【推荐】TensorFlow手把手CNN实践指南

2017 年 8 月 17 日 机器学习研究会
【推荐】TensorFlow手把手CNN实践指南


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摘要
 

转自:爱可可-爱生活

1. Introduction

In the past I have mostly written about ‘classical’ Machine Learning, like Naive Bayes classification, Logistic Regression, and the Perceptron algorithm. In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

For this you will need to have tensorflow installed (see installation instructions) and you should also have a basic understanding of Python programming and the theory behind Convolutional Neural Networks. After you have installed tensorflow, you can run the smaller Neural Networks without GPU, but for the deeper networks you will definitely need some GPU power.
The Internet is full with awesome websites and courses which explain how a convolutional neural network works. Some of them have good visualisations which make it easy to understand [click here for more info]. I don’t feel the need to explain the same things again, so before you continue, make sure you understand how a convolutional neural network works. For example,

  • What is a convolutional layer, and what is the filter of this convolutional layer?

  • What is an activation layer (ReLu layer (most widely used), sigmoid activation or tanh)?

  • What is a pooling layer (max pooling / average pooling), dropout?

  • How does Stochastic Gradient Descent work?

 

The contents of this blog-post is as follows:

  1. Tensorflow basics:

    • 1.1 Constants and Variables

    • 1.2 Tensorflow Graphs and Sessions

    • 1.3 Placeholders and feed_dicts

  2. Neural Networks in Tensorflow

    • 2.1 Introduction

    • 2.2 Loading in the data

    • 2.3 Creating a (simple) 1-layer Neural Network:

    • 2.4 The many faces of Tensorflow

    • 2.5 Creating the LeNet5 CNN

    • 2.6 How the parameters affect the outputsize of an layer

    • 2.7 Adjusting the LeNet5 architecture

    • 2.8 Impact of Learning Rate and Optimizer

  3. Deep Neural Networks in Tensorflow

    • 3.1 AlexNet

    • 3.2 VGG Net-16

    • 3.3 AlexNet Performance

  4. Final words

 

1. Tensorflow basics:

Here I will give a short introduction to Tensorflow for people who have never worked with it before. If you want to start building Neural Networks immediatly, or you are already familiar with Tensorflow you can go ahead and skip to section 2. If you would like to know more about Tensorflow, you can also have a look at this repository, or the notes of lecture 1 and lecture 2 of Stanford’s CS20SI course.


链接:

http://ataspinar.com/2017/08/15/building-convolutional-neural-networks-with-tensorflow/


原文链接:

https://m.weibo.cn/1402400261/4140930397690270

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Google发布的第二代深度学习系统TensorFlow

When I started out, I had a strong quantitative background (chemical engineering undergrad, was taking PhD courses in chemical engineering) and some functional skills in programming. From there, I first dove deep into one type of machine learning (Gaussian processes) along with general ML practice (how to set up ML experiments in order to evaluate your models) because that was what I needed for my project. I learned mostly online and by reading papers, but I also took one class on data analysis for biologists that wasn’t ML-focused but did cover programming and statistical thinking. Later, I took a linear algebra class, an ML survey class, and an advanced topics class on structured learning at Caltech. Those helped me obtain a broad knowledge of ML, and then I’ve gained deeper understandings of some subfields that interest me or are especially relevant by reading papers closely (chasing down references and anything I don’t understand and/or implementing the core algorithms myself).

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