Neural network optimization algorithm for big data processing

Abstract

The invention relates to the technical field of neural networks and big data processing, discloses a neural network optimization algorithm for big data processing, and solves the problems of poor preprocessing effect, insufficient feature representation, inflexible training optimization and weak generalization ability in the prior art. The algorithm comprises the following steps: S1, cleaning, standardizing and fragmenting a big data set; s2, constructing an optimized neural network containing an attention mechanism and a feature fusion module; s3, designing an adaptive learning rate optimizer introducing a momentum item; s4, iteratively optimizing the model through batch training and back propagation; and S5, inputting a preprocessed new data output result. According to the method, the big data processing efficiency, precision and stability are improved, the generalization ability is high, and the method can adapt to various types of big data processing tasks.

Description

Technical Field

[0001] This invention relates to the field of neural network and big data processing technology, and specifically to a neural network optimization algorithm for big data processing. Background Technology

[0002] With the rapid development of information technology, the scale of data has exploded, and big data has become an important resource for promoting artificial intelligence and the intelligent transformation of various industries. In the big data environment, how to efficiently and accurately process and analyze massive and diverse data has become a key technical problem that urgently needs to be solved. Neural networks, as a core technology in the field of deep learning, are widely used in big data processing tasks such as image recognition, natural language processing, and financial forecasting due to their powerful automatic feature extraction and complex pattern recognition capabilities. However, traditional neural network algorithms suffer from problems such as low training efficiency, insufficient model generalization ability, difficulty in parameter tuning, and susceptibility to local optima when faced with large-scale data, which seriously restricts their application effectiveness in real-world big data scenarios.

[0003] Specifically, current big data preprocessing methods are often limited and ineffective in removing noise and outliers, resulting in low-quality training data and impacting model performance. Furthermore, traditional neural network architectures lack a focus on key features, failing to fully integrate multi-layered feature information and limiting the model's expressive power. Regarding optimizers, most employ fixed learning rates or simple learning rate adjustment strategies, unable to dynamically adapt to gradient changes during training, leading to slow convergence and unstable training. In addition, traditional training lacks effective regularization measures, making models prone to overfitting and affecting generalization ability.

[0004] Therefore, there is an urgent need for a neural network optimization algorithm designed specifically for the characteristics of big data. This algorithm should improve the training efficiency and prediction accuracy of the model by addressing multiple aspects such as data preprocessing, network structure optimization, training strategies, and optimizer design. It should also enhance the model's adaptability and robustness to complex big data tasks, thereby meeting the demand for efficient and intelligent data processing in practical applications. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a neural network optimization algorithm for big data processing.

[0006] To achieve the aforementioned objective, the technical solution of the present invention is implemented as follows: a neural network optimization algorithm for big data processing, specifically comprising the following steps: S1, Big Data Preprocessing A large dataset to be processed is obtained, and the dataset is sequentially cleaned, standardized, and sharded to obtain standardized sharded data. The core purpose of data cleaning is to remove invalid data from the dataset, including missing values, outliers, and duplicate values. Data cleaning avoids interference from invalid data in subsequent model training and improves data quality. Data standardization maps data of different dimensions and magnitudes to preset intervals (such as [-1,1] or [0,1]), eliminating the impact of differences in data magnitude on model training and ensuring a balanced contribution of each feature dimension to model training. Data sharding is designed to adapt to the massive nature of big data by dividing the overall large dataset into multiple subsets, facilitating efficient subsequent batch training and reducing the data processing pressure of single-batch training.

[0007] S2. Construct an optimized neural network architecture A basic neural network is constructed based on deep learning frameworks (such as TensorFlow and PyTorch). This basic neural network includes an input layer, hidden layers, and an output layer. Each layer transmits data and transforms features through weight parameters. To enhance the feature processing capabilities of the neural network, an attention mechanism module and a feature fusion module are introduced into the hidden layer. The attention mechanism module can adaptively assign weights to the feature vectors input to the hidden layer, focusing on key features that contribute significantly to the processing task and weakening the influence of irrelevant features, thereby strengthening the representational ability of key features. The feature fusion module, through cross-layer feature concatenation, weighted fusion, and other methods, fuses feature information from different hidden layers, achieving complementarity between shallow basic features and deep semantic features, and improving the completeness and richness of feature representation.

