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TensorFlow API for the Scala Programming Language
| Date | Stars |
|---|---|
| 2026-07-24 | 939 |
| 2026-07-25 | 939 |
| 2026-07-28 | 939 |
| 2026-07-30 | 939 |
| 2026-08-06 | 939 |
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[](http://platanios.org/tensorflow_scala/api/api)
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[](http://platanios.org/tensorflow_scala/api/examples)
This library is a Scala API for [https://www.tensorflow.org](https://www.tensorflow.org). It attempts to provide most of
the functionality provided by the official Python API, while at the same type being strongly-typed and adding some new
features. It is a work in progress and a project I started working on for my personal research purposes. Much of the API
should be relatively stable by now, but things are still likely to change.
[](https://gitter.im/eaplatanios/tensorflow_scala?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
Please refer to the main website for documentation and tutorials. Here
are a few useful links:
- [Installation](https://eaplatanios.github.io/tensorflow_scala/installation.html)
- [Getting Started Guide](https://eaplatanios.github.io/tensorflow_scala/getting_started.html)
- [Library Architecture](https://eaplatanios.github.io/tensorflow_scala/architecture.html)
- [Contributing](https://eaplatanios.github.io/tensorflow_scala/contributing.html)
## Citation
It would be greatly appreciated if you could cite this project using the following BibTex entry, if you end up using it
in your work:
```bibtex
@misc{Platanios:2018:tensorflow-scala,
title = {{TensorFlow Scala}},
author = {Platanios, Emmanouil Antonios},
howpublished = {\url{https://github.com/eaplatanios/tensorflow_scala}},
year = {2018}
}
```
## Main Features
- Easy manipulation of tensors and computations involving tensors (similar to NumPy in Python):
```scala
val t1 = Tensor(1.2, 4.5)
val t2 = Tensor(-0.2, 1.1)
t1 + t2 == Tensor(1.0, 5.6)
```
- Low-level graph construction API, similar to that of the Python API, but strongly typed wherever possible:
```scala
val inputs = tf.placeholder[Float](Shape(-1, 10))
val outputs = tf.placeholder[Float](Shape(-1, 10))
val predictions = tf.nameScope("Linear") {
val weights = tf.variable[Float]("weights", Shape(10, 1), tf.ZerosInitializer)
tf.matmul(inputs, weights)
}
val loss = tf.sum(tf.square(predictions - outputs))
val optimizer = tf.train.AdaGrad(1.0f)
val trainOp = optimizer.minimize(loss)
```
- Numpy-like indexing/slicing for tensors. For example:
```scala
tensor(2 :: 5, ---, 1) // is equivalent to numpy's 'tensor[2:5, ..., 1]'
``Excerpt of 11,885 characters
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matched fp:c915b7397fd1a4ba, topic:deep-learning, topic:tensorflow