Saving and Loading Models in PyTorch Tutorial 17

In this tutorial, we will learn how to save and load PyTorch models. Saving and loading models is an essential skill for machine learning practitioners as it allows you to save the state of your trained models and use them for inference or further training at a later time.

Before we proceed, make sure you have PyTorch installed in your environment. You can install PyTorch using pip by running the following command:

pip install torch torchvision

Now, let’s get started with saving and loading models in PyTorch.

Step 1: Define a Model
First, let’s define a simple neural network model using PyTorch. For this tutorial, we will create a basic feedforward neural network with two hidden layers.

import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

Step 2: Save a Model
To save a PyTorch model, we can use the state_dict() method. This method returns a dictionary containing the model’s parameters. We can save this dictionary to a file using the torch.save() function.

model = SimpleNN(input_size=784, hidden_size=128, num_classes=10)

# Save model
torch.save(model.state_dict(), 'simple_nn_model.pth')

Step 3: Load a Model
To load a saved model, we can instantiate the model class and load the saved state dictionary using the load_state_dict() method. This will load the saved parameters into the model.

# Create model instance
model = SimpleNN(input_size=784, hidden_size=128, num_classes=10)

# Load saved model
model.load_state_dict(torch.load('simple_nn_model.pth'))
model.eval()  # Set model to evaluation mode

Step 4: Use the Model for Inference
Once the model is loaded, we can use it for inference on new data. Here’s an example of how to make predictions using the loaded model:

# Assuming `data` is a tensor of input data
output = model(data)
predicted_class = torch.argmax(output, dim=1)
print(f'Predicted class: {predicted_class.item()}')

That’s it! You have successfully saved and loaded a PyTorch model. This is a fundamental skill in deep learning, and knowing how to save and load models will be invaluable as you work on more complex projects and datasets.

In this tutorial, we covered the basic steps to save and load PyTorch models. Remember to always save the model’s state_dict() and load it into a model instance appropriately. Additionally, make sure to set the model to evaluation mode (model.eval()) before using it for inference.

I hope you found this tutorial helpful. Happy coding!