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