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Title: Understanding PyTorch nn.Linear: A Tutorial with Code Examples
Introduction:
PyTorch is a popular open-source deep learning framework that provides a flexible and dynamic computational graph. One of the fundamental modules in PyTorch is nn.Linear, which represents a fully connected layer. In this tutorial, we will explore the basics of nn.Linear and demonstrate its usage with a simple code example.
Let's create a simple neural network with one linear layer. The nn.Linear module requires two parameters: the input size and the output size.
The nn.Linear layer has two learnable parameters - weights (weight) and biases (bias). We can access and inspect them as follows:
Now, let's generate some input data and pass it through the linear layer.
Print the input and output to see the transformation.
Although this example is simple, in real-world scenarios, you would typically include the linear layer as part of a larger neural network and train it on a specific task. Here's a basic example of a training loop:
This tutorial covered the basics of using PyTorch's nn.Linear module. You learned how to create a simple linear layer, examine its parameters, and perform a forward pass. Incorporating the nn.Linear layer into a complete neural network allows you to build and train more complex models for various tasks. Experiment with different input sizes, output sizes, and hyperparameters to gain a deeper understanding of how linear layers work in PyTorch.
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