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Help me met het ontwerpen en implementeren van PyTorch modeluitlegbaarheid met tegenfeitelijke analyse

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Deze prompt stelt gebruikers in staat een dieper inzicht te krijgen in de beslissingen van PyTorch-modellen door gebruik te maken van tegenfeitelijke analyse, een krachtige maar minder vaak behandelde uitlegbaarheidstechniek. Het helpt aantonen hoe kleine veranderingen in invoerkenmerken modelvoorspellingen kunnen veranderen, wat bruikbare inzichten biedt voor het debuggen, beoordelen van eerlijkheid en het opbouwen van vertrouwen. In vergelijking met standaard uitlegbaarheidsmethoden biedt tegenfeitelijke analyse een meer intuïtieve en scenario-gebaseerde verklaring, wat het waardevol maakt voor zowel technische als niet-technische belanghebbenden.

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Help me met het ontwerpen en implementeren van tegenfeitelijke analyse voor mijn PyTorch-model om te onderzoeken hoe veranderingen in invoerkenmerken de voorspellingen beïnvloeden. Mijn PyTorch-modelarchitectuur: <beschrijf je PyTorch mo ...

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provider: openai
model: gpt-4.1-nano
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provider: deepseek
model: deepseek-chat
# Counterfactual Analysis for PyTorch CNN on Chest X-ray Classification I'll help you implement counterfactual analysis for your pneumonia detection CNN. Counterfactual instances show minimal changes needed to flip the model's prediction, providing valuable insights into decision boundaries. ## Implementation Approach ```python import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np import matplotlib.pyplot as plt from torchvision import transforms from PIL import Image # Define your CNN architecture (assuming this matches your model) class PneumoniaCNN(nn.Module): def __init__(self): super(PneumoniaCNN, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, padding=1) self.conv2 = nn.Conv2d(32, 64, 3, padding=1) self.conv3 = nn.Conv2d(64, 128, 3, padding=1) self.pool = nn.MaxPool2d(2, 2) self.fc1 = nn.Linear(128 * 28 * 28, 512) # Adjust based on your input size self.fc2 = nn.Linear(512, 2) self.dropout = nn.Dropout(0.5) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = self.pool(F.relu(self.conv3(x))) x = x.view(-1, 128 * 28 * 28) # Adjust based on your input size x = self.dropout(F.relu(self.fc1(x))) x = self.fc2(x) return x # Counterfactual Generator Class class CounterfactualGenerator: def __init__(self, model, target_class, lambda_param=0.1, learning_rate=0.01): self.model = model self.target_class = target_class self.lambda_param = lambda_param self.learning_rate = learning_rate self.model.eval() # Set model to evaluation mode def generate_counterfactual(self, original_input, max_iterations=1000, confidence_threshold=0.9, verbose=False): """ Generate counterfactual explanation for a given input """ # Clone and detach the original input counterfactual = original_input.clone().detach().requires_grad_(True) optimizer = optim.Adam([counterfactual], lr=self.learning_rate) for iteration in range(max_iterations): optimizer.zero_grad() # Get model prediction output = self.model(counterfactual) probabilities = F.softmax(output, dim=1) target_prob = probabilities[0, self.target_class] # Loss function: encourage target class + minimize changes prediction_loss = -torch.log(target_prob + 1e-10) # Negative log likelihood distance_loss = torch.norm(counterfactual - original_input, p=2) total_loss = prediction_loss + self.lambda_param * distance_loss total_loss.backward() optimizer.step() # Apply constraints to keep image valid with torch.no_grad(): counterfactual.data = torch.clamp(counterfactual, 0, 1) if verbose and iteration % 100 == 0: print(f"Iteration {iteration}: Target prob={target_prob.item():.4f}, " f"Loss={total_loss.item():.4f}") # Early stopping if we reach desired confidence if target_prob > confidence_threshold: if verbose: print(f"Early stopping at iteration {iteration}") break return counterfactual.detach() # Evaluation and Visualization Functions def evaluate_counterfactual(original, counterfactual, model, class_names): """Evaluate and compare original vs counterfactual predictions""" model.eval() with torch.no_grad(): orig_output = model(original.unsqueeze(0)) cf_output = model(counterfactual.unsqueeze(0)) orig_probs = F.softmax(orig_output, dim=1) cf_probs = F.softmax(cf_output, dim=1) orig_pred = torch.argmax(orig_probs, dim=1).item() cf_pred = torch.argmax(cf_probs, dim=1).item() print(f"Original prediction: {class_names[orig_pred]} " f"(confidence: {orig_probs[0, orig_pred]:.4f})") print(f"Counterfactual prediction: {class_names[cf_pred]} " f"(confidence: {cf_probs[0, cf_pred]:.4f})") # Calculate change magnitude change_magnitude = torch.norm(counterfactual - original).item() print(f"Change magnitude (L2 