Image classifier - CIFAR10
Goal: To improve the average accuracy of ten classes in CIFAR10 classifier. Following link has detailed description of initial code: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py Background: W e will use the CIFAR10 dataset. It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size. As part of initial code, following steps are done in order: Loading and normalizing the CIFAR10 training and test datasets using torchvision Defining a Convolutional Neural Network Defining a loss function Training the network on the training data Testing the network on the test data Basic Definitions: CNN are made up of neurons that have weight and biases. These are mainly applied to image networks. They consist of convolutional layers followed by activation filters and pooling layers...