Benchmarking and evaluation are core to the success of any deep learning exploration. We will develop some simple code to evaluate two key performance measures: the accuracy and the training time. We will use the following model template:
This model is the most common and basic linear template for solving MNIST. You can see we initialize each layer, in theinit method, by creating a class variable that is assigned to a PyTorch nn object. Here, we initialize two linear functions and a ReLU function. The nn.Linear function takes an input size of 28*28 or 784. This is the size of each of the training images. The output channels ...
