A machine learning method, using a neural network as a model, executed by a computer, the machine learning method including dividing a first batch data into a plurality of pieces of second batch data, the first batch data being a set of sample data to be input into the model in a machine learning, allocating the plurality of pieces of second batch data to a plurality of computers, the model having a specified layered structure and a specified parameter of the neural network being applied to the plurality of computers, making the plurality of computers to execute the machine learning based on the plurality of allocated second batch data, obtaining, from each of the plurality of computers, a plurality of correction amounts of the parameter derived by the executed machine learning, and correcting the model by modifying the specified parameter in accordance with the plurality of correction amounts.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning method using a neural network as a model, the machine learning method being executed by a computer, the machine learning method comprising: dividing a first batch data into a plurality of pieces of second batch data, the first batch data being a set of sample data to be input into the model in a machine learning, the first batch data having a specified data size in which a parameter of the model is corrected; allocating the plurality of pieces of second batch data to a plurality of computers, the model having a specified layered structure and a specified parameter of the neural network being applied to the plurality of computers; making each of the plurality of computers to execute the machine learning based on each of the plurality of allocated second batch data; obtaining, from each of the plurality of computers, a plurality of correction amounts of the parameter derived by the executed machine learning; and correcting the model by modifying the specified parameter in accordance with the plurality of correction amounts. 2. The machine learning method according to claim 1, wherein the process comprises: applying, to each of the plurality of computers, a seed value and a random number generation algorithm which defines neurons invalidating input or output among neurons included in the model. 3. The machine learning method according to claim 1, wherein the dividing includes determining a size of each of the plurality of pieces of second batch data in accordance with a memory capacity of each of the plurality of computers. 4. The machine learning method according to claim 1, wherein the process comprises: correcting, in the correcting, the model in accordance with an average value of the plurality of correction amounts. 5. The machine learning method according to claim 1, wherein the process comprises: applying the corrected model to each of the plurality of computers. 6. A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process comprising: dividing a first batch data into a plurality of pieces of second batch data, the first batch data being a set of sample data to be input into a model in a machine learning using a neural network as the model, the first batch data having a specified data size in which a parameter of the model is corrected; allocating the plurality of pieces of second batch data to a plurality of computers, the model having a specified layered structure and a specified parameter of the neural network being applied to the plurality of computers; making each of the plurality of computers to execute the machine learning based on each of the plurality of allocated second batch data; obtaining, from each of the plurality of computers, a plurality of correction amounts of the parameter derived by the executed machine learning; and correcting the model by modifying the specified parameter in accordance with the plurality of correction amounts. 7. An information processing apparatus comprising: a memory; and a processor coupled to the memory and the processor configured to: dividing a first batch data into a plurality of pieces of second batch data, the first batch data being a set of sample data to be input into a model in a machine learning using a neural network as the model, the first batch data having a specified data size in which a parameter of the model is corrected; allocating the plurality of pieces of second batch data to a plurality of computers, the model having a specified layered structure and a specified parameter of the neural network being applied to the plurality of computers; making each of the plurality of computers to execute the machine learning based on each of the plurality of allocated second batch data; obtaining, from each of the plurality of computers, a plurality of correction amounts of the parameter derived by the executed machine learning; and correcting the model by modifying the specified parameter in accordance with the plurality of correction amounts.