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Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.
About Sonnet
Sonnet is a library built on TensorFlow 2 for machine learning research. It organizes code around snt.Module, which can hold parameters, other modules, and methods that process input. It includes predefined modules such as snt.Linear, snt.Conv2D, and snt.BatchNorm, plus networks such as snt.nets.MLP, and supports custom modules by subclassing snt.Module.
Sonnet does not include a training framework, so users build their own or use one built by others. Installation uses pip as dm-sonnet alongside TensorFlow 2.0 and tensorflow-probability.
Key features
- Provides snt.Module abstraction for parameters and nested modules
- Includes predefined layers such as Linear, Conv2D, and BatchNorm
- Includes predefined networks such as snt.nets.MLP
- Supports custom modules by subclassing snt.Module
- Exposes variables and trainable_variables for use with optimizers
- Supports distributed training with snt.distribute
Best for: Researchers building custom neural networks with TensorFlow 2 for supervised, unsupervised, or reinforcement learning.
Good for
- Modules are self-contained and decoupled from one another
- Explains reasons for defaults such as initial parameter values
- Includes examples for MLP prediction, GAN training, and distributed training
Watch out for
- Does not ship with a training framework; users build or adopt their own
Sonnet at a glance
- Website
- github.com/deepmind/sonnet
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Sonnet FAQ
How do I install Sonnet?
Install TensorFlow 2.0 with pip install tensorflow tensorflow-probability, then pip install dm-sonnet.
Does Sonnet include a training framework?
No. Users are encouraged to build their own training code or adopt one built by others.
How do I create a custom layer?
Subclass snt.Module and define initialization and computation in the subclass.
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