diff --git a/neuron activation distribution b/neuron activation distribution
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+++ b/neuron activation distribution	
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+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Step 1: Load a pretrained ResNet model
+resnet = models.resnet18(pretrained=True)
+resnet.eval()
+
+# Step 2: Prepare an input image
+def preprocess_image(image_path):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    image = Image.open(image_path).convert('RGB')
+    return transform(image).unsqueeze(0)
+
+image_path = "/content/example.png"  # Replace with your image path
+input_tensor = preprocess_image(image_path)
+
+# Step 3: Define a hook to capture activations
+activations = {}
+
+def hook_fn(module, input, output):
+    activations[module] = output
+
+# Register hooks for a specific layer
+layer = resnet.layer4[1].conv2  # Example: last convolutional layer
+hook = layer.register_forward_hook(hook_fn)
+
+# Step 4: Forward pass
+with torch.no_grad():
+    resnet(input_tensor)
+
+# Unregister the hook
+hook.remove()
+
+# Step 5: Plot the activation distribution
+activation_data = activations[layer].squeeze().cpu().numpy()  # Shape: [C, H, W]
+flattened_data = activation_data.flatten()
+
+# Use seaborn for better visuals
+sns.histplot(flattened_data, bins=50, kde=True)
+plt.title("Neuron Activation Distribution")
+plt.xlabel("Activation Value")
+plt.ylabel("Frequency")
+plt.show()