Addition of Sparsemax activation

.gitignore

@@ -18,4 +18,5 @@ dist/**
 examples/**/*.jpg
 .python-version
 .coverage
-*coverage.xml
+*coverage.xml
+.ruff_cache

keras/api/_tf_keras/keras/activations/__init__.py

@@ -33,6 +33,7 @@
 from keras.src.activations.activations import softplus
 from keras.src.activations.activations import softsign
 from keras.src.activations.activations import sparse_plus
+from keras.src.activations.activations import sparsemax
 from keras.src.activations.activations import squareplus
 from keras.src.activations.activations import tanh
 from keras.src.activations.activations import tanh_shrink

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

@@ -100,6 +100,7 @@
 from keras.src.ops.nn import softsign
 from keras.src.ops.nn import sparse_categorical_crossentropy
 from keras.src.ops.nn import sparse_plus
+from keras.src.ops.nn import sparsemax
 from keras.src.ops.nn import squareplus
 from keras.src.ops.nn import tanh_shrink
 from keras.src.ops.numpy import abs

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

@@ -45,5 +45,6 @@
 from keras.src.ops.nn import softsign
 from keras.src.ops.nn import sparse_categorical_crossentropy
 from keras.src.ops.nn import sparse_plus
+from keras.src.ops.nn import sparsemax
 from keras.src.ops.nn import squareplus
 from keras.src.ops.nn import tanh_shrink

keras/api/activations/__init__.py

@@ -33,6 +33,7 @@
 from keras.src.activations.activations import softplus
 from keras.src.activations.activations import softsign
 from keras.src.activations.activations import sparse_plus
+from keras.src.activations.activations import sparsemax
 from keras.src.activations.activations import squareplus
 from keras.src.activations.activations import tanh
 from keras.src.activations.activations import tanh_shrink

keras/api/ops/__init__.py

@@ -100,6 +100,7 @@
 from keras.src.ops.nn import softsign
 from keras.src.ops.nn import sparse_categorical_crossentropy
 from keras.src.ops.nn import sparse_plus
+from keras.src.ops.nn import sparsemax
 from keras.src.ops.nn import squareplus
 from keras.src.ops.nn import tanh_shrink
 from keras.src.ops.numpy import abs

keras/api/ops/nn/__init__.py

@@ -45,5 +45,6 @@
 from keras.src.ops.nn import softsign
 from keras.src.ops.nn import sparse_categorical_crossentropy
 from keras.src.ops.nn import sparse_plus
+from keras.src.ops.nn import sparsemax
 from keras.src.ops.nn import squareplus
 from keras.src.ops.nn import tanh_shrink

keras/src/activations/__init__.py

@@ -24,6 +24,7 @@
 from keras.src.activations.activations import softplus
 from keras.src.activations.activations import softsign
 from keras.src.activations.activations import sparse_plus
+from keras.src.activations.activations import sparsemax
 from keras.src.activations.activations import squareplus
 from keras.src.activations.activations import tanh
 from keras.src.activations.activations import tanh_shrink
@@ -59,6 +60,7 @@
     mish,
     log_softmax,
     log_sigmoid,
+    sparsemax,
 }
 
 ALL_OBJECTS_DICT = {fn.__name__: fn for fn in ALL_OBJECTS}

keras/src/activations/activations.py

@@ -617,3 +617,28 @@ def log_softmax(x, axis=-1):
         axis: Integer, axis along which the softmax is applied.
     """
     return ops.log_softmax(x, axis=axis)
+
+
+@keras_export(["keras.activations.sparsemax"])
+def sparsemax(x, axis=-1):
+    """Sparsemax activation function.
+
+    For each batch `i`, and class `j`,
+    sparsemax activation function is defined as:
+
+    `sparsemax(x)[i, j] = max(logits[i, j] - τ(logits[i, :]), 0).`
+
+    Args:
+        logits: Input tensor.
+        axis: `int`, axis along which the sparsemax operation is applied.
+
+    Returns:
+        A tensor, output of sparsemax transformation. Has the same type and
+        shape as `logits`.
+
+    Reference:
+
+    - [Martins et.al., 2016](https://arxiv.org/abs/1602.02068)
+    """
+    x = backend.convert_to_tensor(x)
+    return ops.sparsemax(x, axis)

keras/src/activations/activations_test.py

@@ -896,6 +896,55 @@ def test_linear(self):
         x_int32 = np.random.randint(-10, 10, (10, 5)).astype(np.int32)
         self.assertAllClose(x_int32, activations.linear(x_int32))
 
