Building block factory conversion TensorFlow

Abstract

The invention provides a building block factory conversion technology aiming at the problem of complexity of TensorFlow in model construction. According to the technology, a user can easily design a neural network model through an intuitive front-end dragging interface. A plurality of predefined operators are provided on an interface, and a user can complete model construction by dragging the operators and setting related parameters without paying attention to underlying programming details. And a built-in algorithm at the front end verifies the designed model in real time to ensure the rationality of the model. And the verified model information is stored in a database in the form of SQL statements, so that subsequent management and reuse are facilitated. The back-end system is responsible for reading SQL information, analyzing interlayer parameters, building a model framework according to the SQL information and the interlayer parameters, and finally packaging the model framework into a class capable of running in a TensorFlow environment. The process greatly simplifies the process from design to implementation of the model, provides convenience for non-professional programming users, accelerates model development, and improves research and application efficiency.

Description

Technical Field

[0001] This application relates to a technology for converting TensorFlow using a building block factory. It implements independent design of the model structure through front-end drag-and-drop building blocks. After the algorithm determines that the model is legal, it is stored in SQL. The back-end calls SQL for analysis, sorts the parameters, and finally generates a complete neural network structure class. Background Art

[0002] In the field of deep learning, TensorFlow is a widely used open-source software library for high-performance numerical computation. Its flexible architecture allows users to define and run complex dataflow graphs on a variety of platforms, making it particularly well-suited for machine learning and deep learning tasks. However, for beginners and non-experts, designing and training neural network models using TensorFlow can present challenges. These challenges include understanding the underlying mathematical principles, writing extensive code, and navigating complex configuration processes.

[0003] To simplify this process and enable non-expert users to easily build and train neural networks, this application proposes a technology called "Block Factory" to convert TensorFlow. The core idea of this technology is to use an intuitive front-end interface to allow users to design model structures by dragging and dropping building blocks. This approach is similar to the concept of children's toy building blocks, where users can build complex structures by simply stacking and combining different building blocks. In this technology, each building block represents an operation or layer in the neural network, such as a fully connected layer, a convolutional layer, or a pooling layer.

[0004] After the user completes the model design on the front-end interface, the system uses a built-in algorithm to determine whether the designed model is legal. This step ensures that the user's model meets specific constraints, such as matching input and output dimensions and a reasonable layer order. Once the model is confirmed to be legal, its structure and parameter information are stored in a SQL database. This storage method not only facilitates subsequent data analysis and processing but also provides a method for model persistence, allowing users to continue their work at different times and locations.

[0005] On the backend, the system accesses the model information stored in the SQL database and performs further analysis. This includes sorting and optimizing the model's parameters to ensure performance and efficiency. Ultimately, the system generates a complete neural network structure class that can be used directly in the TensorFlow environment. This class contains all the necessary information, including the model's architecture, parameters, and methods required for training and evaluation.

[0006] In this way, this technology significantly lowers the barrier to entry for deep learning using TensorFlow, enabling users without a deep programming background to easily build and train their own neural network models. This not only helps promote the application of deep learning technology, but also provides a new tool for education and research, allowing more people to participate in this exciting field.

[0007] Compared with traditional building models, the Brick Factory conversion of TensorFlow has the following advantages:

[0008] 1. Improved user-friendliness and accessibility: Traditional neural network design and implementation typically require in-depth programming knowledge and familiarity with deep learning frameworks. This creates a high barrier to entry, especially for individuals without a programming background. By building model structures through an intuitive drag-and-drop interface, this technology greatly simplifies the design process, making it accessible to beginners without programming experience, thereby expanding the audience for deep learning technology.

[0009] 2. Accelerate prototyping and experimentation: Rapid iteration and prototyping are crucial in research and education. Using a building block approach to model building can significantly reduce the time from concept to implementation because users don't need to write and maintain large amounts of code. Users can focus on experimental design and result analysis rather than on programming details, which helps accelerate innovation and improve research efficiency.

