During the holidays, I've been happily playing with keras & tensorflow in RStudio Cloud, on the UCI wine quality dataset. I defined a worfklow, where I download & preprocess the data, then I fit multiple models in order to tune the numerous hyperparameters: a README.md file has been provided so that you can easily rerun the analysis.
The dataset is very small ( ~500 kB), the task is relatively simple (classify the wine quality to one of 4 classes , according to the 11 features), and each model is very small, by modern standards (1 hidden layer, max ~5000 parameters). I try to fit ~400 models, however, I've not been able to fit all of them, not even once. Discussing on the RStudio Cloud channel, it seems to be a memory issue:
However, RStudio Cloud provides 1 GB of RAM, each model is very small, and AFAIU, Tensorflow is not growing the computational graph abnormally (each model is fit in a new graph, so the graph size doesn't keep growing). So, why do Tensorflow & Keras grow their memory occupation so much? Am I doing something wrong? Can you help me fix this? Thanks in advance
