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MaxPool2d

MaxPool2d reduces image size and preserves important features.

MaxPool2d

Pooling shrinks the feature map. nn.MaxPool2d(2) takes the largest value from every 2×2 region: height and width are halved, the channel count doesn't change. It has no parameters:

havuz = nn.MaxPool2d(2)
x = torch.tensor([[[[1., 3., 2., 4.],
                    [5., 6., 1., 2.],
                    [7., 2., 9., 0.],
                    [1., 8., 3., 4.]]]])
print(havuz(x).tolist())   # [[[[6.0, 4.0], [8.0, 9.0]]]]

Pooling gives robustness to small shifts and reduces the computation of the following layers.

Average and global pooling

nn.AvgPool2d takes the region's mean. Modern networks usually use global average pooling at the end: it reduces each channel to a single number, so the model becomes independent of the input size:

gap = nn.AdaptiveAvgPool2d(1)
print(gap(torch.zeros(8, 512, 7, 7)).shape)    # torch.Size([8, 512, 1, 1])
print(gap(torch.zeros(8, 512, 13, 9)).shape)   # still (8, 512, 1, 1)

AdaptiveAvgPool2d takes the target output size and works out the needed window itself.

Practice Pooling

Apply nn.MaxPool2d(2) and nn.AvgPool2d(2) to the given 4×4 image; print the two results with tolist() on separate lines.

Starter code

model.py
import torch
from torch import nn

x = torch.tensor([[[[1., 3., 2., 4.], [5., 6., 1., 2.], [7., 2., 9., 0.], [1., 8., 3., 4.]]]])

# buraya yaz

Sample solution

model.py
import torch
from torch import nn

x = torch.tensor([[[[1., 3., 2., 4.], [5., 6., 1., 2.], [7., 2., 9., 0.], [1., 8., 3., 4.]]]])

print(nn.MaxPool2d(2)(x).tolist())
print(nn.AvgPool2d(2)(x).tolist())

Expected output

[[[[6.0, 4.0], [8.0, 9.0]]]]
[[[[3.75, 2.25], [4.5, 4.0]]]]

Questions and answers

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

1 What is the shape after applying <code>MaxPool2d(2)</code> to a tensor of shape (8, 32, 16, 16)?

  • (8, 16, 16, 16)
  • (4, 32, 16, 16)
  • (8, 32, 14, 14)
  • (8, 32, 8, 8) Correct answer

Explanation: Only the spatial dimensions are halved.

2 Apply 2×2 max pooling.

Question 
havuz = nn.____(2)
Answer 
havuz = nn.MaxPool2d(2)

Explanation: MaxPool2d takes each region's largest value.

3 Match the poolings.

Term Meaning
MaxPool2d(2) Region's maximum
AvgPool2d(2) Region's mean
AdaptiveAvgPool2d(1) One value per channel
parameter count 0

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