The Architecture & Implementation of AlexNet

Let's dive into the AlexNet Architecture The AlexNet neural network architecture consists of 8 learned layers of which 5 are convolution layers, few are max-pooling layers, 3 are fully connected layers, and the output layer is a 1000 channel softmax layer. The pooling used here is Max pool. Why 1000 channels of softmax layer are taken?? This is because the Imagenet dataset contains 1000 different classes of images, so at the final output layer we have one node for each of these 1000 categories and the output layer is the softmax output layer. The input to the AlexNet network is a 227 x 227 size RGB image, so it's having 3 different channels- red, green, and blue.