An information processing apparatus executes 1 learning on each of a plurality of neural networks with regard to target data by duration of at least 1 epoch, and information processing apparatus executes a plurality times of loops of a specific algorithm, each of the plurality of times of loops changes a number of units of each of the plurality of neural networks. The information processing apparatus sets a plurality of learning durations for each of the plurality of neural networks based on a respective accuracy variance and a respective actual performance.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable recording medium having stored therein a learning program that causes a computer to execute a process comprising: executing learning on each of a plurality of neural networks with regard to target data by duration of at least 1 epoch; executing a plurality times of loops of a specific algorithm, each of the plurality of times of loops changes a number of units of each of the plurality of neural networks; and setting a plurality of learning durations for each of the plurality of neural networks based on a respective accuracy variance and a respective actual performance, each of the plurality of learning durations being durations of the learning in each of the plurality of times of loops of the specific algorithm, the respective accuracy variance being variance values of accuracy for each of the plurality of neural networks immediately before start of the respective loop, and the respective actual performance being an actual performance of the plurality of neural networks with regard to the target data immediately before start of the respective loop. 2. The non-transitory computer-readable recording medium according to claim 1, further comprising, generating a plurality of new neural networks whose number is identical to a number of the plurality of neural networks, wherein the setting includes setting a plurality of learning durations for each of the plurality of new neural networks each time the plurality of new neural networks are generated, and the executing includes executing the plurality times of loops of the specific algorithm on the plurality of new neural networks by the plurality of learning durations. 3. The non-transitory computer-readable recording medium according to claim 2, wherein the setting includes, in a case where a variance value of the accuracy of each of the plurality of neural networks, which are previous execution targets, is equal to or more than a threshold, determining that the plurality of learning durations for the plurality of new neural networks is a value that is obtained by subtracting a predetermined number from a plurality of previous learning durations and, in a case where the variance value of the accuracy of each of the plurality of neural networks, which are the previous execution targets, is less than the threshold, determining that the plurality of learning durations is a value that is obtained by adding a predetermined number to the plurality of previous learning durations. 4. The non-transitory computer-readable recording medium according to claim 1, wherein the specific algorithm is a genetic algorithm. 5. A learning method comprising: executing learning on each of a plurality of neural networks with regard to target data by duration of at least 1 epoch, using a processor; executing a plurality times of loops of a specific algorithm, each of the plurality of times of loops changes a number of units of each of the plurality of neural networks, using the processor; and setting a plurality of learning durations for each of the plurality of neural networks based on a respective accuracy variance and a respective actual performance, each of the plurality of learning durations being durations of the learning in each of the plurality of times of loops of the specific algorithm, the respective accuracy variance being variance values of accuracy for each of the plurality of neural networks immediately before start of the respective loop, and the respective actual performance being an actual performance of the plurality of neural networks with regard to the target data immediately before start of the respective loop, using the processor. 6. The learning method according to claim 5, further comprising, generating a plurality of new neural networks whose number is identical to a number of the plurality of neural networks, using the processor, wherein the setting includes setting a plurality of learning durations for each of the plurality of new neural networks each time the plurality of new neural networks are generated, and the executing includes executing the plurality times of loops of the specific algorithm on the plurality of new neural networks by the plurality of learning durations. 7. The learning method according to claim 6, wherein the setting includes, in a case where a variance value of the accuracy of each of the plurality of neural networks, which are previous execution targets, is equal to or more than a threshold, determining that the plurality of learning durations for the plurality of new neural networks is a value that is obtained by subtracting a predetermined number from a plurality of previous learning durations and, in a case where the variance value of the accuracy of each of the plurality of neural networks, which are the previous execution targets, is less than the threshold, determining that the plurality of learning durations is a value that is obtained by adding a predetermined number to the plurality of previous learning durations. 8. The learning method according to claim 5, wherein the specific algorithm is a genetic algorithm. 9. An information processing apparatus comprising: a processor that executes a process including: executing learning on each of a plurality of neural networks with regard to target data by duration of at least 1 epoch; executing a plurality times of loops of a specific algorithm, each of the plurality of times of loops changes a number of units of each of the plurality of neural networks; and setting a plurality of learning durations for each of the plurality of neural networks based on a respective accuracy variance and a respective actual performance, each of the plurality of learning durations being durations of the learning in each of the plurality of times of loops of the specific algorithm, the respective accuracy variance being variance values of accuracy for each of the plurality of neural networks immediately before start of the respective loop, and the respective actual performance being an actual performance of the plurality of neural networks with regard to the target data immediately before start of the respective loop. 10. The information processing apparatus according to claim 9, wherein the process further includes generating a plurality of new neural networks whose number is identical to a number of the plurality of neural networks, wherein the setting includes setting a plurality of learning durations for each of the plurality of new neural networks each time the plurality of new neural networks are generated, and the executing includes executing the plurality times of loops of the specific algorithm on the plurality of new neural networks by the plurality of learning durations. 11. The information processing apparatus according to claim 10, wherein the setting includes, in a case where a variance value of the accuracy of each of the plurality of neural networks, which are previous execution targets, is equal to or more than a threshold, determining that the plurality of learning durations for the plurality of new neural networks is a value that is obtained by subtracting a predetermined number from a plurality of previous learning durations and, in a case where the variance value of the accuracy of each of the plurality of neural networks, which are the previous execution targets, is less than the threshold, determining that the plurality of learning durations is a value that is obtained by adding a predetermined number to the plurality of previous learning durations. 12. The information processing apparatus according to claim 9, wherein the specific algorithm is a genetic algorithm.