Method, apparatus, device and storage medium for model training

An electronic device, comprising:

• a processor and a memory, wherein the memory is configured to store a computer program, and the processor is configured to call and run the computer program stored in the memory to:
• train a motion capture model according to a preset training set, wherein the motion capture model comprises a time series prediction unit;
• perform, through a quantization node in the time series prediction unit, a quantization operation and an inverse quantization operation in sequence on model data passing through the quantization node;
• and
• adjust a weight parameter of the time series prediction unit according to an update on a gradient of the time series prediction unit until the motion capture model converges, wherein the weight parameter comprises a weight scaling parameter and a weight direction.

A non-volatile computer-readable storage medium comprising a computer program, wherein the computer program, when executed by a processor, causes the processor to perform:

• train a motion capture model according to a preset training set, wherein the motion capture model comprises a time series prediction unit;
• perform, through a quantization node in the time series prediction unit, a quantization operation and an inverse quantization operation in sequence on model data passing through the quantization node;
• and
• adjust a weight parameter of the time series prediction unit according to an update on a gradient of the time series prediction unit until the motion capture model converges, wherein the weight parameter comprises a weight scaling parameter and a weight direction.