[0010] 3. Reduce Error Rates and Provide Instant Feedback: When manually writing code using traditional methods, typos, logical errors, and configuration errors are prone to occur, which can be difficult to track down and fix. BrickFactory Conversion uses algorithms to check the legality of model designs in real time, providing instant feedback to help users correct potential problems. This approach not only reduces debugging time but also improves the accuracy and reliability of model building.

[0011] This patent relates to the Brick Factory conversion of TensorFlow technology. Its innovation lies in its unique approach, transforming complex TensorFlow programming tasks into intuitive drag-and-drop block operations. This conversion process itself is a major innovation. By introducing a front-end interface, users can design neural network models through simple graphical operations, which greatly simplifies the model building process. Compared to traditional handwritten code, this method is more intuitive and easier to understand, allowing users to effectively design models and experiment without having to master in-depth programming techniques.

[0012] The technology's second innovation lies in its built-in model validation algorithm. After the user completes model design, the system automatically checks the model's legitimacy, including but not limited to data dimension matching, layer order, and parameter settings. This real-time validation mechanism provides instant feedback, helping users correct design errors and avoiding potential problems during subsequent training. This not only improves design accuracy but also saves significant time on debugging and error correction.

[0013] The third innovation lies in the storage and management of model information. The structure and parameters of valid models are stored in a SQL database, facilitating subsequent data analysis and model management. This storage method not only ensures information persistence but also enables model reuse and sharing. Users can retrieve and use previously designed models at any time, and even share model designs across teams, promoting collaboration and knowledge transfer.

[0014] Finally, back-end analysis and optimization are another innovative highlight of this technology. The system can sort and optimize model parameters stored in the database, further improving model performance. This back-end processing ensures that user-designed models are not only legal but also optimized to meet practical needs. By generating a complete neural network structure class, users can easily conduct further training and evaluation in the TensorFlow environment.

[0015] In summary, this patent provides a simple, easy-to-use front-end interface combined with powerful back-end analytical capabilities, enabling a completely new approach to neural network model design and implementation. This innovative approach not only lowers the barrier to entry for deep learning but also provides professional researchers with highly effective tools, helping to advance the development and application of deep learning technology.

[0016] The implementation steps of this patent reveal a complete process from user interaction to model generation, which converts complex programming tasks into intuitive operations and greatly simplifies the construction of neural network models.

[0017] In the first step, users design their own network model by dragging operators on the front-end interface. The front-end interface provides a variety of predefined operators, such as convolutional layers, fully connected layers, activation functions, etc. Users can select and place these operators according to their needs to form a preliminary network structure. At the same time, users can set parameters for each operator, such as the number of layers, number of nodes, activation function type, etc. Figure 1 This graphical interaction makes model design intuitive and easy to understand without having to delve into the underlying programming details.

[0018] The second step involves front-end algorithms evaluating the rationality of the user-designed model. This step ensures that the user-designed model meets specific constraints, such as matching input and output dimensions and a reasonable layer order. If the model is judged to be unreasonable, the system provides feedback, identifying the problem and guiding the user to make corrections. This real-time verification mechanism provides immediate feedback, helping users correct design errors and avoiding potential problems during subsequent training.

[0019] In the third step, once the model is confirmed to be reasonable, the front-end system exports the relevant SQL statements. These SQL statements contain the model's structural information and parameter settings, facilitating subsequent data processing and model management. By storing model information in the database, this technology not only ensures information persistence but also enables model reuse and sharing. Users can retrieve and use previously designed models at any time, and even share model designs across teams, promoting collaboration and knowledge transfer.

[0020] The fourth step occurs on the backend, where the system reads the SQL statements exported by the frontend and parses the parameter information between the layers of the model. This step lays the foundation for subsequent model construction and ensures that all model information is ready.

[0021] In the fifth step, the backend system builds the model layers based on the parsed model information and encapsulates the entire network into a class. This class contains all necessary information and operations and can be used directly in the TensorFlow environment. This not only makes model implementation more efficient but also provides users with an easy-to-use interface.

