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It's explained in the post: > Have you ever done a dense grid search over neural network hyperparameters? Like a really dense grid search? It looks like this (
by yazzku 3y ago
It's explained in the post:
> Have you ever done a dense grid search over neural network hyperparameters? Like a really dense grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
- catlifeonmars 3y agoI saw this, but am still not clear on what the axes represent. I assume two hyperparameters, or possibly two orthogonal principal components. I guess my point is it’s not clear how/which parameters are mapped onto the image.
- bosco_mcnasty 3y agoyour point is valid but the paper explains it clearly and obviously. they are NOT dimensionally reduced hyperparameters, no. The hyperparameters are learning rates, that's it. X axis, learning rate for input (1 hidden layer). Y axis, learning rate for output layer. So what this is saying, for certain ill-chosen learning weights, model convergence is for lack of a better word, chaotic and unstable.
- ks1723 3y agoJust to add to this, only the two learning rates are changed, everything else including initialization and data is fixed. From the paper: Training consists of 500 (sometimes 1000) iterations of full batch steepest gradient descent. Training is performed for a 2d grid of η0 and η1 hyperparameter values, with all other hyperparameters held fixed (including network initialization and training data).