Introduction

This blog post aims to shed light on the significant updates and improvements brought by TensorFlow 2.0, a powerful open-source machine learning framework.

Keras Integration

The most notable change in TensorFlow 2.0 is the full integration of Keras, a user-friendly deep learning API. This integration makes it easier for beginners to get started with TensorFlow and provides a consistent interface for both low-level Tensor manipulation and high-level neural network creation.

Eager Execution by Default

TensorFlow 2.0 now uses eager execution by default. This means that computations are performed as soon as the operations are defined, rather than waiting for a session to be created, as was the case in TensorFlow 1.x. Eager execution simplifies the development process and makes debugging easier.

Deprecation of tf.Session

With the shift to eager execution, the tf.Session concept has been deprecated. Users are encouraged to use functions like tf.keras.models.compile() or tf.keras.losses.SparseCategoricalCrossentropy() for model compilation and loss definition, respectively.

Reduced Memory Usage

TensorFlow 2.0 introduces improvements in memory usage, thanks to the new dynamic shape and broadcasting features. These changes allow the framework to utilize GPU memory more efficiently, enabling larger models to be trained.

Conclusion

TensorFlow 2.0 represents a significant step forward in the development of machine learning frameworks, with its integration of Keras, adoption of eager execution, and reduced memory usage. These updates make TensorFlow more accessible and efficient for both beginners and experienced developers, paving the way for even more powerful and complex machine learning projects.