Hands-On Graph Neural Networks Using Python

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Now, before I dive into this topic let me first explain what GNNs are and why they’re so ***** cool. Essentially, these networks allow us to analyze data that is structured in a graph-like format, which can be incredibly useful for tasks such as social network analysis or protein structure prediction.

But enough with the boring technical stuff! Let’s get our hands dirty and start coding some GNNs using Python. Here’s what our code might look like:

# Import necessary libraries
import torch
from torch_geometric.datasets import Reddit
from torch_geometric.nn import GCNConv, SGCConv
from torch_geometric.utils import dropout

# Load the dataset and split it into training/validation sets
dataset = Reddit(root='./data', transform=dropout)
train_idx, val_idx = dataset.split()

# Define our model architecture (in this case we're using a simple GCNConv)
class MyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(dataset.num_features, 64) # Define first convolutional layer with input features and output features
        self.conv2 = SGCConv(64, dataset.num_classes) # Define second convolutional layer with input features and output classes
        
    def forward(self, x, edge_index):
        # Apply the first convolutional layer to our input data and edges
        x = self.conv1(x, edge_index)
        # Pass the output through a second convolutional layer (in this case we're using SGCConv for better performance)
        return self.conv2(x, edge_index)
        
# Train our model on the training set and evaluate it on the validation set
model = MyModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # Define optimizer with learning rate
loss_func = torch.nn.CrossEntropyLoss() # Define loss function
for epoch in range(5):
    # Iterate over our training data and update the model's parameters using backpropagation
    for i, (batch, label) in enumerate(dataset[train_idx]):
        output = model(batch.x, batch.edge_index) # Get model's output by passing input data and edges
        loss = loss_func(output, label) # Calculate loss using output and labels
        optimizer.zero_grad() # Clear previous gradients
        loss.backward() # Perform backpropagation to calculate gradients
        optimizer.step() # Update model's parameters using calculated gradients
        
    # Evaluate the model's performance on our validation set and print out some metrics (in this case we're using accuracy as a metric)
    val_loss = 0
    for i, (batch, label) in enumerate(dataset[val_idx]):
        output = model(batch.x, batch.edge_index) # Get model's output by passing input data and edges
        loss = loss_func(output, label) # Calculate loss using output and labels
        val_loss += loss.item() # Add loss to total validation loss
        
    print("Epoch: {}/{}".format(epoch+1, 5))
    print("Training Loss: {:.4f}".format(torch.mean(loss))) # Print average loss for training set
    print("Validation Loss: {:.4f}".format(val_loss / len(dataset[val_idx]))) # Print average loss for validation set
    
# Save our trained model to disk so we can use it later on if needed (in this case we're using the PyTorch checkpoint format)
torch.save({'model': model, 'optimizer': optimizer}, './checkpoints/my-gnn') # Save model and optimizer to specified file path

And that’s it! With just a few lines of code and some basic knowledge about GNNs, we can now train our own models using Python and PyTorch Geometric. So give it a try who knows what kind of amazing insights you might uncover in your data!

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