Model_Building_Blocks.md

graph LR
    Embedding_Layer_Manager["Embedding Layer Manager"]
    Model_Graph_Builder["Model Graph Builder"]
    Transformer_Layer["Transformer Layer"]
    ALBERT_Model_Integrator["ALBERT Model Integrator"]
    Capsule_Layer["Capsule Layer"]
    Attention_Layers["Attention Layers"]
    Pooling_Feature_Layers["Pooling & Feature Layers"]
    Custom_Optimizers["Custom Optimizers"]
    Model_Graph_Builder -- "depends on" --> Embedding_Layer_Manager
    Model_Graph_Builder -- "composes" --> Transformer_Layer
    Model_Graph_Builder -- "composes" --> ALBERT_Model_Integrator
    Model_Graph_Builder -- "composes" --> Capsule_Layer
    Model_Graph_Builder -- "composes" --> Attention_Layers
    Model_Graph_Builder -- "composes" --> Pooling_Feature_Layers
    Model_Graph_Builder -- "configures and applies" --> Custom_Optimizers

Loading

Details

The Model Building Blocks subsystem provides the foundational elements for constructing various text classification models within the Keras framework. It encapsulates core utilities for data representation, graph definition, and a rich set of specialized custom layers and optimizers.

Embedding Layer Manager

Manages the creation and configuration of embedding layers, converting raw text into numerical representations suitable for model input. It handles the initial data preparation for the neural network.

Related Classes/Methods:

• keras_textclassification.base.embedding:1-100

Model Graph Builder

Serves as the central orchestrator for high-level model building, compilation, and training processes. It defines the overall structure of Keras models by integrating various custom layers and pre-trained model components.

Related Classes/Methods:

• keras_textclassification.base.graph:1-100

Transformer Layer

Implements the core logic for Transformer encoder and decoder stacks, including multi-head attention mechanisms. It provides a reusable and configurable Transformer architecture.

Related Classes/Methods:

• keras_textclassification.keras_layers.transformer:1-100
• keras_textclassification.keras_layers.transformer_utils.multi_head_attention:1-100

ALBERT Model Integrator

Facilitates the integration of ALBERT models into the Keras framework, including managing the construction and loading of pre-trained weights.

Related Classes/Methods:

• keras_textclassification.keras_layers.albert.albert:1-100

Capsule Layer

Implements the unique logic of a Capsule layer, including its squash activation function, offering an alternative to traditional convolutional layers for hierarchical feature learning.

Related Classes/Methods:

• keras_textclassification.keras_layers.capsule:1-100

Attention Layers

Provide various attention mechanisms, such as self-attention and dot-product attention, which are crucial for capturing dependencies within sequences.

Related Classes/Methods:

• keras_textclassification.keras_layers.attention_self:1-100
• keras_textclassification.keras_layers.attention_dot:1-100

Pooling & Feature Layers

Offer specialized operations for feature selection (K-Max Pooling), non-linear transformations (Highway networks), and utilities for handling masks in recurrent or attention-based networks.

Related Classes/Methods:

• keras_textclassification.keras_layers.k_max_pooling:1-100
• keras_textclassification.keras_layers.highway:1-100
• keras_textclassification.keras_layers.non_mask_layer:1-100

Custom Optimizers

Provide advanced optimization algorithms (e.g., Lookahead, RAdam) that can improve the training stability and performance of Keras models.

Related Classes/Methods:

• keras_textclassification.keras_layers.keras_lookahead:1-100
• keras_textclassification.keras_layers.keras_radam:1-100

FAQ