Convolutional layer

As explained in Deep Learning with Python, Third Edition

A two-dimensional convolution layer used here with a 1×1 kernel to project a residual to a matching shape.

import keras
from keras import layers

inputs = keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(inputs)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation="relu")(x)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)

How Deep Learning with Python, Third Edition covers it

How deep it goes

Explains patch-wise transformation and feature-map dimensions, contrasting convolution with Dense layers and max pooling.

Examples

Uses MNIST digit classification and dogs-versus-cats classification to show Conv2D stacks.

What it assumes

Builds on familiarity with Dense-based classification while explaining image tensors and feature-map axes.

What you can do after

Apply it via Keras Conv2D to assemble an image classifier and inspect changing feature-map shapes.

How it connects

Links convolutional layers to feature maps, Dense layers, max pooling, and residual connections.

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Also explained in

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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 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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  Evolutionary Deep Learning: Genetic algorithms and neural networksA convolutional layer applies learned local filters across structured input locations to detect spatial or otherwise local patterns.
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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.

Compare how 14 books explain convolutional layer →

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.

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.