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What is CNN?

CNN is a deep learning model successful in image processing.

What is a CNN?

A convolutional neural network (CNN) captures local patterns (edges, corners, textures) in grid-structured data like images by sliding small filters over the whole image. Because the same filter is used at every position, the parameter count stays small and a pattern is recognised wherever it appears in the image.

# 28x28 grey image, fully connected first layer (128 neurons): 784 * 128 = 100352 weights
# 3x3 convolution, 16 filters:                                3 * 3 * 16 = 144 weights

A typical CNN stacks convolution + activation + pooling blocks and uses a fully connected classifier at the end.

The convolution operation

A filter is multiplied element-wise with a small region of the image and summed; the result is a single value in the output map. The filter is shifted by one step and the operation repeats. A filter that finds vertical edges:

img = torch.tensor([[0., 0., 1., 1.],
                    [0., 0., 1., 1.],
                    [0., 0., 1., 1.]])
filtre = torch.tensor([[-1., 1.],
                       [-1., 1.]])
# top-left 2x2 region: (0·-1 + 0·1 + 0·-1 + 0·1) = 0
# middle region:       (0·-1 + 1·1 + 0·-1 + 1·1) = 2  -> edge

CNNs learn the values of these filters from the data themselves.

Practice Convolution by hand

Apply filtre to the given image by hand: at every valid position (stride 1, no padding), sum the element-wise product with the 2×2 region and write the result into a 2×3 list. Print the result.

Starter code

model.py
import torch
from torch import nn

img = torch.tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.]])
filtre = torch.tensor([[-1., 1.], [-1., 1.]])

# buraya yaz

Sample solution

model.py
import torch
from torch import nn

img = torch.tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.]])
filtre = torch.tensor([[-1., 1.], [-1., 1.]])

sonuc = [[(img[i:i + 2, j:j + 2] * filtre).sum().item() for j in range(3)] for i in range(2)]
print(sonuc)

Expected output

[[0.0, 2.0, 0.0], [0.0, 2.0, 0.0]]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What is a CNN's main advantage over a fully connected network on images?

  • Needing no scaling
  • Weight sharing: few parameters and position-independent pattern recognition Correct answer
  • More parameters
  • Needing no labels

Explanation: The same filter is used at all positions.

2 Sum the element-wise product of the region and the filter.

Question 
deger = (bolge ____ filtre).sum()
Answer 
deger = (bolge * filtre).sum()

Explanation: Convolution is an element-wise product and sum over a region.

3 Match the concepts.

Term Meaning
filter Small learned weight matrix
sliding Applying at all positions
weight sharing Few parameters
feature map The filter's output

All lessons in Advanced

1 Neural network basics

  1. What is a neural network?
  2. Neuron
  3. Layers
  4. Activation
  5. Forward propagation
  6. Summary - Neural network

2 PyTorch basics

  1. What is PyTorch?
  2. Tensor
  3. Creating tensors
  4. Tensor properties
  5. GPU support
  6. Summary - PyTorch

3 Tensor operations

  1. Tensor operations
  2. Shape
  3. Dtype
  4. Reshape
  5. Indexing
  6. Summary - Tensor operations

4 Autograd

  1. What is autograd?
  2. requires_grad
  3. backward()
  4. grad
  5. Computational graph
  6. Summary - Autograd

5 Project: Neural network

  1. Preparing tensors
  2. Model definition
  3. Loss computation
  4. Optimizer

6 nn.Module

  1. What is nn.Module?
  2. __init__
  3. forward
  4. nn.Linear
  5. parameters()
  6. Summary - nn.Module

7 Training loop

  1. Training loop
  2. Epoch
  3. Loss
  4. backward()
  5. optimizer.step()
  6. Summary - Training loop

8 CNN

  1. What is CNN?
  2. Conv2d
  3. MaxPool2d
  4. Feature map
  5. Flatten
  6. Summary - CNN

9 RNN

  1. What is RNN?
  2. Hidden state
  3. Sequence
  4. Vanishing gradient
  5. Embedding
  6. Summary - RNN

10 LSTM

  1. What is LSTM?
  2. Gates
  3. Cell state
  4. input_size
  5. hidden_size
  6. Summary - LSTM

11 Project: CNN

  1. Preparing data
  2. CNN model
  3. Loss and optimizer
  4. Training

12 Transfer learning

  1. What is transfer learning?
  2. Pre-trained model
  3. Fine-tuning
  4. Feature extraction
  5. Advantages
  6. Summary - Transfer learning

13 Optimization

  1. What is an optimizer?
  2. SGD
  3. Adam
  4. Learning rate
  5. zero_grad()
  6. Summary - Optimization

14 Advanced final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4