Developers looking to build generative models using TensorFlow.js and Node.js can do so by using a Generative Adversarial Network (GAN). In this post, we'll guide you through the process of building a GAN in TensorFlow.js and Node.js.
A GAN is a type of neural network that is used to generate new data from scratch. The network consists of two parts: a generator and a discriminator. The generator is responsible for generating new data, while the discriminator is responsible for determining whether the data is real or fake.
The first step in building a GAN is to create a new file called gan.js
. We'll start by requiring the tensorflow
and tfjs-node
modules:
const tf = require('tensorflow');
const tfNode = require('tfjs-node');
Next, we'll define the generator and discriminator models:
// Generator
const generator = tf.sequential();
generator.add(tf.layers.dense({units: 100, inputShape: [100]}));
generator.add(tf.layers.batchNormalization());
generator.add(tf.layers.leakyReLU());
generator.add(tf.layers.dense({units: 784, activation: 'tanh'}));
// Discriminator
const discriminator = tf.sequential();
discriminator.add(tf.layers.dense({units: 784, inputShape: [784]}));
discriminator.add(tf.layers.leakyReLU());
discriminator.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
In the code above, we've defined a generator that takes in 100-dimensional noise vectors and outputs 784-dimensional vectors (i.e. 28x28 images). We've also defined a discriminator that takes in 784-dimensional vectors and outputs a single value that indicates whether the vector is real or fake.
Next, we need to compile the models:
// Generator
generator.compile({
optimizer: tf.train.adam(0.0001),
loss: 'binaryCrossentropy'
});
// Discriminator
discriminator.compile({
optimizer: tf.train.adam(0.0001),
loss: 'binaryCrossentropy'
});
In the code above, we've specified that the models should be optimized using the Adam optimizer with a learning rate of 0.0001. We've also specified that the loss function for both models should be binary cross entropy.
Now that we've defined and compiled the models, we can start training the GAN. The training process consists of two steps: training the discriminator and training the generator.
The first step in training the GAN is to train the discriminator. We'll do this by generating a batch of fake data using the generator and a batch of real data using a training dataset. We'll then train the discriminator on these two batches of data.
// Train the discriminator
for (let i = 0; i < 1000; i++) {
// Generate a batch of fake data
const noise = tf.randomNormal([batchSize, 100]);
const fakeData = generator.predict(noise);
// Generate a batch of real data
const realData = tf.data.nextBatch(batchSize, [784]);
// Train the discriminator
const xs = tf.concat([fakeData, realData], 0);
const ys = tf.concat([tf.zeros([batchSize, 1]), tf.ones([batchSize, 1])], 0);
discriminator.fit(xs, ys);
}
In the code above, we've generated a batch of fake data using the generator and a batch of real data using a training dataset. We've then concatenated these two batches of data and trained the discriminator on them.
Now that the discriminator has been trained, we can train the generator. We'll do this by generating a batch of noise vectors and passing them through the generator. We'll then train the generator on these noise vectors.
// Train the generator
for (let i = 0; i < 1000; i++) {
// Generate a batch of noise vectors
const noise = tf.randomNormal([batchSize, 100]);
// Train the generator
const ys = tf.ones([batchSize, 1]);
generator.fit(noise, ys);
}
In the code above, we've generated a batch of noise vectors and passed them through the generator. We've then trained the generator on these noise vectors.
Now that the GAN has been trained, we can use it to generate new data. To do this, we'll generate a batch of noise vectors and pass them through the generator.
// Generate a batch of noise vectors
const noise = tf.randomNormal([batchSize, 100]);
// Generate new data
const generatedData = generator.predict(noise);
In the code above, we've generated a batch of noise vectors and passed them through the generator. This has generated a batch of new data that can be used for any purpose.