title: Caffe predicts same class regardless of image
body: <p>I <a href="https://github.com/jackbrucesimpson/caffe_tests/blob/master/lenet_train_test.prototxt" rel="nofollow">modified the MNIST example</a> and when I train it with my 3 image classes it returns an accuracy of 91%. However, when I modify the <a href="https://github.com/jackbrucesimpson/caffe_tests/blob/master/classification.cpp" rel="nofollow">C++ example</a> with a <a href="https://github.com/jackbrucesimpson/caffe_tests/blob/master/deploy_arch.prototxt" rel="nofollow">deploy prototxt</a> file and <a href="https://github.com/jackbrucesimpson/caffe_tests/blob/master/labels.txt" rel="nofollow">labels file</a>, and try to test it on some images it returns a prediction of the second class (1 circle) with a probability of 1.0 no matter what image I give it - even if it's images that were used in the training set. I've tried a dozen images and it consistently just predicts the one class.</p>
  
  <p>To clarify things, in the C++ example I modified I did scale the image to be predicted just like the images were scaled in the training stage:</p>
  
  <pre><code>img.convertTo(img, CV_32FC1);
  img = img * 0.00390625;
  </code></pre>
  
  <p>If that was the right thing to do, then it makes me wonder if I've done something wrong with the output layers that calculate probability in my <a href="https://github.com/jackbrucesimpson/caffe_tests/blob/master/deploy_arch.prototxt" rel="nofollow">deploy_arch.prototxt</a> file.</p>