Efficient learning of a neural network can be performed. A plurality of DNNs are hierarchically configured, and data of a hidden layer of a DNN of a first hierarchy machine learning/recognizing device is used as input data of a DNN of a second hierarchy machine learning/recognizing device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing system, comprising: a plurality of DNNs which are hierarchically configured, wherein data of a hidden layer of a DNN of a first hierarchy machine learning/recognizing device is used as input data of a DNN of a second hierarchy machine learning/recognizing device. 2. The information processing system according to claim 1, wherein, after supervised learning is performed in the DNN of the first hierarchy machine learning/recognizing device so that an output layer performs a desired output, supervised learning of the DNN of the second hierarchy machine learning/recognizing device is performed. 3. The information processing system according to claim 1, wherein the first hierarchy machine learning/recognizing device includes a unit that stores a score of a recognition result of a recognition process while performing the recognition process and an update request transmitting unit that transmits an update request signal for a neural network structure and a weight coefficient of the DNN of the first hierarchy machine learning/recognizing device to the second hierarchy machine learning/recognizing device in a case in which the recognition result is larger than a predetermined threshold value 1 or smaller than a predetermined threshold value 2 or in a case in which a variance when a histogram of the recognition result is generated is larger than a predetermined value, upon receiving the update request signal of the first hierarchy machine learning/recognizing device, the second hierarchy machine learning/recognizing device updates the neural network structure and the weight coefficient of the DNN of the first hierarchy machine learning/recognizing device, and transmits update data to the first hierarchy machine learning/recognizing device, and the first hierarchy machine learning/recognizing device constructs a new neural network on the basis of the update data. 4. The information processing system according to claim 1, wherein the first hierarchy machine learning/recognizing device includes a learning module that performs a learning process, a storage unit that stores weight coefficient information of a learning result of the learning process, recognition result rating information, and intermediate layer data information, and a unit that transmits the update request signal to the second hierarchy machine learning/recognizing device in a case in which it is necessary to update the neural network of the first hierarchy machine learning/recognizing device. 5. The information processing system according to claim 1, wherein a connection of the first hierarchy machine learning/recognizing device and the second hierarchy machine learning/recognizing device has only an input from the first hierarchy machine learning/recognizing device to the second hierarchy machine learning/recognizing device. 6. The information processing system according to claim 1, wherein the first hierarchy machine learning/recognizing device includes a storage device that temporarily holds a value of the hidden layer of the DNN and a mechanism that holds data of the storage device in the second hierarchy machine learning/recognizing device as an input data database. 7. The information processing system according to claim 1, wherein there are a plurality of first hierarchy machine learning/recognizing devices, and the plurality of first hierarchy machine learning/recognizing devices are connected directly or via a network using at least one of a wired manner and a wireless manner for transmission of the input data from the plurality of first hierarchy machine learning/recognizing devices to the single second hierarchy machine learning/recognizing device. 8. The information processing system according to claim 1, wherein there are a plurality of second hierarchy machine learning/recognizing devices, and data of the hidden layer data from one of the first hierarchy machine learning/recognizing devices is shared by the plurality of second hierarchy machine learning/recognizing devices. 9. The information processing system according to claim 1, wherein a copy of the DNN of the first hierarchy machine learning/recognizing device is installed in the second hierarchy machine learning/recognizing device, and together with learning or a recognition process in with the first hierarchy machine learning/recognizing device, in the second hierarchy machine learning/recognizing device, learning is performed on the basis of input data from the first hierarchy machine learning/recognizing device, and as a result, configuration information of a neural network and weight coefficient information which are a learning result in the second hierarchy machine learning/recognizing device is transmitted to the first hierarchy machine learning/recognizing device, and the neural network and a weight coefficient of the first hierarchy machine learning/recognizing device are updated. 10. The information processing system according to claim 1, wherein a hardware size of the second hierarchy machine learning/recognizing device is larger than a hardware size of the first hierarchy machine learning/recognizing device. 11. A method for operating an information processing system including a plurality of DNNs, comprising: configuring the plurality of DNNs to have a multi-layer structure including a first hierarchy machine learning/recognizing device and a second hierarchy machine learning/recognizing device; wherein information processing capability of the second hierarchy machine learning/recognizing device higher than information processing capability of the first hierarchy machine learning/recognizing device is used, and data of a hidden layer of a DNN of the first hierarchy machine learning/recognizing device is used as input data of a DNN of the second hierarchy machine learning/recognizing device. 