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https://github.com/flyingfathead/neurograph-framework
A versatile tool for visualizing entropy loss in TensorFlow-based neural network training, providing insightful scatter plots with annotations.
https://github.com/flyingfathead/neurograph-framework
data-analysis data-analysis-python data-visualization entropy graph graphs neural-network neural-networks neural-networks-visualization nn python python3 tensorflow tensorflow2 training visualization visualization-tools
Last synced: 2 days ago
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A versatile tool for visualizing entropy loss in TensorFlow-based neural network training, providing insightful scatter plots with annotations.
- Host: GitHub
- URL: https://github.com/flyingfathead/neurograph-framework
- Owner: FlyingFathead
- Created: 2023-08-10T16:23:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-14T18:59:39.000Z (10 months ago)
- Last Synced: 2024-01-14T23:13:13.942Z (10 months ago)
- Topics: data-analysis, data-analysis-python, data-visualization, entropy, graph, graphs, neural-network, neural-networks, neural-networks-visualization, nn, python, python3, tensorflow, tensorflow2, training, visualization, visualization-tools
- Language: Python
- Homepage:
- Size: 148 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Audit: audit_logs_poller.py
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README
# neurograph-framework
**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.
## Features
- 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- Overlay warnings in case of missing or outdated data.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).
## Usage
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! 📊ðŸ§
## About
A terminal-based version of the framework (i.e. for headless training setups): [neurograph](https://github.com/FlyingFathead/neurograph/)
My other projects are at: [github.com/FlyingFathead/](https://github.com/FlyingFathead/)