Implement hanning function in keras.ops

keras/api/_tf_keras/keras/ops/__init__.py

@@ -186,6 +186,7 @@
 from keras.src.ops.numpy import greater as greater
 from keras.src.ops.numpy import greater_equal as greater_equal
 from keras.src.ops.numpy import hamming as hamming
+from keras.src.ops.numpy import hanning as hanning
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
 from keras.src.ops.numpy import identity as identity

keras/api/_tf_keras/keras/ops/numpy/__init__.py

@@ -76,6 +76,7 @@
 from keras.src.ops.numpy import greater as greater
 from keras.src.ops.numpy import greater_equal as greater_equal
 from keras.src.ops.numpy import hamming as hamming
+from keras.src.ops.numpy import hanning as hanning
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
 from keras.src.ops.numpy import identity as identity

keras/api/ops/__init__.py

@@ -186,6 +186,7 @@
 from keras.src.ops.numpy import greater as greater
 from keras.src.ops.numpy import greater_equal as greater_equal
 from keras.src.ops.numpy import hamming as hamming
+from keras.src.ops.numpy import hanning as hanning
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
 from keras.src.ops.numpy import identity as identity

keras/api/ops/numpy/__init__.py

@@ -76,6 +76,7 @@
 from keras.src.ops.numpy import greater as greater
 from keras.src.ops.numpy import greater_equal as greater_equal
 from keras.src.ops.numpy import hamming as hamming
+from keras.src.ops.numpy import hanning as hanning
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
 from keras.src.ops.numpy import identity as identity

keras/src/backend/jax/numpy.py

@@ -47,6 +47,11 @@ def hamming(x):
     return jnp.hamming(x)
 
 
+def hanning(x):
+    x = convert_to_tensor(x)
+    return jnp.hanning(x)
+
+
 def kaiser(x, beta):
     x = convert_to_tensor(x)
     return jnp.kaiser(x, beta)

keras/src/backend/numpy/numpy.py

@@ -315,6 +315,11 @@ def hamming(x):
     return np.hamming(x).astype(config.floatx())
 
 
+def hanning(x):
+    x = convert_to_tensor(x)
+    return np.hanning(x).astype(config.floatx())
+
+
 def kaiser(x, beta):
     x = convert_to_tensor(x)
     return np.kaiser(x, beta).astype(config.floatx())

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -11,6 +11,7 @@ NumpyDtypeTest::test_array
 NumpyDtypeTest::test_bartlett
 NumpyDtypeTest::test_blackman
 NumpyDtypeTest::test_hamming
+NumpyDtypeTest::test_hanning
 NumpyDtypeTest::test_kaiser
 NumpyDtypeTest::test_bitwise
 NumpyDtypeTest::test_ceil
@@ -76,6 +77,7 @@ NumpyOneInputOpsCorrectnessTest::test_array
 NumpyOneInputOpsCorrectnessTest::test_bartlett
 NumpyOneInputOpsCorrectnessTest::test_blackman
 NumpyOneInputOpsCorrectnessTest::test_hamming
+NumpyOneInputOpsCorrectnessTest::test_hanning
 NumpyOneInputOpsCorrectnessTest::test_kaiser
 NumpyOneInputOpsCorrectnessTest::test_bitwise_invert
 NumpyOneInputOpsCorrectnessTest::test_conj
@@ -150,5 +152,6 @@ NumpyOneInputOpsDynamicShapeTest::test_angle
 NumpyOneInputOpsDynamicShapeTest::test_bartlett
 NumpyOneInputOpsDynamicShapeTest::test_blackman
 NumpyOneInputOpsDynamicShapeTest::test_hamming
+NumpyOneInputOpsDynamicShapeTest::test_hanning
 NumpyOneInputOpsDynamicShapeTest::test_kaiser
 NumpyOneInputOpsStaticShapeTest::test_angle

keras/src/backend/tensorflow/numpy.py

@@ -151,6 +151,11 @@ def hamming(x):
     return tf.signal.hamming_window(x, periodic=False)
 
