Deep Learning with GAN using PyTorch, Collecting Random Rollout Data

Introduction

Generative Adversarial Networks (GANs) are a deep learning architecture that has made groundbreaking advances in the field of generative modeling. GANs are used to generate data through the competition between two neural networks: the Generator and the Discriminator. The model learns the given data distribution to generate new data, making it applicable to various fields through this characteristic. This document will implement GAN using the PyTorch framework and explain the concept of Random Rollout Data Collection.

1. Basic Concept of GAN

GAN is a model proposed by Ian Goodfellow, consisting of two networks that compete with each other. It is made up of a generator and a discriminator.

  • Generator: A network that takes random noise as input and generates data samples.
  • Discriminator: A network that determines whether a given data point is real or generated.

The generator tries to create increasingly realistic data to fool the discriminator, while the discriminator learns to identify the generator better. The two networks compete to minimize their respective loss functions. Ultimately, the generator produces data that is so realistic that the discriminator can no longer distinguish it.

1.1 Loss Function of GAN

The loss function of GAN is defined as follows. 


    LD = - Ex~pdata[log(D(x))] - Ez~pz[log(1 - D(G(z)))] 
    LG = - Ez~pz[log(D(G(z)))]

Here, D is the discriminator, and G is the generator. D distinguishes between real and fake data, and G learns to fool D.

2. Implementing GAN

2.1 Setting Up the Environment

In this example, we implement GAN using PyTorch. First, we install PyTorch and the required libraries. 


    !pip install torch torchvision matplotlib

2.2 Preparing Data

We will train GAN using the MNIST dataset. Using PyTorch’s torchvision package makes it easy to download and load data. 


    import torch
    import torchvision.transforms as transforms
    from torchvision import datasets

    # Download dataset
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
    train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)

    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)

2.3 Defining the GAN Model

We define the generator and discriminator networks. Each network is implemented by inheriting from PyTorch’s nn.Module. 


    import torch.nn as nn

    class Generator(nn.Module):
        def __init__(self):
            super(Generator, self).__init__()
            self.model = nn.Sequential(
                nn.Linear(100, 256),
                nn.ReLU(True),
                nn.Linear(256, 512),
                nn.ReLU(True),
                nn.Linear(512, 1024),
                nn.ReLU(True),
                nn.Linear(1024, 784),
                nn.Tanh()  # MNIST values range from -1 to 1
            )
        
        def forward(self, z):
            return self.model(z)

    class Discriminator(nn.Module):
        def __init__(self):
            super(Discriminator, self).__init__()
            self.model = nn.Sequential(
                nn.Linear(784, 1024),
                nn.LeakyReLU(0.2, inplace=True),
                nn.Linear(1024, 512),
                nn.LeakyReLU(0.2, inplace=True),
                nn.Linear(512, 256),
                nn.LeakyReLU(0.2, inplace=True),
                nn.Linear(256, 1),
                nn.Sigmoid()  # Final output is between 0 and 1
            )

        def forward(self, x):
            return self.model(x)

2.4 Setting Up the GAN Training Loop

We set up the training loop to allow GANs to compete between the generator and discriminator. GAN training is iterative; during each iteration, the discriminator learns to distinguish between real data and generated data, while the generator strives to deceive the discriminator. 


    generator = Generator()
    discriminator = Discriminator()

    criterion = nn.BCELoss()  # Binary Cross Entropy Loss
    lr = 0.0002
    num_epochs = 200
    g_optimizer = torch.optim.Adam(generator.parameters(), lr=lr)
    d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=lr)

    for epoch in range(num_epochs):
        for i, (images, _) in enumerate(train_loader):
            # Real data labels
            real_labels = torch.ones(images.size(0), 1)
            # Fake data labels
            fake_labels = torch.zeros(images.size(0), 1)

            # Discriminator training
            outputs = discriminator(images.view(-1, 784))
            d_loss_real = criterion(outputs, real_labels)
            real_score = outputs

            z = torch.randn(images.size(0), 100)
            fake_images = generator(z)
            outputs = discriminator(fake_images.detach())
            d_loss_fake = criterion(outputs, fake_labels)
            fake_score = outputs

            d_loss = d_loss_real + d_loss_fake
            d_optimizer.zero_grad()
            d_loss.backward()
            d_optimizer.step()

            # Generator training
            outputs = discriminator(fake_images)
            g_loss = criterion(outputs, real_labels)

            g_optimizer.zero_grad()
            g_loss.backward()
            g_optimizer.step()
        
        if (epoch+1) % 10 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], d_loss: {d_loss.item():.4f}, g_loss: {g_loss.item():.4f}, '
                  f'D(x): {real_score.mean().item():.2f}, D(G(z)): {fake_score.mean().item():.2f}')

2.5 Visualizing Generated Images

After the GAN training is complete, you can visualize the generated images to evaluate performance. 


    import matplotlib.pyplot as plt

    z = torch.randn(64, 100)
    generated_images = generator(z).view(-1, 1, 28, 28).detach().numpy()

    plt.figure(figsize=(8, 8))
    for i in range(64):
        plt.subplot(8, 8, i+1)
        plt.imshow(generated_images[i][0], cmap='gray')
        plt.axis('off')
    plt.show()

3. Random Rollout Data Collection

The images generated using GAN can be useful for creating new data. However, using such data may depend on specific environments or policies.
Rollout data collection refers to the process of gathering data generated under a given policy or environment. It is an important concept in machine learning and reinforcement learning, used to ensure the diversity of data and improve learning performance.

For example, when training a reinforcement learning agent, it is important for the agent to experience various situations through rollout data collection. This helps the agent learn diverse state-action pairs, leading to the generation of a more generalized policy.

3.1 Implementing an Environment for Rollout Data Collection

Libraries like OpenAI’s Gym make it easy to build reinforcement learning environments. Below is a simple example of collecting rollouts. 


    import gym

    env = gym.make('CartPole-v1')

    def collect_rollouts(env, num_rollouts=5):
        rollouts = []
        for _ in range(num_rollouts):
            state = env.reset()
            done = False
            rollout = []
            while not done:
                action = env.action_space.sample()  # Select a random action
                next_state, reward, done, _ = env.step(action)
                rollout.append((state, action, reward, next_state))
                state = next_state
            rollouts.append(rollout)
        
        return rollouts

    rollouts = collect_rollouts(env, num_rollouts=10)
    print(rollouts)

3.2 Utilizing Collected Data

The collected rollout data can be used for training GAN. By utilizing the collected data, the model can generate data in a variety of situations.
It can be used as input to the GAN model to randomly generate various states or to learn appropriate actions for specific states.

Conclusion

In this article, we explained the basic concepts and implementation methods of GAN and discussed the importance of random rollout data collection in reinforcement learning. Additionally, we explored the process of generating actual data through a GAN implementation example using PyTorch.
These techniques can be applied in various machine learning and deep learning fields, contributing to the effective generation and utilization of data suited for necessary situations.

References