norm): {change_magnitude:.4f}") return orig_probs, cf_probs, change_magnitude def visualize_comparison(original, counterfactual, difference, class_names, orig_probs, cf_probs): """Visualize original, counterfactual, and differences""" fig, axes = plt.subplots(1, 4, figsize=(16, 4)) # Original image axes[0].imshow(original.squeeze().cpu().numpy(), cmap='gray') axes[0].set_title(f'Original\n{class_names[torch.argmax(orig_probs).item()]}: ' f'{orig_probs.max().item():.3f}') axes[0].axis('off') # Counterfactual axes[1].imshow(counterfactual.squeeze().cpu().numpy(), cmap='gray') axes[1].set_title(f'Counterfactual\n{class_names[torch.argmax(cf_probs).item()]}: ' f'{cf_probs.max().item():.3f}') axes[1].axis('off') # Difference diff = (counterfactual - original).squeeze().cpu().numpy() axes[2].imshow(diff, cmap='RdBu_r', vmin=-np.max(np.abs(diff)), vmax=np.max(np.abs(diff))) axes[2].set_title('Changes (amplified)') axes[2].axis('off') # Probability comparison classes = range(len(class_names)) width = 0.35 axes[3].bar([x - width/2 for x in classes], orig_probs.squeeze().cpu().numpy(), width, label='Original', alpha=0.7) axes[3].bar([x + width/2 for x in classes], cf_probs.squeeze().cpu().numpy(), width, label='Counterfactual', alpha=0.7) axes[3].set_xlabel('Classes') axes[3].set_ylabel('Probability') axes[3].set_title('Probability Comparison') axes[3].set_xticks(classes) axes[3].set_xticklabels(class_names) axes[3].legend() plt.tight_layout() plt.show() # Main execution example def main(): # Load your trained model model = PneumoniaCNN() model.load_state_dict(torch.load('path_to_your_model.pth')) model.eval() class_names = ['Normal', 'Pneumonia'] # Load and preprocess your image transform = transforms.Compose([ transforms.Grayscale(), transforms.Resize((224, 224)), # Adjust based on your model input size transforms.ToTensor(), transforms.Normalize(mean=[0.5], std=[0.5]) ]) # Example: Load an image predicted as pneumonia image_path = 'path_to_pneumonia_xray.jpg' original_image = Image.open(image_path) input_tensor = transform(original_image).unsqueeze(0) # Generate counterfactual for pneumonia -> normal generator = CounterfactualGenerator(model, target_class=0, lambda_param=0.5, learning_rate=0.1) counterfactual = generator.generate_counterfactual(input_tensor, max_iterations=500, verbose=True) # Evaluate and visualize orig_probs, cf_probs, change_mag = evaluate_counterfactual( input_tensor.squeeze(0), counterfactual.squeeze(0), model, class_names ) difference = counterfactual.squeeze(0) - input_tensor.squeeze(0) visualize_comparison( input_tensor.squeeze(0), counterfactual.squeeze(0), difference, class_names, orig_probs, cf_probs ) return counterfactual, change_mag if __name__ == "__main__": counterfactual, change_magnitude = main() ``` ## Interpretation and Insights ### How to Interpret Counterfactual Results: 1. **Minimal Changes Analysis**: The difference map shows what minimal changes would flip the prediction 2. **Feature Importance**: Areas with significant changes indicate features the model relies on 3. **Decision Boundaries**: Understand what distinguishes pneumonia from normal cases 4. **Model Robustness**: Small changes causing prediction flips may indicate vulnerability ### Key Insights for Medical Imaging: ```python def analyze_medical_insights(original, counterfactual, model): """Additional medical-specific analysis""" # Extract features from intermediate layers activation = {} def get_activation(name): def hook(model, input, output): activation[name] = output.detach() return hook # Register hooks to intermediate layers hooks = [] for name, layer in model.named_modules(): if isinstance(layer, nn.Conv2d): hooks.append(layer.register_forward_hook(get_activation(name))) # Get activations for both images with torch.no_grad(): _ = model(original.unsqueeze(0)) orig_activations = activation.copy() _ = model(counterfactual.unsqueeze(0)) cf_activations = activation.copy() # Remove hooks for hook in hooks: hook.remove() # Analyze which features changed most feature_changes = {} for layer_name in orig_activations: change = torch.norm(cf_activations[layer_name] - orig_activations[layer_name]).item() feature_changes[layer_name] = change print("Feature change analysis by layer:") for layer, change in sorted(feature_changes.items(), key=lambda x: x[1], reverse=True): print(f"{layer}: {change:.4f}") return feature_changes ``` ### Practical Considerations: 1. **Medical Validation**: Always validate findings with medical experts 2. **Multiple Examples**: Generate counterfactuals for multiple cases to identify patterns 3. **Parameter Tuning**: Adjust λ parameter to balance change magnitude vs prediction confidence 4. **Clinical Relevance**: Ensure changes are medically plausible This implementation provides a robust framework for understanding your pneumonia detection model's decision-making process through counterfactual analysis.
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