<p dir="auto">I've been using the Estimator API with the model_fn and input_fn as shown in the official examples (<a href="https://github.com/tensorflow/models/blob/master/official/resnet/cifar10_main.py">https://github.com/tensorflow/models/blob/master/official/resnet/cifar10_main.py</a> for instance).</p>
<p dir="auto">This all looks great and wonderful. However, I'm now facing an issue for going further with it. I'd like to use a model trained on a dataset and transfer it to another dataset. In practice, I would like to take the weights from the trained model up to the softmax layer and only initialize randomly this final layer. Then, I can do fine-tuning on the new dataset, which has different labels for instance.</p>
<p dir="auto">I haven't found a way to do what I want. Is it something missing in the interface? Can we have something  like a variable list to restore from a checkpoint and some other not? Ideally, it would be also good to specify variables to be frozen. Does that all make sense?</p>
<p dir="auto">The Raspberry Pi is an excellent platform for robotics and automation. The native camera also makes a great combo for portable / embedded / robotics computer vision. Having TensorFlow available on this platform would really open up a lot of options in this context.</p>
<h3 dir="auto">What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?</h3>
<p dir="auto">none</p>
<h3 dir="auto">Environment info</h3>
<p dir="auto">Operating System:</p>
<p dir="auto">Raspbian Jessie on Raspberry Pi 3</p>
<h3 dir="auto">What other attempted solutions have you tried?</h3>
<p dir="auto"><a href="https://github.com/samjabrahams/tensorflow-on-raspberry-pi">https://github.com/samjabrahams/tensorflow-on-raspberry-pi</a></p>
<p dir="auto">It only provides a binary for TensorFlow 0.10. I've had lots of issues with it running DNNs trained on 0.11. I need to train on 0.11 because the only fast GPU I have is an Nvidia Pascal chip, and 0.10 doesn't work well on Pascal (due to CUDA version issues).</p>
<p dir="auto">I've tried to compile 0.11 following the guide there, but there were a lot of errors.</p>