Introducing TensorFlow 2

TensorFlow began its life in 2011 as DisBelief, an internal, closed source project at Google. DisBelief was a machine learning system that employed deep learning neural networks. This system morphed into TensorFlow, which was released to the developer community under an Apache 2.0 open source license, on November 9, 2015. Version 1.0.0 made its appearance on February 11, 2017. There have been a number of point releases since then that have incorporated a wealth of new features.

At the time of writing this book, the most recent version is TensorFlow 2.0.0 alpha release, which was announced at the TensorFlow Dev Summit on March 6, 2019.

TensorFlow takes its name from, well, tensors. A tensor is a generalization of vectors and matrices to possibly higher dimensions. The rank of a tensor is the number of indices it takes to uniquely specify each element of that tensor. A scalar (a simple number) is a tensor of rank 0, a vector is a tensor of rank 1, a matrix is a tensor of rank 2, and a 3-dimensional array is a tensor of rank 3. A tensor has a datatype and a shape (all of the data items in a tensor must have the same type). An example of a 4-dimensional tensor (that is, rank 4) is an image where the dimensions are an example within—batch, height, width, and color channel (for example):

image1 = tf.zeros([7, 28, 28, 3]) #  example-within-batch by height by width by color

Although TensorFlow can be leveraged for many areas of numerical computing in general, and machine learning in particular, its main area of research and development has been in the applications of Deep Neural Networks (DNN), where it has been used in diverse areas such as voice and sound recognition, for example, in the now widespread voice-activated assistants; text-based applications such as language translators; image recognition such as exo-planet hunting, cancer detection, and diagnosis; and time series applications such as recommendation systems.

In this chapter, we will discuss the following:

• Looking at the modern TensorFlow ecosystem
• Installing TensorFlow
• Housekeeping and eager operations
• Providing useful TensorFlow operations