TFJS Models - ONEDOUBT.COM

TensorFlow Models

TesorFlow.js

A JavaScript Library for

Training and Deploying
Machine Learning Models
In the Browser

Tensorflow Models

Models and Layers are important building blocks in Machine Learning.

For different Machine Learning tasks you must combine different types of Layers into a Model that can be trained with data to predict future values.

TensorFlow.js is supporting different types of Models and different types of Layers.

A TensorFlow Model is a Neural Network with one or more Layers.

A Tensorflow Project

A Tensorflow project has this typical workflow:

Collecting Data
Creating a Model
Adding Layers to the Model
Compiling the Model
Training the Model
Using the Model

Example

Suppose you knew a function that defined a strait line:

Y = 1.2X + 5

Then you could calculate any y value with the JavaScript formula:

y = 1.2 * x + 5;

To demonstrate Tensorflow.js, we could train a Tensorflow.js model to predict Y values based on X inputs.

The TensorFlow model does not know the function.

// Create Training Data
const xs = tf.tensor([0, 1, 2, 3, 4]);
const ys = xs.mul(1.2).add(5);

// Define a Linear Regression Model
const model = tf.sequential();
model.add(tf.layers.dense({units:1, inputShape:[1]}));

// Specify Loss and Optimizer
model.compile({loss:'meanSquaredError', optimizer:'sgd'});

// Train the Model
model.fit(xs, ys, {epochs:500}).then(() => {myFunction()});

// Use the Model
function myFunction() {
  const xArr = [];
  const yArr = [];
  for (let x = 0; x <= 10; x++) {
    xArr.push(x);
    let result = model.predict(tf.tensor([Number(x)]));
    result.data().then(y => {
      yArr.push(Number(y));
      if (x == 10) {plot(xArr, yArr)};
    });
  }
}

<html>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tfjs-vis.umd.min.js"></script>
<body>
<h2>TensorFlow.js</h2>
<p id="message">Model is training!</p>

// Define a Linear Regression Model
const model = tf.sequential();
model.add(tf.layers.dense({units:1, inputShape:[1]}));

// Specify Loss and Optimizer
model.compile({loss: 'meanSquaredError', optimizer:'sgd'});

// Train the Model
model.fit(xs, ys, {epochs:500}).then(() => {myFunction()});

// Use the Model
function myFunction() {
const xMax = 10;
const xArr = [];
const yArr = [];
for (let x = 0; x <= xMax; x++) {
let result = model.predict(tf.tensor([Number(x)]));
result.data().then(y => {
xArr.push(x);
yArr.push(Number(y));
if (x == xMax) {plot(xArr, yArr)};
});
}
document.getElementById('message').style.display="none";
}

function plot(xArr, yArr) {
// Define Data
const data = [{x:xArr,y:yArr,mode:"markers",type:"scatter"}];

// Define Layout
const layout = {
xaxis: {range: [0, 10]},
yaxis: {range: [0, 20]},
};

// Display Plot
Plotly.newPlot("myPlot", data, layout);
}
</script>
</body>
</html>

TensorFlow.js

The example is explained below:

Collecting Data

Create a tensor (xs) with 5 x values:

const xs = tf.tensor([0, 1, 2, 3, 4]);

Create a tensor (ys) with 5 correct y answers (multiply xs with 1.2 and add 5):

const ys = xs.mul(1.2).add(5);

Creating a Model

Create a sequential mode:.

const model = tf.sequential();

In a sequential model, the output from one layer is the input to the next layer.

Adding Layers

Add one dense layer to the model.

The layer is only one unit (tensor) and the shape is 1 (one dimentional):

model.add(tf.layers.dense({units:1, inputShape:[1]}));

in a dense the layer, every node is connected to every node in the preceding layer.

Compiling the Model

Compile the model using meanSquaredError as loss function and sgd (stochastic gradient descent) as optimizer function:

model.compile({loss:'meanSquaredError', optimizer:'sgd'});

Tensorflow Optimizers

Adadelta -Implements the Adadelta algorithm.
Adagrad - Implements the Adagrad algorithm.
Adam - Implements the Adam algorithm.
Adamax - Implements the Adamax algorithm.
Ftrl - Implements the FTRL algorithm.
Nadam - Implements the NAdam algorithm.
Optimizer - Base class for Keras optimizers.
RMSprop - Implements the RMSprop algorithm.
SGD - Stochastic Gradient Descent Optimizer.

Training the Model

Train the model (using xs and ys) with 500 repeats (epochs):

model.fit(xs, ys, {epochs:500}).then(() => {myFunction()});

Using the Model

After the model is trained, you can use it for many different purposes.

This example predicts 10 y values, given 10 x values, and calls a function to plot the predictions in a graph:

// Define a Linear Regression Model
const model = tf.sequential();
model.add(tf.layers.dense({units:1, inputShape:[1]}));

// Specify Loss and Optimizer
model.compile({loss: 'meanSquaredError', optimizer:'sgd'});

// Train the Model
model.fit(xs, ys, {epochs:500}).then(() => {myFunction()});

function plot(xArr, yArr) {
// Define Data
const data = [{x:xArr,y:yArr,mode:"markers",type:"scatter"}];

// Define Layout
const layout = {
xaxis: {range: [0, 10]},
yaxis: {range: [0, 20]},
};

// Display Plot
Plotly.newPlot("myPlot", data, layout);
}
</script>
</body>
</html>

TensorFlow.js

This example predicts 10 y values, given 10 x values, and calls a function to display the values:

<html>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<body>
<h2>TensorFlow.js</h2>
<p id="message">Model is training!</p>

// Define a Linear Regression Model
const model = tf.sequential();
model.add(tf.layers.dense({units:1, inputShape:[1]}));

// Specify Loss and Optimizer
model.compile({loss: 'meanSquaredError', optimizer:'sgd'});

// Train the Model
model.fit(xs, ys, {epochs:500}).then(() => {myFunction()});

// Use the Model
function myFunction() {
const xMax = 20;
const xArr = [];
const yArr = [];
for (let x = 10; x <= xMax; x++) {
let result = model.predict(tf.tensor([Number(x)]));
result.data().then(y => {
xArr.push(x);
yArr.push(Number(y));
if (x == xMax) {display(xArr,yArr)};
});
}

}

function display(xArr, yArr) {
let text = "Correct Predicted<br>";
for (let i = 0; i < xArr.length; i++) {
text += (xArr[i]*1.2+5).toFixed(4) + " " + yArr[i].toFixed(4) + "<br>";
}
document.getElementById('message').innerHTML = text;
}
</script>
</body>
</html>

TensorFlow.js

Model is training!

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Frequently Ask

What is VPS ? Explain in 100 word.

TensorFlow Models

TesorFlow.js

Training and DeployingMachine Learning ModelsIn the Browser

Tensorflow Models

A Tensorflow Project

Example

TensorFlow.js

The example is explained below:

Collecting Data

Creating a Model

Adding Layers

Compiling the Model

Tensorflow Optimizers

Training the Model

Using the Model

TensorFlow.js

TensorFlow.js

Frequently Ask

Training and Deploying
Machine Learning Models
In the Browser