Compression of learning model

An information processing apparatus comprising:

• a memory;
• and
• one or a plurality of processors, wherein
• the memory stores a predetermined learning model using a neural network, and a weight learning model, in which each model including at least two models from among a first learning model subjected to distillation processing, a second learning model subjected to pruning processing, and a third learning model subjected to quantization processing is weighted, for the predetermined learning model, and
• the one or plurality of processors execute:
• acquiring predetermined learning data;
• performing machine learning by inputting the predetermined learning data to the weight learning model;
• acquiring a learning result in a case where the machine learning is performed by inputting the predetermined learning data for each weight learning model in which a weight of each of the models is changed;
• performing supervised learning by using learning data including each weight learning model to which each changed weight is given and each learning result obtained when learned by each of the weight learning models;
• and
• generating a prediction model that predicts a learning result for each set of weights in a case where arbitrary learning data is input by the supervised learning.

A non-transitory computer-readable storage medium on which a program is recorded, the program causing one or a plurality of processors included in an information processing apparatus to execute:

• acquiring predetermined learning data;
• performing machine learning by inputting the predetermined learning data to a weight learning model, in which each model including at least two models from among a first learning model subjected to distillation processing, a second learning model subjected to pruning processing, and a third learning model subjected to quantization processing is weighted, for a predetermined learning model using a neural network;
• acquiring a learning result in a case where the machine learning is performed by inputting the predetermined learning data for each weight learning model in which a weight of each of the models is changed;
• performing supervised learning by using learning data including each weight learning model to which each changed weight is given and each learning result obtained when learned by each of the weight learning models;
• and
• generating a prediction model that predicts a learning result for each set of weights in a case where arbitrary learning data is input by the supervised learning.