Table of Contents

Class ClipNeuralNetwork<T>

Namespace
AiDotNet.NeuralNetworks
Assembly
AiDotNet.dll

CLIP (Contrastive Language-Image Pre-training) neural network that encodes both text and images into a shared embedding space, enabling cross-modal similarity and zero-shot classification.

public class ClipNeuralNetwork<T> : NeuralNetworkBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IMultimodalEmbedding<T>, IDisposable

Type Parameters

T

The numeric type used for computations (typically float or double).

Inheritance
ClipNeuralNetwork<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Constructors

ClipNeuralNetwork(NeuralNetworkArchitecture<T>, string, string, ITokenizer, ILossFunction<T>?, int, int, int)

Initializes a new instance of the CLIP neural network.

public ClipNeuralNetwork(NeuralNetworkArchitecture<T> architecture, string imageEncoderPath, string textEncoderPath, ITokenizer tokenizer, ILossFunction<T>? lossFunction = null, int embeddingDimension = 512, int maxSequenceLength = 77, int imageSize = 224)

Parameters

architecture NeuralNetworkArchitecture<T>

The network architecture configuration.

imageEncoderPath string

Path to the ONNX image encoder model.

textEncoderPath string

Path to the ONNX text encoder model.

tokenizer ITokenizer

The tokenizer for text processing.

lossFunction ILossFunction<T>

The loss function (optional for inference-only use).

embeddingDimension int

The embedding dimension (typically 512 or 768).

maxSequenceLength int

Maximum sequence length for text (typically 77 for CLIP).

imageSize int

Expected image size in pixels (typically 224 for CLIP).

Properties

EmbeddingDimension

Gets the embedding dimension of the CLIP model.

public int EmbeddingDimension { get; }

Property Value

int

ImageSize

Gets the expected image size (square images: ImageSize x ImageSize pixels).

public int ImageSize { get; }

Property Value

int

MaxSequenceLength

Gets the maximum sequence length for text input.

public int MaxSequenceLength { get; }

Property Value

int

SupportsTraining

Gets whether this network supports training.

public override bool SupportsTraining { get; }

Property Value

bool

Methods

ComputeSimilarity(Vector<T>, Vector<T>)

Computes similarity between two embeddings.

public T ComputeSimilarity(Vector<T> embedding1, Vector<T> embedding2)

Parameters

embedding1 Vector<T>

The first embedding.

embedding2 Vector<T>

The second embedding.

Returns

T

Similarity score (cosine similarity for normalized embeddings).

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

Dispose(bool)

Protected Dispose pattern implementation.

protected override void Dispose(bool disposing)

Parameters

disposing bool

True if called from Dispose(), false if called from finalizer.

EmbedAsync(string) 

public Task<Vector<T>> EmbedAsync(string text)

Parameters

text string

Returns

Task<Vector<T>>

EmbedBatchAsync(IEnumerable<string>) 

public Task<Matrix<T>> EmbedBatchAsync(IEnumerable<string> texts)

Parameters

texts IEnumerable<string>

Returns

Task<Matrix<T>>

EncodeImage(double[])

Encodes an image into an embedding vector.

public Vector<T> EncodeImage(double[] imageData)

Parameters

imageData double[]

The preprocessed image data as a flattened array in CHW format.

Returns

Vector<T>

A normalized embedding vector.

EncodeImageBatch(IEnumerable<double[]>)

Encodes multiple images into embedding vectors in a batch.

public Matrix<T> EncodeImageBatch(IEnumerable<double[]> imageDataBatch)

Parameters

imageDataBatch IEnumerable<double[]>

The preprocessed images as flattened arrays.

Returns

Matrix<T>

A matrix where each row is an embedding for the corresponding image.

EncodeText(string)

Encodes text into an embedding vector.

public Vector<T> EncodeText(string text)

Parameters

text string

The text to encode.

Returns

Vector<T>

A normalized embedding vector.

EncodeTextBatch(IEnumerable<string>)

Encodes multiple texts into embedding vectors in a batch.

public Matrix<T> EncodeTextBatch(IEnumerable<string> texts)

Parameters

texts IEnumerable<string>

The texts to encode.

Returns

Matrix<T>

A matrix where each row is an embedding for the corresponding text.

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

InitializeLayers()

Initializes the layers of the neural network based on the architecture.

protected override void InitializeLayers()

Remarks

For Beginners: This method sets up all the layers in your neural network according to the architecture you've defined. It's like assembling the parts of your network before you can use it.

Predict(Tensor<T>)

Makes a prediction using the neural network.

public override Tensor<T> Predict(Tensor<T> input)

Parameters

input Tensor<T>

The input data to process.

Returns

Tensor<T>

The network's prediction.

Remarks

For Beginners: This is the main method you'll use to get results from your trained neural network. You provide some input data (like an image or text), and the network processes it through all its layers to produce an output (like a classification or prediction).

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input data.

expectedOutput Tensor<T>

The expected output for the given input.

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

UpdateParameters(Vector<T>)

Updates the network's parameters with new values.

public override void UpdateParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

The new parameter values to set.

Remarks

For Beginners: During training, a neural network's internal values (parameters) get adjusted to improve its performance. This method allows you to update all those values at once by providing a complete set of new parameters.

This is typically used by optimization algorithms that calculate better parameter values based on training data.

ZeroShotClassify(double[], IEnumerable<string>)

Performs zero-shot classification of an image against text labels.

public Dictionary<string, T> ZeroShotClassify(double[] imageData, IEnumerable<string> labels)

Parameters

imageData double[]

The preprocessed image data.

labels IEnumerable<string>

The candidate class labels.

Returns

Dictionary<string, T>

A dictionary mapping each label to its probability score.

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