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CNNs In Browser

Notes on Implementing CNNs In The Browser
To implement our CNN based works in the Browser we need to use Tensorflow.JS 🚀

👷‍♀️ Workflow

  1. 1.
    🚙 Import Tensorflow.js​
  2. 2.
    👷‍♀️ Create models
  3. 3.
    👩‍🏫 Train
  4. 4.
    👩‍⚖️ Do inference

🚙 Importing Tensorflow.js

We can import Tensorflow.js in the way below
<script
src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]">
</script>

👷‍♀️ Creating The Model

😎 Same as we did in Python:
  1. 1.
    🐣 Decalre a Sequential object
  2. 2.
    👩‍🔧 Add layers
  3. 3.
    🚀 Compile the model
  4. 4.
    👩‍🎓 Train (fit)
  5. 5.
    🐥 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);
​
}
}
});
}

👩‍💻 Full Code