#model visualization

Penzai uses JAX to turn neural networks into easy-to-read pytree structures, ideal for post-training model exploration such as reverse-engineering and activation analysis. It includes tools like Treescope for visualization, JAX utilities for data manipulation, and a flexible neural network library. With its Transformer model implementations, Penzai aids in research on model interpretability and dynamics, while version 0.2 enhances workflow with new API features like mutable state management.

TorchExplorer facilitates detailed analysis of neural networks by offering an interactive inspection of the inputs, outputs, parameters, and gradients of each nn.Module during training. Compatible with weights and biases or operable as a standalone solution, it enables the visualization of intermediate tensors, examination of model structures, and tracking of network dynamics, such as gradient and parameter distributions. Easily installable on Linux and Mac, it assists in debugging issues like vanishing gradients or uneven module input distribution, providing a clear graphical interface for enhanced network understanding.

Torchinfo offers in-depth PyTorch model summaries similar to TensorFlow's model.summary(), aiding in debugging neural networks. The project introduces a refined API beyond torchsummary and torchsummaryX, supporting RNNs, LSTMs, and flexible output formats, including use in Jupyter Notebooks. Features such as verbose mode and input data type support make Torchinfo an invaluable resource for developers seeking detailed insights into model parameters, operations, and architecture for PyTorch 1.4.0 and later.