diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index fcaba80908..6279b3e088 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -581,7 +581,7 @@ def training_args(): # ! modified by Ziyao: data configuration isolated. arg_validation_data, Argument("numb_steps", int, optional=False, doc=doc_numb_steps, alias=["stop_batch"]), Argument("seed", [int,None], optional=True, doc=doc_seed), - Argument("disp_file", str, optional=True, default='lcueve.out', doc=doc_disp_file), + Argument("disp_file", str, optional=True, default='lcurve.out', doc=doc_disp_file), Argument("disp_freq", int, optional=True, default=1000, doc=doc_disp_freq), Argument("numb_test", [list,int,str], optional=True, default=1, doc=doc_numb_test), Argument("save_freq", int, optional=True, default=1000, doc=doc_save_freq), diff --git a/doc/train-input-auto.rst b/doc/train-input-auto.rst index ea8da06a61..9201809549 100644 --- a/doc/train-input-auto.rst +++ b/doc/train-input-auto.rst @@ -898,7 +898,7 @@ model: .. _`model/fitting_net[polar]/scale`: scale: - | type: ``list`` | ``float``, optional, default: ``1.0`` + | type: ``float`` | ``list``, optional, default: ``1.0`` | argument path: ``model/fitting_net[polar]/scale`` The output of the fitting net (polarizability matrix) will be scaled by ``scale`` @@ -1102,7 +1102,7 @@ loss: .. _`loss[ener]/start_pref_e`: start_pref_e: - | type: ``int`` | ``float``, optional, default: ``0.02`` + | type: ``float`` | ``int``, optional, default: ``0.02`` | argument path: ``loss[ener]/start_pref_e`` The prefactor of energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the energy label should be provided by file energy.npy in each data system. If both start_pref_energy and limit_pref_energy are set to 0, then the energy will be ignored. @@ -1110,7 +1110,7 @@ loss: .. _`loss[ener]/limit_pref_e`: limit_pref_e: - | type: ``int`` | ``float``, optional, default: ``1.0`` + | type: ``float`` | ``int``, optional, default: ``1.0`` | argument path: ``loss[ener]/limit_pref_e`` The prefactor of energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity. @@ -1118,7 +1118,7 @@ loss: .. _`loss[ener]/start_pref_f`: start_pref_f: - | type: ``int`` | ``float``, optional, default: ``1000`` + | type: ``float`` | ``int``, optional, default: ``1000`` | argument path: ``loss[ener]/start_pref_f`` The prefactor of force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force label should be provided by file force.npy in each data system. If both start_pref_force and limit_pref_force are set to 0, then the force will be ignored. @@ -1126,7 +1126,7 @@ loss: .. _`loss[ener]/limit_pref_f`: limit_pref_f: - | type: ``int`` | ``float``, optional, default: ``1.0`` + | type: ``float`` | ``int``, optional, default: ``1.0`` | argument path: ``loss[ener]/limit_pref_f`` The prefactor of force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity. @@ -1134,7 +1134,7 @@ loss: .. _`loss[ener]/start_pref_v`: start_pref_v: - | type: ``int`` | ``float``, optional, default: ``0.0`` + | type: ``float`` | ``int``, optional, default: ``0.0`` | argument path: ``loss[ener]/start_pref_v`` The prefactor of virial loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the virial label should be provided by file virial.npy in each data system. If both start_pref_virial and limit_pref_virial are set to 0, then the virial will be ignored. @@ -1142,7 +1142,7 @@ loss: .. _`loss[ener]/limit_pref_v`: limit_pref_v: - | type: ``int`` | ``float``, optional, default: ``0.0`` + | type: ``float`` | ``int``, optional, default: ``0.0`` | argument path: ``loss[ener]/limit_pref_v`` The prefactor of virial loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity. @@ -1150,7 +1150,7 @@ loss: .. _`loss[ener]/start_pref_ae`: start_pref_ae: - | type: ``int`` | ``float``, optional, default: ``0.0`` + | type: ``float`` | ``int``, optional, default: ``0.0`` | argument path: ``loss[ener]/start_pref_ae`` The prefactor of atom_ener loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the atom_ener label should be provided by file atom_ener.npy in each data system. If both start_pref_atom_ener and limit_pref_atom_ener are set to 0, then the atom_ener will be ignored. @@ -1158,7 +1158,7 @@ loss: .. _`loss[ener]/limit_pref_ae`: limit_pref_ae: - | type: ``int`` | ``float``, optional, default: ``0.0`` + | type: ``float`` | ``int``, optional, default: ``0.0`` | argument path: ``loss[ener]/limit_pref_ae`` The prefactor of atom_ener loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity. @@ -1166,7 +1166,7 @@ loss: .. _`loss[ener]/relative_f`: relative_f: - | type: ``NoneType`` | ``float``, optional + | type: ``float`` | ``NoneType``, optional | argument path: ``loss[ener]/relative_f`` If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by `relative_f`, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label. @@ -1179,7 +1179,7 @@ loss: .. _`loss[tensor]/pref`: pref: - | type: ``int`` | ``float`` + | type: ``float`` | ``int`` | argument path: ``loss[tensor]/pref`` The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included. @@ -1187,7 +1187,7 @@ loss: .. _`loss[tensor]/pref_atomic`: pref_atomic: - | type: ``int`` | ``float`` + | type: ``float`` | ``int`` | argument path: ``loss[tensor]/pref_atomic`` The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #selected atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0. @@ -1408,7 +1408,7 @@ training: .. _`training/disp_file`: disp_file: - | type: ``str``, optional, default: ``lcueve.out`` + | type: ``str``, optional, default: ``lcurve.out`` | argument path: ``training/disp_file`` The file for printing learning curve.