pep8 ConvGrad3D

theano/tensor/nnet/ConvGrad3D.py

@@ -11,7 +11,7 @@ from theano.gradient import DisconnectedType
 
 class ConvGrad3D(theano.Op):
     """ Gradient of Conv3D with respect to W """
-    def __eq__(self,other):
+    def __eq__(self, other):
         return type(self) == type(other)
 
     def __hash__(self):
@@ -29,26 +29,26 @@ class ConvGrad3D(theano.Op):
         return theano.Apply(self, inputs=[V_, d_, WShape_, dCdH_], outputs = [ T.TensorType(V_.dtype, (False,False,False,False,False))() ] )
 
     def infer_shape(self, node, input_shapes):
-        V,d,W_shape, dCdH = node.inputs
+        V, d, W_shape, dCdH = node.inputs
         return [ ( W_shape[0], W_shape[1], W_shape[2], W_shape[3], W_shape[4] ) ]
 
     def connection_pattern(self, node):
 
         return [[True], [True], [False], [True]]
 
-    def grad(self,inputs, output_gradients):
-        C,d, WShape, B = inputs
-        dLdA ,= output_gradients
+    def grad(self, inputs, output_gradients):
+        C, d, WShape, B = inputs
+        dLdA, = output_gradients
 
-        z = T.zeros_like(C[0,0,0,0,:])
-        dLdC = convTransp3D( dLdA, z, d, B, C.shape[1:4])
+        z = T.zeros_like(C[0, 0, 0, 0, :])
+        dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
         # d actually does affect the outputs, so it's not disconnected
         dLdd = grad_undefined(self, 1, d)
         # The shape of the weights doesn't affect the output elements
         dLdWShape = DisconnectedType()()
-        dLdB = conv3D( C, dLdA, T.zeros_like(B[0,0,0,0,:]), d)
+        dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
 
-        return [ dLdC, dLdd, dLdWShape, dLdB ]
+        return [dLdC, dLdd, dLdWShape, dLdB]
 
     def perform(self, node, inputs, output_storage):
         V, d, WShape, dCdH = inputs
@@ -72,17 +72,15 @@ class ConvGrad3D(theano.Op):
 
         #print 'computing output of shape '+str(WShape)
 
-
-
-        for k in xrange(0,WShape[1]):
-            for l in xrange(0,WShape[2]):
-                for m in xrange(0,WShape[3]):
-                    for i in xrange(0,batchSize):
-                        for p in xrange(0,outputHeight):
-                            for q in xrange(0,outputWidth):
-                                for r in xrange(0,outputDur):
-                                    for j in xrange(0,WShape[0]):
-                                        for z in xrange(0,WShape[4]):
+        for k in xrange(0, WShape[1]):
+            for l in xrange(0, WShape[2]):
+                for m in xrange(0, WShape[3]):
+                    for i in xrange(0, batchSize):
+                        for p in xrange(0, outputHeight):
+                            for q in xrange(0, outputWidth):
+                                for r in xrange(0, outputDur):
+                                    for j in xrange(0, WShape[0]):
+                                        for z in xrange(0, WShape[4]):
                                             dCdW[j,k,l,m,z] +=  dCdH[i,p,q,r,j] * V[i,dr*p+k,dc*q+l,dt*r+m,z]
 
         output_storage[0][0] = dCdW
@@ -277,7 +275,7 @@ class ConvGrad3D(theano.Op):
             ///////////// < /code generated by ConvGradW3D >
         """
 
-        return strutil.renderString(codeSource,locals())
+        return strutil.renderString(codeSource, locals())
 
 
 convGrad3D = ConvGrad3D()