What are the steps to debug incorrect weight initialization in a generative model using a deep learning framework

Question

With the help of a code snippet and explanation, can you explain what the steps are to debug incorrect weight initialization in a generative model using a deep learning framework?

Answer 1

To debug incorrect weight initialization in a generative model, follow these steps:

• Visualize Weight Statistics
  • Check the distribution of weights to ensure they are initialized correctly.
• Inspect Specific Layers
  • Focus on problematic layers like convolutions or batch normalization.
• Verify Initialization Method
  • Ensure weights are initialized using the correct method, like Xavier or He initialization.
• Test Forward Pass Outputs
  • Check if the outputs of each layer are within a reasonable range.
• Diagnose Gradient Flow
  • Check if gradients are vanishing or exploding.
• Use Weight Initialization Debugging Tools
  • Visualize activations or gradients using libraries like TensorBoard or tools like torchinfo.

Here are the code snippets you can refer to explain the following steps:

Hence, By systematically inspecting weights, outputs, and gradients, you can debug and fix incorrect weight initialization.