12. The method for operating the information processing system according to claim 11, wherein a configuration of a neural network of the first hierarchy machine learning/recognizing device DNN is controlled on the basis of a processing result of the second hierarchy machine learning/recognizing device. 13. The method for operating the information processing system according to claim 11, wherein one inspection target is observed using a plurality of first hierarchy machine learning/recognizing devices, the data of the hidden layer of the first hierarchy machine learning/recognizing device obtained in a process of the observation is transferred to the second hierarchy machine learning/recognizing device, in the second hierarchy machine learning/recognizing device, learning is performed on the basis of the data of the hidden layer, and a database for calculating a neural network structure and a weight coefficient of the first hierarchy machine learning/recognizing device is constructed, the learning and the construction period of the database in the second hierarchy machine learning/recognizing device are defined as a learning enhancement period of the first hierarchy machine learning/recognizing device, and the second hierarchy machine learning/recognizing device has an operation form of defining an actual operation period in which the neural network and the weight coefficient of the first hierarchy machine learning/recognizing device are set, and an operation of recognition learning is performed in the first hierarchy machine learning/recognizing device and the second hierarchy machine learning/recognizing device after the learning is completed. 14. The method for operating the information processing system according to claim 11, wherein a first learning period for initial neural network construction in the second hierarchy machine learning/recognizing device in order to construct a plurality of first hierarchy machine learning/recognizing devices is set, then, a second learning period in which learning data acquired in the first learning period is loaded to the first hierarchy machine learning/recognizing device, and supervised learning is performed while actually operating the first hierarchy machine learning/recognizing device is set, and further, after the second learning period ends, a third learning period in which machine learning recognition control using the above first hierarchy machine learning/recognizing device is performed, and cooperative learning with the second hierarchy machine learning/recognizing device is performed if necessary is set. 15. A machine learning operator, comprising: a unit that performs an operation on data of a second layer using data of a first layer and performs an operation on data of the first layer using data of the second layer in a multi-layered neural network, wherein weight data of deciding a relation between each piece of data of the first layer and each piece of data of the second layer in both the operations is provided, and the weight data is stored in one storage holding unit as all weight coefficient matrices to be constructed; an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner, wherein, when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, the operation of the weight coefficient matrix is performed in basic units in which the storage is performed in the storage holding unit, a first row component of the row vector is held in the storage holding unit so that an arrangement order of constituent elements is the same as a column vector of an original matrix, a second row component of the row vector is held in the storage holding unit after shifting the constituent element of the column vector of the original matrix to the right or the left by one element, a third row component of the row vector is held in the storage holding unit after further shifting the constituent element of the column vector of the original matrix by one element in the same direction as a movement direction in the second row component, and an N-th row component of the last row of the row vector is held in the storage holding unit further shifting the constituent element of the column vector of the original matrix by one element in the same direction as a movement direction in an (N−1)-th row component; and an operator configuration in which, in a case in which the data of the first layer is calculated from the data of the second layer using the weight coefficient matrix, the data of the second layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation related to both pieces of data is performed, and an operation result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the second layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the second layer is performed, then, data stored in the accumulator of the same operation unit is added, and a similar operation is performed up to an N-th row of the weight coefficient matrix, and in a case in which the data of the second layer is calculated from the data of the first layer using the weight coefficient matrix, the data of the first layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation is performed, and a result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the first layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the first layer is performed, then, information of the accumulator stored in the operation unit is input to an adding unit of a neighbor operation unit, added to the result of the multiplication operation, and a result is stored in the accumulator, and a similar operation is performed up to the N-th row of the weight matrix.