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TensorFlow APIs

TensorFlow is precise to a python package, and a lot of features are identical to that of Python. But the core of TensorFlow has distributed runtime. This functionality implements in many languages, and one of them is Python.

TensorFlow APIs

It is the diagram of Tensor Flow's distributed Execution engine or the runtime engine. The other way to visualize the above picture is to think of it as a virtual machine whose language like C, C++, R, Java, etc. The use of these API's in TensorFlow is explained below.

TensorFlow APIs 1

C API for TensorFlow

The only APIs having the official backing of TensorFlow are C and Python API (some parts). C APIs should be used whenever we are about to make TensorFlow API for some other languages, as lots of languages have ways to connect with C language.

C++ API for TensorFlow

The runtime of TensorFlow is written in C++, and mostly C++ is connected to TensorFlow through header files in tensor flow. C++ API still is in experimental stages of development, but Google commits to work with C++.

R API for TensorFlow

The R API for TensorFlow made by RStudio has some different approaches for providing API support. R API fully contains the python API, which is different from what TensorFlow goes with its APIs. But the users of R have all the access to features of Python API.

Python API for TensorFlow

Python API is the core language when it comes to Tensor Flow and its development. It is one of the first languages supported by TensorFlow and still supports most of the features. The Python API is so diverse that we will have to choose which level of API in TensorFlow; we want to work on.

APIs inside TensorFlow Project

The APIs inside TensorFlow are still Python-based, and they have low-level options for its users, such as tf.manual or tf.nnrelu, which are used to build neural networks architecture. These APIs also use in designing a deep neural network having higher levels of abstraction.

The functionality available by this collection is as follows-

  • Automatic checkpoints
  • Automatic logging
  • Separate training/ evaluation/prediction
  • Simplified training distribution.

TensorFlow is offering experienced multi-queue, multi-thread, and queue-runner design, which use for loading data. The developers of TensorFlow delivered the dataset API to address this issue and provide a candy interface as a bonus.

TensorFlow APIs 2

APIs Outside TensorFlow Project

Some other TensorFlow APIs that develop outside of the TensorFlow project by machine learning enthusiasts.

  • TFLearn: This API cannot be seen as TF Learn, which is Tensor Flow's tf.contrib.learn. It is a type of separate Python package.
  • Tensor Layer: It comes as a separate package and is different from what Tensor Flow's layers API has in its bag.
  • Pretty Tensor: It is a Google project which offers a fluent interface with chaining.
  • Sonnet: It is a project of Google?s Deep Mind which features a modular approach.

We have to know about the TensorFlow API for different languages. Besides, we also studied how TensorFlow is different from Python and how it got its own identity in machine learning and deep neural network areas.

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