Information processing apparatus, non-transitory computer-readable storage medium, and learning-network learning value computing method

A non-transitory computer readable storage medium having stored therein a program that causes a computer to execute a process including:

• acquiring, in a pooling layer, information on an error gradient including a plurality of elements from an upper layer, when computing a learning value of a learning network including a plurality of layers;
• performing, in a convolution layer, cumulative additions on a plurality of elements included in the information in a lateral direction and a longitudinal direction to convert the information into an integrated image, when acquiring information from a lower layer;
• specifying, in the convolution layer, an area corresponding to the one element among from a plurality of elements included in the integrated image, when computing a value of one element included in a weight gradient;
• dividing, in the convolution layer, the specified area having elements into a plurality of partial areas;
• first computing, in the convolution layer, total values of elements included in the respective partial areas based on characteristics of the integrated image;
• second computing, in the convolution layer, for each of the partial areas, a value based on the one or more total values of the elements included in the one or more partial areas and a value of one of the elements of the error gradient corresponding to the corresponding partial area;
• and
• totalizing, in the convolution layer, the computed values to execute a process for computing the value of the one element.

A learning-network learning value computing method, comprising:

• acquiring, in a pooling layer, information on an error gradient including a plurality of elements from an upper layer, when computing a learning value of a learning network including a plurality of layers, using a processor;
• performing, in a convolution layer, cumulative additions on a plurality of elements included in the information in a lateral direction and a longitudinal direction to convert the information into an integrated image, when acquiring information from a lower layer, using the processor;
• specifying, in the convolution layer, an area corresponding to the one element among from a plurality of elements included in the integrated image, when computing a value of one element included in a weight gradient, using the processor;
• dividing, in the convolution layer, the specified area having elements into a plurality of partial areas, using the processor;
• first computing, in the convolution layer, total values of elements included in the respective partial areas based on characteristics of the integrated image, using the processor;
• second computing, in the convolution layer, for each of the partial areas, a value based on the one or more total values of the elements included in the one or more partial areas and a value of one of the elements of the error gradient corresponding to the corresponding partial area, using the processor;
• and
• totalizing, in the convolution layer, the computed values to execute a process for computing the value of the one element, using the processor.