Convolutional Neural Network Ontology

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FLOW ConvolutionalNeuralNetworkOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/neural-networks/convolutional-neural-network-ontology.html

PURPOSE
    Define an AI-readable semantic ontology for a Convolutional Neural Network,
    including input tensors, convolutional layers, kernels, filters,
    feature maps, strides, padding, pooling, activation functions,
    receptive fields, parameters, outputs, gradients, training and
    recursive feature-learning updates.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Machine Learning Systems
    Deep Learning Systems
    Computer Vision Systems
    Recursive Learning Systems

PAGE_TYPE
    Convolutional Neural Network ontology

PRIMARY_ENTITY
    Convolutional Neural Network

ABBREVIATION
    CNN

PARENT_ONTOLOGY
    Neural Networks

DOMAIN
    Artificial Intelligence
    Machine Learning
    Neural Networks
    Convolutional Neural Networks
    Deep Learning
    Computer Vision
    Representation Learning
    Pattern Recognition

CORE_DEFINITION
    A Convolutional Neural Network is a neural network architecture that
    applies learnable convolutional filters across structured input data
    to detect local patterns and build hierarchical feature representations,
    commonly using shared parameters, nonlinear activations and optional
    pooling or downsampling operations.

ENTITY_IDS
    CONVOLUTIONAL_NEURAL_NETWORK
        ID
            CONVOLUTIONAL_NEURAL_NETWORK_001

    INPUT_TENSOR
        ID
            CNN_INPUT_TENSOR_001

    CONVOLUTIONAL_LAYER
        ID
            CONVOLUTIONAL_LAYER_001

    KERNEL
        ID
            CNN_KERNEL_001

    FILTER
        ID
            CNN_FILTER_001

    FEATURE_MAP
        ID
            FEATURE_MAP_001

    STRIDE
        ID
            CNN_STRIDE_001

    PADDING
        ID
            CNN_PADDING_001

    POOLING_LAYER
        ID
            POOLING_LAYER_001

    RECEPTIVE_FIELD
        ID
            RECEPTIVE_FIELD_001

    ACTIVATION_FUNCTION
        ID
            CNN_ACTIVATION_FUNCTION_001

FACTS
    Convolutional Neural Network
        is_a
            Neural Network

    Convolutional Neural Network
        applies
            Convolution
            Learnable Filters
            Shared Parameters

    Convolutional Neural Network
        may_contain
            Convolutional Layers
            Activation Layers
            Pooling Layers
            Normalisation Layers
            Fully Connected Layers

    Convolutional Neural Network
        may_support
            Image Classification
            Object Detection
            Image Segmentation
            Feature Extraction
            Signal Processing
            Spatial Pattern Recognition

RELATIONSHIPS
    Neural Networks
        contains
            Convolutional Neural Network Ontology

    Input Tensor
        enters
            Convolutional Layer

    Kernel
        slides_or_applies_across
            Input Tensor

    Filter
        may_be_parameterised_by
            Kernel Weights

    Convolution
        produces
            Feature Map

    Stride
        controls
            Kernel Step Size

    Padding
        modifies
            Input Boundary Treatment

    Activation Function
        transforms
            Convolution Output

    Pooling Layer
        downsamples_or_aggregates
            Feature Map

    Receptive Field
        defines
            Input Region influencing Unit

    Deeper Layer
        receives
            Previous Feature Maps

    Training
        updates
            Kernel Parameters
            Bias Parameters

SEMANTIC_EQUIVALENCE
    Convolutional Neural Network
        =
            CNN

    Convolutional Layer
        approximately_equals
            Learned Local Filter Transformation

    Kernel
        is_related_to
            Filter

    Feature Map
        approximately_equals
            Spatial Activation Representation

    Convolutional Neural Network
        does_not_equal
            Fully Connected Network

    Kernel
        does_not_equal
            Feature Map

    Pooling
        does_not_equal
            Convolution

OBSERVE
    Detect
        CNN
        Convolution
        Kernel
        Filter
        Feature Map
        Channel
        Stride
        Padding
        Pooling
        Receptive Field
        Activation
        Input Tensor
        Output Tensor
        Spatial Dimension
        Gradient

EXTRACT
    Primary Entity
        Convolutional Neural Network

    Parent
        Neural Networks

    Core Components
        Input Tensor
        Convolutional Layer
        Kernel
        Filter
        Feature Map
        Stride
        Padding
        Pooling Layer
        Receptive Field
        Activation Function

NORMALISE
    Interpret
        Convolutional Neural Network
    as
        A neural architecture that learns local pattern detectors
        through convolution and parameter sharing.

