Module 4 Lab: CNN feature maps

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Open in Colab

Student guidance: If you are new to Python, start here. In Colab, choose Runtime > Run all, then change one small value in the lab and write what changed. Use Codespaces only when your instructor asks you to work with the full course repository.

Module 4 Lab: CNN feature maps#

Inspect how convolutional filters respond to simple synthetic image patterns.

Run the setup cell, inspect the printed diagnostics, and then complete the exercises at the end. The lab is intentionally small enough to run in GitHub Codespaces without a GPU.

import torch
from torch import nn
import matplotlib.pyplot as plt

images = torch.zeros(4, 1, 16, 16)
images[0, :, 4:12, 7:9] = 1.0
images[1, :, 7:9, 4:12] = 1.0
images[2, :, torch.arange(4, 12), torch.arange(4, 12)] = 1.0
images[3] = torch.rand(1, 16, 16) * 0.4

conv = nn.Conv2d(1, 3, kernel_size=3, padding=1, bias=False)
with torch.no_grad():
    conv.weight[0, 0] = torch.tensor([[-1., 0., 1.], [-1., 0., 1.], [-1., 0., 1.]])
    conv.weight[1, 0] = torch.tensor([[-1., -1., -1.], [0., 0., 0.], [1., 1., 1.]])
    conv.weight[2, 0] = torch.tensor([[0., 1., 0.], [1., -4., 1.], [0., 1., 0.]])
features = conv(images)
print("image batch:", tuple(images.shape))
print("feature maps:", tuple(features.shape))
print("vertical-edge response mean:", features[:, 0].abs().mean().item())
plt.figure(figsize=(4, 3))
plt.imshow(features[0, 0].detach(), cmap="gray")
plt.axis("off")
plt.close()

image batch: (4, 1, 16, 16)
feature maps: (4, 3, 16, 16)
vertical-edge response mean: 0.2178533375263214

Lab exercises#

  1. Change one model or data parameter and rerun the lab.

  2. Record whether the metric improved, worsened, or stayed roughly the same.

  3. Add one sentence connecting the result to Convolutional neural networks for vision.

  4. Identify one limitation of this toy setup before applying the idea to a real dataset.

# Reflection workspace
observation = ""
next_experiment = ""
print({"observation": observation, "next_experiment": next_experiment})

{'observation': '', 'next_experiment': ''}
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