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Summary - CNN

In this group you learned Conv2d, MaxPool2d, and feature maps.

Summary: CNN

In this group:

  • Convolution slides small filters; weight sharing gives few parameters and position-independent recognition.
  • nn.Conv2d: (N, C, H, W); output ⌊(H + 2p - k)/s⌋ + 1; parameters out·in·k·k + out.
  • Pooling: MaxPool2d, AvgPool2d, global AdaptiveAvgPool2d(1).
  • Blocks: Conv → BatchNorm → ReLU → Pool; maps shrink, channels grow, the receptive field widens.
  • A linear classifier after nn.Flatten or global pooling.

Practice CNN with global pooling

Build a model with the body Conv2d(3, 16, 3, padding=1) → ReLU → Conv2d(16, 32, 3, padding=1) → ReLU, followed by AdaptiveAvgPool2d(1), Flatten and Linear(32, 5). Print the output shapes for both a (2, 3, 32, 32) and a (2, 3, 50, 70) input on separate lines.

Starter code

model.py
import torch
from torch import nn

# buraya yaz

Sample solution

model.py
import torch
from torch import nn

model = nn.Sequential(
    nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(),
    nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(),
    nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Linear(32, 5))
print(model(torch.zeros(2, 3, 32, 32)).shape)
print(model(torch.zeros(2, 3, 50, 70)).shape)

Expected output

torch.Size([2, 5])
torch.Size([2, 5])

Questions and answers

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

1 What is the parameter count of a 3×3 convolution with 64 input and 128 output channels?

  • 73856 Correct answer
  • 73728
  • 8192
  • 1152

Explanation: 128 · 64 · 3 · 3 = 73728 weights + 128 biases = 73856.

2 Reduce each channel to a single value.

Question 
gap = nn.____(1)
Answer 
gap = nn.AdaptiveAvgPool2d(1)

Explanation: AdaptiveAvgPool2d(1) produces a 1×1 output independent of the input size.

3 Match the formulas.

Term Meaning
output size ⌊(H + 2p - k)/s⌋ + 1
Conv2d parameters out·in·k·k + out
MaxPool2d(2) H/2, W/2
Flatten C·H·W

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