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fix typo: lcueve.out->lcurve.out #1077

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Sep 1, 2021
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fix typo: lcueve.out->lcurve.out #1077

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2 changes: 1 addition & 1 deletion deepmd/utils/argcheck.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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),
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26 changes: 13 additions & 13 deletions doc/train-input-auto.rst
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Expand Up @@ -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``
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@y1xiaoc Is there some different behaviors to sort type?

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dargs use a set to record dtypes. The order may change from 3.6 to 3.7 since the newer python guarantees that the iteration order is same as the add order.

| argument path: ``model/fitting_net[polar]/scale``

The output of the fitting net (polarizability matrix) will be scaled by ``scale``
Expand Down Expand Up @@ -1102,71 +1102,71 @@ 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.

.. _`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.

.. _`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.

.. _`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.

.. _`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.

.. _`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.

.. _`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.

.. _`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.

.. _`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.
Expand All @@ -1179,15 +1179,15 @@ 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.

.. _`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.
Expand Down Expand Up @@ -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.
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