⚡ HyperZZWOperator

🔴 Advanced ✅ Stable 🔥 Popular

🎯 Overview

The HyperZZWOperator computes context-dependent weights by multiplying inputs with hyper-kernels. This specialized layer takes two inputs: the original input tensor and a context tensor, then generates hyper-kernels from the context to perform context-dependent transformations.

This layer is particularly powerful for specialized transformations where the processing should depend on contextual information, making it ideal for advanced neural network architectures and context-aware processing.

🔍 How It Works

The HyperZZWOperator processes data through context-dependent transformations:

Input Processing: Takes input tensor and context tensor
Hyper-Kernel Generation: Generates hyper-kernels from context
Context-Dependent Transformation: Applies context-dependent weights
Weight Computation: Computes context-dependent weights
Output Generation: Produces transformed features

graph TD
    A[Input Tensor] --> C[Hyper-Kernel Generation]
    B[Context Tensor] --> C
    C --> D[Context-Dependent Weights]
    D --> E[Weight Computation]
    E --> F[Transformed Features]

    G[Hyper-Kernels] --> C
    H[Context Processing] --> C

    style A fill:#e6f3ff,stroke:#4a86e8
    style B fill:#e6f3ff,stroke:#4a86e8
    style F fill:#e8f5e9,stroke:#66bb6a
    style C fill:#fff9e6,stroke:#ffb74d
    style D fill:#f3e5f5,stroke:#9c27b0
    style E fill:#e1f5fe,stroke:#03a9f4

💡 Why Use This Layer?

Challenge	Traditional Approach	HyperZZWOperator's Solution
Context Processing	Fixed transformations	🎯 Context-dependent transformations
Specialized Processing	Generic processing	⚡ Specialized transformations with hyper-kernels
Context Awareness	No context consideration	🧠 Context-aware processing
Advanced Architectures	Standard layer stacking	🔗 Specialized component for advanced architectures

📊 Use Cases

Context-Aware Processing: Processing that depends on contextual information
Specialized Transformations: Advanced transformations with hyper-kernels
Advanced Architectures: Components for sophisticated neural networks
Context-Dependent Weights: Learning context-dependent weight patterns
Specialized Models: Building specialized models like Terminator

🚀 Quick Start

Basic Usage

import keras
from kerasfactory.layers import HyperZZWOperator

# Create sample input data
batch_size, input_dim, context_dim = 32, 16, 8
inputs = keras.random.normal((batch_size, input_dim))
context = keras.random.normal((batch_size, context_dim))

# Apply hyper ZZW operator
zzw_op = HyperZZWOperator(input_dim=16, context_dim=8)
output = zzw_op([inputs, context])

print(f"Input shape: {inputs.shape}")      # (32, 16)
print(f"Context shape: {context.shape}")   # (32, 8)
print(f"Output shape: {output.shape}")     # (32, 16)

In a Sequential Model

import keras
from kerasfactory.layers import HyperZZWOperator

# Note: Sequential models don't work well with multiple inputs
# Use functional API for HyperZZWOperator

In a Functional Model

import keras
from kerasfactory.layers import HyperZZWOperator

# Define inputs
input_tensor = keras.Input(shape=(20,), name='input_features')
context_tensor = keras.Input(shape=(10,), name='context_features')

# Apply hyper ZZW operator
x = HyperZZWOperator(input_dim=20, context_dim=10)([input_tensor, context_tensor])

# Continue processing
x = keras.layers.Dense(32, activation='relu')(x)
x = keras.layers.Dense(16, activation='relu')(x)
outputs = keras.layers.Dense(1, activation='sigmoid')(x)

model = keras.Model([input_tensor, context_tensor], outputs)

Advanced Configuration

# Advanced configuration with multiple hyper ZZW operators
def create_context_aware_network():
    # Define inputs
    input_tensor = keras.Input(shape=(25,), name='input_features')
    context_tensor = keras.Input(shape=(12,), name='context_features')

    # Multiple hyper ZZW operators
    x = HyperZZWOperator(input_dim=25, context_dim=12)([input_tensor, context_tensor])
    x = keras.layers.Dense(64, activation='relu')(x)
    x = keras.layers.BatchNormalization()(x)

    x = HyperZZWOperator(input_dim=64, context_dim=12)([x, context_tensor])
    x = keras.layers.Dense(32, activation='relu')(x)
    x = keras.layers.Dropout(0.2)(x)

    x = HyperZZWOperator(input_dim=32, context_dim=12)([x, context_tensor])
    x = keras.layers.Dense(16, activation='relu')(x)
    x = keras.layers.Dropout(0.1)(x)

