Chapter 4: Building Neural Networks with PyTorch
Abstract : Building neural networks with PyTorch typically involves defining a model, preparing data, and then training the model. 1. Defining the Neural Network Model: Inherit from nn.Module : Create a class for your neural network that inherits from torch.nn.Module . This provides essential functionality for managing layers and parameters. Initialize Layers in __init__ : Define the individual layers of your network (e.g., nn.Linear for fully connected layers, nn.Conv2d for convolutional layers, nn.ReLU for activation functions) within the __init__ method. Define Forward Pass in forward : Implement the forward method, which dictates how data flows through the defined layers to produce an output. Example of a simple feedforward network: Python import torch from torch import nn class SimpleNeuralNetwork (nn.Module): def __init__ ( self ): super().__init...