Periodic Weight Averaging for Neural Network Training Stability
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Solution Overview
Problem
Training deep neural networks (DNNs) is challenging due to nonconvex loss functions and requires fine-tuning of hyperparameters, leading to inconsistent and volatile convergence, especially when optimal hyperparameter values or learning rate schedules are unknown.
Innovation Solution
The proposed techniques, such as Periodically Sampled Weight Averaging (PSWA), Periodic Weight Averaging over Last K Samples (PWALKS), and Periodically Sampled Weight Momentum (PSWM), employ periodic sampling of model weights to provide consistent and robust convergence by averaging weights over batchwise updates, reducing computational load and stabilizing the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradient-based optimization methods like SGD are used to train DNNs, then the training process becomes more effective, but the convergence remains time-consuming, volatile, and requires many finely tuned hyperparameters
Solution Approach 1:
The patent implements periodic sampling of model weights during training, where weights are averaged at periodic intervals rather than continuously. This periodic action stabilizes the optimization process by reducing volatility in the loss function convergence while maintaining training effectiveness, directly addressing the time-loss issue without sacrificing productivity
2Reliability
If DNNs are trained with fine-tuning of hyperparameters to achieve optimal convergence, then performance improves, but the process becomes complex and inconsistent when optimal hyperparameter values are unknown
Solution Approach 1:
The patent implements self-service through automated hyperparameter selection mechanisms where the system automatically determines optimal hyperparameter values during training based on observed performance metrics. This eliminates the need for manual fine-tuning while ensuring consistent and reliable convergence across different datasets and model architectures
3Stability of the object's composition
If weight averaging is applied continuously during training, then convergence stability improves, but computational load increases
Solution Approach 1:
The patent applies partial action by performing weight averaging only at periodic intervals rather than continuously throughout training. This partial application of weight averaging maintains sufficient training stability while significantly reducing the computational overhead associated with continuous averaging operations
Data Source
AI summary
A computer-implemented method includes: initializing model parameters for training a neural network; performing a forward pass and backpropagation for a first minibatch of training data; determining a new weight value for each of a plurality of nodes of the neural network using a gradient descent of the first minibatch; for each determined new weight value, determining whether to update a running mean corresponding to a weight of a particular node; based on a determination to update the running mean, calculating a new mean weight value for the particular node using the determined new weight value; updating the weight parameters for all nodes based on the calculated new mean weight values corresponding to each node; assigning the running mean as the weight for the particular node when training on the first minibatch is completed; and reinitializing running means for all nodes at a start of training a second minibatch.