comply to PEP 8

onn/activation.py

@@ -130,8 +130,9 @@ class Gelu(Activation):
         return X * self.cdf
 
     def backward(self, dY):
-        return dY * (self.cdf \
-            + np.exp(-_inv2 * np.square(self.X)) * self.X * _invsqrt2pi)
+        return dY * (self.cdf
+                     + np.exp(-_inv2 * np.square(self.X))
+                     * self.X * _invsqrt2pi)
 
 
 class Softmax(Activation):

onn/experimental.py

@@ -31,7 +31,7 @@ class AddSignClip(Optimizer):
         self.accum[:] = self.accum * self.mu + dW
 
         signed = np.sign(dW) * np.sign(self.accum)
-        #signed *= decay
+        # signed *= decay
 
         inter = dW * (self.alpha + signed)
 
@@ -64,7 +64,7 @@ class PowerSignClip(Optimizer):
         self.accum[:] = self.accum * self.mu + dW
 
         signed = np.sign(dW) * np.sign(self.accum)
-        #signed *= decay
+        # signed *= decay
 
         if self.use_exp:
             inter = dW * np.exp(signed)

onn/utility.py

@@ -75,14 +75,14 @@ class Folding:
         class_n = np.max(classes) + 1
         sorted_inputs = np.array([inputs[classes == n]
                                   for n in range(class_n)], inputs.dtype)
-        sorted_outputs = np.arange(class_n
-            ).repeat(sorted_inputs.shape[1]).reshape(sorted_inputs.shape[:2])
+        sorted_outputs = np.arange(class_n) \
+            .repeat(sorted_inputs.shape[1]).reshape(sorted_inputs.shape[:2])
 
         # now to interleave the classes instead of having them grouped:
-        inputs = np.swapaxes(sorted_inputs, 0, 1
-            ).reshape(-1, *sorted_inputs.shape[2:])
-        outputs = np.swapaxes(sorted_outputs, 0, 1
-            ).reshape(-1, *sorted_outputs.shape[2:])
+        inputs = np.swapaxes(sorted_inputs, 0, 1) \
+            .reshape(-1, *sorted_inputs.shape[2:])
+        outputs = np.swapaxes(sorted_outputs, 0, 1) \
+            .reshape(-1, *sorted_outputs.shape[2:])
 
         # one final thing: we need to make our outputs one-hot again.
         self.inputs = inputs