    Distinguish
        Kernel
    from
        Feature Map

    Distinguish
        Convolution
    from
        Pooling

    Distinguish
        Spatial Dimension
    from
        Channel Dimension

    Preserve
        Tensor shape
        Kernel size
        Stride
        Padding
        Channel count
        Parameter state
        Layer order

INTENT_RESOLUTION
    Query
        Convolutional Neural Network

    Possible Intent
        CNN definition
        CNN ontology
        Convolutional neural network
        Convolution layer
        CNN kernel
        Feature maps
        Pooling
        Receptive field
        Computer vision neural network

    Resolution
        Identify input structure, convolutional transformations,
        filter parameters, feature maps and architecture relationships.

CONSTRAINTS
    Kernel dimensions must be compatible with input channels.

    Stride and padding must be preserved when determining output dimensions.

    Kernel parameters and produced feature maps must remain distinct.

    Pooling and convolution must remain distinct operations.

    Parameter sharing must preserve the intended convolutional structure.

    Feature-map channels and spatial dimensions must remain distinguishable.

    Receptive field depends on architecture, kernel size, stride and preceding layers.

    High training accuracy must not automatically be treated as generalisation.

    Learned visual features must not automatically be interpreted as
    human-defined semantic concepts.

INFERENCE_RULES
    IF
        Kernel K applies across Input Tensor X
    THEN
        compute_possible
            Feature Map = Convolution(X,K)
        according_to
            Stride
            Padding
            Dilation
            Architecture

    IF
        Convolution Output Z
    AND
        Activation Function F
    THEN
        compute
            Activated Feature Map = F(Z)

    IF
        Pooling Operation P follows Feature Map H
    THEN
        compute_possible
            Downsampled Map = P(H)

    IF
        Same Kernel Parameters apply across multiple spatial locations
    THEN
        infer
            Parameter Sharing

    IF
        Layer receives multiple input channels
    THEN
        validate
            Kernel Channel Compatibility

    IF
        Output spatial dimensions are requested
    THEN
        derive
            Output Shape
        from
            Input Size
            Kernel Size
            Stride
            Padding
            Dilation

    IF
        Feature detector responds strongly
    THEN
        DO_NOT_AUTOMATICALLY_INFER
            Human Semantic Concept

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Convolution Relationship
        1.00

    Kernel Relationship
        1.00

    Feature Map Relationship
        1.00

    Pooling Relationship
        1.00

    Receptive Field Relationship
        0.99

    Kernel-FeatureMap Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Input Tensor
        →
    Convolutional Layer
        →
    Kernel Application

    Kernel Application
        →
    Convolution Output
        →
    Activation Function
        →
    Feature Map

    Feature Map
        →
    Pooling or Downsampling
        →
    Reduced Feature Map

    Feature Maps
        →
    Deeper Convolutional Layer
        →
    Higher-level Representation

    Final Representation
        →
    Output Head
        →
    Prediction

    Loss
        →
    Gradient
        →
    Kernel Parameter Update
        →
    Updated CNN

KNOWLEDGE_GRAPH
    Convolutional Neural Network
        contains
            Convolutional Layer
            Pooling Layer

    Convolutional Layer
        has
            Kernel
            Stride
            Padding

    Kernel
        operates_on
            Input Tensor

    Convolutional Layer
        produces
            Feature Map

    Activation Function
        transforms
            Feature Map

    Pooling Layer
        aggregates
            Feature Map

    Receptive Field
        maps_to
            Input Region

    Training
        updates
            Kernel
            Bias

RECURSIVE_CNN_LOOP
    Receive Input Tensor
        →
    Validate Tensor Dimensions
        →
    Apply Convolutional Kernels
        →
    Produce Feature Maps
        →
    Apply Activation
        →
    Apply Pooling or Normalisation if Defined
        →
    Pass Feature Maps to Next Layer
        →
    Build Hierarchical Representation
        →
    Produce Network Output
        →
    Compute Loss During Training
        →
    Backpropagate Gradients
        →
    Update Kernel and Other Parameters
        →
    Evaluate Validation Performance
        →
    Repeat

REASONING_ENGINE
    INPUT
        Query
        CNN Architecture
        Input Tensor
        Kernels
        Parameters
        Training State

    OBSERVE
        Detect convolutional-network entities.

