Building an Image Classifier with a Single-Layer Neural Network in PyTorch
A single-layer neural network, also known as a single-layer perceptron, is the simplest type of neural network. It consists of only one layer of neurons, which are connected to the input layer and the output layer. In case of an image classifier, the input layer would be an image and the output layer would be a class label.
To build an image classifier using a single-layer neural network in PyTorch, you’ll first need to prepare your data. This typically involves loading the images and labels into a PyTorch dataloader, and then splitting the data into training and validation sets. Once your data is prepared, you can define your neural network.
Next, you can use PyTorch’s built-in functions to train