 
+def hanning(x):
+    x = convert_to_tensor(x, dtype=tf.int32)
+    return tf.signal.hann_window(x, periodic=False)
+
+
 def kaiser(x, beta):
     x = convert_to_tensor(x, dtype=tf.int32)
     return tf.signal.kaiser_window(x, beta=beta)

keras/src/backend/torch/numpy.py

@@ -440,6 +440,11 @@ def hamming(x):
     return torch.signal.windows.hamming(x)
 
 
+def hanning(x):
+    x = convert_to_tensor(x)
+    return torch.signal.windows.hann(x)
+
+
 def kaiser(x, beta):
     x = convert_to_tensor(x)
     return torch.signal.windows.kaiser(x, beta=beta)

keras/src/ops/numpy.py

@@ -1270,6 +1270,37 @@ def hamming(x):
     return backend.numpy.hamming(x)
 
 
+class Hanning(Operation):
+    def call(self, x):
+        return backend.numpy.hanning(x)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=backend.floatx())
+
+
+@keras_export(["keras.ops.hanning", "keras.ops.numpy.hanning"])
+def hanning(x):
+    """Hanning window function.
+
+    The Hanning window is defined as:
+    `w[n] = 0.5 - 0.5 * cos(2 * pi * n / (N - 1))` for `0 <= n <= N - 1`.
+
+    Args:
+        x: Scalar or 1D Tensor. The window length.
+
+    Returns:
+        A 1D tensor containing the Hanning window values.
+
+    Example:
+    >>> x = keras.ops.convert_to_tensor(5)
+    >>> keras.ops.hanning(x)
+    array([0. , 0.5, 1. , 0.5, 0. ], dtype=float32)
+    """
+    if any_symbolic_tensors((x,)):
+        return Hanning().symbolic_call(x)
+    return backend.numpy.hanning(x)
+
+
 class Kaiser(Operation):
     def __init__(self, beta):
         super().__init__()

keras/src/ops/numpy_test.py

@@ -1232,6 +1232,10 @@ def test_hamming(self):
         x = np.random.randint(1, 100 + 1)
         self.assertEqual(knp.hamming(x).shape[0], x)
 
+    def test_hanning(self):
+        x = np.random.randint(1, 100 + 1)
+        self.assertEqual(knp.hanning(x).shape[0], x)
+
     def test_kaiser(self):
         x = np.random.randint(1, 100 + 1)
         beta = float(np.random.randint(10, 20 + 1))
@@ -3625,6 +3629,12 @@ def test_hamming(self):
 
         self.assertAllClose(knp.Hamming()(x), np.hamming(x))
 
+    def test_hanning(self):
+        x = np.random.randint(1, 100 + 1)
+        self.assertAllClose(knp.hanning(x), np.hanning(x))
+
+        self.assertAllClose(knp.Hanning()(x), np.hanning(x))
+
     def test_kaiser(self):
         x = np.random.randint(1, 100 + 1)
         beta = float(np.random.randint(10, 20 + 1))
@@ -5639,6 +5649,22 @@ def test_hamming(self, dtype):
             expected_dtype,
         )
 
+    @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
+    def test_hanning(self, dtype):
+        import jax.numpy as jnp
+
+        x = knp.ones((), dtype=dtype)
+        x_jax = jnp.ones((), dtype=dtype)
+        expected_dtype = standardize_dtype(jnp.hanning(x_jax).dtype)
+
+        self.assertEqual(
+            standardize_dtype(knp.hanning(x).dtype), expected_dtype
+        )
+        self.assertEqual(
+            standardize_dtype(knp.Hanning().symbolic_call(x).dtype),
+            expected_dtype,
+        )
+
     @parameterized.named_parameters(named_product(dtype=ALL_DTYPES))
     def test_kaiser(self, dtype):
         import jax.numpy as jnp