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Dense: validate units argument #21902

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Dense: validate units argument #21902

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3d6fbae
Dense: validate units argument
RohitYandigeri Dec 7, 2025
89cca3d
Dense: robust integer validation and parameterized tests
RohitYandigeri Dec 7, 2025
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6 changes: 6 additions & 0 deletions keras/src/layers/core/dense.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,12 @@ def __init__(
lora_alpha=None,
**kwargs,
):
if not isinstance(units, int) or units <= 0:
raise ValueError(
"Received an invalid value for `units`, expected a positive integer. "
f"Received: units={units}"
)
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@gemini-code-assist gemini-code-assist bot Dec 7, 2025

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medium

The validation isinstance(units, int) is a bit too strict as it will reject numpy integer types (e.g., numpy.int64), which are commonly used in the scientific Python ecosystem. This can lead to unexpected errors for users.

A more robust approach is to check if units is integer-like by trying to convert it to an int and checking for equality. This correctly handles integers, numpy integers, and integer-like floats (e.g., 2.0), while rejecting non-integer floats and other types like strings.

Suggested change
if not isinstance(units, int) or units <= 0:
raise ValueError(
"Received an invalid value for `units`, expected a positive integer. "
f"Received: units={units}"
)
try:
is_int_like = int(units) == units
except (TypeError, ValueError):
is_int_like = False
if not is_int_like or units <= 0:
raise ValueError(
"Received an invalid value for `units`, expected a positive integer. "
f"Received: units={units}"
)


super().__init__(activity_regularizer=activity_regularizer, **kwargs)
self.units = units
self.activation = activations.get(activation)
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16 changes: 16 additions & 0 deletions keras/src/layers/core/dense_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,22 @@ def test_dense_basics(self):
expected_num_losses=2, # we have 2 regularizers.
supports_masking=True,
)

def test_dense_invalid_units_raises(self):
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(0)

with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(-3)

with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(2.5)

with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(None)

with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense("64")
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medium

These tests for invalid units values are great. To make the test more concise and easier to extend in the future, you could use parameterized.named_parameters which is already used in this test file.

Suggested change
def test_dense_invalid_units_raises(self):
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(0)
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(-3)
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(2.5)
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(None)
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense("64")
@parameterized.named_parameters(
("zero", 0),
("negative", -3),
("float", 2.5),
("none", None),
("string", "64"),
)
def test_dense_invalid_units_raises(self, units):
with self.assertRaisesRegex(ValueError, "positive integer"):
layers.Dense(units)


def test_dense_correctness(self):
# With bias and activation.
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