diff --git a/RELEASE.md b/RELEASE.md
index d0ba03f..09ae177 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -3,8 +3,8 @@
 ## Release 0.1.1-dev0
 
 ### Includes
--  Minor bugfixes.
--  100% line coverage.
+- Minor bugfixes.
+- 100% line coverage.
 
 ## Release 0.1.0-dev0
 
diff --git a/prototorch/functions/activations.py b/prototorch/functions/activations.py
index 2df8100..1ed141e 100644
--- a/prototorch/functions/activations.py
+++ b/prototorch/functions/activations.py
@@ -8,9 +8,10 @@ ACTIVATIONS = dict()
 # def register_activation(scriptf):
 #     ACTIVATIONS[scriptf.name] = scriptf
 #     return scriptf
-def register_activation(f):
-    ACTIVATIONS[f.__name__] = f
-    return f
+def register_activation(function):
+    """Add the activation function to the registry."""
+    ACTIVATIONS[function.__name__] = function
+    return function
 
 
 @register_activation
@@ -55,6 +56,7 @@ def swish_beta(x, beta=torch.tensor([10])):
 
 
 def get_activation(funcname):
+    """Deserialize the activation function."""
     if callable(funcname):
         return funcname
     if funcname in ACTIVATIONS:
diff --git a/prototorch/functions/distances.py b/prototorch/functions/distances.py
index 5446d53..b88082e 100644
--- a/prototorch/functions/distances.py
+++ b/prototorch/functions/distances.py
@@ -4,8 +4,7 @@ import torch
 
 
 def squared_euclidean_distance(x, y):
-    """
-    Compute the squared Euclidean distance between :math:`x` and :math:`y`.
+    """Compute the squared Euclidean distance between :math:`x` and :math:`y`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -18,8 +17,7 @@ def squared_euclidean_distance(x, y):
 
 
 def euclidean_distance(x, y):
-    """
-    Compute the Euclidean distance between :math:`x` and :math:`y`.
+    """Compute the Euclidean distance between :math:`x` and :math:`y`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -30,8 +28,7 @@ def euclidean_distance(x, y):
 
 
 def lpnorm_distance(x, y, p):
-    r"""
-    Compute :math:`{\langle x, y \rangle}_p`.
+    r"""Compute :math:`{\langle x, y \rangle}_p`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -41,8 +38,7 @@ def lpnorm_distance(x, y, p):
 
 
 def omega_distance(x, y, omega):
-    r"""
-    Omega distance.
+    r"""Omega distance.
 
     Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
 
@@ -57,8 +53,7 @@ def omega_distance(x, y, omega):
 
 
 def lomega_distance(x, y, omegas):
-    r"""
-    Localized Omega distance.
+    r"""Localized Omega distance.
 
     Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
 
diff --git a/prototorch/functions/initializers.py b/prototorch/functions/initializers.py
index d685119..18a4e04 100644
--- a/prototorch/functions/initializers.py
+++ b/prototorch/functions/initializers.py
@@ -7,9 +7,10 @@ import torch
 INITIALIZERS = dict()
 
 
-def register_initializer(func):
-    INITIALIZERS[func.__name__] = func
-    return func
+def register_initializer(function):
+    """Add the initializer to the registry."""
+    INITIALIZERS[function.__name__] = function
+    return function
 
 
 def labels_from(distribution):
@@ -84,6 +85,7 @@ def stratified_random(x_train, y_train, prototype_distribution):
 
 
 def get_initializer(funcname):
+    """Deserialize the initializer."""
     if callable(funcname):
         return funcname
     if funcname in INITIALIZERS: