gan.py

import numpy as np
from nnLayer import nnLayer
import json

class GenerativeAdverserialNetworks:
    def __init__(self, latent_dim, generator_layers, discriminator_layers, data_shape):
        """
        Initialize GAN with generator and discriminator networks
        
        Args:
            latent_dim: Dimension of latent/noise vector for generator
            generator_layers: List of layer sizes for generator network
            discriminator_layers: List of layer sizes for discriminator network
            data_shape: Shape of real data samples
        """
        self.latent_dim = latent_dim
        self.generator_layers = generator_layers
        self.discriminator_layers = discriminator_layers
        self.data_shape = data_shape
        self.debug = []
        
        # Initialize generator network
        # TODO: Create generator layers using nnLayer
        # TODO: Generator takes latent vector and outputs fake data
        self.generator = []
        
        # Initialize discriminator network
        # TODO: Create discriminator layers using nnLayer
        # TODO: Discriminator takes real/fake data and outputs probability
        self.discriminator = []
        
        self.debug.append({
            'message': 'GAN initialized',
            'latent_dim': latent_dim,
            'generator_layers': generator_layers,
            'discriminator_layers': discriminator_layers,
            'data_shape': data_shape
        })
    
    def sample_noise(self, batch_size):
        """
        Sample random noise vectors for generator input
        
        Args:
            batch_size: Number of noise samples to generate
        
        Returns:
            Random noise vectors
        """
        # TODO: Sample from normal distribution or uniform distribution
        # TODO: Shape should be (batch_size, latent_dim)
        # TODO: Return noise vectors
        pass
    
    def generator_forward(self, noise):
        """
        Forward pass through generator network
        
        Args:
            noise: Random noise vectors (batch_size, latent_dim)
        
        Returns:
            Generated fake data samples
        """
        # TODO: Pass noise through generator layers
        # TODO: Apply appropriate activations (ReLU, LeakyReLU, Tanh)
        # TODO: Final layer should match data_shape
        # TODO: Store intermediate values for backward pass
        
        self.debug.append({
            'generator_input_shape': noise.shape,
            'message': 'Generator forward pass'
        })
        
        # TODO: Return generated fake samples
        pass
    
    def discriminator_forward(self, data, training=True):
        """
        Forward pass through discriminator network
        
        Args:
            data: Input data (real or fake samples)
            training: Whether in training mode (affects batch norm, dropout)
        
        Returns:
            Probability that input is real data
        """
        # TODO: Pass data through discriminator layers
        # TODO: Apply appropriate activations (LeakyReLU, Sigmoid)
        # TODO: Final layer outputs probability (0-1)
        # TODO: Store intermediate values for backward pass
        
        self.debug.append({
            'discriminator_input_shape': data.shape,
            'training': training,
            'message': 'Discriminator forward pass'
        })
        
        # TODO: Return probability scores
        pass
    
    def discriminator_loss(self, real_output, fake_output):
        """
        Compute discriminator loss
        
        Args:
            real_output: Discriminator output for real data
            fake_output: Discriminator output for fake data
        
        Returns:
            Discriminator loss value
        """
        # TODO: Compute binary cross-entropy loss
        # TODO: Real data should have labels = 1
        # TODO: Fake data should have labels = 0
        # TODO: Total loss = loss_real + loss_fake
        # TODO: Return total discriminator loss
        pass
    
    def generator_loss(self, fake_output):
        """
        Compute generator loss
        
        Args:
            fake_output: Discriminator output for generated fake data
        
        Returns:
            Generator loss value
        """
        # TODO: Generator wants discriminator to classify fake as real
        # TODO: Use labels = 1 for fake data in loss computation
        # TODO: This is adversarial training objective
        # TODO: Return generator loss
        pass
    
    def train_discriminator(self, real_data, learning_rate=0.0002):
        """
        Training step for discriminator
        
        Args:
            real_data: Batch of real data samples
            learning_rate: Learning rate for discriminator
        
        Returns:
            Discriminator loss
        """
        batch_size = real_data.shape[0]
        
        # TODO: Generate fake data using generator
        # TODO: Forward pass real data through discriminator
        # TODO: Forward pass fake data through discriminator
        # TODO: Compute discriminator loss
        # TODO: Backward pass and update discriminator parameters
        # TODO: Do NOT update generator parameters here
        
        self.debug.append({
            'batch_size': batch_size,
            'message': 'Discriminator training step'
        })
        
        # TODO: Return discriminator loss
        pass
    
    def train_generator(self, batch_size, learning_rate=0.0002):
        """
        Training step for generator
        
        Args:
            batch_size: Size of batch for training
            learning_rate: Learning rate for generator
        
        Returns:
            Generator loss
        """
        # TODO: Sample noise vectors
        # TODO: Generate fake data using generator
        # TODO: Forward pass fake data through discriminator
        # TODO: Compute generator loss (wants to fool discriminator)
        # TODO: Backward pass through discriminator to generator
        # TODO: Update only generator parameters
        # TODO: Do NOT update discriminator parameters here
        
        self.debug.append({
            'batch_size': batch_size,
            'message': 'Generator training step'
        })
        
        # TODO: Return generator loss
        pass
    
    def train_step(self, real_data, d_learning_rate=0.0002, g_learning_rate=0.0002):
        """
        Complete training step for both networks
        
        Args:
            real_data: Batch of real data
            d_learning_rate: Discriminator learning rate
            g_learning_rate: Generator learning rate
        
        Returns:
            Dictionary with both losses
        """
        # TODO: Train discriminator first
        # TODO: Train generator second
        # TODO: Return both losses for monitoring
        
        losses = {
            'discriminator_loss': 0.0,  # TODO: actual discriminator loss
            'generator_loss': 0.0       # TODO: actual generator loss
        }
        
        return losses
    
    def generate_samples(self, num_samples):
        """
        Generate new samples using trained generator
        
        Args:
            num_samples: Number of samples to generate
        
        Returns:
            Generated samples
        """
        # TODO: Sample noise vectors
        # TODO: Forward pass through generator
        # TODO: Return generated samples (no gradient computation needed)
        pass
    
    def discriminator_backward(self, grad_output, real_data, fake_data, learning_rate):
        """
        Backward pass for discriminator
        
        Args:
            grad_output: Gradient from loss
            real_data: Real data batch
            fake_data: Fake data batch  
            learning_rate: Learning rate
        """
        # TODO: Compute gradients for discriminator parameters
        # TODO: Backpropagate through discriminator layers
        # TODO: Update discriminator weights and biases
        # TODO: Do not propagate gradients to generator
        pass
    
    def generator_backward(self, grad_output, noise, learning_rate):
        """
        Backward pass for generator
        
        Args:
            grad_output: Gradient from discriminator
            noise: Input noise vectors
            learning_rate: Learning rate
        """
        # TODO: Compute gradients for generator parameters
        # TODO: Backpropagate through generator layers
        # TODO: Update generator weights and biases
        pass
    
    def mode_collapse_detection(self, generated_samples):
        """
        Detect if generator is suffering from mode collapse
        
        Args:
            generated_samples: Recent generated samples
        
        Returns:
            Boolean indicating potential mode collapse
        """
        # TODO: Analyze diversity of generated samples
        # TODO: Compute statistics (mean, variance, etc.)
        # TODO: Compare with expected diversity
        # TODO: Return True if mode collapse detected
        pass
    
    def apply_spectral_normalization(self, layer_weights):
        """
        Apply spectral normalization to improve training stability
        
        Args:
            layer_weights: Weight matrix to normalize
        
        Returns:
            Spectrally normalized weights
        """
        # TODO: Compute largest singular value
        # TODO: Normalize weights by this value
        # TODO: This helps stabilize GAN training
        pass
    
    def get_debug_json(self):
        """Return debug information as JSON string"""
        return json.dumps(self.debug, indent=2)