How to run examples on CPU?

[dongbeiwudaxian]

I tried to run it on CPU:
THEANO_FLAGS='floatX=float32,device=cpu,lib.cnmem=1' ipython experiments/lenet/lenet5-ard.py
and got the error:
lib/python2.7/site-packages/lasagne/layers/dnn.py in ()
40 else:
41 raise ImportError(
---> 42 "requires GPU support -- see http://lasagne.readthedocs.org/en/"
43 "latest/user/installation.html#gpu-support") # pragma: no cover
44

ImportError: requires GPU support -- see http://lasagne.readthedocs.org/en/latest/user/installation.html#gpu-support
After i read a answer “you replace GPU Convolution (dnn.dnn_conv) in Conv2DVarDropOutARD on CPU one it will fix the issue.”
I find GPU Convolution(dnn.dnn_conv in /home/tom/variational-dropout-sparsifies-dnn/nets/layers.py，but i am not familiar to theano,has anyone tried to change this to CPU？？？？
if deterministic:
conved = dnn.dnn_conv(img=input, kerns=T.switch(T.ge(log_alpha, thresh), 0, self.W),
subsample=self.stride, border_mode=border_mode,
conv_mode=conv_mode)
else:
W = self.W
if train_clip:
W = T.switch(clip_mask, 0, W)
conved_mu = dnn.dnn_conv(img=input, kerns=W,
subsample=self.stride, border_mode=border_mode,
conv_mode=conv_mode)
conved_si = T.sqrt(1e-8+dnn.dnn_conv(img=input * input, kerns=T.exp(log_alpha) * W * W,
subsample=self.stride, border_mode=border_mode,
conv_mode=conv_mode))
conved = conved_mu + conved_si * self._srng.normal(conved_mu.shape, avg=0, std=1)
return conved