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larsbratholm
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Sep 28, 2018
larsbratholm
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Sep 28, 2018
SilviaAmAm
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Oct 4, 2018
* 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 #10 * another attempt at fixing #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 #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
SilviaAmAm
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Dec 12, 2018
* 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 #10 * another attempt at fixing #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 #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)
larsbratholm
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Feb 5, 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 #10 * another attempt at fixing #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 #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) * Added a function to the atomscaler that enables to revert back * Relaxed tolerance in tests
andersx
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Jun 26, 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 #10 * another attempt at fixing #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 #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) * Added a function to the atomscaler that enables to revert back * Relaxed tolerance in tests * Fixed bug in the padding of the representation in the ARMP network used in the pipeline * Made a modification to how the Fortran ACSF are generated that helps with how much memory is used. Currrently only float32 ACSF are available * Added a check to make sure there are no NANs in the representations. * Small mistake corrected in aglaia * Fixed extra space before -lpthread flag * Removed what I added * Implemented MRMP representations from xyz * Generate atomic slatm from data * Fixed typo * Fixed problem with slatm and ARMP * Fixed bug for MRMP tensorboard logger * Actually fixed the tensorboard bug for MRMP and added tests to catch future errors * Fixed another tensorboard bug * Changed the behaviour of logging to tensorboard in MRMP
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
zaspel
pushed a commit
to zaspel/qml
that referenced
this pull request
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)
zaspel
pushed a commit
to zaspel/qml
that referenced
this pull request
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) * Added a function to the atomscaler that enables to revert back * Relaxed tolerance in tests
zaspel
pushed a commit
to zaspel/qml
that referenced
this pull request
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) * Added a function to the atomscaler that enables to revert back * Relaxed tolerance in tests * Fixed bug in the padding of the representation in the ARMP network used in the pipeline * Made a modification to how the Fortran ACSF are generated that helps with how much memory is used. Currrently only float32 ACSF are available * Added a check to make sure there are no NANs in the representations. * Small mistake corrected in aglaia * Fixed extra space before -lpthread flag * Removed what I added * Implemented MRMP representations from xyz * Generate atomic slatm from data * Fixed typo * Fixed problem with slatm and ARMP * Fixed bug for MRMP tensorboard logger * Actually fixed the tensorboard bug for MRMP and added tests to catch future errors * Fixed another tensorboard bug * Changed the behaviour of logging to tensorboard in MRMP
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