Beginner's Guide to PyTorch: Build Your First Neural Network Model Step by Step

This article is an introductory PyTorch tutorial that explains core operations by building a fully connected neural network (MLP) model based on the MNIST dataset. First, install PyTorch (CPU/GPU version), load the MNIST dataset using torchvision, convert it to tensors with ToTensor, normalize with Normalize, and then use DataLoader for batch processing (batch_size=64). The model is defined as an MLP with an input layer of 784 (flattened 28×28 images), a hidden layer of 128 (ReLU activation), and an output layer of 10 (Softmax), implemented by inheriting nn.Module for forward propagation. CrossEntropyLoss is chosen as the loss function, and SGD with lr=0.01 is used as the optimizer. The model is trained for 5 epochs, with forward propagation, loss calculation, backpropagation, and parameter updates executed cyclically, printing the loss every 100 batches. During testing, the model is set to eval mode, gradient computation is disabled, and the accuracy on the test set is calculated. The tutorial also suggests extension directions, such as adjusting the network structure, replacing optimizers, or changing datasets.