What is TensorFlow (TF)?
TensorFlow is a powerful open source software library developed by the Google Brain team for deep neural networks, the topic covered in this book. It was first made available under the Apache 2.0 License in November 2015 and has since grown rapidly; as of May 2019, its GitHub repository (https://github.com/tensorflow/tensorflow) has more than 51,000 commits, with roughly 1,830 contributors. This in itself provides a measure of the popularity of TensorFlow.
Let us first learn what exactly TensorFlow is and why it is so popular among deep neural network researchers and engineers. Google calls it "an open source software library for machine intelligence," but since there are so many other deep learning libraries like PyTorch (https://pytorch.org/), Caffe (https://caffe.berkeleyvision.org/), and MxNet (https://mxnet.apache.org/), what makes TensorFlow special? Most other deep learning libraries – like TensorFlow – have auto-differentiation (a useful mathematical tool used for optimization), many are open source platforms, most of them support the CPU/GPU option, have pretrained models, and support commonly used NN architectures like recurrent neural networks, convolutional neural networks, and deep belief networks.
So, what else is there in TensorFlow? Let me list the top features:
- It works with all popular languages such as Python, C++, Java, R, and Go.
- Keras – a high-level neural network API that has been integrated with TensorFlow (in 2.0, Keras became the standard API for interacting with TensorFlow). This API specifies how software components should interact.
- TensorFlow allows model deployment and ease of use in production.
- Support for eager computation (see Chapter 2, TensorFlow 1.x and 2.x) has been introduced in TensorFlow 2.0, in addition to graph computation based on static graphs.
- Most importantly, TensorFlow has very good community support.
The number of stars on GitHub (see Figure 1) is a measure of popularity for all open source projects. As of March 2019, TensorFlow, Keras, and PyTorch have 123,000, 39,000, and 25,000 stars respectively, which makes TensorFlow the most popular framework for machine learning:
Figure 1: Number of stars for various deep learning projects on GitHub
Google Trends is another measure of popularity, and again TensorFlow and Keras are the two top frameworks (late 2019), with PyTorch rapidly catching up (see Figure 2).
Figure 2: Google Trends for various deep learning projects
