Uh oh!
There was an error while loading. Please reload this page.
Features: maximize information entropy and variable reporting interval. - #170
Merged
Conversation
The new argument sets the reporting interval, i.e., the number of epochs between printing the cost function to the screen and writing it to the cost.plt file.
This commit adds the `--bins` command-line argument, which sets the number of phase-space hypercubes across the range of each of the 5 output variables. Only one data point per bin retained for use in training a neural network. This choice maximizes the information entropy for a given retained training data set size.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This pull request adds
--reportand--binscommand-line arguments in the train-cloud-microphysics app:--reportsets the reporting interval, i.e., the number of epochs between printing the cost function to the screen and writing it to thecost.pltfile.--binssets the number of phase-space hypercubes across the range of each of the 5 output variables. Only one data point per bin retained for using in training a neural network. This choice maximizes the information entropy for a given retained training data set size.