What is the difference between Tensorflow and Keras?
TensorFlow and Keras are closely related, and their relationship has evolved over time. Here’s a breakdown of their differences and how they are connected:
TensorFlow:
- TensorFlow is an open-source machine learning library developed by the Google Brain team.
- It is a comprehensive library that provides tools for building and deploying machine learning models, including deep learning models.
Functionality:
TensorFlow is more than just a deep learning library; it offers a wide range of functionalities for machine learning, including low-level operations, automatic differentiation, optimization, and deployment tools.
Low-Level and High-Level APIs:
- TensorFlow provides both low-level APIs (allowing fine-grained control over model architecture and training) and high-level APIs for easier model development.
Ecosystem:
- TensorFlow has a vast and mature ecosystem, including TensorFlow Extended (TFX) for production deployment, TensorFlow Lite for mobile and embedded devices, TensorFlow.js for browser-based applications, and more.
Graph-Based Execution:
- TensorFlow traditionally uses a static computation graph for execution. In TensorFlow 2.0 and later, eager execution is enabled by default, allowing dynamic computation without the need for graph construction.
Keras:
- Keras is an open-source high-level neural networks API originally developed by François Chollet.
- It is designed to be user-friendly, with a focus on simplicity and ease of use for building and experimenting with neural networks.
Integration with TensorFlow:
- Keras was originally a standalone library but has been integrated into TensorFlow as the official high-level API for building neural networks. This integration started with TensorFlow 1.x and became more seamless in TensorFlow 2.0.
User-Friendly API:
- Keras provides a simple and intuitive API for constructing neural networks. Its syntax is designed to be accessible to both beginners and experts in machine learning.
High-Level Abstraction:
Keras abstracts away much of the complexity associated with low-level TensorFlow operations. It allows users to define models using high-level building blocks like layers, making it easy to experiment with different architectures.
Modularity:
- Keras is modular, allowing users to create models by stacking together building blocks (layers). It is extensible and enables the development of custom layers and models.
Relationship:
Integration: As of TensorFlow 2.0 and later versions, Keras is tightly integrated into TensorFlow as tf.keras. This means that when you import tensorflow in your Python script, you can access Keras directly as tf.keras.
Default High-Level API: With TensorFlow 2.0 and later, tf.keras is the default high-level API for building and training neural networks. It simplifies the user experience and aligns with Keras's philosophy.
In summary, while TensorFlow provides a comprehensive machine learning platform with a broader set of functionalities, Keras (as part of TensorFlow or standalone) offers a user-friendly high-level API specifically designed for building and experimenting with neural networks. In practice, many users leverage tf.keras for its simplicity and integration with TensorFlow.
