Abstract: The invention discloses a neural network retraining and gradient sparse method based on the aging sensing of memristors. For the accuracy rate of hardware online reasoning decreases after cross-array aging, the extreme values of programmable weights under a current aging condition is calculated by using the known aging information of memristor, and a neural network model is retrained according to this, so as to improve the accuracy rate of the current hardware online reasoning. In the process of retraining, network weights exceeding the extreme values of programmable weights are automatically truncated. For the working life of the memristor, the sparsity of derivatives of the neural network is utilized to discard the derivatives with a small absolute value in hardware adjustment process, so as to ensure that the memristor corresponding to small derivatives would not be applied voltage, prevent the aging process of the memristors and prolong the service life thereof.

Abstract: Embodiments of the present disclosure disclose an image processing method and apparatus, and a computer readable storage medium. The method includes: obtaining at least one page image of a current full-screen page in a webpage application; converting the at least one page image into at least one file object, and creating at least one link path of the at least one file object, each of the at least one file object corresponding to the respective at least one link path; and creating at least one picture object, and associatively saving the at least one picture object and the at least one link path in webpage application cache to complete taking a screenshot of the current full-screen page. Through the embodiments of the present disclosure, the screenshot capturing performance of the webpage application can be improved.