- Introduction and Setup
- Course Introduction
- Environment Setup
- Advanced Neural Network Architectures
- Advanced Convolutional Neural Networks (CNNs)
- Implementing ResNet for Image Classification Tasks from Scratch
- Recurrent Neural Networks (RNNs) and LSTMs
- Understanding RNNs
- Code Sample for RNN
- Understanding LSTMs
- Code Sample for LSTM
- Understanding GRUs
- Code Sample for GRU
- Generative Adversarial Networks (GANs)
- Understanding the Architecture of GANs
- The Role of the Generator and Discriminator
- Implementing a Simple GAN for Image Generation
- Advanced GAN Techniques
- Implementing a Conditional GAN for MNIST Digit Generation
- Wasserstein GANs (WGANs)
- Implementing a WGAN for MNIST Digit Generation
- CycleGANs for image translation
- Implementing a CycleGAN for Image Translation
- Variational Autoencoders (VAEs)
- Understanding the Architecture and Functioning of VAEs
- The Concept of Latent Space and Reconstruction
- Practical Coding: Implementing VAEs
- Enhancing the VAE
- Transformer Models
- Architecture of the Transformer model
- The Attention Mechanism and Its Importance
- Practical Coding: Implementing Transformers
- Application: Implementing a Transformer for text generation
- Advanced Applications of Generative Models
- Fine-tuning GPT-3 for Custom Text Generation Tasks
- Enhancing Fine-Tuning with More Training Data
- Implementing neural style transfer
- Using GANs for High-Resolution Image Synthesis
- Music and Audio Generation
- Optimization and Fine-Tuning
- Techniques for Optimizing Model Performance
- Using Grid Search and Random Search for Hyperparameter Tuning
- Transfer Learning with BERT Model
- Real-World Projects
- Generating Realistic Faces with GANs
- Building a Chatbot with Transformer Models
- Best Practices and Advanced Tips
- Model Evaluation and Validation
- Measurements of Quantitative Importance for Text Generation
- Deploying Generative Models
- Future Trends and Research Directions
- Emerging Trends in Generative AI
- Ethical Considerations
- Conclusion
Implementing a CycleGAN for Image Translation
Implementing a CycleGAN for Image Translation
1. Import Libraries
| import tensorflow as tf from tensorflow.keras import layers, models import numpy as np import matplotlib.pyplot as plt |
To start, we import TensorFlow and Keras to build and train our models, as well as NumPy to work with data and Matplotlib to see the pictures that were made.
2. Load and Preprocess the Data
| dataset, metadata = tfds.load('cycle_gan/horse2zebra', with_info=True, as_supervised=True) train_horses, train_zebras = dataset['trainA'], dataset['trainB'] def preprocess_image(image, label): image = tf.image.resize(image, [286, 286]) image = tf.image.random_crop(image, size=[256, 256, 3]) image = tf.image.random_flip_left_right(image) image = (image / 127.5) - 1 return image train_horses = train_horses.map(preprocess_image).batch(1) train_zebras = train_zebras.map(preprocess_image).batch(1) |
We use TensorFlow Datasets to load the horse2zebra dataset. The pictures are enlarged, cropped at random to 256x256 pixels, and made to be normal across the [-1, 1] range. Random horizontal flips are also used to add to the training data.
3. Define the Generators
| def build_generator(): inputs = layers.Input(shape=[256, 256, 3]) down1 = layers.Conv2D(64, 4, strides=2, padding='same')(inputs) down1 = layers.LeakyReLU()(down1) down2 = layers.Conv2D(128, 4, strides=2, padding='same')(down1) down2 = layers.InstanceNormalization()(down2) down2 = layers.LeakyReLU()(down2) up1 = layers.Conv2DTranspose(128, 4, strides=2, padding='same')(down2) up1 = layers.InstanceNormalization()(up1) up1 = layers.ReLU()(up1) up2 = layers.Conv2DTranspose(64, 4, strides=2, padding='same')(up1) up2 = layers.InstanceNormalization()(up2) up2 = layers.ReLU()(up2) outputs = layers.Conv2D(3, 7, padding='same', activation='tanh')(up2) return models.Model(inputs, outputs) |
A set of convolutional and inverted convolutional layers make up the generator model. Normalization of instances and activations of the ReLU help keep training stable. A tanh activation is used in the last layer to make outputs in the [-1, 1] band.