+    def test_sparsemax(self):
+        # result check with 1d
+        x_1d = np.linspace(1, 12, num=12)
+        expected_result = np.zeros_like(x_1d)
+        expected_result[-1] = 1.0
+        self.assertAllClose(expected_result, activations.sparsemax(x_1d))
+
+        # result check with 2d
+        x_2d = np.linspace(1, 12, num=12).reshape(-1, 2)
+        expected_result = np.zeros_like(x_2d)
+        expected_result[:, -1] = 1.0
+        self.assertAllClose(expected_result, activations.sparsemax(x_2d))
+
+        # result check with 3d
+        x_3d = np.linspace(1, 12, num=12).reshape(-1, 1, 3)
+        expected_result = np.zeros_like(x_3d)
+        expected_result[:, :, -1] = 1.0
+        self.assertAllClose(expected_result, activations.sparsemax(x_3d))
+
+        # result check with axis=-2 with 2d input
+        x_2d = np.linspace(1, 12, num=12).reshape(-1, 2)
+        expected_result = np.zeros_like(x_2d)
+        expected_result[-1, :] = 1.0
+        self.assertAllClose(
+            expected_result, activations.sparsemax(x_2d, axis=-2)
+        )
+
+        # result check with axis=-2 with 3d input
+        x_3d = np.linspace(1, 12, num=12).reshape(-1, 1, 3)
+        expected_result = np.ones_like(x_3d)
+        self.assertAllClose(
+            expected_result, activations.sparsemax(x_3d, axis=-2)
+        )
+
+        # result check with axis=-3 with 3d input
+        x_3d = np.linspace(1, 12, num=12).reshape(-1, 1, 3)
+        expected_result = np.zeros_like(x_3d)
+        expected_result[-1, :, :] = 1.0
+        self.assertAllClose(
+            expected_result, activations.sparsemax(x_3d, axis=-3)
+        )
+
+        # result check with axis=-3 with 4d input
+        x_4d = np.linspace(1, 12, num=12).reshape(-1, 1, 1, 2)
+        expected_result = np.ones_like(x_4d)
+        self.assertAllClose(
+            expected_result, activations.sparsemax(x_4d, axis=-3)
+        )
+
     def test_get_method(self):
         obj = activations.get("relu")
         self.assertEqual(obj, activations.relu)

keras/src/backend/jax/nn.py

@@ -142,6 +142,24 @@ def log_softmax(x, axis=-1):
     return jnn.log_softmax(x, axis=axis)
 
 
+def sparsemax(logits, axis=-1):
+    # Sort logits along the specified axis in descending order
+    logits = convert_to_tensor(logits)
+    logits_sorted = -1.0 * jnp.sort(logits * -1.0, axis=axis)
+    logits_cumsum = jnp.cumsum(logits_sorted, axis=axis)  # find cumulative sum
+    r = jnp.arange(1, logits.shape[axis] + 1)  # Determine the sparsity
+    r_shape = [1] * logits.ndim
+    r_shape[axis] = -1  # Broadcast to match the target axis
+    r = r.reshape(r_shape)
+    support = logits_sorted - (logits_cumsum - 1) / r > 0
+    # Find the threshold
+    k = jnp.sum(support, axis=axis, keepdims=True)
+    logits_cumsum_safe = jnp.where(support, logits_cumsum, 0.0)
+    tau = (jnp.sum(logits_cumsum_safe, axis=axis, keepdims=True) - 1) / k
+    output = jnp.maximum(logits - tau, 0.0)
+    return output
+
+
 def _convert_to_spatial_operand(
     x,
     num_spatial_dims,

keras/src/backend/numpy/nn.py

@@ -191,6 +191,24 @@ def log_softmax(x, axis=None):
     return x - max_x - logsumexp
 
 
+def sparsemax(logits, axis=-1):
+    # Sort logits along the specified axis in descending order
+    logits = convert_to_tensor(logits)
+    logits_sorted = -1.0 * np.sort(-1.0 * logits, axis=axis)
+    logits_cumsum = np.cumsum(logits_sorted, axis=axis)
+    r = np.arange(1, logits.shape[axis] + 1)
+    r_shape = [1] * logits.ndim
+    r_shape[axis] = -1  # Broadcast to match the target axis
+    r = r.reshape(r_shape)
+    support = logits_sorted - (logits_cumsum - 1) / r > 0
+    # Find the threshold
+    k = np.sum(support, axis=axis, keepdims=True)
+    logits_cumsum_safe = np.where(support, logits_cumsum, 0.0)
+    tau = (np.sum(logits_cumsum_safe, axis=axis, keepdims=True) - 1) / k
+    output = np.maximum(logits - tau, 0.0)
+    return output
+
+
 def _convert_to_spatial_operand(
     x,
     num_spatial_dims,

keras/src/backend/tensorflow/nn.py

@@ -151,6 +151,24 @@ def log_softmax(x, axis=-1):
     return tf.nn.log_softmax(x, axis=axis)
 
 
+def sparsemax(logits, axis=-1):
+    # Sort logits along the specified axis in descending order
+    logits = convert_to_tensor(logits)
+    logits_sorted = tf.sort(logits, direction="DESCENDING", axis=axis)
+    logits_cumsum = tf.cumsum(logits_sorted, axis=axis)
+    r = tf.range(1, tf.shape(logits)[axis] + 1, dtype=logits.dtype)
+    r_shape = [1] * len(logits.shape)
+    r_shape[axis] = -1  # Broadcast to match the target axis
+    r = tf.reshape(r, r_shape)  # Reshape for broadcasting
+    support = logits_sorted - (logits_cumsum - 1) / r > 0
+    # Find the threshold
+    logits_cumsum_safe = tf.where(support, logits_cumsum, 0.0)
+    k = tf.reduce_sum(tf.cast(support, logits.dtype), axis=axis, keepdims=True)
+    tau = (tf.reduce_sum(logits_cumsum_safe, axis=axis, keepdims=True) - 1) / k
+    output = tf.maximum(logits - tau, 0.0)
+    return output
+
+
 def _transpose_spatial_inputs(inputs):
     num_spatial_dims = len(inputs.shape) - 2
     # Tensorflow pooling does not support `channels_first` format, so

keras/src/backend/torch/nn.py

@@ -174,6 +174,28 @@ def log_softmax(x, axis=-1):
     return cast(output, dtype)
 
