Unlike energy, which is a scalar, one may want to fit some high dimensional physical quantity, like dipole (vector) and polarizability (matrix, shorted as polar). Deep Potential has provided different APIs to do this. In this example, we will show you how to train a model to fit a water system. A complete training input script of the examples can be found in
$deepmd_source_dir/examples/water_tensor/dipole/dipole_input.json
$deepmd_source_dir/examples/water_tensor/polar/polar_input.jsonThe 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 <model>, {ref}learning_rate <learning_rate>, {ref}loss <loss> and {ref}training <training>. Most keywords remain the same as ener mode, and their meaning can be found here. To fit a tensor, one needs to modify {ref}model/fitting_net <model/fitting_net> and {ref}loss <loss>.
The {ref}fitting_net <model/fitting_net> section tells DP which fitting net to use.
The JSON of dipole type should be provided like
"fitting_net" : {
"type": "dipole",
"sel_type": [0],
"neuron": [100,100,100],
"resnet_dt": true,
"seed": 1,
},The JSON of polar type should be provided like
"fitting_net" : {
"type": "polar",
"sel_type": [0],
"neuron": [100,100,100],
"resnet_dt": true,
"seed": 1,
},typespecifies which type of fitting net should be used. It should be eitherdipoleorpolar. Note thatglobal_polarmode in version 1.x is already deprecated and is merged intopolar. To specify whether a system is global or atomic, please see here.sel_typeis a list specifying which type of atoms have the quantity you want to fit. For example, in the water system,sel_typeis[0]since0represents atomO. If left unset, all types of atoms will be fitted.- The rest arguments have the same meaning as they do in
enermode.
DP supports a combinational training of the global system (only a global tensor label, i.e. dipole or polar, is provided in a frame) and atomic system (labels for each atom included in sel_type are provided). In a global system, each frame has just one tensor label. For example, when fitting polar, each frame will just provide a 1 x 9 vector which gives the elements of the polarizability tensor of that frame in order XX, XY, XZ, YX, YY, YZ, XZ, ZY, ZZ. By contrast, in an atomic system, each atom in sel_type has a tensor label. For example, when fitting a dipole, each frame will provide a #sel_atom x 3 matrices, where #sel_atom is the number of atoms whose type are in sel_type.
The {ref}loss <loss> section tells DP the weight of these two kinds of loss, i.e.
loss = pref * global_loss + pref_atomic * atomic_lossThe loss section should be provided like
"loss" : {
"type": "tensor",
"pref": 1.0,
"pref_atomic": 1.0
},- {ref}
type <loss/type>should be written astensoras a distinction fromenermode. - {ref}
pref <loss[tensor]/pref>and {ref}pref_atomic <loss[tensor]/pref_atomic>respectively specify the weight of global loss and atomic loss. It can not be left unset. If set to 0, the corresponding label will NOT be included in the training process.
In tensor mode, the identification of the label's type (global or atomic) is derived from the file name. The global label should be named dipole.npy/raw or polarizability.npy/raw, while the atomic label should be named atomic_dipole.npy/raw or atomic_polarizability.npy/raw. If wrongly named, DP will report an error
ValueError: cannot reshape array of size xxx into shape (xx,xx). This error may occur when your label mismatch it's name, i.e. you might store global tensor in `atomic_tensor.npy` or atomic tensor in `tensor.npy`.In this case, please check the file name of the label.
The training command is the same as ener mode, i.e.
dp train input.jsonThe detailed loss can be found in lcurve.out:
# step rmse_val rmse_trn rmse_lc_val rmse_lc_trn rmse_gl_val rmse_gl_trn lr
0 8.34e+00 8.26e+00 8.34e+00 8.26e+00 0.00e+00 0.00e+00 1.0e-02
100 3.51e-02 8.55e-02 0.00e+00 8.55e-02 4.38e-03 0.00e+00 5.0e-03
200 4.77e-02 5.61e-02 0.00e+00 5.61e-02 5.96e-03 0.00e+00 2.5e-03
300 5.68e-02 1.47e-02 0.00e+00 0.00e+00 7.10e-03 1.84e-03 1.3e-03
400 3.73e-02 3.48e-02 1.99e-02 0.00e+00 2.18e-03 4.35e-03 6.3e-04
500 2.77e-02 5.82e-02 1.08e-02 5.82e-02 2.11e-03 0.00e+00 3.2e-04
600 2.81e-02 5.43e-02 2.01e-02 0.00e+00 1.01e-03 6.79e-03 1.6e-04
700 2.97e-02 3.28e-02 2.03e-02 0.00e+00 1.17e-03 4.10e-03 7.9e-05
800 2.25e-02 6.19e-02 9.05e-03 0.00e+00 1.68e-03 7.74e-03 4.0e-05
900 3.18e-02 5.54e-02 9.93e-03 5.54e-02 2.74e-03 0.00e+00 2.0e-05
1000 2.63e-02 5.02e-02 1.02e-02 5.02e-02 2.01e-03 0.00e+00 1.0e-05
1100 3.27e-02 5.89e-02 2.13e-02 5.89e-02 1.43e-03 0.00e+00 5.0e-06
1200 2.85e-02 2.42e-02 2.85e-02 0.00e+00 0.00e+00 3.02e-03 2.5e-06
1300 3.47e-02 5.71e-02 1.07e-02 5.71e-02 3.00e-03 0.00e+00 1.3e-06
1400 3.13e-02 5.76e-02 3.13e-02 5.76e-02 0.00e+00 0.00e+00 6.3e-07
1500 3.34e-02 1.11e-02 2.09e-02 0.00e+00 1.57e-03 1.39e-03 3.2e-07
1600 3.11e-02 5.64e-02 3.11e-02 5.64e-02 0.00e+00 0.00e+00 1.6e-07
1700 2.97e-02 5.05e-02 2.97e-02 5.05e-02 0.00e+00 0.00e+00 7.9e-08
1800 2.64e-02 7.70e-02 1.09e-02 0.00e+00 1.94e-03 9.62e-03 4.0e-08
1900 3.28e-02 2.56e-02 3.28e-02 0.00e+00 0.00e+00 3.20e-03 2.0e-08
2000 2.59e-02 5.71e-02 1.03e-02 5.71e-02 1.94e-03 0.00e+00 1.0e-08
One may notice that in each step, some of the local loss and global loss will be 0.0. This is because our training data and validation data consist of the global system and atomic system, i.e.
--training_data
>atomic_system
>global_system
--validation_data
>atomic_system
>global_system
During training, at each step when the lcurve.out is printed, the system used for evaluating the training (validation) error may be either with only global or only atomic labels, thus the corresponding atomic or global errors are missing and are printed as zeros.