pep8

theano/tensor/nnet/abstract_conv.py

@@ -103,10 +103,10 @@ def conv2d(input,
            border_mode='valid',
            subsample=(1, 1),
            filter_flip=True):
-    """
-    This function will build the symbolic graph for convolving a mini-batch of a
-    stack of 2D inputs with a set of 2D filters. The implementation is modelled
-    after Convolutional Neural Networks (CNN).
+    """This function will build the symbolic graph for convolving a
+    mini-batch of a stack of 2D inputs with a set of 2D filters. The
+    implementation is modelled after Convolutional Neural Networks
+    (CNN).
 
     :type input: symbolic 4D tensor
     :param input: mini-batch of feature map stacks, of shape
@@ -153,7 +153,8 @@ def conv2d(input,
     :param filter_flip: If ``True``, will flip the filter rows and columns
         before sliding them over the input. This operation is normally referred
         to as a convolution, and this is the default. If ``False``, the filters
-        are not flipped and the operation is referred to as a cross-correlation.
+        are not flipped and the operation is referred to as a
+        cross-correlation.
 
     :rtype: symbolic 4D tensor
     :return: set of feature maps generated by convolutional layer. Tensor is
@@ -169,9 +170,10 @@ def conv2d(input,
 
 
 class BaseAbstractConv2d(Op):
-    """
-    Base class for AbstractConv
-    Define an abstract convolution op that will be replaced with the appropriate implementation
+    """Base class for AbstractConv
+
+    Define an abstract convolution op that will be replaced with the
+    appropriate implementation
 
     :type imshp: None, tuple/list of len 4 of int or Constant variable
     :param imshp: The shape of the input parameter.
@@ -211,7 +213,9 @@ class BaseAbstractConv2d(Op):
     :param filter_flip: If ``True``, will flip the filter rows and columns
         before sliding them over the input. This operation is normally referred
         to as a convolution, and this is the default. If ``False``, the filters
-        are not flipped and the operation is referred to as a cross-correlation.
+        are not flipped and the operation is referred to as a
+        cross-correlation.
+
     """
     check_broadcast = False
     __props__ = ('border_mode', 'subsample', 'filter_flip', 'imshp', 'kshp')
@@ -270,7 +274,8 @@ class AbstractConv2d(BaseAbstractConv2d):
                  subsample=(1, 1),
                  filter_flip=True):
         super(AbstractConv2d, self).__init__(imshp, kshp,
-                                             border_mode, subsample, filter_flip)
+                                             border_mode, subsample,
+                                             filter_flip)
 
     def make_node(self, img, kern):
         if img.type.ndim != 4:
@@ -319,7 +324,9 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
                  subsample=(1, 1),
                  filter_flip=True):
         super(AbstractConv2d_gradWeights, self).__init__(imshp, kshp,
-                                                         border_mode, subsample, filter_flip)
+                                                         border_mode,
+                                                         subsample,
+                                                         filter_flip)
 
     # Update shape/height_width
     def make_node(self, img, topgrad, shape):
@@ -336,7 +343,8 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
         return Apply(self, [img, topgrad, shape], [output])
 
     def perform(self, node, inp, out_):
-        raise NotImplementedError('AbstractConv2d_gradWeight theano optimization failed')
+        raise NotImplementedError(
+            'AbstractConv2d_gradWeight theano optimization failed')
 
     def grad(self, inp, grads):
         bottom, top = inp[:2]
@@ -344,7 +352,10 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
         d_bottom = AbstractConv2d_gradInputs(self.imshp, self.kshp,
                                              self.border_mode,
                                              self.subsample,
-                                             self.filter_flip)(weights, top, bottom.shape[-2:])
+                                             self.filter_flip)(
+                                                 weights,
+                                                 top,
+                                                 bottom.shape[-2:])
         d_top = AbstractConv2d(self.imshp,
                                self.kshp,
                                self.border_mode,
@@ -373,7 +384,9 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
                  subsample=(1, 1),
                  filter_flip=True):
         super(AbstractConv2d_gradInputs, self).__init__(imshp, kshp,
-                                                        border_mode, subsample, filter_flip)
+                                                        border_mode,
+                                                        subsample,
+                                                        filter_flip)
 
     # Update shape/height_width
     def make_node(self, kern, topgrad, shape):
@@ -390,16 +403,20 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
         return Apply(self, [kern, topgrad, shape], [output])
 
     def perform(self, node, inp, out_):
-        raise NotImplementedError('AbstractConv2d_gradWeight theano optimization failed')
+        raise NotImplementedError(
+            'AbstractConv2d_gradWeight theano optimization failed')
 
     def grad(self, inp, grads):
         weights, top = inp[:2]
         bottom, = grads
         d_weights = AbstractConv2d_gradWeights(self.imshp, self.kshp,
                                                self.border_mode,
-                                               self.subsample)(bottom, top, weights.shape[-2:])
+                                               self.subsample)(
+                                                   bottom, top,
+                                                   weights.shape[-2:])
         d_top = AbstractConv2d(self.imshp, self.kshp,
-                               self.border_mode, self.subsample)(bottom, weights)
+                               self.border_mode, self.subsample)(
+                                   bottom, weights)
         d_height_width = (theano.gradient.DisconnectedType()(),)
         return (d_weights, d_top) + d_height_width