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https://github.com/eaplatanios/tensorflow_scala
TensorFlow API for the Scala Programming Language
https://github.com/eaplatanios/tensorflow_scala
deep-learning machine-learning scala tensorflow
Last synced: about 2 months ago
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TensorFlow API for the Scala Programming Language
- Host: GitHub
- URL: https://github.com/eaplatanios/tensorflow_scala
- Owner: eaplatanios
- License: apache-2.0
- Created: 2017-04-01T18:00:29.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-06-22T21:53:34.000Z (over 2 years ago)
- Last Synced: 2024-11-15T07:33:10.574Z (about 2 months ago)
- Topics: deep-learning, machine-learning, scala, tensorflow
- Language: Scala
- Homepage: http://platanios.org/tensorflow_scala/
- Size: 44.8 MB
- Stars: 939
- Watchers: 65
- Forks: 95
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scala - **tensorflow_scala** - activity/y/eaplatanios/tensorflow_scala) (Table of Contents / Science and Data Analysis)
README
-----------------
[](https://circleci.com/gh/eaplatanios/tensorflow_scala/tree/master)
[](https://www.codacy.com/app/eaplatanios/tensorflow_scala?utm_source=github.com&utm_medium=referral&utm_content=eaplatanios/tensorflow_scala&utm_campaign=Badge_Grade)
[](http://platanios.org/tensorflow_scala/api/api)
[](http://platanios.org/tensorflow_scala/api/jni)
[](http://platanios.org/tensorflow_scala/api/data)
[](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]'
```
- High-level API for creating, training, and using neural networks. For example, the following code shows how simple it
is to train a multi-layer perceptron for MNIST using TensorFlow for Scala. Here we omit a lot of very powerful
features such as summary and checkpoint savers, for simplicity, but these are also very simple to use.
```scala
// Load and batch data using pre-fetching.
val dataset = MNISTLoader.load(Paths.get("/tmp"))
val trainImages = tf.data.datasetFromTensorSlices(dataset.trainImages.toFloat)
val trainLabels = tf.data.datasetFromTensorSlices(dataset.trainLabels.toLong)
val trainData =
trainImages.zip(trainLabels)
.repeat()
.shuffle(10000)
.batch(256)
.prefetch(10)
// Create the MLP model.
val input = Input(FLOAT32, Shape(-1, 28, 28))
val trainInput = Input(INT64, Shape(-1))
val layer = Flatten[Float]("Input/Flatten") >>
Linear[Float]("Layer_0", 128) >> ReLU[Float]("Layer_0/Activation", 0.1f) >>
Linear[Float]("Layer_1", 64) >> ReLU[Float]("Layer_1/Activation", 0.1f) >>
Linear[Float]("Layer_2", 32) >> ReLU[Float]("Layer_2/Activation", 0.1f) >>
Linear[Float]("OutputLayer", 10)
val loss = SparseSoftmaxCrossEntropy[Float, Long, Float]("Loss") >>
Mean("Loss/Mean")
val optimizer = tf.train.GradientDescent(1e-6f)
val model = Model.simpleSupervised(input, trainInput, layer, loss, optimizer)
// Create an estimator and train the model.
val estimator = InMemoryEstimator(model)
estimator.train(() => trainData, StopCriteria(maxSteps = Some(1000000)))
```
And by changing a few lines to the following code, you can get checkpoint capability, summaries, and seamless
integration with TensorBoard:
```scala
val loss = SparseSoftmaxCrossEntropy[Float, Long, Float]("Loss") >>
Mean("Loss/Mean") >>
ScalarSummary(name = "Loss", tag = "Loss")
val summariesDir = Paths.get("/tmp/summaries")
val estimator = InMemoryEstimator(
modelFunction = model,
configurationBase = Configuration(Some(summariesDir)),
trainHooks = Set(
SummarySaver(summariesDir, StepHookTrigger(100)),
CheckpointSaver(summariesDir, StepHookTrigger(1000))),
tensorBoardConfig = TensorBoardConfig(summariesDir))
estimator.train(() => trainData, StopCriteria(maxSteps = Some(100000)))
```
If you now browse to `https://127.0.0.1:6006` while training, you can see the training progress:
- Efficient interaction with the native library that avoids unnecessary copying of data. All tensors are created and
managed by the native TensorFlow library. When they are passed to the Scala API (e.g., fetched from a TensorFlow
session), we use a combination of weak references and a disposing thread running in the background. Please refer to
`tensorflow/src/main/scala/org/platanios/tensorflow/api/utilities/Disposer.scala`, for the implementation.
## Compiling from Source
Note that in order to compile TensorFlow Scala on your
machine you will need to first install the TensorFlow
Python API. You also need to make sure that you have a
`python3` alias for your python binary. This is used by
CMake to find the TensorFlow header files in your
installation.
## Tutorials
- [Object Detection using Pre-Trained Models](https://brunk.io/deep-learning-in-scala-part-3-object-detection.html)
## Funding
Funding for the development of this library has been generously provided by the following sponsors:
|
|
|
|
|:---------------------------------------:|:---------------------------------:|:-----------------------------------------------:|
| **CMU Presidential Fellowship** | **National Science Foundation** | **Air Force Office of Scientific Research** |
| awarded to Emmanouil Antonios Platanios | Grant #: IIS1250956 | Grant #: FA95501710218 |
TensorFlow, the TensorFlow logo, and any related marks are trademarks of Google Inc.