HMM in TensorFlow
TensorFlow and NumPy implementations of HMM Viterbi and forward/backward algorithms for sequence modeling.
What is HMM in TensorFlow?
tensorflow_hmm is an open-source library that implements Hidden Markov Model algorithms—specifically the Viterbi and forward/backward algorithms—in TensorFlow and NumPy. It solves the problem of integrating traditional probabilistic sequence modeling into modern deep learning pipelines by providing differentiable HMM operations that work within TensorFlow computational graphs.
Machine learning researchers and developers working on sequence modeling tasks such as speech recognition, bioinformatics, natural language processing, or time-series analysis who need HMM functionality within TensorFlow/Keras workflows.
Developers choose tensorflow_hmm because it provides production-ready, GPU-accelerated implementations of core HMM algorithms that seamlessly integrate with TensorFlow's automatic differentiation and Keras API, unlike standalone statistical packages that don't interoperate with deep learning frameworks.
Overview
A tensorflow implementation of an HMM layer
Use Cases
Best For
- Adding HMM layers to TensorFlow/Keras neural networks
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