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Notes on Implementing CNNs In The Browser |
To implement our CNN based works in the Browser we need to use Tensorflow.JS 🚀
- 🚙 Import Tensorflow.js
- 👷♀️ Create models
- 👩🏫 Train
- 👩⚖️ Do inference
We can import Tensorflow.js in the way below
<script
src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest">
</script>
😎 Same as we did in Python:
- 🐣 Decalre a Sequential object
- 👩🔧 Add layers
- 🚀 Compile the model
- 👩🎓 Train (fit)
- 🐥 Use the model to predict
// create sequential
const model = tf.sequential();
// add layer(s)
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// set compiling parameters and compile the model
model.compile({loss:'meanSquaredError',
optimizer:'sgd'});
// get summary of the mdoel
model.summary();
// create sample data set
const xs = tf.tensor2d([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], [6, 1]);
const ys = tf.tensor2d([-3.0, -1.0, 2.0, 3.0, 5.0, 7.0], [6, 1]);
// train
doTraining(model).then(() => {
// after training
predict = model.predict(tf.tensor2d([10], [1,1]));
predict.print();
});
([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], [6, 1])
[-1.0, 0.0, 1.0, 2.0, 3.0, 4.0]: Data set values
[6, 1]: Shape of input
👁🗨 Attention
- 🐢 Training is a long process so that we have to do it in an asynchronous function
async function doTraining(model){
const history =
await model.fit(xs, ys,
{ epochs: 500,
callbacks:{
onEpochEnd: async(epoch, logs) =>{
console.log("Epoch:"
+ epoch
+ " Loss:"
+ logs.loss);
}
}
});
}