Patent ID: 11907834
Assignee: DEEPMENTOR INC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 4:
5. The method of claim 1, wherein:
the data-recognition model includes a hidden layer set including a plurality of hidden layers that are connected in series, each of the plurality of hidden layers including a plurality of neurons, each of the neurons having a weight matrix containing a plurality of weights; and
the method further comprises
E) for each of the neurons included in the plurality of hidden layers,
calculating an average of the plurality of weights of the neuron, and a variance of the plurality of weights,
when it is determined that a ratio between the average of the plurality of weights and the variance of the plurality of weights is larger than a predetermined threshold, keeping the weight matrix unchanged,
when it is determined that the ratio between the average of the plurality of weights and the variance of the plurality of weights is not larger than the predetermined threshold, calculating a reference value for the neuron based on the average of the plurality of weights,
when it is determined that the reference value is substantially equal to zero, deleting the neuron from the data-recognition model, and, when it is determined that the reference value is substantially not equal to zero, substituting the reference value for the weight matrix of the neuron; and
F) after step E), applying at least one golden sample to the data-recognition model, so as to obtain a plurality of outputs respectively from the neurons in the data-recognition model, and for each of the neurons included in the plurality of hidden layers and having the weight matrix, deleting the neuron from the data-recognition model when it is determined that the output of the neuron is substantially equal to zero and retaining the neuron when otherwise.