tensorflow/tfjs-examples
Tfjs Examples
This repository provides a collection of practical demonstrations and implementation guides for machine learning tasks using TensorFlow.js. It serves as a resource for developers to explore model architectures, training workflows, and data manipulation techniques across domains such as computer vision, natural language processing, and reinforcement learning.
The project covers the full lifecycle of machine learning development, including tensor-based mathematical operations, model construction via high-level layer APIs or low-level tensor logic, and model serialization for various storage mediums. It includes utilities for converting models into browser-compatible formats and provides infrastructure for executing these models across diverse backends, including WebGL, WebAssembly, and CPU-accelerated environments.
Documentation and examples are organized by task type, allowing users to browse implementations for regression, object detection, and generative models. The repository also includes deployment guides for hosting server-side applications on cloud platforms, alongside tools for managing tensor memory and asynchronous training processes.
Features
- Manual Memory Management - Layer and model objects provide a manual disposal method to free GPU-resident memory, as the environment may lack automatic garbage collection for these resources.
- Core Model APIs - A low-level API allowing for manual weight initialization and custom model logic by defining functions that operate directly on tensors for specialized machine learning tasks.
- Model Execution APIs - Specialized loading interfaces to fetch and execute converted model assets, supporting both fixed-parameter graph models and trainable layer-based models.
- Asynchronous Training Utilities - An asynchronous model training method that returns a promise, preventing UI thread blocking during long-running training operations.
- CPU Backends - A server-side backend accelerated by native binaries that optimizes linear algebra computation on the CPU across multiple operating systems.
- Backend Kernel Implementations - Backend-specific kernels provide optimized logic for operations by directly reading and writing memory, registered via a central registry for dispatch during execution.
- Tensor Memory Management - Explicit memory management utilities including manual disposal and automated cleanup scopes for managing tensor lifecycles, particularly critical when using GPU-based backends.