Wasserstein GAN implementation using Ruby ->

Implementing a Wasserstein GAN (Generative Adversarial Network) using Ruby involves using a machine learning library that supports deep learning, such as TensorFlow or Keras, and building the necessary components of the GAN architecture. In this example, we'll use TensorFlow and the Keras API for simplicity.

Here's a basic outline of the steps involved in implementing a Wasserstein GAN using Ruby:

Install the necessary libraries: Make sure you have TensorFlow and Keras installed. You can install them by adding the following lines to your Gemfile:

gem 'tensorflow'
gem 'keras'

Import the required libraries in your Ruby script:

require 'tensorflow'
require 'keras'

Define the generator and discriminator networks using the Keras API. Here's a simple example:

generator = Keras::Sequential.new([
Keras::Layers::Dense.new(256, input_shape: [latent_dim]),
Keras::Layers::LeakyReLU.new(0.2),
Keras::Layers::Dense.new(512),
Keras::Layers::LeakyReLU.new(0.2),
Keras::Layers::Dense.new(1024),
Keras::Layers::LeakyReLU.new(0.2),
Keras::Layers::Dense.new(n_features, activation: 'tanh')
])

discriminator = Keras::Sequential.new([
Keras::Layers::Dense.new(512, input_shape: [n_features]),
Keras::Layers::LeakyReLU.new(0.2),
Keras::Layers::Dense.new(256),
Keras::Layers::LeakyReLU.new(0.2),
Keras::Layers::Dense.new(1)
])

Define the Wasserstein loss function and compile the discriminator:

def wasserstein_loss(y_true, y_pred)
return Keras::Backend.mean(y_true * y_pred)
end

discriminator.compile(optimizer: Keras::Optimizers::RMSprop.new(lr: 0.00005), loss: wasserstein_loss)

Combine the generator and discriminator into a Wasserstein GAN model:

wgan = Keras::Model.new(generator.inputs, discriminator.call(generator.output))
wgan.compile(optimizer: Keras::Optimizers::RMSprop.new(lr: 0.00005), loss: wasserstein_loss)

Train the Wasserstein GAN by alternating between training the discriminator and the generator :

def train_wgan(generator, discriminator, wgan, x_train, n_epochs, batch_size)
n_batches = x_train.shape[0] / batch_size

for epoch in 0...n_epochs
for batch in 0...n_batches
# Train the discriminator
for _ in 0...n_critic
real_samples = x_train[batch * batch_size, (batch + 1) * batch_size]
noise = random_noise(batch_size, latent_dim)
fake_samples = generator.predict(noise)

    discriminator.trainable = true
    discriminator_loss = discriminator.train_on_batch(real_samples, -1)
    discriminator_loss += discriminator.train_on_batch(fake_samples, 1)
    discriminator.trainable = false
  end

  # Train the generator
  noise = random_noise(batch_size, latent_dim)
  generator_loss = wgan.train_on_batch(noise, -1)
end

end
end

Note that in the training loop, the discriminator is trained multiple times (n_critic) for every generator update. This is a key characteristic of such features.

Let us test them on MRI Scans or cryo-EM Images :

To test the Wasserstein GAN implementation on MRI scans or cryo-EM images, you'll need a dataset of MRI scans or cryo-EM images to train the GAN. You can use publicly available datasets or your own dataset, depending on your requirements.

Here's an example of how you can modify the code to train the Wasserstein GAN using MRI scans:

Preprocess the MRI scans or cryo-EM images:

Convert the images to a compatible format (e.g., numpy arrays) and normalize the pixel values.
Split the dataset into training and validation sets if necessary.

Modify the input shape of the generator and discriminator networks to match the image dimensions:

Load and preprocess the MRI scans or cryo-EM images dataset for training:
ruby

x_train = load_dataset() # Load and preprocess the MRI scans or cryo-EM images dataset

Train the Wasserstein GAN:

batch_size = 32
n_epochs = 100
n_critic = 5
latent_dim = 100

train_wgan(generator, discriminator, wgan, x_train, n_epochs, batch_size)

Generate synthetic MRI scans or cryo-EM images using the trained generator:

def generate_images(generator, n_samples)
noise = random_noise(n_samples, latent_dim)
generated_images = generator.predict(noise)

Post-process generated_images if necessary

return generated_images
end

synthetic_images = generate_images(generator, 10) # Generate 10 synthetic MRI scans or cryo-EM images

Remember to adapt the code to the specific dimensions and requirements of your dataset, as well as adjust hyperparameters and other settings based on your specific needs.

How to display the processed images ?

To display the processed images generated by the Wasserstein GAN in Ruby, you can use various libraries depending on your preferred method of visualization. One popular library for image processing and visualization in Ruby is RMagick. Here's an example of how you can use RMagick to display the processed images:

Install the RMagick gem by adding the following line to your Gemfile:

gem 'rmagick'

Require the RMagick library in your Ruby script:

require 'rmagick'

Assuming synthetic_images is a Ruby array containing the processed images generated by the Wasserstein GAN, you can iterate over the array and display the images using RMagick:

synthetic_images.each do |image|

Create an RMagick image object from the processed image array

rmagick_image = Magick::Image.from_blob(image.tostring)[0]

Display the RMagick image

rmagick_image.display
end

This code iterates over each image in synthetic_images, converts it to an RMagick image object using Magick::Image.from_blob, and then displays it using the display method.

Make sure you have an image viewer installed on your system that is compatible with RMagick's display method, such as ImageMagick.

Note: The above code assumes that the processed images are in a compatible format for RMagick. If the processed images are in a different format, you might need to convert them or adjust the code accordingly.

Testing in progress - Thanks from Dr.Nirmal - Informatics R&D - Inspire others ALWAYS - Nonprofit Research,

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Post-process generated_images if necessary

Create an RMagick image object from the processed image array

Display the RMagick image

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