In this section, we will introduce TensorFlow 2.00 alpha. We will begin with an overview of the major features of this machine learning ecosystem and see some examples of its use. We will then introduce TensorFlow's high-level Keras API. We will end the section with an investigation of artificial neural network technologies and techniques.
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
![]()
Let's discuss eager execution. The first incarnation of TensorFlow involved constructing a computational graph made up of operations and tensors, which had to be subsequently evaluated in what Google termed as session (this is known as declarative programming). This is still a common way to write TensorFlow programs. However, eager execution, available from release 1.5 onward in research form and baked into TensorFlow proper from release 1.7, involves the immediate evaluation of operations, with the consequence that tensors can be treated like NumPy arrays (this is known as imperative programming).
Google says that eager execution is the preferred method for research and development but that computational graphs are to be preferred for serving TensorFlow production applications.
tf.data is an API that allows you to build complicated data input pipelines from simpler, reusable parts. The highest level abstraction is Dataset, which comprises both elements of nested structures of tensors and a plan of transformations that are to act on those elements. There are classes for the following:
- There's Dataset consisting of fixed length record sets from at least one binary file (FixedLengthRecordDataset)
- There's Dataset consisting of records from at least one TFRecord file (TFRecordDataset)
- There's Dataset consisting of records that are lines from at least one text file (TFRecordDataset)
- There is also a class that represents the state of iterating through Dataset (tf.data.Iterator)
Let's move on to the estimator, which is a high-level API that allows you to build greatly simplified machine learning programs. Estimators take care of training, evaluation, prediction, and exports for serving.
TensorFlow.js is a collection of APIs that allow you to build and train models using either the low-level JavaScript linear algebra library or the high-level layers API. Hence, models can be trained and run in a browser.
TensorFlow Lite is a lightweight version of TensorFlow for mobile and embedded devices. It consists of a runtime interpreter and a set of utilities. The idea is that you train a model on a higher-powered machine and then convert your model into the .tflite format using the utilities. You then load the model into your device of choice. At the time of writing, TensorFlow Lite is supported on Android and iOS with a C++ API and has a Java wrapper for Android. If an Android device supports the Android Neural Networks (ANN) API for hardware acceleration, then the interpreter will use this, or else it will default to the CPU for execution.
TensorFlow Hub is a library designed to foster the publication, discovery, and use of reusable modules of machine learning models. In this context, a module is a self-contained piece of a TensorFlow graph together with its weights and other assets. The module can be reused in different tasks in a method known as transfer learning. The idea is that you t...
