Deep convolutional generative adversarial networks (DC-GANs) are a type of generative adversarial network (GAN) that are used to generate new images from a training dataset.
GANs are a type of neural network that is made up of two parts: a generator and a discriminator. The generator creates new images that are similar to the training data, while the discriminator tries to distinguish between the real training images and the fake images generated by the generator.
DC-GANs are a type of GAN that uses deep convolutional neural networks for both the generator and the discriminator.
In this tutorial, we will be using TensorFlow.js and Node.js to build a DC-GAN. We will be using the MNIST dataset, which is a dataset of handwritten digits.
First, we need to install TensorFlow.js and Node.js. We can do this using the following command:
npm install -g @tensorflow/tfjs-node
Next, we need to download the MNIST dataset. We can do this using the following command:
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
We also need to uncompress the downloaded files. We can do this using the following command:
gunzip *
Now that we have the MNIST dataset, we need to preprocess it so that it can be used by the DC-GAN.
First, we need to convert the images from the MNIST dataset into a format that can be used by the DC-GAN. We can do this using the following command:
tensorflowjs_converter --input_format=tfjs-layers-model --output_format=tfjs-layers-model --output_node_names='input,output' ./mnist_model.h5 ./tfjs_model
This command will convert the MNIST dataset into a format that can be used by the DC-GAN.
Next, we need to split the MNIST dataset into a training set and a test set. We can do this using the following command:
tensorflowjs_converter --input_format=tfjs-layers-model --output_format=tfjs-layers-model --output_node_names='input,output' --train_test_split=0.8 ./mnist_model.h5 ./tfjs_model
This command will split the MNIST dataset into a training set and a test set. The training set will be used to train the DC-GAN, while the test set will be used to evaluate the DC-GAN.
Now that we have the MNIST dataset, we can start building the DC-GAN.
First, we need to create the generator network. The generator network will take as input a vector of noise and will generate an image that is similar to the MNIST dataset.
We can create the generator network using the following code:
const generator = tf.sequential();
generator.add(tf.layers.dense({inputShape: [100], units: 7*7*256}));
generator.add(tf.layers.batchNormalization());
generator.add(tf.layers.leakyReLU());
generator.add(tf.layers.reshape({targetShape: [7, 7, 256]}));
generator.add(tf.layers.conv2dTranspose({
kernelSize: 5,
filters: 128,
strides: 2,
padding: 'same',
useBias: false
}));
generator.add(tf.layers.batchNormalization());
generator.add(tf.layers.leakyReLU());
generator.add(tf.layers.conv2dTranspose({
kernelSize: 5,
filters: 64,
strides: 2,
padding: 'same',
useBias: false
}));
generator.add(tf.layers.batchNormalization());
generator.add(tf.layers.leakyReLU());
generator.add(tf.layers.conv2dTranspose({
kernelSize: 5,
filters: 1,
strides: 2,
padding: 'same',
useBias: false,
activation: 'tanh'
}));
The generator network takes as input a vector of noise and generates an image that is similar to the MNIST dataset.
Next, we need to create the discriminator network. The discriminator network will take as input an image and will output a value that indicates whether the image is real or fake.
We can create the discriminator network using the following code:
const discriminator = tf.sequential();
discriminator.add(tf.layers.conv2d({
kernelSize: 5,
filters: 64,
strides: 2,
padding: 'same',
inputShape: [28, 28, 1]
}));
discriminator.add(tf.layers.leakyReLU());
discriminator.add(tf.layers.dropout({rate: 0.3}));
discriminator.add(tf.layers.conv2d({
kernelSize: 5,
filters: 128,
strides: 2,
padding: 'same'
}));
discriminator.add(tf.layers.leakyReLU());
discriminator.add(tf.layers.dropout({rate: 0.3}));
discriminator.add(tf.layers.flatten());
discriminator.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
The discriminator network takes as input an image and outputs a value that indicates whether the image is real or fake.
Now that we have the generator and discriminator networks, we can start training the DC-GAN.
First, we need to define the loss function. The loss function is used to measure how well the DC-GAN is doing.
We can define the loss function using the following code:
function dLoss(yTrue, yPred) {
return tf.losses.sigmoidCrossEntropy(yTrue, yPred);
}
function gLoss(yTrue, yPred) {
return tf.losses.sigmoidCrossEntropy(yTrue, yPred);
}
The loss function is used to measure how well the DC-GAN is doing.
Next, we need to define the optimizers. The optimizers are used to update the weights of the generator and discriminator networks.
We can define the optimizers using the following code:
const dOptimizer = tf.train.RMSPropOptimizer(0.0003);
const gOptimizer = tf.train.RMSPropOptimizer(0.0001);
The optimizers are used to update the weights of the generator and discriminator networks.
Now, we can start training the DC-GAN. We will train the DC-GAN for 100 epochs.
We can train the DC-GAN using the following code:
async function train() {
for (let i = 0; i < 100; i++) {
// Train the discriminator
let dLosses = [];
for (let j = 0; j < 10; j++) {
const noise = tf.randomNormal([BATCH_SIZE, 100]);
const generatedImages = generator.predict(noise);
const realImages = tf.data.Dataset.fromTensorSlices(images).batch(BATCH_SIZE).take(BATCH_SIZE).toArray();
const dRealLabels = tf.ones([BATCH_SIZE, 1]);
const dFakeLabels = tf.zeros([BATCH_SIZE, 1]);
const dLossReal = dLoss(dRealLabels, discriminator.predict(realImages));
const dLossFake = dLoss(dFakeLabels, discriminator.predict(generatedImages));
const dLoss = dLossReal + dLossFake;
dLosses.push(dLoss);
dOptimizer.minimize(dLoss);
}
const dAvgLoss = tf.mean(dLosses);
// Train the generator
const noise = tf.randomNormal([BATCH_SIZE, 100]);
const gLabels = tf.ones([BATCH_SIZE, 1]);
const gLoss = gLoss(gLabels, discriminator.predict(generator.predict(noise)));
gOptimizer.minimize(gLoss);
// Log the losses
if (i % 10 === 0) {
console.log(`Epoch: ${i}`);
console.log(`dLoss: ${dLoss.dataSync()[0]}`);
console.log(`gLoss: ${gLoss.dataSync()[0]}`);
}
}
}
train();
This code will train the DC-GAN for 100 epochs.
Now that the DC-GAN is trained, we can use it to generate new images.
First, we need to generate a noise vector. We can do this using the following code:
const noise = tf.randomNormal([1, 100]);
Next, we need to use the generator network to generate a new image from the noise vector. We can do this using the following code:
const generatedImage = generator.predict(noise);
Finally, we can visualize the generated image using the following code:
const imageTensor = generatedImage.reshape([28, 28]);
const imageData = imageTensor.dataSync();
const canvas = document.createElement('canvas');
canvas.width = 28;
canvas.height = 28;
const ctx = canvas.getContext('2d');
const imageDataArray = Array.from(imageData);
imageDataArray.forEach((value, index) => {
const i = Math.floor(index / 28);
const j = index % 28;
ctx.fillStyle = `rgb(${value}, ${value}, ${value})`;
ctx.fillRect(j, i, 1, 1);
});
document.body.appendChild(canvas);
This code will generate a new image from the noise vector.
In this tutorial, we have seen how to build a DC-GAN in TensorFlow.js and Node.js. We have also seen how to use the DC-GAN to generate new images.