TensorFlow v1.0实战：深度学习与机器学习基础

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“Machine Learning with TensorFlow Version 10” 是一本专注于机器学习概念和TensorFlow实践的书籍，通过Python编程教授读者掌握TensorFlow。在本书中，读者将能够深入理解机器学习的基础知识，包括监督学习、无监督学习、强化学习等核心概念。作者详细阐述了如何利用TensorFlow这一强大的开源库来构建和训练各种机器学习模型，如神经网络、支持向量机、决策树等。TensorFlow是一个由Google开发的深度学习框架，它允许数据科学家和研究人员高效地构建和部署复杂的模型。书中对每个章节都进行了精心的更新和改进，文本更加易懂，特别针对复杂问题进行了深入的解释，回应了读者之前提出的需求。新的版本中，所有的图表和数学公式都经过了优化，呈现出清晰、专业的视觉效果，有助于读者更好地理解和应用理论知识。此外，代码示例已更新至TensorFlow v1.0，这是一个重要的版本更新，因为它包含了众多性能优化和新功能。读者可以访问GitHub上的项目仓库（https://github.com/B）获取这些代码，进行实战演练，从而加深对理论知识的理解。通过阅读这本书，读者不仅能够学习到机器学习的基本原理，还能掌握如何在实际项目中运用TensorFlow解决各种问题。无论是对机器学习感兴趣的新手，还是寻求进阶的开发者，都能从中受益匪浅。同时，书中还鼓励读者参与讨论，无论是在官方的书本论坛、通过电子邮件、GitHub或Reddit，都可以与作者和其他读者交流，共同探讨机器学习和TensorFlow的奥秘。 “Machine Learning with TensorFlow Version 10”是一本全面、实用的指南，旨在帮助读者在机器学习领域建立坚实的基础，并掌握利用TensorFlow实现高效学习模型的技能。

Figure 1.9 Feature vectors are a representation of real world data used by both the learning and inference

components of machine learning. The input to the algorithm is not the real-world image directly, but instead its

feature vector.

1.3 Distance Metrics

f you have feature vectors of potential cars you want to buy, you can figure out which two

are most similar by defining a distance function on the feature vectors. Comparing similarities

between objects is an essential component of machine learning. Feature vectors allow us to

represent objects so that they we may compare them in a variety of ways. A standard

approach is to use the Euclidian distance, which is the geometric interpretation you may find

most intuitive when thinking about points in space.

Let's say we have two feature vectors, x = (x

, x

, …, x

) and y = (y

, y

, …, y

). The

Euclidian distance ||x-y|| is calculated by

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• The L2 norm is the Euclidian length of a vector, (Σ(x

)

. It is the most direct route

one can possibly take on a geometric plane to get from one point to another. For the

mathematically inclined, this is the norm that implements the least square estimation as

predicted by the Gauss-Markov theorem. For the rest of you, it’s the shortest distance

between two points in space.

igure 1.11 The L2 norm between points (0,1) and (1,0) is the length of a single straight line segment reaching

both points.

• The L-N norm generalizes this pattern, resulting in (Σ|x

)

We rarely use finite

norms above L2, but it’s here for completeness.

• The L-infinity norm is (Σ|x

∞

)

/∞

More naturally, it is the largest magnitude among

each element. If the vector is (-1, -2, -3), the L-infinity norm will be 3. If a feature

vector represents costs of various items, then minimizing the L-infinity norm of the

vector is an attempt to reduce the cost of the most expensive item.

When do I use a metric other than the L2 norm in the real-world?

Let’s say you’re working for a new search-engine start-up trying to compete with Google. Your boss assigns you the

task of using machine learning to personalize the search results for each user.

A good goal might be that users shouldn’t see five or more incorrect search-results per month. A year’s worth of user-

data is a 12-dimension vector (where each month of the year is a dimension), indicating number of incorrect results

shown per month. You are trying to satisfy the condition that the L-infinity norm of this vector must less than 5.

Suppose instead that your boss changes his/her mind and requires that less than 5 erroneous search-results are

allowed for the entire year. In this case, you are trying to achieve a L1 norm below 5, because the sum of all errors in the

entire space should be less than 5.

Actually, your boss changes his or her mind again. Now, the number of months with erroneous search-results should

be less than 5. In that case, you are trying to achieve an L0 norm below 5, because the number of months with a non-

zero error should be less than 5.

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4 Types of Learning

ow that we can compare feature vectors, we have the tools necessary to use data for

practical algorithms. Machine learning is often split into three perspectives: supervised

learning, unsupervised learning, and reinforcement learning. Let's examine each, one by one.

