Graphical Neural Network Design Tool for Code Generation
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Solution Overview
Problem
The complexity of deep neural network architectures makes it difficult for designers to generate, analyze, understand, and modify them, as existing methods require extensive coding and obscure the network's operation, hindering analysis and modification capabilities.
Innovation Solution
A computer-implemented method using a graphical user interface (GUI) to generate, analyze, and modify neural networks, including a network generator that updates code based on user interactions, a network analyzer for layer-level analysis, and a network evaluator for comprehensive training data evaluation, along with a network descriptor for natural language descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks with complex architectures are used to achieve high accuracy, then the accuracy of task performance is improved, but the difficulty of designing and generating the network increases
Solution Approach 1:
The patent introduces an intermediary system that automatically generates, analyzes, and modifies neural network code based on high-level specifications. This intermediary handles the complexity of deep network architectures by translating abstract design requirements into detailed code implementations, allowing designers to achieve high accuracy networks without directly managing the inherent complexity.
2Ease of manufacture
If programming libraries are used to simplify deep neural network design, then the ease of generating networks is improved, but the designer's understanding of network operations deteriorates
Solution Approach 1:
The system provides feedback mechanisms that automatically analyze and explain network operations to designers. By implementing tools that generate detailed analysis reports, visualize network behavior, and provide modifications based on design goals, the system maintains designer understanding while preserving ease of use through automated assistance.
3Measurement precision
If large volumes of code are written to define neural network layers and connections, then the functionality and accuracy of the network is improved, but the difficulty of locating and modifying specific code portions increases
Solution Approach 1:
The patent applies segmentation by automatically dividing the neural network code into distinct, manageable modules corresponding to specific layers and connections. Each segment is independently analyzable and modifiable, allowing designers to locate and modify specific code portions without navigating through large volumes of monolithic code, while maintaining full network functionality.
4Ease of operation
If conventional training algorithms that record only accuracy are used, then the simplicity of the training process is maintained, but the ability to evaluate and explain network performance deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically generating comprehensive performance evaluation frameworks before training completes. These pre-configured evaluation mechanisms capture detailed performance metrics, intermediate results, and behavioral data throughout training, enabling thorough analysis without requiring complex manual setup, thus maintaining simplicity while preventing information loss.
Data Source
AI summary
As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.