    # Multi-task output
    classification = keras.layers.Dense(3, activation='softmax', name='classification')(x)
    regression = keras.layers.Dense(1, name='regression')(x)

    return keras.Model([input_tensor, context_tensor], [classification, regression])

model = create_context_aware_network()
model.compile(
    optimizer='adam',
    loss={'classification': 'categorical_crossentropy', 'regression': 'mse'},
    loss_weights={'classification': 1.0, 'regression': 0.5}
)

📖 API Reference

kerasfactory.layers.HyperZZWOperator

This module implements a HyperZZWOperator layer that computes context-dependent weights by multiplying inputs with hyper-kernels. This is a specialized layer for the Terminator model.

Classes

HyperZZWOperator

HyperZZWOperator(
    input_dim: int,
    context_dim: int | None = None,
    name: str | None = None,
    **kwargs: Any
)

A layer that computes context-dependent weights by multiplying inputs with hyper-kernels.

This layer takes two inputs: the original input tensor and a context tensor. It generates hyper-kernels from the context and performs a context-dependent transformation of the input.

Parameters:

Name	Type	Description	Default
`input_dim`	`int`	Dimension of the input features.	required
`context_dim`	`int \| None`	Optional dimension of the context features. If not provided, it will be inferred.	`None`
`name`	`str \| None`	Optional name for the layer.	`None`

Input

A list of two tensors: - inputs[0]: Input tensor with shape (batch_size, input_dim). - inputs[1]: Context tensor with shape (batch_size, context_dim).

Output shape

2D tensor with shape: (batch_size, input_dim) (same as input)

Example

import keras
from kerasfactory.layers import HyperZZWOperator

# Create sample input data
inputs = keras.random.normal((32, 16))  # 32 samples, 16 features
context = keras.random.normal((32, 8))  # 32 samples, 8 context features

# Create the layer
zzw_op = HyperZZWOperator(input_dim=16, context_dim=8)
context_weights = zzw_op([inputs, context])
print("Output shape:", context_weights.shape)  # (32, 16)

Initialize the HyperZZWOperator.

Parameters:

Name	Type	Description	Default
`input_dim`	`int`	Input dimension.	required
`context_dim`	`int \| None`	Context dimension.	`None`
`name`	`str \| None`	Name of the layer.	`None`
`**kwargs`	`Any`	Additional keyword arguments.	`{}`

Source code in kerasfactory/layers/HyperZZWOperator.py

def __init__(
    self,
    input_dim: int,
    context_dim: int | None = None,
    name: str | None = None,
    **kwargs: Any,
) -> None:
    """Initialize the HyperZZWOperator.

    Args:
        input_dim: Input dimension.
        context_dim: Context dimension.
        name: Name of the layer.
        **kwargs: Additional keyword arguments.
    """
    # Set public attributes
    self.input_dim = input_dim
    self.context_dim = context_dim

    # Validate parameters
    self._validate_params()

    # Call parent's __init__
    super().__init__(name=name, **kwargs)

🔧 Parameters Deep Dive

`input_dim` (int)

Purpose: Dimension of the input features
Range: 1 to 1000+ (typically 16-256)
Impact: Determines the input feature dimension
Recommendation: Match the actual input feature dimension

`context_dim` (int, optional)

Purpose: Dimension of the context features
Range: 1 to 1000+ (typically 8-128)
Impact: Determines the context feature dimension
Recommendation: Use appropriate context dimension for your use case

📈 Performance Characteristics

Speed: ⚡⚡⚡ Fast for small to medium models, scales with dimensions
Memory: 💾💾💾 Moderate memory usage due to hyper-kernel computation
Accuracy: 🎯🎯🎯🎯 Excellent for context-dependent processing
Best For: Context-aware processing and specialized transformations

🎨 Examples

Example 1: Context-Aware Processing

import keras
import numpy as np
from kerasfactory.layers import HyperZZWOperator

# Create a context-aware processing model
def create_context_aware_model():
    # Define inputs
    input_tensor = keras.Input(shape=(20,), name='input_features')
    context_tensor = keras.Input(shape=(8,), name='context_features')

    # Context-aware processing
    x = HyperZZWOperator(input_dim=20, context_dim=8)([input_tensor, context_tensor])
    x = keras.layers.Dense(32, activation='relu')(x)
    x = keras.layers.BatchNormalization()(x)

    x = HyperZZWOperator(input_dim=32, context_dim=8)([x, context_tensor])
    x = keras.layers.Dense(16, activation='relu')(x)
    x = keras.layers.Dropout(0.2)(x)

