From d3dc7dbee8437864f246ff21504d638a1d5637c6 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Fri, 24 Jun 2022 00:11:08 -0400 Subject: [PATCH] docs: fix arg reference --- doc/model/train-energy.md | 4 ++-- doc/model/train-fitting-tensor.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/model/train-energy.md b/doc/model/train-energy.md index cbe6ad1801..20f173bc9d 100644 --- a/doc/model/train-energy.md +++ b/doc/model/train-energy.md @@ -42,6 +42,6 @@ The {ref}`loss ` section in the `input.json` is "limit_pref_v": 0 } ``` -The options {ref}`start_pref_e `, {ref}`limit_pref_e `, {ref}`start_pref_f `, {ref}`limit_pref_f `, {ref}`start_pref_v ` and {ref}`limit_pref_v ` determine the start and limit prefactors of energy, force and virial, respectively. +The options {ref}`start_pref_e `, {ref}`limit_pref_e `, {ref}`start_pref_f `, {ref}`limit_pref_f `, {ref}`start_pref_v ` and {ref}`limit_pref_v ` determine the start and limit prefactors of energy, force and virial, respectively. -If one does not want to train with virial, then he/she may set the virial prefactors {ref}`start_pref_v ` and {ref}`limit_pref_v ` to 0. +If one does not want to train with virial, then he/she may set the virial prefactors {ref}`start_pref_v ` and {ref}`limit_pref_v ` to 0. diff --git a/doc/model/train-fitting-tensor.md b/doc/model/train-fitting-tensor.md index 6d48c34c86..240f126aa3 100644 --- a/doc/model/train-fitting-tensor.md +++ b/doc/model/train-fitting-tensor.md @@ -9,7 +9,7 @@ $deepmd_source_dir/examples/water_tensor/polar/polar_input.json The training and validation data are also provided our examples. But note that **the data provided along with the examples are of limited amount, and should not be used to train a production model.** -Similar to the `input.json` used in `ener` mode, training json is also divided into {ref}`model `, {ref}`learning_rate `, {ref}`loss ` and {ref}`training `. Most keywords remains the same as `ener` mode, and their meaning can be found [here](train-se-e2-a.md). To fit a tensor, one need to modify {ref}`model/fitting_net` and {ref}`loss `. +Similar to the `input.json` used in `ener` mode, training json is also divided into {ref}`model `, {ref}`learning_rate `, {ref}`loss ` and {ref}`training `. Most keywords remains the same as `ener` mode, and their meaning can be found [here](train-se-e2-a.md). To fit a tensor, one need to modify {ref}`model/fitting_net ` and {ref}`loss `. ## The fitting Network