Neural Network Development Interface with Reusable Layer Units
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Solution Overview
Problem
Existing neural network development methods, such as those described in Patent Literature 1, require repetitive arrangement of components corresponding to each layer, leading to inefficiencies when multiple layers are repeated, as users must manually arrange and set properties for each layer, which is time-consuming and error-prone, especially for layerings with tens of layers.
Innovation Solution
The method provides a form-based interface for building neural networks where a unit can be defined as a group of multiple components, allowing these units to be arranged like individual components, thereby simplifying the process of creating and editing neural networks by enabling users to treat repeated layerings as a single entity, reducing the need for manual arrangement of individual components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If components corresponding to each layer are arranged manually to build a neural network, then the neural network can be created with precise control over each layer, but the development process becomes time-consuming and inefficient when multiple layers are repeated
Solution Approach 1:
The patent merges multiple layer components into a single reusable unit definition. By defining a unit that encapsulates multiple layers with their properties and connections, users can create complex neural networks by arranging these units rather than individually arranging each layer component, significantly reducing the time and effort required for network development
Solution Approach 2:
The patent implements preliminary action by allowing users to pre-define unit structures that contain multiple layers and their configurations. These predefined units can then be reused multiple times in the neural network architecture, eliminating the need to repeatedly configure identical layer structures and improving development efficiency
2Ease of operation
If each layer is represented by a separate component, then detailed control over individual layers is achieved, but the complexity of managing and manipulating multiple repeated layers increases
Solution Approach 1:
The patent reduces operational complexity by merging multiple layer components into a single unit object. This allows users to manipulate entire groups of layers as one entity, making it easier to copy, move, and configure repeated structures without having to individually manage each layer component
Solution Approach 2:
The patent applies segmentation by dividing the neural network into hierarchical levels: individual layers within units, and units within the overall network architecture. This hierarchical segmentation allows for manageable complexity by enabling users to work at the appropriate level of abstraction, defining units once and instantiating them multiple times throughout the network
Data Source
AI summary
[Problem] To enable an increase in efficiency of developing a neural network.[Solution] An information processing method including, by a processor, providing a form for creating a program to build a neural network based on arrangement of a component corresponding to a layer and properties that are set in the component; and representing statistical information on the neural network, wherein the providing a form further includes providing a function of, on the form, defining a unit formed of the multiple components, and providing the defined unit such that the defined unit can be arranged like the component, is provided.