Fix discretization discrepancy

keras/src/layers/preprocessing/discretization.py

@@ -95,9 +95,6 @@ def __init__(
         dtype=None,
         name=None,
     ):
-        if dtype is None:
-            dtype = "int64" if output_mode == "int" else backend.floatx()
-
         super().__init__(name=name, dtype=dtype)
 
         if sparse and not backend.SUPPORTS_SPARSE_TENSORS:
@@ -155,6 +152,10 @@ def __init__(
     def input_dtype(self):
         return backend.floatx()
 
+    @property
+    def output_dtype(self):
+        return self.compute_dtype if self.output_mode != "int" else "int32"
+
     def adapt(self, data, steps=None):
         """Computes bin boundaries from quantiles in a input dataset.
 
@@ -213,7 +214,7 @@ 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)
+        return backend.KerasTensor(shape=inputs.shape, dtype=self.output_dtype)
 
     def load_own_variables(self, store):
         if len(store) == 1:
@@ -234,7 +235,7 @@ def call(self, inputs):
             indices,
             output_mode=self.output_mode,
             depth=len(self.bin_boundaries) + 1,
-            dtype=self.compute_dtype,
+            dtype=self.output_dtype,
             sparse=self.sparse,
             backend_module=self.backend,
         )

keras/src/layers/preprocessing/discretization_test.py

@@ -205,3 +205,29 @@ 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_model_call_vs_predict_consistency(self):
+        """Test that model(input) and model.predict(input) produce consistent outputs."""  # noqa: E501
+        # Test with int output mode
+        layer = layers.Discretization(
+            bin_boundaries=[-0.5, 0, 0.1, 0.2, 3],
+            output_mode="int",
+        )
+        x = np.array([[0.0, 0.15, 0.21, 0.3], [0.0, 0.17, 0.451, 7.8]])
+
+        # Create model
+        inputs = layers.Input(shape=(4,), dtype="float32")
+        outputs = layer(inputs)
+        model = models.Model(inputs=inputs, outputs=outputs)
+
+        # Test both execution modes
+        model_call_output = model(x)
+        predict_output = model.predict(x)
+
+        # Check consistency
+        self.assertAllClose(model_call_output, predict_output)
+        self.assertEqual(
+            backend.standardize_dtype(model_call_output.dtype),
+            backend.standardize_dtype(predict_output.dtype),
+        )
+        self.assertTrue(backend.is_int_dtype(model_call_output.dtype))