How to eat TensorFlow2 in 30 days ?🔥🔥

Switching to Chinese version: 中文版 🎈

📚 URL to gitbook (Only in Chinese version for now): https://lyhue1991.github.io/eat_tensorflow2_in_30_days

🚀 URL to github repo (Chinese): https://github.com/lyhue1991/eat_tensorflow2_in_30_days/tree/master

🚀 URL to github repo (English): https://github.com/lyhue1991/eat_tensorflow2_in_30_days/tree/english

1. TensorFlow2 🍎 or Pytorch🔥

Conclusion first:

For the engineers, priority goes to TensorFlow2.

For the students and researchers,first choice should be Pytorch.

The best way is to master both of them if having sufficient time.

Reasons:

    1. Model implementation is the most important in the industry. Only deployment supports for tensorflow models (not Pytorch) is the present situation in the majority of the internet enterprises (in China). What’s more, the industry prefers the models with higher availability; in most cases, they use well-validated modeling architectures with the minimized requirements of adjustment.
    1. Fast iterative development and publication is the most important for the researchers since they need to test a lot of new models. Pytorch has advantages in accessing and debugging comparing with TensorFlow2. Pytorch is most frequently used in academy since 2019 with a large amount of the cutting-edge results.
    1. Overall, TensorFlow2 and Pytorch are quite similar in programming nowadays, so mastering one helps learning the other. Mastering both framework provides you a lot more open-sourced models and helps you switching between them.

2. Keras🍏 and tf.keras 🍎

Conclusion first:

Keras will be discontinued in development after version 2.3.0, so use tf.keras.

Keras is a high-level API for the deep learning frameworks. It help the users to define and training DL networks with a more intuitive way.

The Keras libraries installed by pip implement this high-level API for the backends in tensorflow, theano, CNTK, etc.

tf.keras is the high-level API just for Tensorflow, which is based on low-level APIs in Tensorflow.

Most but not all of the functions in tf.keras are the same for those in Keras (which is compatible to many kinds of backend). tf.keras has a tighter combination to TensorFlow comparing to Keras.

With the acquisition by Google, Keras will not update after version 2.3.0 , thus the users should use tf.keras from now on, instead of using Keras installed by pip.

3. What Should You Know Before Reading This Book 📖?

It is suggested that the readers have foundamental knowledges of machine/deep learning and experience of modeling using Keras or TensorFlow 1.0.

For those who have zero experience of machine/deep learning, it is strongly suggested to refer to “Deep Learning with Python” along with reading this book.

“Deep Learning with Python” is written by François Chollet, the inventor of Keras. This book is based on Keras and has no machine learning related prerequisites to the reader.

“Deep Learning with Python” is easy to understand as it uses various examples to demonstrate. No mathematical equation is in this book since it focuses on cultivating the intuitive to the deep learning.

4. Writing Style 🍉 of This Book

This is a introduction reference book which is extremely friendly to human being. The lowest goal of the authors is to avoid giving up due to the difficulties, while “Don’t let the readers think” is the highest target.

This book is mainly based on the official documents of TensorFlow together with its functions.

However, the authors made a thorough restructuring and a lot optimizations on the demonstrations.

It is different from the official documents, which is disordered and contains both tutorial and guidance with lack of systematic logic, that our book redesigns the content according to the difficulties, readers’ searching habits, and the architecture of TensorFlow. We now make it progressive for TensorFlow studying with a clear path, and an easy access to the corresponding examples.

In contrast to the verbose demonstrating code, the authors of this book try to minimize the length of the examples to make it easy for reading and implementation. What’s more, most of the code cells can be used in your project instantaneously.

Given the level of difficulty as 9 for learning Tensorflow through official documents, it would be reduced to 3 if learning through this book.

This difference could be demonstrated as the following figure:

How to eat TensorFlow2 in 30 days ?🔥🔥 - 图1

5. How to Learn With This Book ⏰

(1) Study Plan

The authors wrote this book using the spare time, especially the two-month unexpected “holiday” of COVID-19. Most readers should be able to completely master all the content within 30 days.

Time required everyday would be between 30 minutes to 2 hours.

This book could also be used as library examples to consult when implementing machine learning projects with TensorFlow2.

Click the blue captions to enter the corresponding chapter.