[0022] In summary, this patent realizes a new approach to neural network model design and implementation by providing a simple and easy-to-use front-end interface and powerful back-end processing capabilities. Users can design model structures through intuitive drag-and-drop operations without having to delve into the underlying programming details. The system's real-time verification mechanism and back-end optimization processing ensure that the designed model is not only reasonable but also optimized to meet the needs of actual use. This innovative approach not only lowers the entry threshold for deep learning, but also provides professional researchers with efficient tools, helping to promote the development and application of deep learning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Designing the interface for the front end DETAILED DESCRIPTION

[0024] The specific implementation of this patent discloses an integrated system that covers front-end user interface design, real-time model validation, database storage, and back-end model construction. The system allows users to build an initial network model on the front-end interface through intuitive drag-and-drop operations. The front-end provides a rich set of predefined operators, such as convolutional layers, fully connected layers, and activation functions, for users to select and configure according to their needs. This graphical operation simplifies the model design process, making it easy for users without a programming background to get started.

[0025] After the user completes model building, the front-end's built-in algorithms immediately perform a rationality check on the designed model. This process checks whether the model meets necessary constraints, such as the dimensionality of the data flow and the order of the layers. If any issues are found, the system provides specific feedback to guide the user in making adjustments to ensure the designed model is structurally feasible.

[0026] Once the model passes validation, the front-end converts its structure and parameter information into SQL statements and exports them. This information is then stored in a database, facilitating subsequent analysis and processing. The use of an SQL database not only ensures the persistence of model information but also facilitates model retrieval, reuse, and sharing.

[0027] The backend system reads the SQL statements exported by the frontend from the database and parses the inter-layer parameters required by the model. Next, the backend builds the model framework based on these parameters and encapsulates it into a class that can be run in the TensorFlow environment. This class integrates all the model details and required methods, providing users with a convenient interface for model training and evaluation.

[0028] The implementation method of this patent greatly simplifies the process from model design to implementation through a continuous front-end to back-end process. Users can build complex neural network models by simply dragging and dropping and configuring, while the system's real-time verification and back-end optimization ensure the model's rationality and performance. This innovative approach not only lowers the threshold for deep learning technology but also improves the efficiency of model development in research and education, and is of great significance to promoting the development of related fields.

Claims

1. Claims Building Block Factory to convert TensorFlow, characterized by: The network structure built by the front-end building blocks is transferred to the back-end through the database, analyzed by data processing methods, and finally a model structure class that can be directly instantiated and used is generated. This model class has the same functions as the model structure class built by ordinary code and can be used to train models, analyze models, etc. The specific steps are as follows: Step 1) Drag and drop operators on the front-end interface and set parameters to generate the front-end network; Step 2) The front-end algorithm determines the rationality of the model; Step 3) Export SQL from the front end; Step 4) The backend reads the SQL and parses the parameters between the layers in the model; Step 5) The backend builds the model layer and encapsulates the network into a class.

2. The building block factory conversion to TensorFlow according to claim 1, characterized in that Step 1) Drag the operator on the front end and set the parameters to generate the front-end network. In this step, there is sufficient autonomy in defining the network. Based on the pre-made network, the system allows users to design the network with maximum freedom.

3. The building block factory conversion to TensorFlow according to claim 1 is characterized in that Step 2) Front-end algorithms assess the model's rationality. This allows users to easily observe changes in data after each layer is connected, facilitating parameter adjustments. Furthermore, the algorithm simulates actual model operations to facilitate error correction during the construction process.

4. The building block factory conversion to TensorFlow according to claim 1, characterized in that Step 3) The front-end exports the SQL and stores it in the form of a database, which ensures security while compressing the file size and facilitating parsing.

5. The building block factory conversion to TensorFlow according to claim 1, characterized in that Step 4) The backend reads the SQL and parses the parameters between layers in the model. A self-developed third-party model dependency library is associated here, and the model building order can be obtained based on the relevant indexes and keys in the SQL.

6. The building block factory conversion to TensorFlow according to claim 1, characterized in that Step 5) The backend builds the model layer and encapsulates the network into a class. This step constructs a relatively complex model function, generating a class based on parameters. It is easy to use and can be implemented by simply passing in the output list of step 4).

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