Just two months ago Google’s TensorFlow, one of the most popular machine learning platforms celebrated its third birthday. Last year in August, Martin Wicke, engineer at Google, posted the list of what’s expected in TensorFlow 2.0, an open source machine learning framework, on the Google group. The key features listed by him include:
The early preview of TensorFlow 2.0 is expected soon. TensorFlow 2.0 is expected to come with high-level APIs, robust model deployment, powerful experimentation for research and simplified API.
This release will come with Keras, a user-friendly API standard for machine learning which will be used for building and training the models. As Keras provides various model-building APIs including sequential, functional, and subclassing, it becomes easier for users to choose the right level of abstraction for their project.
TensorFlow 2.0 will also feature eager execution, which will be used for immediate iteration and debugging. The tf.function will easily translate the Python programs into TensorFlow graphs. The performance optimizations will remain optimum and by adding the flexibility, tf.function will ease the use of expressing programs in simple Python. Further, the tf.data will be used for building scalable input pipelines.
The team at TensorFlow has made it much easier for those who are not into building a model from scratch. Users will soon get a chance to use models from TensorFlow Hub, a library for reusable parts of machine learning models to train a Keras or Estimator model.
Many APIs are removed in this release, some of which are tf.app, tf.flags, and tf.logging. The main tf.* namespace will be cleaned by moving lesser used functions into sub packages such as tf.math. Few APIs have been replaced with their 2.0 equivalents like tf.keras.metrics, tf.summary, and tf.keras.optimizers. The v2 upgrade script can be used to automatically apply these renames.
To know about this news, check out the post by the TensorFlow team on Medium.
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