title: Error using pre-trained model in DIGITS with different dataset. How to modify layers based on new datasets?
body: <p>I try to use pretrained model (VGG 16) to DIGITS but I got this error. </p>
  
  <blockquote>
    <p>ERROR: Check failed: error == cudaSuccess (2 vs. 0) out of memory</p>
  </blockquote>
  
  <p>and </p>
  
  <pre><code>conv2_2 does not need backward computation.
  relu2_1 does not need backward computation.
  conv2_1 does not need backward computation.
  pool1 does not need backward computation.
  relu1_2 does not need backward computation.
  conv1_2 does not need backward computation.
  relu1_1 does not need backward computation.
  conv1_1 does not need backward computation.
  data does not need backward computation.
  This network produces output label
  This network produces output softmax
  Network initialization done.
  Solver scaffolding done.
  Finetuning from /home/digits/digits/jobs/20161020-095911-9d01/model.caffemodel
  Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/digits/digits/jobs/20161020-095911-9d01/model.caffemodel
  Successfully upgraded file specified using deprecated V1LayerParameter
  Attempting to upgrade input file specified using deprecated input fields: /home/digits/digits/jobs/20161020-095911-9d01/model.caffemodel
  Successfully upgraded file specified using deprecated input fields.
  Note that future Caffe releases will only support input layers and not input fields.
  Check failed: error == cudaSuccess (2 vs. 0)  out of memory
  </code></pre>
  
  <p>I uploaded <code>deploy.prototxt</code> and <code>VGG_ILSVRC_16_layers.caffemodel</code> and <code>synset_words.txt</code> successfully into DIGITS and tested with my data-set which has two classes. </p>