4 results about "Feed forward neural" patented technology

Embodiments of the present disclosure provide a data processing method and device, electronic equipment and storage medium. The method comprises: acquiring an image, determining a to-be-processed feature corresponding to the image; inputting the to-be-processed feature into an autoregressive sequence generation model to obtain a target feature of the image; wherein the autoregressive sequence generation model comprises a lightweight self-attention subnetwork and a feedforward neural subnetwork; and performing analysis and processing on the image based on the target feature. The technical solution of the embodiments of the present disclosure reduces the requirement of the model on computing power, and even if the model is deployed on a mobile terminal, good data processing effect can be achieved. At the same time, the calculation amount in the model running process is reduced, and real-time processing of related data is facilitated.

The application discloses an image classification system and method based on a lightweight LA Transformer network, comprising a feature extraction module, an LA Transformer main network and an image classification module. The feature extraction module is responsible for local feature extraction of an input RGB image, realizes down-sampling of the input image, and outputs the image as an input of the LA Transformer main network. The LA Transformer main network is responsible for further feature extraction of the image. A local self-attention subnetwork is used to model spatial correlation information between feature map patches and patches by using a local self-attention mechanism, and an attention feedforward neural subnetwork is used to model correlation information between feature map channels and channels. The image classification processing module is responsible for generating a probability for each image classification, and completes classification of the input RGB image. The application can achieve higher image classification accuracy while having lower parameter quantity and calculation amount.

This application discloses a model loading / unloading method and electronic device, relating to the field of server resource management technology. The method includes: preloading each feedforward neural subnetwork onto preset hardware in a preset order based on historical operation data; performing a first calculation on first resource usage information, second resource usage information, and memory usage information at preset time intervals to obtain a first resource usage function value; performing a second calculation on second resource usage information and memory usage information at preset time intervals to obtain a second resource usage function value; and unloading the feedforward neural subnetwork from a first processor to a second processor when the second resource usage function value is detected to be less than a second predefined lower bound of a resource usage function value threshold. This invention solves the problem of low model feedback efficiency caused by the uneven distribution of subnetworks in the model across different hardware components of the server in related technologies.

The application discloses a model deployment method and device, and the method comprises the following steps: acquiring a pre-trained visual convolution model, setting an accuracy target and search parameters for pruning the visual convolution model, wherein the search parameters at least comprise an initial pruning ratio and a search interval length; searching a first target pruning ratio of an attention mechanism module and a second target pruning ratio of a feedforward neural module respectively; optimizing the visual convolution model according to the first target pruning ratio and the second target pruning ratio to obtain a target visual convolution model; and deploying the target visual convolution model on an edge computing device of a substation. In this way, after the visual convolution model is pruned, the model size is in a minimum state, so that the requirement for the running environment is reduced. The visual convolution model is deployed on the edge computing device, real-time reception of monitoring data is realized, so that real-time detection and warning can be achieved, and the safety of the power system is improved.