Training Method and Computer System Using the Same

A computer system, applied to train a computing model-X, which is configured with at least one inference input node and at least one output node, comprising:

• a processing unit;
• and
• a storage unit, storing a program code, wherein the program code instructs the processing unit to execute the following steps:
• obtaining an inference input signal of at least one inference input signal from a training dataset and a teacher signal;
• transmitting the inference input signal to the at least one inference input node and transmitting the teacher signal to at least one teacher input node;
• receiving an output signal from the at least one output node;
• and
• calculating the teacher signal according to the output signal;
• wherein the training dataset comprises at least one predetermined set of inference input signal and corresponding golden output signal;
• wherein when the computing model-X finishes training with an associative learning rule, the computing model-X is configured to infer things;
• wherein the computing model-X calculates and updates parameters thereof according to the inference input signal, the teacher signal, and the output signal with the associative learning rule;
• wherein all of the at least one teacher input node is added when the computing model-X training starts.