Syed Muhammad Ali Raza1. Choose a neural network framework
- TensorFlow.js: Allows machine learning models to be defined, trained, and run in the browser.
- ONNX.js: Supports running pre-built ONNX (Open Neural Network Exchange) models in the browser.
- Brain.js: A JavaScript library that simplifies working with neural networks.
2. Prepare Your Neural Network Model
- Train your model: You can train your model using frameworks like TensorFlow, PyTorch, or other machine learning frameworks.
- Export the model: After training, export the model to a format compatible with the chosen frontend framework (eg TensorFlow.js format or ONNX format).
3. Set Up Your Frontend Project
Create Project: Create a new frontend project using a framework like meta, Vue or plain JavaScript / HTML.
-
Build required libraries:
npm @tensorflow/build tfjsor
npm install onnxjsdepending on the library you plan to use.
4. Load and Run the Model in the Frontend
-
Load Model:
// For TensorFlow.js * import as tf from @tensorflow/tfjs'; async functionModel() { const model = await tf.loadLayersModel( 'path/to/model.json' ); return model; }
For ONNX.js
```javascript
// For ONNX.js
const onnx = required ( 'onnxjs' );
async functionModel() {
const session = new onnx.InferenceSession();
await session.loadModel("path/to/model.onnx");
return;
}
```
-
How the model works:
// For TensorFlow.js function async guess(input) { const model = await loadModel(); const output = model.predict(tf.tensor(input)); return; }// For ONNX.js function async guess(input) { const session = await loadModel(); const tensor = onnx.new Tensor(input, 'float32'); const outputMap = await session.run([tensor]); const outputTensor = outputMap.values (). Next(). price returnTensor; }
5. Integrate with the Frontend
- Create UI Components: Design login forms or other UI components to collect data from users.
- Prediction Triggers: Add event listeners or handlers to send user data to neural network for prediction.
- Result Display: Process the output of the neural network and display it in the UI.
Example: A simple React application with TensorFlow.js
1. Build the project
npx create-react-app neural-network-frontend
cd neural-network-front-end
npm @tensorflow/build tfjs
2. Create Components
App.js
import React useState from React;
* import as tf from @tensorflow/tfjs';
const App = () => {
const [input, setInput] = useState('');
const [output, setOutput] = useState(null);
const loadModel = async() => {
const model = await tf.loadLayersModel('/path/to/model.json');
return model;
};
const handlePredict = async() => {
const model = await loadModel();
const tensor = tf.tensor2d([parseFloat(input)], [1, 1]);
const prediction = model.predict(tensor);
setOutput(estimate.dataSync()[0]);
};
return (
<div>
<h1> Front Nervous System Integration</h1>
<input type = "text" value = {input} onChange = {e => setInput(e.target.value)} />
<button click={handlePredict}>prediction</button>
{Log off! == null && <h2> Output: {output} </h2>}
</div>
);
};
export the main program;
3. Run the Project
npm start
Examples
1. TensorFlow.js Integration
Setup
- Create a React Project
npx create-react-app tfjs-frontend
cd tfjs-frontend
npm install @tensorflow/tfjs
- Create Components
App.js
import React, { useState } from 'react';
import * as tf from '@tensorflow/tfjs';
const App = () => {
const [input, setInput] = useState('');
const [output, setOutput] = useState(null);
const loadModel = async () => {
const model = await tf.loadLayersModel('/path/to/model.json'); // Update path to your model
return model;
};
const handlePredict = async () => {
const model = await loadModel();
const tensor = tf.tensor2d([parseFloat(input)], [1, 1]);
const prediction = model.predict(tensor);
const result = prediction.dataSync()[0];
setOutput(result);
};
return (
<div>
<h1>TensorFlow.js Integration</h1>
<input type="text" value={input} onChange={e => setInput(e.target.value)} />
<button onClick={handlePredict}>Predict</button>
{output !== null && <h2>Output: {output}</h2>}
</div>
);
};
export default App;
Running the Application
npm start
2. ONNX.js Integration
Setup
-
Create a React Project
npx create-react-app onnxjs-frontend cd onnxjs-frontend npm install onnxjs Create Components
App.js
import React, { useState } from 'react';
import * as onnx from 'onnxjs';
const App = () => {
const [input, setInput] = useState('');
const [output, setOutput] = useState(null);
const loadModel = async () => {
const session = new onnx.InferenceSession();
await session.loadModel('/path/to/model.onnx'); // Update path to your model
return session;
};
const handlePredict = async () => {
const session = await loadModel();
const tensor = new onnx.Tensor([parseFloat(input)], 'float32', [1, 1]);
const outputMap = await session.run([tensor]);
const result = outputMap.values().next().value.data[0];
setOutput(result);
};
return (
<div>
<h1>ONNX.js Integration</h1>
<input type="text" value={input} onChange={e => setInput(e.target.value)} />
<button onClick={handlePredict}>Predict</button>
{output !== null && <h2>Output: {output}</h2>}
</div>
);
};
export default App;
Running the Application
npm start
3. Brain.js Integration
Setup
-
Create a React Project
bash npx create-react-app brainjs-frontend cd brainjs-frontend npm install brain.js
- Create Components
App.js
import React, { useState } from 'react';
import { NeuralNetwork } from 'brain.js';
const App = () => {
const [input, setInput] = useState('');
const [output, setOutput] = useState(null);
const loadModel = () => {
const net = new NeuralNetwork();
net.fromJSON(require('./path/to/model.json')); // Update path to your model
return net;
};
const handlePredict = () => {
const net = loadModel();
const result = net.run([parseFloat(input)])[0];
setOutput(result);
};
return (
<div>
<h1>Brain.js Integration</h1>
<input type="text" value={input} onChange={e => setInput(e.target.value)} />
<button onClick={handlePredict}>Predict</button>
{output !== null && <h2>Output: {output}</h2>}
</div>
);
};
export default App;
Running the Application
npm start
Note: If you have any Query DM me on Linkedin Syed Muhammad Ali Raza
