Tensor2Tensor is a library of deep learning models and datasets designed to make deep learning research and experimentation fast and accessible. Developed by the Google Brain team and part of the TensorFlow ecosystem, it provides a standardized, modular, and extensible framework for training deep learning models—especially sequence models.
It includes implementations of many state-of-the-art models such as the original Transformer model, and it supports training across multiple GPUs and TPUs. T2T has been widely used in tasks like machine translation, text summarization, image classification, speech recognition, and more.
Key Features
Modular Design:
Easily swap models, datasets, and hyperparameters via a command-line interface.
Wide Model Library:
Includes advanced models like:
Transformer (Vaswani et al.)
LSTM and GRU-based models
ResNet for image classification
Universal Transformer
Reinforcement learning models
Multi-Domain Support:
NLP, vision, speech, and reinforcement learning.
Hyperparameter Optimization:
Built-in tools for tuning and scheduling hyperparameters.
Android DevHub
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