A learning apparatus performs a learning process for a feed-forward multilayer neural network with supervised learning. The network includes an input layer, an output layer, and at least one hidden layer having at least one probing neuron that does not transfer an output to an uppermost layer side of the network. The learning apparatus includes a learning unit and a layer quantity adjusting unit. The learning unit performs a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning. The layer quantity adjusting unit removes at least one uppermost layer from the network based on the cost derived by the output from the probing neuron, and sets, as the output layer, the probing neuron in the uppermost layer of the remaining layers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus that performs a learning process for a feed-forward multilayer neural network with supervised learning, the multilayer neural network comprising: a plurality of layers configured by an input layer that is a lowermost layer of a layered structure of the multilayer neural network, an output layer that is an uppermost layer of the layered structure, and at least one hidden layer that is located between the input layer and the output layer, each of the layers including a given number of units that receive an input from the lowermost layer side, perform a predetermined calculation based on the input and a weight to produce an output, and transfer the output toward the uppermost layer side, the at least one hidden layer including at least one probing neuron that receives an input from the lowermost layer side, performs a predetermined calculation based on the input and a weight to produce an output, but does not transfer the output to the uppermost layer side, the learning apparatus comprising: a learning unit that performs a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning; and a layer quantity adjusting unit that removes at least one uppermost layer from the multilayer neural network based on the cost derived by the output from the at least one probing neuron, and sets, as the output layer, the at least one probing neuron in the uppermost layer of the remaining layers. 2. The learning apparatus according to claim 1, further comprising: a relearning unit that performs a relearning process for the multilayer neural network from which the at least one layer has been removed by the layer quantity adjusting unit. 3. The learning apparatus according to claim 2, wherein: the layer quantity adjusting unit further removes at least one uppermost layer from the multilayer neural network based on the cost of the at least one probing neuron, for the multilayer neural network that has been relearned by the relearning unit, and sets, as the output layer, the probing neuron in the uppermost layer of the remaining layers. 4. The learning apparatus according to claim 1, wherein: the layer quantity adjusting unit sets, as the output layer, the at least one probing neuron of which the cost is the lowest. 5. The learning apparatus according to claim 3, wherein: when the cost of the output layer is not the lowest, the layer quantity adjusting unit removes at least one uppermost layer from the multilayer neural network so as to keep the at least one probing neuron of the at least one hidden layer at an upper layer side compared to the at least one hidden layer that has the at least one probing neuron of which the cost is the lowest. 6. The learning apparatus according to claim 1, wherein: a section connected to the probing neuron has a layered structure. 7. A learning system comprising: a first learning apparatus that is configured by the learning apparatus according to claim 1: and a second learning apparatus that performs a learning process for a feed-forward multilayer neural network with supervised learning, the second learning apparatus using, as an initial feed-forward multilayer neural network, the feed-forward multilayer neural network that has been learned by the first learning apparatus from which the at least one probing neuron have been removed. 8. A discriminator for performing a discrimination process using the feed-forward multilayer neural network obtained by the learning apparatus according to claim 1, wherein: the discriminator is configured to perform a discrimination process using the probing neuron of the feed-forward multilayer neural network and perform an early rejection in the feed-forward multilayer neural network. 9. The discriminator according to claim 8, wherein: the discriminator is configured to perform, using the probing neuron, the discrimination process by determining whether to correspond to the units of the output layer and perform an early rejection in the feed-forward multilayer neural network. 10. A learning apparatus that performs a learning process for a feed-forward convolutional neural network with supervised learning, the convolutional neural network repeatedly performing, for an input image, a convolution process using a filter and a pooling process to reduce a resolution of the input image, and produces compressed information of convoluted two-dimensional array of a map forming at least one hidden layer, the convolutional neural network including at least one probing neuron connected to the map in the at least one hidden layer; and the learning apparatus comprising: a learning unit that performs a learning process by calculation of a cost derived by a cost function defined in the convolutional neural network using a training data set for supervised learning; and a layer quantity adjusting unit that removes at least one layer at an uppermost layer side from the convolutional neural network based on the cost derived by the output from the at least one probing neuron, and sets, as an output layer for the convolution process, the at least one probing neuron in the uppermost layer of the remaining layers. 11. A non-transitory computer-readable storage medium storing a learning program for enabling a computer to function as a learning apparatus that performs a learning process for a feed-forward multilayer neural network with supervised learning, the multilayer neural network comprising: a plurality of layers configured by an input layer that is a lowermost layer of a layered structure of the feed-forward multilayer neural network, an output layer that is an uppermost layer of the layered structure, and at least one hidden layer that is located between the input layer and the output layer, each of the layers including a given number of units that receive an input from the lowermost layer side, perform a predetermined calculation based on the input and a weight to produce an output, and transfer the output to the uppermost layer side, the at least one hidden layer including at least one probing neuron that receives an input from the lowermost layer side, performs a predetermined calculation based on the input and a weight to produce an output, but does not transfer the output to the uppermost layer side, the learning apparatus comprising: a learning unit that performs a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning; and a layer quantity adjusting unit that removes at least one uppermost layer from the multilayer neural network based on the cost derived by the output from the at least one probing neuron, and sets, as the output layer, the at least one probing neuron in the uppermost layer of the remaining layers. 12. A learning method for performing a learning process for a feed-forward multilayer neural network with supervised learning, the multilayer neural network comprising: a plurality of layers configured by an input layer that is a lowermost layer side of the multilayer neural network, an output layer that is an uppermost layer side of feed-forward multilayer neural network, and at least one hidden layer that is located between the input layer and the output layer, each of the layers including a given number of units that receive an input from the lowermost layer side, perform a predetermined calculation based on the input and a weight to produce an output, and transfer the output to the uppermost layer side, the at least one hidden layer including at least one probing neuron that receives an input from the lowermost layer side, performs a predetermined calculation based on the input and a weight to produce an output, but does not transfer the output to the uppermost layer side, the learning method comprising: performing, by a learning unit, a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning; and removing, by a layer quantity adjusting unit, at least one uppermost layer from the feed-forward multilayer neural network based on the cost derived by the output from the at least one probing neuron, and setting, as the output layer, the at least one probing neuron in the uppermost layer of the remaining layers.