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Sure, I'd be happy to provide you with an informative tutorial on nn.ModuleList in PyTorch, along with a code example.
PyTorch provides the nn.ModuleList container to manage a list of PyTorch modules. This is useful when you want to define a neural network with a variable number of sub-modules and manage them collectively. nn.ModuleList allows you to use container-like operations (such as indexing) on the list of modules, and it ensures that all modules in the list are properly registered and can be optimized during training.
Let's consider a simple example where we want to create a custom neural network with multiple layers, and the number of layers is determined dynamically. We will use nn.ModuleList to store and manage these layers.
In this example, DynamicNet is a neural network with a variable number of hidden layers. The number of hidden layers is specified by the hidden_sizes list. The nn.ModuleList is used to store these hidden layers dynamically. The forward method defines the forward pass through these layers.
This example demonstrates how to use nn.ModuleList to create a flexible neural network architecture where the number of hidden layers can be easily adjusted. You can extend this example for more complex architectures by adding different types of layers, activation functions, etc.
I hope this tutorial helps you understand how to use nn.ModuleList in PyTorch for building modular and dynamic neural networks. If you have any further questions or need clarification on any part of the code, feel free to ask!
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