Introduction
Writing is an important skill for both our career and communication because it helps us understand each other. And, Rhetorical Analysis is one of the most important skills in writing that shows the effectiveness of communications between authors and their audiences. Therefore learning how to rhetorical analysis documents is critical for us. In order to improve skills, read good papers in the field and analysis them is one of the best ways.
In this Rhetorical Analysis, I will be analyzing the article “Image Recognition”(about six pages) from the official website of TensorFlow. This article doesn’t have a specific author, instead, it is created and has been updated(the latest update happened on September 28th) constantly by a team called Google Brain team. This article is basically a tutorial for people to learn how to use Inception-v3( “Inception-v3 is trained for the ImageNet Large Visual Recognition Challenge using the data from 2012.” ) to implement Image Recognition on their devices. As we all know, Google is the bellwether in AI(Artificial Intelligence) development (or deep learning) area and Image Recognition is based on AI.
The intended audiences of this article are students or people working in the AI field. And the readers need to have a basic knowledge of Python or C++. This article introduced what is Inception-v3 and how to implement it using Python API(Application Program Interface) and C++ API, and the authors give examples to show how Inception-v3 works. In what follows I will be discussing style, rhetorical appeals, and two rhetorical strategies the document used. Analysis of Rhetorical Strategies Style Style is the way an author writes a piece of writing. This basically affects the impression a reader has after reading the article. In this piece of writing, Google Brain team uses a formal style and have a professional tone at all times. As they mention at the beginning of the article, this is a tutorial to teach people new knowledge. For instance, the author provides a lot of examples of codes and commands such as, “cd models/tutorials/image/imagenet” “python classify_image.py”
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The strong professional tone and style like “You’ll learn how to classify images into 1,000 classes in Python or C++.” have not changed throughout the article and make this a very trustworthy tutorial. Rhetorical Appeals Ethos Ethos is basically an ethical appeal that is dependent on the credibility of the writer. There is a use of ethos in which the author uses the reference of GitHub, which is the biggest online community of software engineer in the world. The authors provide many examples of codes, commands, and images so that the readers can operate on their own computer to find that the information from this tutorial is credible and not something that was made up. Since this tutorial is on the official website of TensorFlow, the readers can feel free to check more of it. This puts the author’s credibility at a much higher level. At the end of the article, the authors mention other resources about neural networks(closely related to AI development). Moreover, they demonstrate that their code examples are licensed under the authority of this field.“Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 3.0 License, and code samples are licensed under the Apache 2.0 License.” With the name of the authority in this field, the credibility of the article goes to a higher level. The use of ethos in the article establishes that the explanations and examples given have credibility. Pathos Pathos is when the author tries to persuade the reader with emotions. Emotions are a good way for authors to communicate with readers thoughts and heart. However, In computer sciences and engineering fields, the claims being made rarely have emotional components, so the use of pathos is not strong in the article and is present only at the beginning of the article. In the introduction part, the authors put some Pathos element in it such as “ It doesn’t take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human’s face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.” By writing this sentence at the beginning of the article, the authors use common sense to introduce the idea of this article and make people more interested in reading this tutorial.
Strategies Division and Classification
Division and Classification are to splits the whole article up into parts and then places the divided information into various categories. This article uses division well to clarify the content of the tutorial. First Google Brain team introduce the topic, and then the article is divided into two big sections – Usage with Python API and Usage with C++ API. The two different parts are for two different kinds of audiences( people with Python skills or people with C++ skills) The first paragraph discusses how to set up Inception-v3 with Python and then implement an example on based on that. C++ users could just skip the first paragraph and go to the second part which is written for them. Since C++ is a deeper programming language than python, more specific examples and explanations are given in the second part. By dividing this article into these sections, the authors save the time for different readers of that they would not read the parts that they don’t understand.
Exemplification
Exemplification is explaining an idea by giving examples like facts, cases, and data. This strategy would make it easy for readers to understand the topic. The authors give an example of commands implementing Inception-v3 with Python API: “cd models/tutorials/image/imagenet python classify_image.py (Image of a panda) The above command will classify a supplied image of a panda bear. If the model runs correctly, the script will produce the following output: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.88493) indri, indri, Indri indri, Indri brevicaudatus (score = 0.00878) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00317) custard apple (score = 0.00149) earthstar (score = 0.00127)” This example will help audiences understand how Inception-v3 work in Python deeper. Then the authors give an example of how to implementing the same function using a picture of Admiral Grace Hopper in C++. Moreover, they give examples of built-in functions of Inception-v3 like “GetTopLabels()”, “ToGraphDef()”, and “PrintTopLabels()” to make readers further understand how Inception-v3 works.
At last, Google Brain team also provide more examples that readers can exercise more themselves. All these examples provided the reader with problems and solutions that they can make use of. Conclusions This article is quite professional and informative at the same time. The authors use many examples of code and images to explain how to use Inception-v3 with Tensorflow. The examples are very easy to understand to the proper audience and make this tutorial much more concrete and practical. The use of pathos and ethos cause the reader to trust Google Brain team and further to learn knowledge with their writing such as using a lot of licensed code resources and proper citation. Further, the tone and style enhance the specialty of this piece of writing. It is pretty obvious that rhetorical devices leave a huge impact on the audience. They target the thought process, opinions and emotions of the audience, therefore, with the use of rhetorical devices, a piece of writing becomes very powerful and convincing.
Cutting-edge knowledge might not be one hundred percent true, but with the addition of ethos and pathos, it becomes easier to believe in. By authors’ examples and explanation of the procedure, they try to explain the concept and functionality of Inception-v3 and then, they teach the readers to implement it on readers’ own computer. In this informative article, writers used rhetorical strategies to help readers to understand easily what they are talking about, and learn from this document. After reading this tutorial, I am able to create an image recognition myself using their package without a problem. Choosing the best strategies for the rhetorical situation is extremely important for the writers to achieve their purpose. Obviously, Google Brain team do a good job using the rhetorical strategies in this article.
Reference
- Image recognition, Step 28th, Google Brain team, www.tensorflow.org
