# carpedm20/NTM-tensorflow

"Neural Turing Machine" in Tensorflow

Repository: https://github.com/carpedm20/NTM-tensorflow
Canonical: https://ross.abutalabs.com/products/ntm-tensorflow
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow, neural-turing-machines
Last push: 2017-05-18T06:31:02+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3920, "days_push": 3394, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1047, forks 212 (observed 2026-08-28T04:03:21.860979+00:00)

## What it is
A TensorFlow implementation of the Neural Turing Machine (NTM) architecture with an LSTM controller and support for multiple read/write heads. It includes training and testing scripts for tasks like the copy task, intended primarily as a reference implementation.

## Use cases
- implement a neural turing machine in tensorflow
- train an NTM on a copy task
- study memory-augmented neural network architectures
- compare NTM implementations against the original torch code
- experiment with LSTM controllers and multiple read/write heads

## When to choose
- you want a readable reference implementation of NTM in TensorFlow
- you are studying memory-augmented networks and want example code
- you need a starting point to fork and modify for research

## When to avoid
- you need production-ready or well-maintained code
- you require variable-length NTM inputs, which the author says are poorly supported
- you need a modern TensorFlow 2.x or PyTorch implementation
- you cannot tolerate training instability such as NaN losses

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python
- tags: neural-turing-machine, tensorflow, lstm, research-code, jupyter-notebook

## Member repositories
- carpedm20/NTM-tensorflow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.860979+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:01:06.063572+00:00, confidence not recorded.
  - readme: https://github.com/carpedm20/NTM-tensorflow (fetched 2026-08-28T04:03:21.860979+00:00, sha cc7275b25a69)
- Data as of 2026-08-30T08:39:29.467469+00:00.