4. Define the Discriminators
| def build_discriminator(): inputs = layers.Input(shape=[256, 256, 3]) down1 = layers.Conv2D(64, 4, strides=2, padding='same')(inputs) down1 = layers.LeakyReLU()(down1) down2 = layers.Conv2D(128, 4, strides=2, padding='same')(down1) down2 = layers.InstanceNormalization()(down2) down2 = layers.LeakyReLU()(down2) outputs = layers.Conv2D(1, 4, padding='same')(down2) return models.Model(inputs, outputs) |
The discriminator model is a type of convolutional network that tells the difference between real and fake pictures. To make things run faster, it uses LeakyReLU activations and instance normalization.
5. Compile the Models
| generator_g = build_generator() generator_f = build_generator() discriminator_x = build_discriminator() discriminator_y = build_discriminator() generator_g.compile(optimizer=tf.keras.optimizers.Adam(2e-4, beta_1=0.5), loss='mse') generator_f.compile(optimizer=tf.keras.optimizers.Adam(2e-4, beta_1=0.5), loss='mse') discriminator_x.compile(optimizer=tf.keras.optimizers.Adam(2e-4, beta_1=0.5), loss='mse') discriminator_y.compile(optimizer=tf.keras.optimizers.Adam(2e-4, beta_1=0.5), loss='mse') |
We use the Adam algorithm and mean squared error loss to help us build the generators and discriminators. This setup helps keep the mechanics of training fixed.
6. Training the CycleGAN
| def train_cyclegan(epochs, batch_size=1): for epoch in range(epochs): for image_x, image_y in tf.data.Dataset.zip((train_horses, train_zebras)): with tf.GradientTape(persistent=True) as tape: fake_y = generator_g(image_x, training=True) fake_x = generator_f(image_y, training=True) cycle_x = generator_f(fake_y, training=True) cycle_y = generator_g(fake_x, training=True) disc_real_x = discriminator_x(image_x, training=True) disc_real_y = discriminator_y(image_y, training=True) disc_fake_x = discriminator_x(fake_x, training=True) disc_fake_y = discriminator_y(fake_y, training=True) gen_g_loss = tf.reduce_mean(tf.square(disc_fake_y - 1.0)) gen_f_loss = tf.reduce_mean(tf.square(disc_fake_x - 1.0)) cycle_loss = tf.reduce_mean(tf.abs(image_x - cycle_x)) + tf.reduce_mean(tf.abs(image_y - cycle_y)) total_gen_g_loss = gen_g_loss + cycle_loss total_gen_f_loss = gen_f_loss + cycle_loss disc_x_loss = (tf.reduce_mean(tf.square(disc_real_x - 1.0)) + tf.reduce_mean(tf.square(disc_fake_x))) / 2.0 disc_y_loss = (tf.reduce_mean(tf.square(disc_real_y - 1.0)) + tf.reduce_mean(tf.square(disc_fake_y))) / 2.0 gradients_g = tape.gradient(total_gen_g_loss, generator_g.trainable_variables) gradients_f = tape.gradient(total_gen_f_loss, generator_f.trainable_variables) gradients_disc_x = tape.gradient(disc_x_loss, discriminator_x.trainable_variables) gradients_disc_y = tape.gradient(disc_y_loss, discriminator_y.trainable_variables) generator_g.optimizer.apply_gradients(zip(gradients_g, generator_g.trainable_variables)) generator_f.optimizer.apply_gradients(zip(gradients_f, generator_f.trainable_variables)) discriminator_x.optimizer.apply_gradients(zip(gradients_disc_x, discriminator_x.trainable_variables)) discriminator_y.optimizer.apply_gradients(zip(gradients_disc_y, discriminator_y.trainable_variables)) print(f'Epoch: {epoch}, Generator G Loss: {total_gen_g_loss.numpy()}, Generator F Loss: {total_gen_f_loss.numpy()}') train_cyclegan(epochs=100) |
The training loop is controlled by the train_cyclegan function. It uses gradient descent to update the generators and discriminators every time. Translations are useful and correct because of the cycle consistency loss, and pictures that are made are lifelike because of the discriminators. The producers are taught how to trick the discriminators, and the discriminators are taught how to tell the difference between real and fake pictures.
You can set up a CycleGAN to do single image-to-image translation by following these steps. This method makes it possible to change images in flexible and useful ways without using paired datasets. It opens up a lot of new options in computer vision and image processing.
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