How deeply does Deep Learning with PyTorch, Second Edition cover pooling?

Defines regional aggregation, compares three downsampling methods, and focuses on non-overlapping 2 × 2 max-pooling tiles.

Pooling

As explained in Deep Learning with PyTorch, Second Edition

A downsampling technique that reduces spatial resolution by aggregating local regions of a feature map.

Pooling, also called downsampling or subsampling, reduces a feature map’s spatial dimensions by aggregating values from local regions. It lets successive convolutions work with a larger effective scope without using oversized kernels. section 8.2.3

Common ways to compute each lower-resolution value include:

Average pooling computes the mean of each region; it was common early on but is less favored. section 8.2.3
Max pooling selects the largest value, preserving the strongest response while discarding the other values. section 8.2.3
A strided convolution calculates outputs at intervals while still incorporating input from the preceding layer. section 8.2.3

A 2 × 2 max-pooling step

Applying max pooling to non-overlapping 2 × 2 tiles replaces each tile with its largest value. A three-channel 32 × 32 input becomes a three-channel 16 × 16 output. section 8.2.3

# In[21]:
pool = nn.MaxPool2d(2)
output = pool(img.unsqueeze(0))

img.unsqueeze(0).shape, output.shape

# Out[21]:
(torch.Size([1, 3, 32, 32]), torch.Size([1, 3, 16, 16]))

The PyTorch-style example constructs a 2 × 2 max-pooling layer and shows the resulting tensor shapes. section 8.2.3

How Deep Learning with PyTorch, Second Edition covers it

Cover of Deep Learning with PyTorch, Second Edition

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Also explained in

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Inside Deep Learning: Math, Algorithms, ModelsA neural-network operation that downsamples local neighborhoods by aggregating their values.
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Natural Language Processing in Action, Second EditionA pooling layer is a neural-network layer that aggregates neighboring activations to reduce their representation while retaining salient information.
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Math and Architectures of Deep LearningA downsampling operation on feature maps that reduces spatial resolution, often by local averaging or max selection.
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Build a Text-to-Image Generator (from Scratch)A pooling layer aggregates values within local spatial windows to produce a reduced or transformed feature representation.
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Evolutionary Deep Learning: Genetic algorithms and neural networksPooling is a neural-network operation that aggregates values within local regions to reduce spatial or feature-map dimensions.
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Graph Neural Networks in ActionA graph operation that reduces a collection of representations to a smaller representation, often for graph-level prediction.
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AI-Powered SearchPooling is the aggregation of multiple representation values into a single lower-level or fixed-size representation.

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Related concepts

convolutional neural networkused by · A convolutional neural network is a neural-network architecture that applies learned convolutional operations to extract patterns from structured data.
max poolingincludes · Max pooling is an aggregation operation that replaces each local or grouped region with its maximum value, often to downsample a representation.
downsamplingequivalent to · Downsampling is the reduction of data quantity, spatial resolution, temporal resolution, or sampling rate by retaining, selecting, or aggregating fewer representative values or observations.
upsamplingalternative to · Upsampling is a process that increases the number, density, or spatial or temporal resolution of samples or values in a dataset or representation by adding, duplicating, or estimating them.
gradientuses · A gradient is the vector of partial derivatives of a scalar function with respect to its variables, indicating its local rate and direction of change.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.