 
+def sparsemax(logits, axis=-1):
+    # Sort logits along the specified axis in descending order
+    logits = convert_to_tensor(logits)
+    logits_sorted, _ = torch.sort(logits, dim=axis, descending=True)
+    logits_cumsum = torch.cumsum(logits_sorted, dim=axis)
+    r = torch.arange(
+        1, logits.size(axis) + 1, device=logits.device, dtype=logits.dtype
+    )
+    r_shape = [1] * logits.ndim
+    r_shape[axis] = -1  # Broadcast to match the target axis
+    r = r.view(r_shape)
+    support = logits_sorted - (logits_cumsum - 1) / r > 0
+    # Find the threshold
+    k = torch.sum(support, dim=axis, keepdim=True)
+    logits_cumsum_safe = torch.where(
+        support, logits_cumsum, torch.tensor(0.0, device=logits.device)
+    )
+    tau = (torch.sum(logits_cumsum_safe, dim=axis, keepdim=True) - 1) / k
+    output = torch.clamp(logits - tau, min=0.0)
+    return output
+
+
 def _compute_padding_length(
     input_length, kernel_length, stride, dilation_rate=1
 ):

keras/src/ops/nn.py

@@ -951,6 +951,48 @@ def log_softmax(x, axis=-1):
         return backend.nn.log_softmax(x, axis=axis)
 
 
+class Sparsemax(Operation):
+    def __init__(self, axis=-1):
+        super().__init__()
+        self.axis = axis
+
+    def call(self, x):
+        return backend.nn.sparsemax(x, axis=self.axis)
+
+    def compute_output_spec(self, x):
+        return KerasTensor(x.shape, dtype=x.dtype)
+
+
+@keras_export(["keras.ops.sparsemax", "keras.ops.nn.sparsemax"])
+def sparsemax(x, axis=-1):
+    """Sparsemax activation function.
+
+    For each batch `i`, and class `j`,
+    sparsemax activation function is defined as:
+
+    `sparsemax(x)[i, j] = max(logits[i, j] - τ(logits[i, :]), 0).`
+
+    Args:
+        logits: Input tensor.
+        axis: `int`, axis along which the sparsemax operation is applied.
+
+    Returns:
+        A tensor, output of sparsemax transformation. Has the same type and
+        shape as `logits`.
+
+    Example:
+
+    >>> x = np.array([-1., 0., 1.])
+    >>> x_sparsemax = keras.ops.sparsemax(x)
+    >>> print(x_sparsemax)
+    array([0., 0., 1.], shape=(3,), dtype=float64)
+
+    """
+    if any_symbolic_tensors((x,)):
+        return Sparsemax(axis).symbolic_call(x)
+    return backend.nn.sparsemax(x, axis=axis)
+
+
 class MaxPool(Operation):
     def __init__(
         self,

keras/src/ops/nn_test.py

@@ -200,6 +200,10 @@ def test_log_softmax(self):
         self.assertEqual(knn.log_softmax(x, axis=1).shape, (None, 2, 3))
         self.assertEqual(knn.log_softmax(x, axis=-1).shape, (None, 2, 3))
 
+    def test_sparsemax(self):
+        x = KerasTensor([None, 2, 3])
+        self.assertEqual(knn.sparsemax(x).shape, (None, 2, 3))
+
     def test_max_pool(self):
         data_format = backend.config.image_data_format()
         if data_format == "channels_last":
@@ -861,6 +865,10 @@ def test_log_softmax(self):
         self.assertEqual(knn.log_softmax(x, axis=1).shape, (1, 2, 3))
         self.assertEqual(knn.log_softmax(x, axis=-1).shape, (1, 2, 3))
 
+    def test_sparsemax(self):
+        x = KerasTensor([1, 2, 3])
+        self.assertEqual(knn.sparsemax(x).shape, (1, 2, 3))
+
     def test_max_pool(self):
         data_format = backend.config.image_data_format()
         if data_format == "channels_last":
@@ -1487,6 +1495,13 @@ def test_log_softmax_correctness_with_axis_tuple(self):
             )
             self.assertAllClose(normalized_sum_by_axis, 1.0)
 
+    def test_sparsemax(self):
+        x = np.array([-0.5, 0, 1, 2, 3], dtype=np.float32)
+        self.assertAllClose(
+            knn.sparsemax(x),
+            [0.0, 0.0, 0.0, 0.0, 1.0],
+        )
+
     def test_max_pool(self):
         data_format = backend.config.image_data_format()
         # Test 1D max pooling.