In this post, we'll be looking at how to build generative models with TensorFlow.js and Node.js. Generative models are a powerful tool for learning how to generate new data, and TensorFlow.js is a great way to get started with deep learning in JavaScript.
Generative models are a type of machine learning model that can learn to generate new data. For example, a generative model could learn to generate new images of faces, or new pieces of text.
There are many different types of generative models, but one of the most popular types is the Generative Adversarial Network (GAN). GANs are a type of neural network that consists of two parts: a generator and a discriminator.
The generator is trained to generate new data, while the discriminator is trained to distinguish between real and generated data. The two networks compete with each other, and as they train, the generator gets better and better at fooling the discriminator.
Now that we've seen what generative models are, let's take a look at how to build one with TensorFlow.js.
There are two ways to use TensorFlow.js to build a generative model: you can either use the high-level layers API, or you can use the lower-level API.
If you're just getting started with TensorFlow.js, we recommend using the high-level layers API. The layers API makes it easy to build models with just a few lines of code, and it's easy to use with existing TensorFlow.js code.
To use the layers API, we first need to install TensorFlow.js. We can do this using npm:
npm install @tensorflow/tfjs
Once TensorFlow.js is installed, we can import it into our Node.js program:
const tf = require('@tensorflow/tfjs');
Now that we have TensorFlow.js installed, let's take a look at how to use it to build a generative model.
We'll start by building a simple generator network. The generator network will take in a vector of noise, and it will generate an image from that noise.
First, we'll need to create a function that takes in a noise vector and returns a generator network. We'll use the layers API to build our generator network:
function createGenerator(noise_size) {
const generator = tf.sequential();
// First, we'll build a fully connected layer that takes in our noise vector
// and generates an image from that noise.
generator.add(tf.layers.dense({
inputShape: [noise_size],
units: 7 * 7 * 256,
useBias: false,
kernelInitializer: tf.initializers.truncatedNormal({ stddev: 0.02 })
}));
// Next, we'll add a batch normalization layer.
generator.add(tf.layers.batchNormalization({
epsilon: 1e-5,
momentum: 0.9
}));
// Then, we'll add a Leaky ReLU activation layer.
generator.add(tf.layers.leakyReLU(0.2));
// Next, we'll reshape our vector into a 7x7x256 image.
generator.add(tf.layers.reshape({
targetShape: [7, 7, 256]
}));
// We'll then add a series of convolutional layers.
generator.add(tf.layers.conv2dTranspose({
kernelSize: [3, 3],
filters: 256,
strides: [2, 2],
padding: 'same',
kernelInitializer: tf.initializers.truncatedNormal({ stddev: 0.02 })
}));
generator.add(tf.layers.batchNormalization({
epsilon: 1e-5,
momentum: 0.9
}));
generator.add(tf.layers.leakyReLU(0.2));
generator.add(tf.layers.conv2dTranspose({
kernelSize: [3, 3],
filters: 128,
strides: [2, 2],
padding: 'same',
kernelInitializer: tf.initializers.truncatedNormal({ stddev: 0.02 })
}));
generator.add(tf.layers.batchNormalization({
epsilon: 1e-5,
momentum: 0.9
}));
generator.add(tf.layers.leakyReLU(0.2));
// Finally, we'll add a convolutional layer that outputs a 28x28x1 image.
generator.add(tf.layers.conv2dTranspose({
kernelSize: [3, 3],
filters: 1,
strides: [2, 2],
padding: 'same',
kernelInitializer: tf.initializers.truncatedNormal({ stddev: 0.02 }),
activation: 'tanh'
}));
// We'll return the generator model.
return generator;
}
Now that we have our generator network, let's take a look at how to use it to generate images.
First, we'll need to create a noise vector. We can do this by creating a Tensor with a random normal distribution:
const noise = tf.randomNormal([1, 100]);
Next, we'll create our generator network:
const generator = createGenerator(100);
Now that we have our generator network, we can use it to generate an image from our noise vector:
const generated_image = generator.predict(noise);
The generated image will be a 28x28x1 Tensor. We can convert it to a JavaScript array and plot it using the plotly.js library:
const image_array = generated_image.reshape([28, 28]).arraySync();
const data = [
{
z: image_array,
type: 'heatmap'
}
];
Plotly.newPlot('generated-image', data);
You can run the code above to generate an image. You should see something like this:
In this post, we've seen how to build a generative model with TensorFlow.js and Node.js. Generative models are a powerful tool for learning how to generate new data, and TensorFlow.js is a great way to get started with deep learning in JavaScript.