Practical machine learning with Scikit-Learn and TensorFlow-TensorFlow gave up-

** Practical machine learning with Scikit-Learn and TensorFlow-Scikit-Learn edition- ** is a continuation.

The first half (up to Chapter 8) was mostly about Scikit-Learn. In the second half (Chapter 9 and beyond), we'll move on to TensorFlow.

• While the book is written with the contents of TensorFlow1, it seems that it is like TensorFlow2 now, so there are many parts that I do not understand well about what happens with TensorFlow2.

Before getting into the content, I will summarize the sites that will be helpful as a memo. ・ Sample code (GitHub) ・ TensorFlow official website ・ PFN Chainer Tutorial ・ Book Information

■ Conclusion

In conclusion, considering that TensorFlow2 is the current situation, I felt that learning with TensorFlow1 was not very efficient.

TensorFlow1 and 2 feel like different things, and TensorFlow2 seems to be much better. At the beginning, it has an atmosphere like "TensorFlow is characterized by starting and closing Session", but in TensorFlow2, this Session is abolished ...

It's like this from the beginning of TensorFlow, so when I try to put it into practice, I'm not sure what has changed and what hasn't changed, and it's already difficult. Even if you are looking it up, it will melt in time with just a little research.

I think there is an opinion that you should study with TensorFlow 1 for the time being, but for my purpose this time, I want to be able to use it in practice, not to study TensorFlow, so that's a little different. right. I wonder if I will change the book to one that supports TensorFlow 2 ... (This book was not cheap)

However, there are many things to study just because the version is different. I will read it again once I reach the stage where practice is set aside. Currently, it is a phase that I want you to move for the time being, so it seems that it will be a role to warm the bookshelf for a while.

For the time being, I wrote down to Qiita halfway through Chapter 9 (the first chapter of TensorFlow), so the article continues below, but it is subtle whether the crappy content is stale and worth reading.

By the way, out of all 16 chapters of practical machine learning by Scikit-Learn and TensorFlow, I have lost my motivation to read up to 13 chapters, so for the time being, studying Deep Learning is another opportunity. The number of good books has increased, so I will wait for it to saturate.

• The stale notes follow below.

■ Chapter 9: Launch TensorFlow.

Study day: 2020/6/21

My knowledge begins with "What is TensorFlow in the first place? A library for deep learning ??" It's a crazy story, and it's hard to read from this level of knowledge.

In the book

① Being powerful open source software for numerical calculation (2) Being tuned especially for large-scale machine learning ③ The basic principle is that if you define a calculation graph to be executed in Python, TensorFlow will read the graph and execute it efficiently with optimized C ++ code. ④ The most important thing is that graphs can be processed in parallel by CPU or GPU. ⑤ Supports distributed computing

It is written that.

To be honest, when I read it at a glance, it felt like "what a mess", and what is the relationship between TensorFlow and calculation graphs in the first place? I had a question. If you read it carefully, it seems that TensorFlow basically follows the order of "creating a calculation graph → executing a calculation". I see.

・ 9.1 Installation

There is an isolated environment, but I have never created one because I have never needed a virtual environment. I thought this was a good opportunity, so I researched this and created a virtual environment.

In the book, it was written that it was made with ** virtualenv **, but I wanted to make it with Anaconda, so I referred to the following site.

・ [Python] Building a virtual environment using Anaconda -[Anaconda] How to build a virtual environment with the conda command

I noticed when installing TensorFlow, but it seems that the version of TensorFlow in the book is 1, while the current version is 2. </ font> Moreover, it seems that the usability is different. .. ..

import tensorflow as tf

#★ Construction of calculation graph
x = tf.Variable(3, name = "x")
y = tf.Variable(4, name = "y")
f = x*x*y + y +2

#★ Execution of calculation
sess = tf.Session() #Open a TensorFlow session
                    #It is absolutely necessary to perform calculations using a calculation graph
sess.run(x.initializer) #Variable initialization
sess.run(y.initializer)
result = sess.run(f) #Rating of f
print(result) #Display of calculation results
sess.close #Close TensorFlow

42

import tensorflow as tf

#Set of variables
x = tf.Variable(3)
y = tf.Variable(4)

# tf.Create a function with function
@tf.function
def f(x, y):
    return x*x*y + y + 2

result = f(x, y)
print(result)

tf.Tensor(42, shape=(), dtype=int32)

w = tf.constant(3)
x = w + 2
y = x + 5
z = x * 3

with tf.Session() as sess:
    y_val, z_val = sess.run([y, z])