Reusing Trained Layers of Convolutional Neural Networks to Shorten Hyperparameters Tuning Time

Abstract

Hyperparameters tuning is a time-consuming approach, particularly when the architecture of the neural network is decided as part of this process. For instance, in convolutional neural networks (CNNs), the selection of the number and the characteristics of the hidden (convolutional) layers may be decided. This implies that the search process involves the training of all these candidate network architectures.This paper describes a proposal to reuse the weights of hidden (convolutional) layers among different trainings to shorten this process. The rationale is that if a set of convolutional layers have been trained to solve a given problem, the weights calculated in this training may be useful when a new convolutional layer is added to the network architecture. This idea has been tested using the CIFAR-10 dataset, testing different CNNs architectures with up to 3 convolutional layers and up to 3 fully connected layers. The experiments compare the training time and the validation loss when reusing and not reusing convolutional layers. They confirm that this strategy reduces the training time while it even increases the accuracy of the resulting neural network. This finding opens up the future possibility of integrating this strategy in existing AutoML methods with the purpose of reducing the total search time.

Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition

“…We set λ 2 = λ 3 = 1 following the work [32] and set λ 1 to 1. Since tuning the hyperparameters is time-consuming [46,47], it may be possible to improve accuracy by adjusting these parameters.…”