Visualize model weights with tensorboard

[ph4s3r]

The method I described using tensorboardX for PyTorch primarily visualizes the structure of your model – that is, it shows the layers and how data flows through the network. This is particularly useful for understanding and verifying the architecture of your model.

However, if you're interested in visualizing the weights or other training aspects of your model, TensorBoard offers several other features that can be quite useful:

Weight Histograms: TensorBoard can display histograms of the weights and biases in your model. This allows you to observe how the distributions of these parameters change over time during training. You can log these histograms using add_histogram in tensorboardX.

Feature Maps: While not directly supported in TensorBoard, you can visualize the feature maps (outputs of different layers) by extracting these outputs during a forward pass and logging them as images using add_image in tensorboardX.

Scalars for Training Metrics: You can track and visualize training metrics such as loss, accuracy, etc., by logging them as scalars in TensorBoard. This is done using the add_scalar function.

Embeddings: TensorBoard also supports the visualization of high-dimensional data (like embeddings) using t-SNE or PCA, which can be useful for understanding the learned feature representations.

Gradient Visualizations: It's also possible to visualize gradients, which can be critical for debugging training issues. Similar to weights, you can log gradients using add_histogram.

To utilize these features, you'll need to modify your training loop to log the relevant data at each iteration or epoch. Here's an example of how you might log weights and training loss:

writer = SummaryWriter('runs/training_visualization')

for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        # Forward pass, backward pass, and optimize
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Log loss
        writer.add_scalar('Loss/train', loss.item(), epoch * len(train_loader) + i)

        # Log weights
        for tag, value in model.named_parameters():
            tag = tag.replace('.', '/')
            writer.add_histogram(tag, value.data.cpu().numpy(), epoch)
            if value.grad is not None:
                writer.add_histogram(tag+'/grad', value.grad.data.cpu().numpy(), epoch)

writer.close()

Remember, visualizing training dynamics like weights, gradients, and activations can be very resource-intensive, especially for large models, so you might want to log these less frequently or selectively.