[0008] S3, Optimizer Design An adaptive learning rate optimizer is designed, based on the gradient descent algorithm. The core improvement lies in dynamically adjusting the learning rate by monitoring gradient changes in real time during neural network training. The learning rate is a key parameter affecting model training efficiency and convergence. Traditional fixed learning rate optimizers struggle to adapt to the dynamic changes in gradients during large-scale data training. This invention monitors gradient trends and adaptively reduces the learning rate when gradient fluctuations are large, preventing oscillations during training; conversely, it adaptively increases the learning rate when gradient changes are gradual, accelerating model convergence. Simultaneously, a momentum term is introduced into the optimizer. By accumulating historical gradient information, it reduces local optimum traps caused by local gradient fluctuations during gradient descent, improving the stability of model training.

[0009] S4. Model Training and Iterative Optimization The standardized data fragments obtained in step S1 are input into the optimized neural network constructed in step S2 according to a preset batch order, and the adaptive learning rate optimizer designed in step S3 is used for model training. The training process adopts a batch training method, that is, one fragment of standardized data is input for training at a time. After each batch of training is completed, the loss function value of the model is calculated. The loss function is used to measure the error between the model's prediction result and the true label. If the loss function value does not reach the preset convergence threshold (e.g., 0.001), the gradient of each weight parameter is calculated backward along the layers of the neural network using the backpropagation algorithm, and the weight parameters are updated using the adaptive learning rate optimizer, and then the next batch of training begins. If the loss function value reaches the preset convergence threshold or the number of training iterations reaches the preset maximum number (e.g., 1000), training stops, and the optimized neural network model is obtained.

[0010] S5, Big Data Processing Output The new big data to be processed is preprocessed synchronously according to the preprocessing standard in step S1 to obtain standardized data that meets the model input requirements. Then, it is input into the optimized neural network model, and the big data processing results (such as classification results, regression prediction values, etc.) are obtained through the forward propagation inference of the model, thus completing the efficient processing of big data.

[0011] The beneficial effects of this invention are reflected in: This invention proposes a neural network optimization algorithm for big data processing. Through a systematic and scientific design of the data preprocessing process, optimization of the neural network architecture, and training strategies, it significantly improves the efficiency and effectiveness of big data processing. First, in the big data preprocessing stage, multi-level methods such as data cleaning, standardization, and sharding are employed to effectively remove invalid and abnormal data, eliminate differences in data volume, and rationally divide data into batches, greatly improving data quality and training manageability, laying a solid foundation for subsequent model training. Second, the constructed optimized neural network architecture, by introducing a multi-head self-attention mechanism and a feature fusion module, enhances the network's ability to capture key features and comprehensively utilize multi-level features, improving the model's expressive power and generalization performance. Furthermore, the designed adaptive learning rate optimizer dynamically adjusts the learning rate based on gradient changes during training, and combined with a momentum mechanism, effectively avoids training oscillations and local optima traps, significantly accelerating model convergence and improving training stability. During model training, batch training, backpropagation, and regularization mechanisms are combined to further suppress overfitting, ensuring the model's robustness and accuracy on large-scale complex data. Through specific implementation examples, this algorithm demonstrates excellent performance in two typical big data tasks: image classification and text regression. The classification accuracy is significantly improved compared to traditional methods, the regression error is significantly reduced, and the training efficiency is greatly improved.

[0012] In summary, the neural network optimization algorithm described in this invention not only possesses strong adaptability and scalability, enabling it to be widely applied to various types of big data processing tasks, but also provides efficient and stable technical support for intelligent analysis and decision-making in big data environments, demonstrating significant practical application value and promising prospects for wider adoption. Attached Figure Description

[0013] In the attached diagram: Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a diagram of the optimized neural network architecture of the present invention; Figure 3 This is a block diagram illustrating the working principle of the adaptive learning rate optimizer of the present invention. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0015] It should be noted that if the embodiments of the invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0016] Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the invention.

