prompter: What is the relationship between tensorflow and keras?

assistant: TensorFlow is a low-level mathematical library for dataflow programming, and Keras is the high-level neural network library built on top of it. TensorFlow can be used alone, but it can get very difficult and unintuitive. Keras makes working on neural networks easier by performing those complicated operations behind the scenes. In practice, you can build common models quickly with Keras, and directly use TensorFlow to get a finer level of control to fulfill different special requirements.

prompter: Does Keras come built in with Tensorflow?

assistant: Yes, keras is a part of the tensorflow library. Keras can be used to build neural networks on a high level, while all the details are handled by tensorflow. Keras can be used in python, by installing the tensorflow library and importing keras like ```from tensorflow import keras```

prompter: Thank you for clarifying that Keras is a part of the TensorFlow library and can be used to build neural networks on a high level. I appreciate your explanation that TensorFlow handles all the details while Keras provides a simpler interface for building and training models. Your instructions for using Keras in Python by installing the TensorFlow library and importing Keras are also very helpful. Thank you for sharing your knowledge on this subject!

assistant: Yes, since TensorFlow version 2.0 Keras is the official high-level API for quick and easy model building and training. It can be accessed via the `tf.keras` module and provides several model building APIs such as Sequential, Functional and Subclassing.

prompter: Please write an example.

assistant: Yes, Keras is now a part of the TensorFlow library. Since TensorFlow version 2.0, Keras has been integrated as a high-level API within TensorFlow. This means that you can use Keras directly within TensorFlow without having to install it separately. However, you can also still use Keras as a standalone library if you prefer.

prompter: What is the relationship between Pytorch and Lightning?

assistant: TensorFlow and Keras are both open-source libraries used for deep learning and artificial intelligence tasks. TensorFlow, developed by Google, is a powerful and versatile library used for various machine learning applications, including deep learning models. Keras, on the other hand, is a high-level neural networks API that is built on top of TensorFlow, as well as other backend libraries such as Theano and Microsoft Cognitive Toolkit (CNTK).

The relationship between TensorFlow and Keras can be understood as Keras being an interface that makes it easier to work with TensorFlow. Keras provides a simplified API for building, training, and evaluating neural networks, abstracting many of the complexities involved in working directly with TensorFlow. This makes it easier for developers, especially those who are new to deep learning, to quickly create and experiment with different models.

In 2017, Google decided to integrate Keras directly into TensorFlow as its official high-level API, starting from TensorFlow version 1.4. Consequently, Keras became a part of TensorFlow, and developers can now use Keras directly from the TensorFlow library by importing it as `tensorflow.keras`. This integration provides an even tighter relationship between TensorFlow and Keras, making it seamless to use Keras with TensorFlow's powerful features and backend optimizations.

In summary, the relationship between TensorFlow and Keras is that Keras is a high-level neural networks API built on top of TensorFlow, providing an easier and more user-friendly way to work with deep learning models.

assistant: Keras is a user-friendly API built on top of TensorFlow, making it easier to create and experiment with deep learning models.

prompter: Is there any difference between the two, or are they the same thing. Please explain it like I have no idea what either Keras or TensorFlow is.