pep8

theano/sandbox/cuda/tests/test_dnn.py

@@ -1060,8 +1060,7 @@ def test_dnn_conv_grad():
 def get_conv3d_test_cases():
     # Every element of test_shapes follows the format
     # [input_shape, filter_shape, subsample]
-    test_shapes = [  # Test with standard size inputs and kernels
-                   [(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1)],
+    test_shapes = [[(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1)],
                    [(8, 4, 20, 12, 15), (5, 4, 6, 12, 4), (2, 2, 2)],
                    [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 3, 3)],
                    [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
@@ -1154,9 +1153,9 @@ def test_conv3d_fwd():
 
         # Compile a theano function for the reference implementation
         conv_ref = theano.tensor.nnet.conv3D(
-                                V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
-                                W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
-                                b=bias, d=subsample)
+            V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
+            W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
+            b=bias, d=subsample)
         f_ref = theano.function([], conv_ref.dimshuffle(0, 4, 1, 2, 3))
 
         # Compare the results of the two implementations
@@ -1222,9 +1221,9 @@ def test_conv3d_bwd():
 
         # Compile a theano function for the reference implementation
         conv_ref = theano.tensor.nnet.conv3D(
-                                V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
-                                W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
-                                b=bias, d=subsample)
+            V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
+            W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
+            b=bias, d=subsample)
         (grad_padded_i_ref,
          grad_w_ref) = theano.tensor.grad(conv_ref.sum(),
                                           [padded_inputs, filters])
@@ -1235,10 +1234,10 @@ def test_conv3d_bwd():
         else:
             shp = grad_padded_i_ref.shape
             grad_i_ref = grad_padded_i_ref[
-                                    :, :,
-                                    pad_per_dim[0]:shp[2] - pad_per_dim[0],
-                                    pad_per_dim[1]:shp[3] - pad_per_dim[1],
-                                    pad_per_dim[2]:shp[4] - pad_per_dim[2]]
+                :, :,
+                pad_per_dim[0]:shp[2] - pad_per_dim[0],
+                pad_per_dim[1]:shp[3] - pad_per_dim[1],
+                pad_per_dim[2]:shp[4] - pad_per_dim[2]]
 
         f_ref = theano.function([], [grad_i_ref, grad_w_ref])