flake8

theano/sandbox/cuda/tests/test_abstractconv.py

@@ -97,7 +97,7 @@ class TestConv2d(unittest.TestCase):
     def run_gradweight(self, inputs_shape, filters_shape, output_shape,
                        ref=dnn_gradweight, subsample=(1, 1), filter_flip=True,
                        verify_grad=True, mode=mode_without_gpu, border_mode='valid',
-                       device='cpu', provide_shape = False):
+                       device='cpu', provide_shape=False):
 
         inputs_val = numpy.random.random(inputs_shape).astype('float32')
         output_val = numpy.random.random(output_shape).astype('float32')
@@ -143,7 +143,7 @@ class TestConv2d(unittest.TestCase):
 
     def run_gradinput(self, inputs_shape, filters_shape, output_shape, ref=dnn_gradinput,
                       subsample=(1, 1), filter_flip=True, verify_grad=True, mode=mode_without_gpu,
-                      border_mode='valid', device='cpu', provide_shape = False):
+                      border_mode='valid', device='cpu', provide_shape=False):
 
         output_val = numpy.random.random(output_shape).astype('float32')
         filters_val = numpy.random.random(filters_shape).astype('float32')

theano/tensor/nnet/abstract_conv2d.py

 """
-FIXME
+Define abstract conv2d interface
 """
-
-__docformat__ = "restructuredtext en"
-
 import logging
 
 import theano
@@ -18,6 +15,7 @@ from theano.tensor.nnet import conv2d as cpu_conv2d, ConvOp
 from theano.tensor.nnet.ConvGrad3D import convGrad3D
 from theano.tensor.nnet.ConvTransp3D import convTransp3D
 
+__docformat__ = "restructuredtext en"
 
 _logger = logging.getLogger("theano.tensor.nnet.conv2d")
 
@@ -144,7 +142,7 @@ class BaseAbstractConv2d(Op):
     def __init__(self,
                  imshp=None, kshp=None,
                  border_mode="valid", subsample=(1, 1),
-                 filter_flip = True):
+                 filter_flip=True):
         if isinstance(border_mode, int):
             border_mode = (border_mode, border_mode)
         if isinstance(border_mode, tuple):
@@ -192,7 +190,7 @@ class AbstractConv2d(BaseAbstractConv2d):
                  kshp=None,
                  border_mode="valid",
                  subsample=(1, 1),
-                 filter_flip = True):
+                 filter_flip=True):
         super(AbstractConv2d, self).__init__(imshp, kshp,
                                              border_mode, subsample, filter_flip)