Training in Pytorch

Now that we have mastered optimization and training, let’s get our hands dirty and try to train an NN in Pytorch.

We will build a network with an input of size 3072, 3 linear layers with dimensions 128, 64, 10, and 2 Relu layers in between.

For our training data, we will use a well-known image dataset called CIFAR. CIFAR includes 32*32 RGB (3 channels) images with real-world objects. Our goal is to classify the image into 10 different classes. This is why the input features will be 32∗32∗3, and the output will be the number of classes, which is ...