In this post we’ll introduce the changes and additions to TensorFlow 2 Contrib. We’ll also show how to get started with the new features.
For more information check out our video:
Introducing TensorFlow 2 Contrib
TensorFlow 2 Contrib is a library of contributions to the TensorFlow machine learning platform. The library includes a wide range of features, from experimental model architectures to tools for debugging and visualizing TensorFlow models.
In this release, we’ve added many new features and improvements, including:
– A new `TensorBoard` debugger that lets you visualize the internal state of your TensorFlow model as it runs.
– A `tf.data` module that makes it easy to build efficient data pipelines for training and inference.
– New helper functions for constructing layers, losses, and other common neural network building blocks.
– Many new contributed modules, including a text classifier and an image completion network.
To get started with TensorFlow 2 Contrib, check out the documentation at https://tensorflow.org/contrib/.
New features and improvements
TensorFlow 2 Contrib is a project that provides additional libraries and tools for TensorFlow 2.0. The most recent release of Contrib includes several new features and improvements, including:
– Support for additional layers, including 3D convolutional layers, depthwise separable convolution, and pooling layers
– Improved support for training on multiple GPUs
– New datasets and input pipelines, including a new AudioSet dataset and a generic image classification pipeline
– A new Model Maker library that makes it easier to convert existing models to TensorFlow 2.0 format
– A number of bug fixes and performance improvements
TensorFlow 2 Contrib in action
TensorFlow 2.0 contrib is an open source library that provides high quality implementations of common machine learning algorithms. The library is designed to work with the TensorFlow ecosystem and to be easy to use, extensible, and portable.
Some of the features of TensorFlow 2 contrib include:
· Easy model building with Keras and Estimators
· Support for custom layers and models
· Implementation of common machine learning algorithms
· Tools for data visualization and debugging
· Support for different hardware platforms
How to get started with TensorFlow 2 Contrib
TensorFlow 2 Contrib is a new library that contains many features that are not yet ready for production use but are well on their way. This guide shows you how to get started with TensorFlow 2 Contrib.
First, install TensorFlow 2 Contrib. You can do this with pip:
pip install tensorflow-2-contrib
Next, create a file called hello_world.py and copy the following code into it:
import tensorflow as tf
import tensorflow_2_contrib as tfc
print(tf.__version__) # Should be 2.0.0-dev20180912
Now run the code:
TensorFlow 2 Contrib is now installed and you’re ready to start using it!
What’s next for TensorFlow 2 Contrib
TensorFlow 2 contrib is moving forward! Currently, work is being done on integrating ORC files and optimizing for Windows. After that, the focus will shift to improving the documentation.
Keyword: What’s New in TensorFlow 2 Contrib