fix test following the removing of set_flops.

theano/sandbox/cuda/tests/test_mlp.py

@@ -187,7 +187,6 @@ def run_conv_nnet1(use_gpu):
     lr = tensor.fscalar('lr')
 
     conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
-    conv_op.set_flops()
 
     hid = tensor.tanh(conv_op(x, w) + b.dimshuffle((0, 'x', 'x')))
     hid_flat = hid.reshape((n_batch, n_hid))
@@ -278,8 +277,6 @@ def run_conv_nnet2(use_gpu):  # pretend we are training LeNet for MNIST
     conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
     conv_op1 = conv.ConvOp((n_kern, logical_hid_shape[0] / 2,
          logical_hid_shape[1] / 2), shape_kern1[2:], n_kern1, n_batch, 1, 1)
-    conv_op.set_flops()
-    conv_op1.set_flops()
 
     hid = tensor.tanh(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x')))
     hid1 = tensor.tanh(conv_op1(hid[:, :, ::2, ::2], w1) + b1.dimshuffle((
@@ -371,8 +368,6 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
     conv_op1 = conv.ConvOp(
         (n_kern, logical_hid_shape[0] / 2, logical_hid_shape[1] / 2),
         shape_kern1[2:], n_kern1, n_batch, 1, 1,verbose=verbose, version=version)
-    conv_op.set_flops()
-    conv_op1.set_flops()
 
     ds_op = downsample.DownsampleFactorMax((2, 2), ignore_border=False)
     if downsample_ops: