# 3.10. Type embedding approach

We generate specific a type embedding vector for each atom type so that we can share one descriptor embedding net and one fitting net in total, which decline training complexity largely.

The training input script is similar to that of se_e2_a, but different by adding the type_embedding section.

## 3.10.1. Type embedding net

The model defines how the model is constructed, adding a section of type embedding net:

    "model": {
"type_map":	["O", "H"],
"type_embedding":{
...
},
"descriptor" :{
...
},
"fitting_net" : {
...
}
}


The model will automatically apply the type embedding approach and generate type embedding vectors. If the type embedding vector is detected, the descriptor and fitting net would take it as a part of the input.

The construction of type embedding net is given by type_embedding. An example of type_embedding is provided as follows

	"type_embedding":{
"neuron":		[2, 4, 8],
"resnet_dt":	false,
"seed":		1
}

• The neuron specifies the size of the type embedding net. From left to right the members denote the sizes of each hidden layer from the input end to the output end, respectively. It takes a one-hot vector as input and output dimension equals to the last dimension of the neuron list. If the outer layer is twice the size of the inner layer, then the inner layer is copied and concatenated, then a ResNet architecture is built between them.

• If the option resnet_dt is set to true, then a timestep is used in the ResNet.

• seed gives the random seed that is used to generate random numbers when initializing the model parameters.

A complete training input script of this example can be found in the directory.

\$deepmd_source_dir/examples/water/se_e2_a_tebd/input.json


See here for further explanation of type embedding.

Note

You can’t apply the compression method while using the atom type embedding.