A Complete Example of Tensorflow Queues
The story behind this is that I kept seeing mention of Tensorflow Queues and that feed_dict shouldn't be used in serious Tensorflow code, but the documentation didn't have a complete example of how to use them. This post
was the most helpful resource I found when I was learning how to use Queues.
I have provided a complete example (albeit simple) that covers all the way from cleaning the data to evaluating the model on a test set, all the while using Tensorflow Queues.
It's pretty neat when you realize you don't have to use feed_dict because the underlying Session graph already knows where to get your train/validation/test data!
On my NVIDIA GTX 1080, this takes up about 6 GB since the data can be transferred directly onto the GPU. The model itself is only a few hundred MBs. This is much faster than having to copy each mini-batch from the CPU -> GPU each time you call feed_dict; in fact, the data is copied over twice.
This example is a modified version of the Convolutional Neural Network from this tutorial.
The MNIST dataset is included under MNIST_data. Run
python mnist-to-jpg.pyto unpack the data, then do
python cnn.pyIt should take about half an hour to run on a CPU, or a few minutes on a GPU.
The code has been tested with Python 3.5 and Tensorflow 1.1.
If using Anaconda, make sure to have scipy and Pillow also installed.