Abstract
Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily depends upon their architecture, which is handcrafted by domain expertise and type of investigated problem. On the other hand, for researchers who do not have proficiency in using CNNs, it has been very difficult to explore this topic in their problem statements. In this paper, we have come up with a rank and gradient descent-based optimized genetic algorithm to automatically find the architecture design of CNNs that is vigorously competent in exploring the best CNN architecture for maneuvering the tasks of image classification. In the proposed algorithm, there is no requirement for handcrafted pre- and post-processing, which implies that the algorithm is fully mechanized. The validation of the proposed algorithm on conventional benchmarked datasets has been done by comparing the run time of a graphics processing unit (GPU) throughout the training process and assessing the accuracy of various measures. The experimental results show that the proposed algorithm accomplishes better and more persistent ‘classification accuracy’ than the original genetic algorithm on the CIFAR datasets by using fifty percent less intensive computing resources for training the individual CNN and the entire population.
Highlights
Convolutional neural networks (CNNs) are one of the leading techniques of ‘deep learning’ [1] and have exhibited superior performance in several real-world problems over the various customary machine learning algorithms [2]
The goal of this research was to develop an autonomous architecture design method for CNNs using the updated genetic algorithm (GA), which is efficient for finding the best CNN architecture in tackling image classification challenges for users who do not have expertise in adjusting CNN architectures
In comparison to most of its peers, the CNN found by CNN-GA-differential architecture search (DART) has few parameters
Summary
Convolutional neural networks (CNNs) are one of the leading techniques of ‘deep learning’ [1] and have exhibited superior performance in several real-world problems over the various customary machine learning algorithms [2]. The development of such an algorithm is exigent to perform the following tasks: Automatically best CNN architecture design for given data Use of limited computational resources No manual assistance. In 1988, Fukushima discovered the architecture of a neural network known as CNN [3] This was the first CNN, which later became the basis for all subsequent CNNs. In CNNs, there are two important layers: convolution and pooling. The power of a CNN mainly depends upon the manner of filters’ usage and the way the layers are connected, gradient back propagation is the main learning algorithm for all types of CNNs. To design an optimized CNN architecture, it is mandatory to know the parameter calculation of each layer. According to [12], the size of a CNN layer is calculated based on the below equation
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