diff --git a/microwakeword/mixednet.py b/microwakeword/mixednet.py
index 076d467..75cbb9f 100644
--- a/microwakeword/mixednet.py
+++ b/microwakeword/mixednet.py
@@ -231,7 +231,7 @@ def __call__(self, inputs):
         return x
 
 
-class SpatialAttention(tf.keras.layers.Layer):
+class SpatialAttention:
     """Spatial Attention Layer based on CBAM: Convolutional Block Attention Module
     https://arxiv.org/pdf/1807.06521v2
 
@@ -239,14 +239,12 @@ class SpatialAttention(tf.keras.layers.Layer):
         object (_type_): _description_
     """
 
-    def __init__(self, kernel_size, ring_buffer_size, **kwargs):
-        super().__init__(**kwargs)
-
+    def __init__(self, kernel_size, ring_buffer_size):
         self.kernel_size = kernel_size
         self.ring_buffer_size = ring_buffer_size
 
-    def call(self, inputs):
-        tranposed = tf.transpose(inputs, perm=[0, 1, 3, 2])
+    def __call__(self, inputs):
+        tranposed = tf.keras.ops.transpose(inputs, axes=[0, 1, 3, 2])
         channel_avg = tf.keras.layers.AveragePooling2D(
             pool_size=(1, tranposed.shape[2]), strides=(1, tranposed.shape[2])
         )(tranposed)
@@ -254,6 +252,7 @@ def call(self, inputs):
             pool_size=(1, tranposed.shape[2]), strides=(1, tranposed.shape[2])
         )(tranposed)
         pooled = tf.keras.layers.Concatenate(axis=-1)([channel_avg, channel_max])
+
         attention = stream.Stream(
             cell=tf.keras.layers.Conv2D(
                 1,
@@ -275,12 +274,6 @@ def call(self, inputs):
 
         return net * attention
 
-    def get_config(self):
-        return {
-            "kernel_size": self.kernel_size,
-            "ring_buffer_size": self.ring_buffer_size,
-        }
-
 
 def model(flags, shape, batch_size):
     """MixedNet model.
@@ -368,7 +361,10 @@ def model(flags, shape, batch_size):
 
     if net.shape[1] > 1:
         if flags.spatial_attention:
-            net = SpatialAttention(4, net.shape[1] - 1)(net)
+            net = SpatialAttention(
+                kernel_size=4,
+                ring_buffer_size=net.shape[1] - 1,
+            )(net)
         else:
             net = stream.Stream(
                 cell=tf.keras.layers.Identity(),