pytorch save model checkpoint

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In machine learning, it is common to save model checkpoints during the training process. This ensures that if your training process is interrupted or you want to continue training later, you can resume from the last saved state. PyTorch provides a convenient way to save and load model checkpoints using the torch.save() and torch.load() functions. In this tutorial, we'll explore how to save and load model checkpoints in PyTorch with a practical code example.
Make sure you have PyTorch installed (pip install torch) before running the code.
For the sake of this tutorial, let's create a simple neural network class.
For the sake of this tutorial, we'll simulate a training loop.
In this example, we save the model's state dictionary, optimizer's state dictionary, epoch, and loss into a checkpoint dictionary. You can customize the information you want to save in the checkpoint.
Now, let's see how to load a model checkpoint.
Make sure to adjust the filename ('model_checkpoint_epoch_4.pth' in this example) based on the epoch you want to load.
This tutorial covers the basics of saving and loading model checkpoints in PyTorch. Depending on your use case, you might want to include additional information in the checkpoint, such as training metrics or additional model configurations.
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