Convolutional layer

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Demonstrates sliding learned filters and Keras layer construction, then encodes filter and kernel choices for evolutionary architectures.

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

A convolutional layer applies learned local filters across structured input locations to detect spatial or otherwise local patterns.

A convolutional layer applies learned filters, also called kernels, across an image. Each filter slides over local patches—typically one pixel at a time—and produces output values that form a feature map. The filter weights are learned, so different filters can extract different image features. section 7.1

In Keras, `Conv2D` specifies the number of filters, kernel dimensions, activation, padding, and, for the first layer, input shape. A simple Fashion-MNIST feature extractor starts with 64 filters on a 28×28×1 image, then uses 32 and 16 filters in later convolutional layers. section 7.1

Listing 7.2 EDL_7_1_Keras_CNN.ipynb: Building CNN layers

model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding="same", 
 input_shape=(28, 28, 1)))                                 #1
model.add(layers.MaxPooling2D((2, 2), padding="same"))       #2
model.add(layers.Conv2D(32, (3, 3), activation='relu', 
 [padding="same"))                                         #3
model.add(layers.MaxPooling2D((2, 2)))                       #4
model.add(layers.Conv2D(16, (3, 3), activation='relu'))      #5
model.add(layers.MaxPooling2D((2, 2)))                       #6
 
model.summary()

This sequential model alternates `Conv2D` and `MaxPooling2D`: convolutions expand or transform channels, while pooling reduces spatial dimensions between feature-extraction stages. section 7.1

Convolutional layers are combined with neighboring layer types for different jobs:

Use max pooling after convolution to summarize feature maps and reduce their spatial size. section 7.1
Use convolution plus pooling in an autoencoder encoder to form a latent image representation. section 8.1.2
Use convolution plus upsampling in an autoencoder decoder to rebuild an image, ending with a layer that produces the required channel count. section 8.1.2
Encode filter count and kernel size as gene values when evolving CNN or autoencoder architectures. section 8.2.1

A convolutional autoencoder path

For a grayscale 28×28 input, an encoder can apply 64, 32, and 16-filter convolutional layers with 3×3 kernels, pooling after each layer. A decoder then convolves and upsamples through corresponding stages before a final one-filter convolution restores a single-channel output. section 8.1.2

Layer design remains an experimental task. Change kernel sizes, pooling sizes, or the number of convolutional layers and rerun the model to inspect the effect. Because choosing an effective CNN architecture can be difficult, filter counts and kernel sizes can also become variables in an evolutionary search. section 7.1.2 section 7.2

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Natural Language Processing in Action, Second EditionA convolutional layer is a neural-network layer that applies learned filters across local regions of its input to produce feature maps.
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Math and Architectures of Deep LearningA neural-network layer that applies convolution operations to an input, commonly used to extract local features from images.
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Deep Learning with PyTorch, Second EditionA PyTorch 2D convolution layer module that applies learned filters over spatial input regions.
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Deep Learning with Python, Third EditionA two-dimensional convolution layer used here with a 1×1 kernel to project a residual to a matching shape.
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Deep Learning with R, Third EditionA convolutional layer is a neural-network layer that applies learned local filters across spatial or sequential input dimensions.
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Grokking AI Algorithms, Second EditionA convolutional layer is a neural-network layer that applies shared filters to local regions of an input representation while preserving spatial organization.
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Optimization Algorithms: AI techniques for design, planning, and control problemsA transformation that applies shared learned weights to neighboring feature representations.
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Learn Generative AI with PyTorchA PyTorch convolution layer that applies learned 2D filters to an input tensor to produce feature maps.
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Sutskever's ListA convolutional layer is a neural-network layer that applies learned local filters across an input to produce spatial feature responses.
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Machine Learning Algorithms in DepthA convolutional layer is a neural-network layer that applies learned convolution kernels to an input representation.
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Build a Text-to-Image Generator (from Scratch)Conv2d is a two-dimensional convolution layer that applies learned spatial filters to image-like tensors.
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Distributed Machine Learning PatternsA convolutional layer is a neural-network layer that applies learned convolutional filters to its input.
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Grokking Machine LearningA convolutional layer is a neural-network layer that produces a filtered representation by applying convolutions across an input.

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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.
feature mapproduces · A feature map is a spatially organized array of neural-network activations representing the presence or strength of learned features at different locations.
downsamplingused by · Downsampling is the reduction of data quantity, spatial resolution, temporal resolution, or sampling rate by retaining, selecting, or aggregating fewer representative values or observations.
convolution kerneluses · 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.
batch sizeuses · Batch size is the number of items grouped together for processing or computation as a single batch.
encoderused by · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.

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