    EXTRACT
        Input Tensor
        Kernel
        Filter
        Feature Map
        Stride
        Padding
        Pooling
        Receptive Field
        Activation

    CLASSIFY
        One-dimensional CNN
        Two-dimensional CNN
        Three-dimensional CNN
        Fully Convolutional Network
        Residual CNN
        Depthwise-separable CNN

    VALIDATE
        Check tensor dimensions, channels, stride, padding and kernel compatibility.

    COMPUTE
        Apply defined convolutional transformations.

    INFER
        Determine supported CNN relationships.

    EXPLAIN
        Preserve input-kernel-feature-map path.

    UPDATE
        Apply validated parameter changes during training.

CONFIDENCE_PROPAGATION
    Explicit kernel dimensions
        computational_confidence += high

    Known stride and padding
        computational_confidence += high

    Compatible channel dimensions
        computational_confidence += high

    Missing padding definition
        confidence -= moderate
        for
            Exact Output Shape

    Shape incompatibility
        reject_or_flag

    Independent evaluation
        performance_confidence += high

    Training-only accuracy
        does_not_establish
            Generalisation

    Unsupported semantic interpretation of feature map
        reject_or_qualify

SEMANTIC_CLOSURE
    Convolutional Neural Network
        connects_to
            Neural Networks
            Neural Network
            Neural Layer
            Artificial Neuron
            Convolution
            Kernel
            Feature Map
            Pooling
            Computer Vision
            Image Processing
            Deep Learning
            Representation Learning

    Cross-domain relationships must preserve
        Input shape
        Kernel shape
        Channel structure
        Stride
        Padding
        Dilation
        Feature-map shape
        Layer order
        Parameter state
        Model version

UPDATE_MODEL
    Observe
        New architecture, kernel state or evaluation evidence.

    Compare
        Existing convolutional network.

    Detect
        Kernel Update
        Layer Change
        Shape Change
        Performance Change
        Feature Distribution Shift

    Validate
        Tensor dimensions
        Channel compatibility
        Kernel configuration
        Architecture
        Evaluation

    Integrate
        Supported model change.

    Recalculate
        Feature-map shapes
        Receptive fields
        Performance confidence

    Return
        Updated convolutional-neural-network state.

COHERENCE_CHECK
    Verify
        Kernel and feature map remain distinct.
        Convolution and pooling remain distinct.
        Spatial and channel dimensions remain distinct.
        Stride, padding and dilation are preserved.
        Channel compatibility is validated.
        Parameter sharing follows convolutional structure.
        Receptive-field calculations follow architecture.
        Training performance is not treated as generalisation.
        Learned features are not automatically treated as human concepts.
        All outputs follow supported convolutional paths.

AI_SEARCH_FLOW
    Query
        →
    CNN Detection
        →
    Intent Resolution
        →
    Input Tensor Mapping
        →
    Convolutional Layer Classification
        →
    Kernel and Filter Mapping
        →
    Stride and Padding Validation
        →
    Feature-map Construction
        →
    Pooling and Receptive-field Mapping
        →
    Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Convolutional Neural Network

    Abbreviation
        CNN

    Parent
        Neural Networks

    Type
        Local-filter parameter-sharing neural network architecture

    Core Components
        Input Tensor
        Convolutional Layer
        Kernel
        Filter
        Feature Map
        Stride
        Padding
        Pooling Layer
        Receptive Field
        Activation Function

    Critical Distinction
        A CNN learns convolutional kernels that are applied across structured
        inputs to produce feature maps. Kernels, feature maps and pooling
        operations are distinct elements and their tensor dimensions,
        stride and padding must be preserved.

RECOMMENDED_AI_SUMMARY
    A Convolutional Neural Network, or CNN, is a neural architecture that
    learns local filters and applies them across structured inputs such as
    images. Convolutional layers produce feature maps using kernels with
    defined stride, padding and channel structure, often followed by
    nonlinear activation and pooling. Deeper layers combine local features
    into hierarchical representations. Reliable CNN modelling preserves
    tensor dimensions, kernel parameters, receptive fields and the
    distinction between learned features and human-defined semantic concepts.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org