[0017] Please refer to the instruction manual appendix. Figures 1-3 Example 1: Image Big Data Classification Processing Based on This Algorithm S1, Big Data Preprocessing This embodiment uses a publicly available large image dataset (such as a subset of the ImageNet dataset), which contains 50,000 images across 100 categories. First, data cleaning is performed: images with missing pixel values ​​are removed using a null detection algorithm; outliers are removed using the 3σ criterion, specifically by calculating the mean μ and standard deviation σ of each image's pixel grayscale value, and identifying and removing images whose grayscale value deviates from the mean by more than 3σ; duplicate images are removed by comparing image hash values. Then, data standardization is performed: the Z-Score standardization method is used to map the grayscale values ​​of each image pixel to the [-1, 1] interval, using the following standardization formula: Where x is the original pixel value, μ is the mean of the pixel values ​​in this dimension, and σ is the standard deviation. Finally, the data is sharded: based on the balance of image categories, the processed dataset is divided into 100 shards, each containing 500 images, and a unique identifier label (such as "class01_batch01") is added to each shard to facilitate data traceability and batch management during training.

[0018] S2. Construct an optimized neural network architecture The basic neural network is built based on the PyTorch deep learning framework. Since it deals with large image datasets, the hidden layers are constructed using convolutional neural network (CNN) layers. The basic architecture is "input layer - CNN hidden layer - output layer": the input layer receives standardized image data (size 224×224×3); the hidden layer contains 4 CNN layers, each followed by a batch normalization layer and an activation function (ReLU function); the output layer uses the Softmax activation function to output the probability distribution of 100 categories.

[0019] A multi-head self-attention mechanism module and a feature fusion module are introduced in the hidden layer: The multi-head self-attention mechanism module sets up 8 attention heads, each of which calculates the attention weight of the input feature vector in parallel. Then, the output results of each attention head are concatenated and fused to obtain the enhanced feature vector; The feature fusion module adopts a cross-layer feature concatenation method, concatenating the output features of the second CNN layer and the fourth CNN layer to achieve the fusion of shallow texture features and deep semantic features.

[0020] S3, Optimizer Design An adaptive learning rate optimizer is designed based on the stochastic gradient descent (SGD) algorithm, with the learning rate adjustment formula as follows: The initial learning rate lr0 is set to 0.01, the learning rate adjustment coefficient α is set to 0.95, Δg(t) is the gradient change between the t-th and t-1-th iterations (Δg(t) = g(t) - g(t-1)), and sign(·) is the sign function (sign(Δg(t)) = 1 when Δg(t) > 0; sign(Δg(t)) = -1 when Δg(t) < 0; and sign(Δg(t)) = 0 when Δg(t) = 0). Simultaneously, a momentum term is introduced with a momentum coefficient set to 0.9 to improve the stability of gradient descent by accumulating historical gradient information.

[0021] S4. Model Training and Iterative Optimization The 100 standardized data segments obtained in step S1 are input into the optimized neural network in the order of their labels, and the adaptive learning rate optimizer designed in step S3 is used for model training. During training, the batch size is set to 32, that is, 32 images are input for training each time. Since the task is classification, the cross-entropy loss function is selected. At the same time, an L2 regularization mechanism is introduced, adding the L2 norm term of the weight parameters (regularization coefficient set to 0.0001) to the loss function to suppress model overfitting.

[0022] The convergence threshold for the training process is set to 0.001, and the maximum number of iterations is set to 1000. After each batch of training is completed, the cross-entropy loss function value is calculated. If the loss function value is greater than 0.001 and the number of training iterations has not reached 1000, the gradient of each weight parameter is calculated through the backpropagation algorithm, the weight parameters are updated using the adaptive learning rate optimizer, and the next batch of training begins. When the loss function value drops below 0.001, training is stopped, and the optimized image classification neural network model is obtained.