    # Output
    outputs = keras.layers.Dense(1, activation='sigmoid')(x)

    return keras.Model([input_tensor, context_tensor], outputs)

model = create_context_aware_model()
model.compile(optimizer='adam', loss='binary_crossentropy')

# Test with sample data
sample_inputs = keras.random.normal((100, 20))
sample_context = keras.random.normal((100, 8))
predictions = model([sample_inputs, sample_context])
print(f"Context-aware predictions shape: {predictions.shape}")

Example 2: Specialized Transformation

# Create a specialized transformation model
def create_specialized_transformation():
    # Define inputs
    input_tensor = keras.Input(shape=(15,), name='input_features')
    context_tensor = keras.Input(shape=(6,), name='context_features')

    # Specialized transformation
    x = HyperZZWOperator(input_dim=15, context_dim=6)([input_tensor, context_tensor])
    x = keras.layers.Dense(24, activation='relu')(x)
    x = keras.layers.BatchNormalization()(x)

    x = HyperZZWOperator(input_dim=24, context_dim=6)([x, context_tensor])
    x = keras.layers.Dense(12, activation='relu')(x)
    x = keras.layers.Dropout(0.1)(x)

    # Output
    outputs = keras.layers.Dense(1, activation='sigmoid')(x)

    return keras.Model([input_tensor, context_tensor], outputs)

model = create_specialized_transformation()
model.compile(optimizer='adam', loss='binary_crossentropy')

Example 3: Context Analysis

# Analyze context-dependent behavior
def analyze_context_behavior():
    # Create model with HyperZZWOperator
    input_tensor = keras.Input(shape=(12,))
    context_tensor = keras.Input(shape=(6,))

    x = HyperZZWOperator(input_dim=12, context_dim=6)([input_tensor, context_tensor])
    outputs = keras.layers.Dense(1, activation='sigmoid')(x)

    model = keras.Model([input_tensor, context_tensor], outputs)

    # Test with different context patterns
    test_inputs = keras.random.normal((10, 12))
    test_contexts = [
        keras.random.normal((10, 6)),  # Random context
        keras.random.normal((10, 6)) * 2,  # Scaled context
        keras.random.normal((10, 6)) + 1,  # Shifted context
    ]

    print("Context Behavior Analysis:")
    print("=" * 40)

    for i, test_context in enumerate(test_contexts):
        prediction = model([test_inputs, test_context])
        print(f"Context {i+1}: Prediction mean = {keras.ops.mean(prediction):.4f}")

    return model

# Analyze context behavior
# model = analyze_context_behavior()

💡 Tips & Best Practices

Input Dimension: Must match the actual input feature dimension
Context Dimension: Use appropriate context dimension for your use case
Context Quality: Ensure context information is meaningful and relevant
Multiple Inputs: Use functional API for multiple input models
Specialized Use: Best for specialized transformations and context-aware processing
Architecture: Use as components in advanced architectures

⚠️ Common Pitfalls

Input Format: Must be a list of [input_tensor, context_tensor]
Input Dimension: Must be positive integer
Context Dimension: Must be positive integer
Memory Usage: Scales with input and context dimensions
Complexity: More complex than standard layers

SlowNetwork - Multi-layer network processing
GatedResidualNetwork - Gated residual networks
TransformerBlock - Transformer processing
SparseAttentionWeighting - Sparse attention weighting

📚 Further Reading

Hyper-Kernels - Hyper-parameter concepts
Context-Aware Processing - Context awareness concepts
Specialized Transformations - Transformation concepts
KerasFactory Layer Explorer - Browse all available layers
Feature Engineering Tutorial - Complete guide to feature engineering

⚡ HyperZZWOperator

⚡ HyperZZWOperator

🎯 Overview

🔍 How It Works

💡 Why Use This Layer?

📊 Use Cases

🚀 Quick Start

Basic Usage

In a Sequential Model

In a Functional Model

Advanced Configuration

📖 API Reference

kerasfactory.layers.HyperZZWOperator

Classes

HyperZZWOperator

🔧 Parameters Deep Dive

input_dim (int)

context_dim (int, optional)

📈 Performance Characteristics

🎨 Examples

Example 1: Context-Aware Processing

Example 2: Specialized Transformation

Example 3: Context Analysis

💡 Tips & Best Practices

⚠️ Common Pitfalls

🔗 Related Layers

📚 Further Reading

`input_dim` (int)

`context_dim` (int, optional)