neurograph-framework is a versatile tool designed to help you visualize entropy loss in TensorFlow-based neural network training. It generates insightful scatter plots with annotations to aid in understanding and analyzing your training progress.

• Creates a line out of average entropy losses along with scatter plots that display entropy loss over single training iterations. Useful for tracking per-iteration scatter and underlying model trends during training / fine-tuning.
• Annotations for minimum and maximum loss values as well as per-iteration scatter.
• Indication of the latest iteration number and average loss value.
• Overlay warnings in case of missing or outdated data.
• Customizable to suit various types of iteration data, suitable for all kinds of visualization purposes.
• Intended to visualize entropy losses as effectively as possible (min/max lines, median, per-iteration scatter etc).

1. Clone this repository to your local machine:

git clone https://github.com/FlyingFathead/neurograph-framework/

2. Navigate to the cloned directory:

cd neurograph-framework/

3. Install the PyPi requirements with pip install -r requirements.txt

(or, make sure you have these installed):

matplotlib>=3.5.1
Pillow>=9.1.0
numpy>=1.23.5

4. Run the audit_subprocess.py script to start visualizing your neural network training data:

python audit_subprocess.py setname logs_directory

The plotter graphs are updated every 20 seconds by default.

Happy training and analyzing with neurograph-framework! 📊🧠

A terminal-based version of the framework (i.e. for headless training setups): neurograph

My other projects are at: github.com/FlyingFathead/