Neural Network Learning Method, Neural Network Generation Method, Trained Device, Mobile Terminal Device, Learning Processing Device and Recording Medium

A neural network learning method, comprising:

• setting each neuron in a neural network to a random variable allowed to take a binary value;
• expressing a connection weight between neurons in the neural network as a plurality of synapses obtained by multiplying each synapse by a required connection coefficient, and setting the plurality of synapses to random variables allowed to take binary values;
• giving training data to a neuron in each of an input layer and an output layer, and giving initial data to a neuron in a middle layer;
• repeating a process of updating each state value of each neuron in the middle layer and each synapse in the neural network by performing sampling based on a Markov chain Monte Carlo method from a conditional probability distribution under a condition that a random variable of a neuron in each of the input layer and the output layer is a value of the training data;
• and
• calculating a connection weight between neurons based on the updated state value of each synapse.

A neural network learning method, comprising:

• giving training data to a neuron in each of an input layer and an output layer of a neural network;
• giving initial data to a connection weight between neurons of a neural network and a neuron in the middle layer of the neural network;
• updating a state value of a neuron in the middle layer based on a value obtained by converting a sum of a sum of signal values input to the neuron and a bias value from a posterior neuron connected to the neuron by an activation function;
• and
• updating a connection weight between neurons based on an updated state value of each neuron.

A neural network generation method, comprising:

• setting each neuron in a neural network to a random variable allowed to take a binary value;
• expressing a connection weight between neurons in the neural network as a plurality of synapses obtained by multiplying each synapse by a required connection coefficient, and setting the plurality of synapses to random variables allowed to take binary values;
• giving training data to a neuron in each of an input layer and an output layer, and giving initial data to a neuron in a middle layer;
• repeating a process of updating each state value of each neuron in the middle layer and each synapse in the neural network by performing sampling based on a Markov chain Monte Carlo method from a conditional probability distribution under a condition that a random variable of a neuron in each of the input layer and the output layer is a value of the training data;
• and
• generating a neural network by calculating a connection weight between neurons based on an updated state value of each synapse.

A neural network generation method, comprising:

• giving training data to a neuron in each of an input layer and an output layer of a neural network;
• giving initial data to a connection weight between neurons of a neural network and a neuron in the middle layer of the neural network;
• updating a state value of a neuron in the middle layer based on a value obtained by converting a sum of a sum of signal values input to the neuron and a bias value from a posterior neuron connected to the neuron by an activation function;
• and
• updating a connection weight between neurons based on an updated state value of each neuron.

A trained device having a neural network, the trained device being generated by causing a computer to execute processes of:

• setting each neuron in the neural network to a random variable allowed to take a binary value;
• expressing a connection weight between neurons in the neural network as a plurality of synapses obtained by multiplying each synapse by a required connection coefficient, and setting the plurality of synapses to random variables allowed to take binary values;
• giving training data to a neuron in each of an input layer and an output layer, and giving initial data to a neuron in a middle layer;
• repeatedly updating each state value of each neuron in the middle layer and each synapse in the neural network by performing sampling based on a Markov chain Monte Carlo method from a conditional probability distribution under a condition that a random variable of a neuron in each of the input layer and the output layer is a value of the training data;
• and
• calculating a connection weight between neurons based on the updated state value of each synapse.

A trained device having a neural network, the trained device being generated by causing a computer to execute processes of:

• giving training data to a neuron in each of an input layer and an output layer of the neural network;
• giving initial data to a connection weight between neurons of a neural network and a neuron in the middle layer of the neural network;
• updating a state value of a neuron in the middle layer based on a value obtained by converting a sum of a sum of signal values input to the neuron and a bias value from a posterior neuron connected to the neuron by an activation function;
• and
• updating a connection weight between neurons based on an updated state value of each neuron.

A learning processing device, comprising

• a processor,
• the learning processing device training a neural network,
• the processor executing processes of
• setting each neuron in the neural network to a random variable allowed to take a binary value,
• expressing a connection weight between neurons in the neural network as a plurality of synapses obtained by multiplying each synapse by a required connection coefficient, and setting the plurality of synapses to random variables allowed to take binary values,
• giving training data to a neuron in each of an input layer and an output layer, and giving initial data to a neuron in a middle layer,
• repeatedly updating each state value of each neuron in the middle layer and each synapse in the neural network by performing sampling based on a Markov chain Monte Carlo method from a conditional probability distribution under a condition that a random variable of a neuron in each of the input layer and the output layer is a value of the training data, and
• calculating a connection weight between neurons based on the updated state value of each synapse.

A learning processing device, comprising

• a processor,
• the learning processing device training a neural network,
• the processor executing processes of
• giving training data to a neuron in each of an input layer and an output layer of a neural network,
• giving initial data to a connection weight between neurons of a neural network and a neuron in the middle layer of the neural network,
• updating a state value of a neuron in the middle layer based on a value obtained by converting a sum of a sum of signal values input to the neuron and a bias value from a posterior neuron connected to the neuron by an activation function, and
• updating a connection weight between neurons based on an updated state value of each neuron.

A computer readable non-transitory recording medium recording a computer program causing a computer to execute processes of:

• setting each neuron in a neural network to a random variable allowed to take a binary value;
• expressing a connection weight between neurons in the neural network as a plurality of synapses obtained by multiplying each synapse by a required connection coefficient, and setting the plurality of synapses to random variables allowed to take binary values;
• giving training data to a neuron in each of an input layer and an output layer, and giving initial data to a neuron in a middle layer;
• repeatedly updating each state value of each neuron in the middle layer and each synapse in the neural network by performing sampling based on a Markov chain Monte Carlo method from a conditional probability distribution under a condition that a random variable of a neuron in each of the input layer and the output layer is a value of the training data;
• and
• calculating a connection weight between neurons based on the updated state value of each synapse.

A computer readable non-transitory recording medium recording a computer program causing a computer to execute processes of:

• giving training data to a neuron in each of an input layer and an output layer of a neural network;
• giving initial data to a connection weight between neurons of a neural network and a neuron in the middle layer of the neural network;
• updating a state value of a neuron in the middle layer based on a value obtained by converting a sum of a sum of signal values input to the neuron and a bias value from a posterior neuron connected to the neuron by an activation function;
• and
• updating a connection weight between neurons based on an updated state value of each neuron.