One can linearly combine existing models with arbitrary coefficients:
"model": {
"type": "linear_ener",
"models": [
{
"type": "frozen",
"model_file": "model0.pb"
},
{
"type": "frozen",
"model_file": "model1.pb"
}
],
"weights": [0.5, 0.5]
},
{ref}weights <model[linear_ener]/weights>
can be a list of floats, mean
, or sum
.
To obtain the model, one needs to execute dp train
to do a zero-step training with {ref}numb_steps <training/numb_steps>
set to 0
, and then freeze the model with dp freeze
.