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cs501r_f2016:lab5 [2016/09/15 16:13] wingated created |
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**Note:** when using ''pip'' to install python packages, make sure that you're using the anaconda version of ''pip'' (as opposed to any ''pip'' programs that are part of your system distro)! You can always tell which version you're running by running ''which pip'' in a terminal. | **Note:** when using ''pip'' to install python packages, make sure that you're using the anaconda version of ''pip'' (as opposed to any ''pip'' programs that are part of your system distro)! You can always tell which version you're running by running ''which pip'' in a terminal. | ||
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+ | Also note that there are two versions of Tensorflow -- one that runs on GPUs, and one that runs only on the CPU. You may want to try the GPU version first; if it works (and you have a GPU in your computer!) it may be **considerably** faster than the CPU only version. Performance won't be a big deal for this lab, but it will matter more later on. | ||
**Part 2: implement basic MNIST tutorial** | **Part 2: implement basic MNIST tutorial** | ||
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Note that you will need to pick the size of the hidden layer. Try different values, and see what works. | Note that you will need to pick the size of the hidden layer. Try different values, and see what works. | ||
- | Adding a second layer, adjusting my initialization, changing my step size to 0.05, and running for 2000 epochs, I was able to achieve 92% classification accuracy. My new classification curve is shown in the first section of this document. | + | Adding a second layer, adjusting my initialization, changing my step size to 0.05, and running for 2000 epochs, I was able to achieve 92% classification accuracy. Using a larger steps size (0.1) allowed me to get to 94% accuracy. My new classification curve is shown in the first section of this document. |
You are welcome (and encouraged!) to see what happens as you add more and more layers! | You are welcome (and encouraged!) to see what happens as you add more and more layers! |