Caffe vs Theano MNIST example
<p>I'm trying to learn (and compare) different deep learning frameworks, by the time they are Caffe and Theano.</p>

<p><a href="http://caffe.berkeleyvision.org/gathered/examples/mnist.html" rel="nofollow">http://caffe.berkeleyvision.org/gathered/examples/mnist.html</a></p>

<p>and </p>

<p><a href="http://deeplearning.net/tutorial/lenet.html" rel="nofollow">http://deeplearning.net/tutorial/lenet.html</a></p>

<p>I follow the tutorial to run those frameworks on MNIST dataset. However, I notice a quite difference in term of accuracy and performance.</p>

<p>For Caffe, it's extremely fast for the accuracy to build up to ~97%. In fact, it only takes 5 mins to finish the program (using GPU) which the final accuracy on test set of over 99%. How impressive!</p>

<p>However, on Theano, it is much poorer. It took me more than 46 minutes (using same GPU), just to achieve 92% test performance.</p>

<p>I'm confused as it should not have so much difference between the frameworks running relatively same architectures on same dataset.</p>

<p>So my question is. Is the accuracy number reported by Caffe is the percentage of correct prediction on test set? If so, is there any explanation for the discrepancy?</p>

<p>Thanks.</p>