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Develop #98
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Develop #98
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…raining because it leads to a problem with predictions
…dded representations. Currently works *ONLY* when there is one molecule for the whole data set.
But elements are still not working Sorted elements
Cannot be any transforms of the input data
Code review etc
Keeping updated with upstream
… start the binning can be set by the user
larsbratholm
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Dec 12, 2018
zaspel
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Jul 29, 2019
* Corrected small bug in predict function * Started updating so that model can be trained after its been reloaded * Minor modifications * Updated model so one can predict from xyz and disabled shuffling in training because it leads to a problem with predictions * Fix for the problem of shuffling * Added some tests to make sure the predictions work * Fixed a tensorboard problem * The saving of the model doesn't cause an error if the directory already exists * Fixed a bug that made a test fail * Modified the name of a parameter * Made modifications to make te symmetry functions more numerically stable * Added a hack that makes ARMP work with fortran ACSF when there are padded representations. Currently works *ONLY* when there is one molecule for the whole data set. * corrected bug in score function for padded molecules * Changes that make the model work quickly even when there is padding. * Fixed discrepancies between fortran and TF acsf * Corrected bug in setting of ACSF parameters * Attempt at fixing issue qmlcode#10 * another attempt at fixing qmlcode#10 * Removed a pointless line * set-up * Added the graceful killer * Modifications which prevent installation from breaking on BC4 * Modification to add neural networks to qmlearn * Fix for issue qmlcode#8 * Random comment * Started including the atomic model * Made the atomic neural network work * Fixed a bug with the indices * Now training and predictions don't use the default graph, to avoid problems * uncommented examples * Removed unique_elements in data class This can be stored in the NN class, but I might reverse the change later * Made tensorflow an optional dependency The reason for this approach is that pip would just auto install tensorflow and you might want the gpu version or your own compiled one. * Made is_numeric non-private and removed legacy code * Added 1d array util function * Removed QML check and moved functions from utils to tf_utils * Support for linear models (no hidden layers) * fixed import bug in tf_utils * Added text to explain that you are scoring on training set * Restructure. But elements are still not working Sorted elements * Moved documentation from init to class * Constant features will now be removed at fit/predict time * Moved get_batch_size back into utils, since it doesn't depend on tf * Made the NeuralNetwork class compliant with sklearn Cannot be any transforms of the input data * Fixed tests that didn't pass * Fixed mistake in checks of set_classes() in ARMP * started fixing ARMP bugs for QM7 * Fixed bug in padding and added examples that give low errors * Attempted fix to make representations single precision * Hot fix for AtomScaler * Minor bug fixes * More bug fixes to make sure tests run * Fixed some tests that had failures * Reverted the fchl tests to original * Fixed path in acsf test * Readded changes to tests * Modifications after code review * Version with the ACSF basis functions starting at 0.8 A * Updated ACSF representations so that the minimum distance at which to start the binning can be set by the user * Modified the name of the new parameter (minimum distance of the binning in ACSF)
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Bug fixes and modification of ACSF so that the user can set the minimum distance at which to start binning the distances.