networks/jwst-spec-v1-and-image-v2 #58
Merged
Conversation
This file contains 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
alphasentaurii
added
documentation
alphasentaurii
added a commit
that referenced
this pull request
Apr 6, 2024
* use updated encoder keypairs for ingest, scrub, and predict * update visitype encodings * added new tests, updated docstrings for jwst predict * update zenodo ID * updated hash vals and sizes for keras archive formatted model files * add blueprint for spec3_reg * set keras_archive=True for all pretrained networks * min scikit-learn 1.3.2 * update predictions for new models * create parent directory path before saving keras archive * ruff fix * fix conditional check
Sign up for free
to 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Outdated suggestions cannot be applied.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.
builder.trained_networksjwst_cal.zip includes updated (v2)img3_regand newspec3_regpredictive models for image and spectroscopic dataskopes.jwst.cal.predictgenerates predictions for spectrosopic datasets in addition to image data. This update also allows further customization of user arguments:obsto specify selection of a program ID + observation numberinput_pathaccepts either a directory (default) or a filename. If filename, the script will try to find any input exposures that belong to the same program and observation number as that file.sfxattribute is now customizable on instantiation of the class object (default is '_uncal.fits')All pre-trained networks saved and loaded using keras archive format