Feature map

As explained in Deep Learning with Python, Third Edition

A spatial grid of activations produced by a convolutional or pooling layer, where each channel encodes the presence of a learned feature over locations.

>>> model.summary()
Model: "functional"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                      ┃ Output Shape             ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer (InputLayer)          │ (None, 28, 28, 1)        │             0 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ conv2d (Conv2D)                   │ (None, 26, 26, 64)       │           640 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)      │ (None, 13, 13, 64)       │             0 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)                 │ (None, 11, 11, 128)      │        73,856 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)    │ (None, 5, 5, 128)        │             0 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)                 │ (None, 3, 3, 256)        │       295,168 │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ global_average_pooling2d          │ (None, 256)              │             0 │
│ (GlobalAveragePooling2D)          │                          │               │
├───────────────────────────────────┼──────────────────────────┼───────────────┤
│ dense (Dense)                     │ (None, 10)               │         2,570 │
└───────────────────────────────────┴──────────────────────────┴───────────────┘
 Total params: 372,234 (1.42 MB)
 Trainable params: 372,234 (1.42 MB)
 Non-trainable params: 0 (0.00 B)

import matplotlib.pyplot as plt

plt.matshow(first_layer_activation[0, :, :, 5], cmap="viridis")

How Deep Learning with Python, Third Edition covers it

How deep it goes

Defines rank-3 shape and channel meaning, then works through convolution, pooling, and activation shapes without deriving filter calculations mathematically.

Examples

Uses MNIST digit shapes, a small ConvNet’s layer summary, and separately plotted channels from a cat-image activation.

What it assumes

Introduces image-tensor channels through RGB and grayscale examples while assuming familiarity with Dense classifiers.

What you can do after

Apply it via Keras to read ConvNet output shapes and visualize individual channels of intermediate activations.

How it connects

Links feature maps to convolutional filters, max pooling, spatial hierarchies, global average pooling, and intermediate activations.

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Also explained in

• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA feature map is a spatial array of activations indicating the presence or strength of a learned visual pattern at different image locations.
• 

  liveBookmanning.com
  Grokking Machine LearningA mapping that sends data points from an original input space into another feature space, usually of higher dimension.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionA feature map is a spatially organized array of activation values representing learned features at different locations in an input or intermediate image representation.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningA spatial array of activation values produced by applying a convolution or pooling operation over an input image or prior layer output.
• 

  liveBookmanning.com
  Sutskever's ListA spatial array of responses produced by applying a convolutional filter to an input.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchAn intermediate activation map in a neural network whose spatial resolution can be increased or preserved by convolutional operations.
• 

  liveBookmanning.com
  Machine Learning Algorithms in DepthA feature map is a function that represents input objects as vectors in a feature space, possibly an infinite-dimensional one.

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Related concepts

• convolutional layerproduced by · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
• kernel functionproduced by · A kernel function is a mathematical function that measures similarity between pairs of inputs, often representing an inner product in an implicit feature space.
• convolution kernelproduced by · A convolution kernel is a small array or tensor of weights applied across local input regions to compute weighted responses and detect or transform patterns.
• Region Proposal Networkused by · A Region Proposal Network is a neural-network module that generates candidate object regions from image features for a two-stage detector.
• channelhas part · A channel is an independent component or dimension of image or tensor data carrying a distinct color, feature, activation, or value stream.
• max poolingused by · Max pooling is an aggregation operation that replaces each local or grouped region with its maximum value, often to downsample a representation.

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