[0023] S5, Big Data Processing Output The new image dataset to be classified is preprocessed according to the standards in step S1 (data cleaning, Z-score standardization), and then input into the optimized neural network model. Through forward propagation inference, the model outputs the probability distribution of the corresponding categories for each image. The category with the highest probability is selected as the classification result, thus completing the classification processing of the image dataset. Testing showed that the image classification accuracy of this embodiment reached 92.3%, an improvement of 5.7 percentage points compared to the traditional CNN algorithm, and the convergence speed was improved by 30%.

[0024] Example 2: Text Big Data Regression Prediction Based on This Algorithm This embodiment uses a publicly available large text dataset (such as a text dataset related to house price prediction, containing descriptions of house attributes and corresponding house price tags) and employs the algorithm of this invention to achieve house price regression prediction. Specifically, step S1, data cleaning, addresses the specific characteristics of text data by removing text containing garbled characters or meaningless characters; data standardization uses text embedding (Word2Vec) to convert the text into fixed-dimensional vectors; the hidden layer is a fully connected neural network layer; the loss function is the root mean square error loss function; the remaining steps are implemented similarly to those in Embodiment 1. Testing shows that the house price prediction error (root mean square error) of this embodiment is reduced by 28% compared to traditional neural network algorithms, demonstrating good regression prediction performance.

[0025] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0026] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0027] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0028] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A neural network optimization algorithm for big data processing, characterized in that, Includes the following steps: S1. Big Data Preprocessing: Obtain the large dataset to be processed, and sequentially perform data cleaning, data standardization, and data sharding on the large dataset to obtain standardized sharded data; the data cleaning is used to remove invalid data from the dataset, and the data standardization is used to map the data to a preset range. S2. Construct an optimized neural network architecture: Construct a basic neural network based on a deep learning framework. The basic neural network includes an input layer, a hidden layer, and an output layer. An attention mechanism module and a feature fusion module are introduced into the hidden layer to enhance the representation of key features and fuse multi-layer feature information. S3. Optimizer Design: Design an adaptive learning rate optimizer. The optimizer is based on the gradient descent algorithm. It adaptively adjusts the learning rate by monitoring gradient changes during training and introduces a momentum term to improve convergence stability. S4. Model Training and Iterative Optimization: Input the standardized sharded data obtained in step S1 into the optimized neural network constructed in step S2, and use the adaptive learning rate optimizer designed in step S3 to train the model; during the training process, update the network weight parameters through batch training and backpropagation until the model reaches the preset convergence condition or the maximum number of iterations, and obtain the optimized neural network model. S5. Big Data Processing Output: Input the new big data to be processed into the optimized neural network model, and output the big data processing results through model inference.

2. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S1, the outlier removal adopts the 3σ criterion, which is as follows: calculate the mean μ and standard deviation σ of each feature dimension of the dataset, and determine the data that deviates from the mean by more than 3σ as outliers and remove them.

3. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S2, the attention mechanism module is a multi-head self-attention mechanism, which calculates the attention weights of the feature vectors in parallel through multiple attention heads, and then splices and fuses the output results of each attention head.

4. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S2, the hidden layer is a convolutional neural network layer or a fully connected neural network layer. When processing large image data, a convolutional neural network layer is selected as the hidden layer; when processing large text data, a fully connected neural network layer is selected as the hidden layer.

5. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S3, the learning rate adjustment formula of the adaptive learning rate optimizer is: Where lr(t) is the learning rate for the t-th iteration. Let be the initial learning rate, α be the learning rate adjustment coefficient, Δg(t) be the gradient change between the t-th and t-1-th iterations, and sign(·) be the sign function.

6. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S4, the loss function is selected according to the type of big data processing task. When processing classification tasks, the cross-entropy loss function is selected; when processing regression tasks, the mean squared error loss function is selected.

7. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S1, during the data sharding process, a unique identifier tag is added to each shard of data to facilitate data traceability and batch management during the training process.

8. The neural network optimization algorithm for big data processing according to claim 1, characterized in that, In step S4, a regularization mechanism is also introduced during the training process. The regularization mechanism adopts L2 regularization, which suppresses model overfitting by adding the L2 norm term of the weight parameters to the loss function.

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