Neural Network Training with Shuffled Accelerated Gradient Convergence
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Solution Overview
Problem
Existing neural network training methods, particularly those using stochastic gradient descent, suffer from inefficient convergence rates, especially when dealing with nonconvex cost functions, which can lead to local minima and hinder the achievement of a global solution.
Innovation Solution
The method involves generating an index sequence of training data to update model parameters using an accelerated gradient with shuffling, incorporating a momentum term to enhance convergence, and setting initial values to predetermined or random values, ensuring the cost function meets specific properties to achieve global convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stochastic gradient descent is used to train neural networks, then memory efficiency and computation efficiency are improved, but convergence rate deteriorates
Solution Approach 1:
The patent applies preliminary action by performing a deterministic shuffle of training data indices before each epoch, establishing a predictable sequence that accelerates convergence. This pre-arranged shuffling strategy prepares the optimization path in advance, allowing the stochastic gradient descent to converge faster without sacrificing memory efficiency.
Solution Approach 2:
The patent changes the parameter of data shuffling from random to deterministic based on epoch number. By modifying the shuffling strategy parameter, the system achieves faster convergence rates while maintaining the computational efficiency of stochastic gradient descent. This parameter change transforms the optimization trajectory to reach global minima more quickly.
2Measurement precision
If nonconvex cost functions are used to model complex real-world behavior, then model accuracy is improved, but convergence to global solution becomes unreliable due to local minima
Solution Approach 1:
The patent applies preliminary action by deterministically shuffling training data before each epoch, which systematically guides the optimization process through the nonconvex cost function landscape. This pre-arranged shuffling strategy helps escape local minima and reliably converge to global solutions, making nonconvex cost functions dependable for complex modeling tasks.
Solution Approach 2:
The patent implements feedback by using the epoch number to determine the shuffling strategy, creating a adaptive optimization process. The deterministic shuffle pattern provides structured feedback to the gradient descent algorithm, helping it navigate nonconvex landscapes reliably while maintaining high model accuracy.
3Measurement precision
If gradient descent updates model parameters after evaluating all training examples, then convergence precision is improved, but computation efficiency deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the training process into epochs with deterministic shuffling, where each epoch processes training data in a systematically reordered sequence. This segmentation allows stochastic gradient descent to maintain computational efficiency while achieving convergence precision through structured data presentation across multiple epochs.
Solution Approach 2:
The patent changes the parameter of data presentation from random to deterministic sequence based on epoch number. This parameter modification enables the system to achieve both computational efficiency and convergence precision by systematically varying the training data sequence across epochs.
Data Source
AI summary
An index sequence specifying an index of training data corresponding to a component of a cost function is generated. A first model parameter in the set of model parameters is set to an initial value. Using the index sequence, a neural network model comprising a set of weights is trained. As part of the training, using the index sequence, a learning rate, and a set of gradients, a subset of the set of model parameters is updated. As part of the training, a momentum term is set. As part of the training, using the momentum term as the first model parameter, the updating and the setting are repeated until reaching a training completion condition. The trained neural network model is used to predict an outcome by analyzing live data.