https://github.com/voyrox/neuralgraph
NeuralGraph is a powerful AI training data visualization tool designed to analyze and interpret training data for neural networks. It provides comprehensive visualizations of loss and accuracy trends over epochs, enabling users to gain deep insights into the training process and evaluate the performance of their AI models.
https://github.com/voyrox/neuralgraph
neuralgraph tensorflow tensorflowjs
Last synced: about 1 year ago
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NeuralGraph is a powerful AI training data visualization tool designed to analyze and interpret training data for neural networks. It provides comprehensive visualizations of loss and accuracy trends over epochs, enabling users to gain deep insights into the training process and evaluate the performance of their AI models.
- Host: GitHub
- URL: https://github.com/voyrox/neuralgraph
- Owner: Voyrox
- Created: 2023-06-30T06:34:28.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-10T10:16:25.000Z (over 1 year ago)
- Last Synced: 2025-04-11T23:13:35.696Z (about 1 year ago)
- Topics: neuralgraph, tensorflow, tensorflowjs
- Language: EJS
- Homepage:
- Size: 642 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# NeuralGraph
NeuralGraph is an AI training data visualization tool that helps analyze and interpret training data for neural networks. It provides visualizations of loss and accuracy trends over epochs, allowing users to gain insights into the training process and assess the model's performance. When you call it simply create a popup app displaying the data.
## Features
- Line charts for loss and accuracy trends over epochs
- Calculation of average accuracy
- Feedback message indicating the success or failure of AI training
- Responsive and intuitive user interface
- 3D Graph of you model
- Nested Table to fully understand you model (Full Breakdown)
## Coming soon:
- Flow Charts
- FAQ
## Installation
Follow these steps to set up NeuralGraph:
1. Install:
```shell
npm i neuralgraph
```
2. Call needed functions:
```js
const { GenerateGraph, updateGraph } = require("neuralgraph");
```
3. Call this just before model.fit:
```js
GenerateGraph(model); //Parse the model directly into this to be able to see a 3D view of you model
```
4. Update your model.fit callback:
```js
callbacks: {
onEpochEnd: async (epoch, logs) => {
console.log(
`Epoch: ${epoch} Loss: ${logs.loss * 100} Accuracy: ${logs.acc}`
);
updateGraph(epoch, logs);
};
}
```
## Data Format
NeuralGraph expects data in a specific format (default):
- `lossData`: An array of loss values corresponding to each epoch.
- `accuracyData`: An array of accuracy values corresponding to each epoch.
Example data format:
```json
{
"epoch": [0.5, 0.4, 0.3, 0.2, 0.1],
"logs": [0.6, 0.7, 0.8, 0.9, 0.95]
}
```
## Example Tensorflow.js code
```js
const tf = require('@tensorflow/tfjs-node');
const { GenerateGraph, updateGraph } = require('neuralgraph');
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['accuracy'] });
const xs = tf.tensor2d([-1, 0, 1, 2, 3, 4], [6, 1]);
const ys = tf.tensor2d([-3, -1, 1, 3, 5, 7], [6, 1]);
GenerateGraph(model);
model.fit(xs, ys, {
epochs: 100,
callbacks: {
onEpochEnd: async (epoch, logs) => {
console.log(`Epoch: ${epoch} Loss: ${logs.loss * 100} Accuracy: ${logs.acc}`);
updateGraph(epoch, logs);
}
}
});
```
## Contributing
Contributions are welcome! To contribute to NeuralGraph, follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make the necessary changes and commit them.
4. Push your changes to your forked repository.
5. Submit a pull request to the main repository.
6. Please ensure that your code follows the existing coding style and conventions.