diff --git a/n3fit/src/n3fit/layers/rotations.py b/n3fit/src/n3fit/layers/rotations.py
index 686d3c5bbb..ed58cdbe15 100644
--- a/n3fit/src/n3fit/layers/rotations.py
+++ b/n3fit/src/n3fit/layers/rotations.py
@@ -56,44 +56,39 @@ def __init__(
         super().__init__(rotation_matrix, axes=1, **kwargs)
 
 
-class FkRotation(MetaLayer):
+class FkRotation(Rotation):
     """
-    Applies a transformation from the dimension-8 evolution basis
+    Applies a transformation from the dimension-9 evolution basis
     to the dimension-14 evolution basis used by the fktables.
 
     The input to this layer is a `pdf_raw` variable which is expected to have
-    a shape (1,  None, 8), and it is then rotated to an output (1, None, 14)
+    a shape (1,  None, 9), and it is then rotated to an output (1, None, 14)
     """
-
-    # TODO: Generate a rotation matrix in the input and just do tf.tensordot in call
-    # the matrix should be: (8, 14) so that we can just do tf.tensordot(pdf, rotmat, axes=1)
-    # i.e., create the matrix and inherit from the Rotation layer above
     def __init__(self, output_dim=14, name="evolution", **kwargs):
         self.output_dim = output_dim
-        super().__init__(name=name, **kwargs)
-
-    def call(self, pdf_raw):
-        # Transpose the PDF so that the flavour index is the first one
-        x = op.transpose(pdf_raw)
-        pdf_raw_list = [
-            0 * x[0],  # photon
-            x[0],  # sigma
-            x[1],  # g
-            x[2],  # v
-            x[3],  # v3
-            x[4],  # v8
-            x[8],  # v15
-            x[2],  # v24
-            x[2],  # v35
-            x[5],  # t3
-            x[6],  # t8
-            x[0] - 4 * x[7],  # t15 (c-)
-            x[0],  # t24
-            x[0],  # t35
-        ]
-        ret = op.concatenate(pdf_raw_list)
-        # Concatenating destroys the batch index so we have to regenerate it
-        return op.batchit(ret)
+        rotation_matrix = self._create_rotation_matrix()
+        super().__init__(rotation_matrix, axes=1, name=name, **kwargs)
+
+    def _create_rotation_matrix(self):
+        """Create the rotation matrix"""
+        array = np.array([
+            [0, 0, 0, 0, 0, 0, 0, 0, 0], # photon
+            [1, 0, 0, 0, 0, 0, 0, 0, 0], # sigma
+            [0, 1, 0, 0, 0, 0, 0, 0, 0], # g
+            [0, 0, 1, 0, 0, 0, 0, 0, 0], # v
+            [0, 0, 0, 1, 0, 0, 0, 0, 0], # v3
+            [0, 0, 0, 0, 1, 0, 0, 0, 0], # v8
+            [0, 0, 0, 0, 0, 0, 0, 0, 1], # v15
+            [0, 0, 1, 0, 0, 0, 0, 0, 0], # v24
+            [0, 0, 1, 0, 0, 0, 0, 0, 0], # v35
+            [0, 0, 0, 0, 0, 1, 0, 0, 0], # t3
+            [0, 0, 0, 0, 0, 0, 1, 0, 0], # t8
+            [1, 0, 0, 0, 0, 0, 0,-4, 0], # t15 (c-)
+            [1, 0, 0, 0, 0, 0, 0, 0, 0], # t24
+            [1, 0, 0, 0, 0, 0, 0, 0, 0], # t35
+        ])
+        tensor = op.numpy_to_tensor(array.T)
+        return tensor
 
 
 class AddPhoton(MetaLayer):