Building a Generative AI with Ruby: Unleashing Creativity through Code

Photo by Jezael Melgoza on Unsplash

Introduction:

Welcome back, fellow developers and AI enthusiasts! Today, we are diving into the fascinating world of generative artificial intelligence (AI) using Ruby. As a Senior Ruby on Rails developer, I am thrilled to share with you an exciting journey of creating a generative AI model that can unleash its creative potential. So, let’s get started!

What is Generative AI?

Generative AI refers to a branch of artificial intelligence that focuses on creating models capable of generating new and original content. These models learn from existing data and use it to produce novel outputs, such as images, music, or even text. By leveraging the power of machine learning algorithms, we can train our AI models to generate content that exhibits creativity and uniqueness.

Setting Up Our Environment:

Before we dive into the code, let’s ensure we have the necessary tools and libraries installed. We’ll be using Ruby and the popular machine learning library, TensorFlow, along with the TensorFlow Ruby API. Make sure you have these dependencies set up in your development environment.

Defining the Problem:

For our generative AI project, let’s focus on generating unique and artistic images. We’ll train our model on a dataset of images and then use it to generate new images that resemble the training data but possess their own creative flair.

Data Preparation:

To begin, we need a dataset of images to train our model. You can either collect your own dataset or use publicly available datasets. Ensure that your dataset is diverse and contains a wide range of images to encourage creativity in the generated outputs.

Training the Model:

Now, let’s dive into the code! We’ll start by importing the necessary libraries and defining our model architecture. TensorFlow provides a high-level API that simplifies the process of building neural networks. Here’s a sample code snippet to get you started:

#ruby
require 'tensorflow'
# Define the model architecture
model = TensorFlow::Keras::Sequential.new
model.add(TensorFlow::Keras::Layers::Dense.new(64, activation: 'relu', input_shape: [100]))
model.add(TensorFlow::Keras::Layers::Dense.new(128, activation: 'relu'))
model.add(TensorFlow::Keras::Layers::Dense.new(784, activation: 'sigmoid'))
# Compile the model
model.compile(optimizer: 'adam', loss: 'binary_crossentropy')

In this example, we create a simple feedforward neural network with three layers. The input layer has 100 neurons, followed by two hidden layers with 64 and 128 neurons, respectively. The output layer consists of 784 neurons, representing the flattened image.

Next, we’ll load and preprocess our dataset, splitting it into training and validation sets. TensorFlow provides various utilities for data preprocessing, such as normalization and resizing.

# Load and preprocess the dataset
(x_train, _), (x_test, _) = TensorFlow::Keras::Datasets::Mnist.load_data
x_train = x_train / 255.0
x_test = x_test / 255.0
# Train the model
model.fit(x_train, x_train, epochs: 10, batch_size: 32, validation_data: [x_test, x_test])

In this snippet, we load the MNIST dataset, which consists of grayscale images of handwritten digits. We normalize the pixel values to the range [0, 1] and use the same data for both input and output, as we aim to reconstruct the input images.

Generating New Images:

Once our model is trained, we can utilize it to generate new images. We’ll provide random noise as input to the model and let it generate an output image. Here’s a code snippet to demonstrate this:

# Generate new images
noise = TensorFlow::Random.normal([1, 100])
generated_image = model.predict(noise)
# Display the generated image
display_image(generated_image)

In this example, we generate random noise using TensorFlow’s random module. We then pass this noise through our trained model using the <predict> method, which generates an output image. Finally, we can display the generated image using a suitable visualization library or save it to disk.

Conclusion:

Congratulations! You have successfully built a generative AI model using Ruby and TensorFlow. By training our model on a diverse dataset, we can unlock its creative potential and generate unique images. Remember, this is just the tip of the iceberg when it comes to generative AI. Feel free to experiment with different architectures, datasets, and techniques to push the boundaries of creativity even further.

In our next blog post, we’ll explore more advanced techniques, such as generative adversarial networks (GANs), to create even more realistic and captivating generative AI models. Stay tuned!

Happy coding and happy generating!