Write a python script that optimizes a machine learning model of your choice using GPyOpt:
- Your script should optimize at least 5 different hyperparameters. E.g. learning rate, number of units in a layer, dropout rate, L2 regularization weight, batch size
- Your model should be optimized on a single satisficing metric
- Your model should save a checkpoint of its best iteration during each training session
- The filename of the checkpoint should specify the values of the hyperparameters being tuned
- Your model should perform early stopping
- Bayesian optimization should run for a maximum of 30 iterations
- Once optimization has been performed, your script should plot the convergence
- Your script should save a report of the optimization to the file
'bayes_opt.txt' - There are no restrictions on imports
Once you have finished your script, write a blog post describing your approach to this task. Your blog post should include:
- A description of what a Gaussian Process is
- A description of Bayesian Optimization
- The particular model that you chose to optimize
- The reasons you chose to focus on your specific hyperparameters
- The reason you chose your satisficing matric
- Your reasoning behind any other approach choices
- Any conclusions you made from performing this optimization
- Final thoughts
Your posts should have examples and at least one picture, at the top. Publish your blog post on Medium or LinkedIn, and share it at least on LinkedIn.
