So far, we have been using trivial examples to demonstrate core concepts in PyTorch. We are now ready to explore a more real-world example. The dataset we will be using is the MNIST dataset of hand-written digits from 0 to 9. The task is to correctly identify each sample image with the correct digit.
The classification model we will be building consists of several layers and these are outlined in the following diagram:
The images we are working with are 28 x 28 pixels in size, and each pixel in each image is characterized by a single number, indicating its gray scale. This is why we need 28 x 28 or 784 inputs to the model. The first layer is a linear layer with 10 outputs, one output for each label. These outputs are fed into to the softmax activation layer and cross-entropy loss layer. The 10 output dimensions represent the 10 possible classes...