The network consists of a kernel or filters with size 11 x 11, 5 x 5, 3 x 3, 3 x 3 and 3 x 3 for its five convolutional layers respectively.
As an activation function, the ReLU function is used by the network which shows improved performance over sigmoid and tanh functions. Some of the convolutional layers of the model are followed by max-pooling layers. The AlexNet proposed by Alex Krizhevsky in his work has eight layers including five convolutional layers followed by three fully connected layers. Alex Krizhevsky competed in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012) in the year 2012 where he used the AlexNet model and achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up.
Hinton, a well-known name in the field of deep learning research. This model was proposed by Alex Krizhevsky as his research work. AlexNetĪlexNet is a deep learning model and it is a variant of the convolutional neural network. In the end, we will evaluate the performance of this model in classification. In this article, we will discuss the architecture and implementation of AlexNet using Keras library without using transfer learning approach. AlexNet is one of the variants of CNN which is also referred to as a Deep Convolutional Neural Network. There is a variety of Convolutional Neural Network (CNN) architectures. Convolutional neural networks are one of the popular deep learning models that have a wide range of applications in the field of computer vision. Every deep learning model has a specific architecture and is trained in that specific way. As of now, there may be more than hundreds of deep learning models that have proven their capabilities in handling millions of images and producing accurate results. The computer vision is being applied in a variety of applications across the domains and thanks to the deep learning that is continuously giving new frameworks to be used in the computer vision space.