diff --git a/nobrainer/distrubuted_learning/dwc.py b/nobrainer/distributed_learning/dwc.py
similarity index 100%
rename from nobrainer/distrubuted_learning/dwc.py
rename to nobrainer/distributed_learning/dwc.py
diff --git a/nobrainer/models/__init__.py b/nobrainer/models/__init__.py
index 8bc4d125..34da1027 100644
--- a/nobrainer/models/__init__.py
+++ b/nobrainer/models/__init__.py
@@ -1,3 +1,5 @@
+from .attention_unet import attention_unet
+from .attention_unet_with_inception import attention_unet_with_inception
 from .autoencoder import autoencoder
 from .dcgan import dcgan
 from .highresnet import highresnet
@@ -29,6 +31,8 @@ def get(name):
         "progressivegan": progressivegan,
         "progressiveae": progressiveae,
         "dcgan": dcgan,
+        "attention_unet": attention_unet,
+        "attention_unet_with_inception": attention_unet_with_inception,
     }
 
     try:
diff --git a/nobrainer/models/attention_unet.py b/nobrainer/models/attention_unet.py
index 879d3b22..8d7bd5f9 100644
--- a/nobrainer/models/attention_unet.py
+++ b/nobrainer/models/attention_unet.py
@@ -3,8 +3,8 @@
 """
 
 import tensorflow as tf
-import tensorflow.keras.layers as L
 from tensorflow.keras import layers
+import tensorflow.keras.layers as L
 from tensorflow.keras.models import Model
 
 
@@ -71,7 +71,7 @@ def attention_unet(n_classes, input_shape):
     outputs = layers.Activation(final_activation)(outputs)
 
     """ Model """
-    return Model(inputs=inputs, outputs=outputs, name="Attention U-Net")
+    return Model(inputs=inputs, outputs=outputs, name="Attention_U-Net")
 
 
 if __name__ == "__main__":
diff --git a/nobrainer/models/attention_unet_with_inception.py b/nobrainer/models/attention_unet_with_inception.py
index f0d6ed20..fa002ea6 100644
--- a/nobrainer/models/attention_unet_with_inception.py
+++ b/nobrainer/models/attention_unet_with_inception.py
@@ -2,12 +2,10 @@
 Adapted from https://github.com/robinvvinod/unet
 """
 
-import tensorflow.keras.backend as K
 from tensorflow.keras import layers
+import tensorflow.keras.backend as K
 from tensorflow.keras.models import Model
 
-from hyperparameters import alpha
-
 K.set_image_data_format("channels_last")
 
 
@@ -44,7 +42,7 @@ def conv3d_block(
     )(input_tensor)
     if batchnorm:
         conv = layers.BatchNormalization()(conv)
-    output = layers.LeakyReLU(alpha=alpha)(conv)
+    output = layers.LeakyReLU(alpha=0.1)(conv)
 
     for _ in range(recurrent - 1):
         conv = layers.Conv3D(
@@ -57,7 +55,7 @@ def conv3d_block(
         )(output)
         if batchnorm:
             conv = layers.BatchNormalization()(conv)
-        res = layers.LeakyReLU(alpha=alpha)(conv)
+        res = layers.LeakyReLU(alpha=0.1)(conv)
         output = layers.Add()([output, res])
 
     return output
@@ -141,7 +139,7 @@ def transpose_block(
         ),
         kernel_initializer="he_normal",
     )(input_tensor)
-    conv = layers.LeakyReLU(alpha=alpha)(conv)
+    conv = layers.LeakyReLU(alpha=0.1)(conv)
 
     act = conv3d_block(
         conv,
diff --git a/nobrainer/models/tests/models_test.py b/nobrainer/models/tests/models_test.py
index 68f4b985..d8a3534c 100644
--- a/nobrainer/models/tests/models_test.py
+++ b/nobrainer/models/tests/models_test.py
@@ -4,6 +4,8 @@
 
 from nobrainer.bayesian_utils import default_mean_field_normal_fn
 
+from ..attention_unet import attention_unet
+from ..attention_unet_with_inception import attention_unet_with_inception
 from ..autoencoder import autoencoder
 from ..bayesian_vnet import bayesian_vnet
 from ..bayesian_vnet_semi import bayesian_vnet_semi
@@ -241,3 +243,13 @@ def test_vox2vox():
     pred_shape = (1, 2, 2, 2, 1)
     out = vox_discriminator(inputs=[y, x])
     assert out.shape == pred_shape
+
+
+def test_attention_unet():
+    model_test(attention_unet, n_classes=1, input_shape=(1, 64, 64, 64, 1))
+
+
+def test_attention_unet_with_inception():
+    model_test(
+        attention_unet_with_inception, n_classes=1, input_shape=(1, 64, 64, 64, 1)
+    )