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Tensorflow.js Linear Regression
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| <html> | |
| <head> | |
| <title>Tensor Flow</title> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"></script> | |
| <script> | |
| const nr_epochs=2048; // higher=better but slower | |
| var tfinterface; | |
| const model = tf.sequential(); | |
| function initTF() { | |
| // 1. TRAINING DATA - learn this 10x table | |
| const xs = tf.tensor2d([1,2,3,4,5], [5,1]); | |
| const ys = tf.tensor2d([10,20,30,40,50], [5,1]); | |
| // 2. ML MODEL - linear regression model | |
| model.add(tf.layers.dense({units: 1, inputShape: [1]})); | |
| // Prep for training | |
| model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); | |
| // train -- the higher the number the more accurate you'll get (but longer run time) | |
| tfinterface=model.fit(xs,ys,{epochs: nr_epochs}); | |
| } | |
| // 3. WRAPPER AROUND PREDICT | |
| function predict(n) { | |
| return tfinterface.then(()=> { return model.predict(tf.tensor2d([n],[1,1])); }); | |
| } | |
| // Helper for form | |
| function formpredict(v,r) { | |
| predict(v).then(function(res) { | |
| // alert(res.get([0])); | |
| r.innerHTML=res.get([0]); | |
| }); | |
| } | |
| </script> | |
| </head> | |
| <!-- HTML --> | |
| <body onLoad='initTF();'> | |
| <p> | |
| <div id='res'>Wait</div> | |
| <p> | |
| <form name='iForm' onSubmit='formpredict(this.val.value,document.getElementById("res")); return false;')> | |
| <script> | |
| document.getElementById("res").innerHTML=" "; | |
| </script> | |
| Input Number: <input name='val'><input type=submit> | |
| </form> | |
| </body> | |
| </html> |
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