Date Contents Difficulties Est. Time Update Status
  Chapter 1: Modeling Procedure of TensorFlow ⭐️ 0hour
Day 1 1-1 Example: Modeling Procedure for Structured Data ⭐️⭐️⭐️ 1hour
Day 2 1-2 Example: Modeling Procedure for Images ⭐️⭐️⭐️⭐️ 2hours
Day 3 1-3 Example: Modeling Procedure for Texts ⭐️⭐️⭐️⭐️⭐️ 2hours
Day 4 1-4 Example: Modeling Procedure for Temporal Sequences ⭐️⭐️⭐️⭐️⭐️ 2hours
  Chapter 2: Key Concepts of TensorFlow ⭐️ 0hour
Day 5 2-1 Data Structure of Tensor ⭐️⭐️⭐️⭐️ 1hour
Day 6 2-2 Three Types of Graph ⭐️⭐️⭐️⭐️⭐️ 2hours
Day 7 2-3 Automatic Differentiate ⭐️⭐️⭐️ 1hour
  Chapter 3: Hierarchy of TensorFlow ⭐️ 0hour
Day 8 3-1 Low-level API: Demonstration ⭐️⭐️⭐️⭐️ 1hour
Day 9 3-2 Mid-level API: Demonstration ⭐️⭐️⭐️ 1hour
Day 10 3-3 High-level API: Demonstration ⭐️⭐️⭐️ 1hour
  Chapter 4: Low-level API in TensorFlow ⭐️ 0hour
Day 11 4-1 Structural Operations of the Tensor ⭐️⭐️⭐️⭐️⭐️ 2hours
Day 12 4-2 Mathematical Operations of the Tensor ⭐️⭐️⭐️⭐️ 1hour
Day 13 4-3 Rules of Using the AutoGraph ⭐️⭐️⭐️ 0.5hour
Day 14 4-4 Mechanisms of the AutoGraph ⭐️⭐️⭐️⭐️⭐️ 2hours
Day 15 4-5 AutoGraph and tf.Module ⭐️⭐️⭐️⭐️ 1hour
  Chapter 5: Mid-level API in TensorFlow ⭐️ 0hour
Day 16 5-1 Dataset ⭐️⭐️⭐️⭐️⭐️ 2hours
Day 17 5-2 feature_column ⭐️⭐️⭐️⭐️ 1hour
Day 18 5-3 activation ⭐️⭐️⭐️ 0.5hour
Day 19 5-4 layers ⭐️⭐️⭐️ 1hour
Day 20 5-5 losses ⭐️⭐️⭐️ 1hour
Day 21 5-6 metrics ⭐️⭐️⭐️ 1hour
Day 22 5-7 optimizers ⭐️⭐️⭐️ 0.5hour
Day 23 5-8 callbacks ⭐️⭐️⭐️⭐️ 1hour
  Chapter 6: High-level API in TensorFlow ⭐️ 0hour
Day 24 6-1 Three Ways of Modeling ⭐️⭐️⭐️ 1hour
Day 25 6-2 Three Ways of Training ⭐️⭐️⭐️⭐️ 1hour
Day 26 6-3 Model Training Using Single GPU ⭐️⭐️ 0.5hour
Day 27 6-4 Model Training Using Multiple GPUs ⭐️⭐️ 0.5hour
Day 28 6-5 Model Training Using TPU ⭐️⭐️ 0.5hour
Day 29 6-6 Model Deploying Using tensorflow-serving ⭐️⭐️⭐️⭐️ 1hour
Day 30 6-7 Call Tensorflow Model Using spark-scala ⭐️⭐️⭐️⭐️⭐️ 2hours
  Epilogue: A Story Between a Foodie and Cuisine ⭐️ 0hour

(2) Software environment for studying

All the source codes are tested in jupyter. It is suggested to clone the repository to local machine and run them in jupyter for an interactive learning experience.

The authors would suggest to install jupytext that converts markdown files into ipynb, so the readers would be able to open markdown files in jupyter directly.

  1. #For the readers in mainland China, using gitee will allow cloning with a faster speed
  2. #!git clone https://gitee.com/Python_Ai_Road/eat_tensorflow2_in_30_days
  3. #It is suggested to install jupytext that converts and run markdown files as ipynb.
  4. #!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U jupytext
  5. #It is also suggested to install the latest version of TensorFlow to test the demonstrating code in this book
  6. #!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U tensorflow
  1. import tensorflow as tf
  2. #Note: all the codes are tested under TensorFlow 2.1
  3. tf.print("tensorflow version:",tf.__version__)
  4. a = tf.constant("hello")
  5. b = tf.constant("tensorflow2")
  6. c = tf.strings.join([a,b]," ")
  7. tf.print(c)
  1. tensorflow version: 2.1.0
  2. hello tensorflow2

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