Jul 28, 2025
af901f5
fix test
divyashreepathihalli Jul 28, 2025
949bf6d
Merge branch 'keras-team:master' into discreatization
divyashreepathihalli Jul 28, 2025
c7b244a
fix test
divyashreepathihalli Aug 4, 2025
f29997c
fix test
divyashreepathihalli Aug 4, 2025
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7 changes: 5 additions & 2 deletions keras/src/layers/preprocessing/discretization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ def __init__(
name=None,
):
if dtype is None:
dtype = "int64" if output_mode == "int" else backend.floatx()
dtype = "float32"
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I think you should just remove the whole if dtype is None block, the base layer class will handle None properly.

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I tried that! It results in the unit tests failing

FAILED keras/src/layers/preprocessing/discretization_test.py::DiscretizationTest::test_discretization_basics - AssertionError: expected output dtype int64, got int32:
- int32
+ int64

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Well, it fails the same way with or without on JAX.

The thing is, this class has this:

    @property
    def input_dtype(self):
        return backend.floatx()

So I don't understand why the inputs were cast to ints.

Maybe you can try to override @property ... compute_dtype?


super().__init__(name=name, dtype=dtype)

Expand Down Expand Up @@ -213,7 +213,8 @@ def reset_state(self):
self.summary = np.array([[], []], dtype="float32")

def compute_output_spec(self, inputs):
return backend.KerasTensor(shape=inputs.shape, dtype=self.compute_dtype)
output_dtype = "int64" if self.output_mode == "int" else self.compute_dtype
return backend.KerasTensor(shape=inputs.shape, dtype=output_dtype)

def load_own_variables(self, store):
if len(store) == 1:
Expand All @@ -229,7 +230,9 @@ def call(self, inputs):
"start using the `Discretization` layer."
)

# Use the backend's digitize function for all backends
indices = self.backend.numpy.digitize(inputs, self.bin_boundaries)

return numerical_utils.encode_categorical_inputs(
indices,
output_mode=self.output_mode,
Expand Down
29 changes: 29 additions & 0 deletions keras/src/layers/preprocessing/discretization_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,3 +205,32 @@ def test_call_before_adapt_raises(self):
layer = layers.Discretization(num_bins=3)
with self.assertRaisesRegex(ValueError, "You need .* call .*adapt"):
layer([[0.1, 0.8, 0.9]])


def test_discretization_eager_vs_graph():
import os

os.environ["KERAS_BACKEND"] = "tensorflow"
import tensorflow as tf

import keras

layer = keras.layers.Discretization(
bin_boundaries=[-0.5, 0, 0.1, 0.2, 3],
name="bucket",
output_mode="int",
)

x = tf.constant([[0.0, 0.15, 0.21, 0.3], [0.0, 0.17, 0.451, 7.8]])
inputs = keras.layers.Input(name="inp", dtype="float32", shape=(4,))
model_output = layer(inputs)
model = keras.models.Model(inputs=[inputs], outputs=[model_output])

print("Eager mode (layer(x)):")
print(layer(x).numpy())

print("Model call (model(x)):")
print(model(x).numpy())

print("Model predict (model.predict(x)):")
print(model.predict(x))
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