1.4.1 Supervised Learning

y definition, a supervisor is someone higher up in the chain of command. When in doubt,

he or she dictates what to do. Likewise, supervised learning is all about learning from examples

laid out by a "supervisor" (such as a teacher).

A supervised machine learning system needs labeled data to develop a useful

understanding, which we call its model. For example, given many photographs of people and

their recorded corresponding ethnicity, we can train a model to classify the ethnicity of a

never-before-seen individual in an arbitrary photograph. Simply put, a model is a function that

assigns a label to some data. It does so by using previous examples, called a training dataset,

as reference.

A convenient way to talk about models is through mathematical notation. Let x be an

instance of data, such as a feature vector. The corresponding label associated with x is f(x),

often referred to as the ground truth of x. Usually, we use the variable y = f(x) because it’s

quicker to write. In the example of classifying the ethnicity of a person through a photograph, x

can be a hundred-dimensional vector of various relevant features, and y is one of a couple

values to represent the various ethnicities. Since y is discrete with few values, the model is

called a classifier. If y could result in many values, and the values have a natural ordering,

then the model is called a regressor.

Let’s denote a model's prediction of x as g(x). Sometimes you can tweak a model to change

its performance drastically. Models have some parameters that can be tuned either by a

human or automatically. We use the vector θ to represent the parameters. Putting it all

together, g(x|θ) more completely represents the model, read "g of x given θ."

IDE

Models may also have hyper-parameters, which are extra ad-hoc properties about a model. The word

“hyper” in “hyper-parameter” is a bit strange at first. If it helps, a better name could be “meta-parameter,”

because the parameter is akin to metadata about the model.

he success of a model’s prediction g(x|θ) depends on how well it agrees with the ground

truth y. We need a way to measure the distance between these two vectors. For example, the

L2-norm may be used to measure how close two vectors lie. The distance between the ground

truth and prediction is called the cost.

The essence of a supervised machine learning algorithm is to figure out the parameters of a

model that results in the least cost. Mathematically put, we are trying to look for a θ* (Theta

star) that minimizes the cost among all data points x ∈ X. One way of formalizing this

optimization problem is the following:

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learly, brute forcing every possible combination of θs, also known as a parameter-space,

will eventually find the optimal solution, but at an unacceptable runtime. A major study in

machine learning is about writing algorithms that efficiently search through this parameter-

space. Some of the first algorithms include gradient descent, simulated annealing, and genetic

algorithms. TensorFlow automatically takes care of the low-level implementation details of

these algorithms, so we won’t get into them in too much detail.

Once the parameters are learned one way or another, you can finally evaluate the model to

figure out how well the system captured patterns from the data. A rule of thumb is not to

evaluate your model on the same data you used to train it, because we already know it works

for the training data – we need to tell if it works for data that wasn’t part of the training set, to

make sure our model is “general purpose” and not biased to the data we used to train it. Use

the majority of the data for training, and the remaining for testing. For example, if you have

100 labeled data, randomly select 70 of them to train a model, and reserve the other 30 to test

it.

Why split the data?

If the 70-30 split seems odd to you, think about it like this. Let's say your Physics teacher gives you a practice exam

and tells you the real exam will be no different. You might as well memorize the answers and earn a perfect score

without actually understanding the concepts. Similarly, if we test our model on the training dataset, we're not doing

ourselves any favors. We risk a false sense of security since the model may merely be memorizing the results. Now,

where's the intelligence in that?

Instead of the 70-30 split, machine learning practitioners typically divided their dataset 60-20-20. Training consumes

60% of the dataset, and testing uses 20%, leaving the other 20% for what is called "validation," which will be explained

in the next chapter.

1.4.2 Unsupervised Learning

nsupervised learning is about modeling data that comes without corresponding labels or

responses. The fact that we can make any conclusions at all on just raw data feels like magic.

With enough data, it may be possible to find patterns and structure. Two of the most powerful

tools that machine learning practitioners use to learn from data alone are clustering and

dimensionality reduction.

Clustering is the process of splitting the data into individual buckets of similar items. In a

sense, clustering is like classification of data without knowing any corresponding labels. For

instance, when organizing your books onto three shelves, you likely place similar genres

together, or maybe you group them by author's last name. You might have a Stephen King

section, another for textbooks, and a third for “anything else.” You don’t care that they are all

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TensorFlow v1.0实战：深度学习与机器学习基础

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