While I was reading an article on Google last Deep Learning achievement, I was reminded of a previous discussion with former colleagues about replacing reservoir simulations with neural networks. At the time, I dismissed the idea as ridiculous due to the complexity of the task and the requirement for the training.
But now, Google seems to have done it. Or have they?
A few weeks ago, on StackOverflow, a user asked for an accuracy measure on the embedded space for an autoencoder. This was with Keras, but I thought it would be a nice exercise for Tensorflow as well.
The idea in this case is to add a few layers to the embedded space to create a classifier and measure its accuracy while we optimize the autoencoder.
We will train the autoencoder in alternation with the classifier. When one is updated, the other will be frozen, and then we can measure classification accuracy and reconstruction loss concurrently in Tensorboard.