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Syed Muhammad Ali Raza
Syed Muhammad Ali Raza

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Neural Network Integration in Frontend

#frontend #tensorflow #javascript #devto

1. 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 tfjs

    or

     npm install onnxjs

    depending 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; } ```
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  • How ​​the model works:

     // For TensorFlow.js function async guess(input) { const model = await loadModel(); const output = model.predict(tf.tensor(input)); return; } 
 ```javascript // 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; } ```
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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
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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;
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3. Run the Project 

npm start
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Examples

1. TensorFlow.js Integration

Setup

  1. Create a React Project 
npx create-react-app tfjs-frontend cd tfjs-frontend npm install @tensorflow/tfjs
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  1. 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;
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Running the Application 

npm start
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2. ONNX.js Integration

Setup

  1. Create a React Project 
npx create-react-app onnxjs-frontend cd onnxjs-frontend npm install onnxjs
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  1. 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;
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Running the Application 

npm start
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3. Brain.js Integration

Setup

  1. Create a React Project 
npx create-react-app brainjs-frontend cd brainjs-frontend npm install brain.js
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  1. 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;
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Running the Application 

npm start
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Note: If you have any Query DM me on Linkedin Syed Muhammad Ali Raza

Top comments (1)

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brysenrom profile image
Brysen-Rom •

what a next level frontend
any microservice for this