Add parameter axis to tversky loss

keras/src/losses/losses.py

@@ -1388,6 +1388,7 @@ def __init__(
         beta=0.5,
         reduction="sum_over_batch_size",
         name="tversky",
+        axis=None,
         dtype=None,
     ):
         super().__init__(
@@ -1397,13 +1398,17 @@ def __init__(
             dtype=dtype,
             alpha=alpha,
             beta=beta,
+            axis=axis,
         )
         self.alpha = alpha
         self.beta = beta
+        self.axis = axis
 
     def get_config(self):
         config = Loss.get_config(self)
-        config.update({"alpha": self.alpha, "beta": self.beta})
+        config.update(
+            {"alpha": self.alpha, "beta": self.beta, "axis": self.axis}
+        )
         return config
 
 
@@ -2465,7 +2470,7 @@ def dice(y_true, y_pred, axis=None):
 
 
 @keras_export("keras.losses.tversky")
-def tversky(y_true, y_pred, alpha=0.5, beta=0.5):
+def tversky(y_true, y_pred, alpha=0.5, beta=0.5, axis=None):
     """Computes the Tversky loss value between `y_true` and `y_pred`.
 
     This loss function is weighted by the alpha and beta coefficients
@@ -2479,6 +2484,7 @@ def tversky(y_true, y_pred, alpha=0.5, beta=0.5):
         y_pred: tensor of predicted targets.
         alpha: coefficient controlling incidence of false positives.
         beta: coefficient controlling incidence of false negatives.
+        axis: tuple for which dimensions the loss is calculated.
 
     Returns:
         Tversky loss value.
@@ -2490,12 +2496,13 @@ def tversky(y_true, y_pred, alpha=0.5, beta=0.5):
     y_pred = ops.convert_to_tensor(y_pred)
     y_true = ops.cast(y_true, y_pred.dtype)
 
-    inputs = ops.reshape(y_true, [-1])
-    targets = ops.reshape(y_pred, [-1])
+    inputs = y_true
+    targets = y_pred
+
+    intersection = ops.sum(inputs * targets, axis=axis)
+    fp = ops.sum((1 - targets) * inputs, axis=axis)
+    fn = ops.sum(targets * (1 - inputs), axis=axis)
 
-    intersection = ops.sum(inputs * targets)
-    fp = ops.sum((1 - targets) * inputs)
-    fn = ops.sum(targets * (1 - inputs))
     tversky = ops.divide(
         intersection,
         intersection + fp * alpha + fn * beta + backend.epsilon(),

keras/src/losses/losses_test.py

@@ -1630,6 +1630,16 @@ def test_binary_segmentation(self):
         output = losses.Tversky()(y_true, y_pred)
         self.assertAllClose(output, 0.77777773)
 
+    def test_binary_segmentation_with_axis(self):
+        y_true = np.array(
+            [[[[1.0], [1.0]], [[0.0], [0.0]]], [[[1.0], [1.0]], [[0.0], [0.0]]]]
+        )
+        y_pred = np.array(
+            [[[[0.0], [1.0]], [[0.0], [1.0]]], [[[0.4], [0.0]], [[0.0], [0.9]]]]
+        )
+        output = losses.Tversky(axis=(1, 2, 3), reduction=None)(y_true, y_pred)
+        self.assertAllClose(output, [0.5, 0.75757575])
+
     def test_binary_segmentation_custom_coefficients(self):
         y_true = np.array(
             ([[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]])
@@ -1640,6 +1650,18 @@ def test_binary_segmentation_custom_coefficients(self):
         output = losses.Tversky(alpha=0.2, beta=0.8)(y_true, y_pred)
         self.assertAllClose(output, 0.7916667)
 
+    def test_binary_segmentation_custom_coefficients_with_axis(self):
+        y_true = np.array(
+            [[[[1.0], [1.0]], [[0.0], [0.0]]], [[[1.0], [1.0]], [[0.0], [0.0]]]]
+        )
+        y_pred = np.array(
+            [[[[0.0], [1.0]], [[0.0], [1.0]]], [[[0.4], [0.0]], [[0.0], [0.9]]]]
+        )
+        output = losses.Tversky(
+            alpha=0.2, beta=0.8, axis=(1, 2, 3), reduction=None
+        )(y_true, y_pred)
+        self.assertAllClose(output, [0.5, 0.7222222])
+
     def test_dtype_arg(self):
         y_true = np.array(([[1, 2], [1, 2]]))
         y_pred = np.array(([[4, 1], [6, 1]]))