原文链接：[BUILDING A NEURAL NET FROM SCRATCH IN GO](https://www.datadan.io/building-a-neural-net-from-scratch-in-go/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com)
I'm super pumped that my new book [Machine Learning with Go](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-go) is now available! Writing the book allowed me to get a complete view of the current state of machine learning in Go, and let's just say that I'm pretty excited to see how the community growing!
我很高兴我的新书[Machine Learning with Go](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-go)现已推出！写这本书让我可以全面了解Go中机器学习的现状，我很高兴看到社区如何成长！
In the book (and for my own edification), I decided that I would build a neural network from scratch in Go. Turns out, this is fairly easy, and I thought it would be great to share my little neural net here.
(If you are interested in leveraging pre-existing Go packaging for machine learning, check out [all the great existing packages](https://github.com/gopherdata/resources/tree/master/tooling), and be sure to watch [Chris Benson's recent talk](https://youtu.be/CHzMEamGZDA) at GolangUK about Deep Learning in Go)
There are a whole variety of ways to accomplish this task of building a neural net in Go, but I wanted to adhere to the following guidelines:
在Go中完成构建神经网络的任务有多种方法，但我想遵循以下准则：
-**No cgo** - I want my little neural net to compile nicely to a statically linked binary, and I also want to highlight the numerical functionality that is available natively in Go.
-**gonum matrix input** - I want supply matrices to my neural network for training, similar to how you would supply `numpy` arrays to most Python machine learning functions.
-**Variable numbers of nodes** - Although I will only illustrate one architecture here, I wanted my code to be flexible, such that I could tweak the numbers of nodes in each layer for other scenarios.
This type of single layer neural net might not be very "deep," but it has proven to be very useful for a huge majority of simple classification tasks. In our case, we will be training our model to classify iris flowers based on the [famous iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set). This should be more than enough to solve that problem with a high degree of accuracy.
Each of the **nodes** in the network will take in one or more inputs, combine those together linearly (using **weights** and a **bias**), and then apply a non-linear **activation function**. By optimizing the weights and the biases, with a process called [**backpropagation**](https://en.wikipedia.org/wiki/Backpropagation), we will be able to mimic the relationships between our inputs (measurements of flowers) and what we are trying to predict (species of flowers). We will then be able to feed new inputs through the optimized network (i.e., we will **feed** them **forward**) to predict the corresponding output.
(If you are new to neural nets, you might also check out [this great intro](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/), or, of course, you can read the relevant section in [Machine Learning with Go](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-go).)
We also need to define our activation function and it's derivative, which we will utilize during backpropagation. There are many choices for activation functions, but here we are going to use the [sigmoid function](http://mathworld.wolfram.com/SigmoidFunction.html). This function has various advantages, including probabilistic interpretations and a convenient expression for it's derivative.
With the definitions above taken care of, we can write an implementation of the [backpropagation method](https://en.wikipedia.org/wiki/Backpropagation) for training, or optimizing, the weights and biases of our network. The backpropagation method involves:
1.Initializing our weights and biases (e.g., randomly).
2.Feeding training data through the neural net forward to produce output.
3.Comparing the output to the correct output to get errors.
4.Calculating changes to our weights and biases based on the errors.
5.Propagating the changes back through the network.
6.Repeating steps 2-5 for a given number of **epochs** or until a stopping criteria is satisfied.
1.初始化我们的权重和偏差（例如，随机）。
2.通过神经网络向前馈送训练数据以产生输出。
3.将输出与正确的输出进行比较以获得错误。
4.根据错误计算我们的权重和偏差的变化。
5.通过网络传播更改。
6.对于给定数量的**批次**重复步骤2-5或直到满足停止标准。
In steps 3-5, we will utilize [**stochastic gradient descent**](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)(SGD) to determine the updates for our weights and biases.
To implement this network training, I created a method on `neuralNet` that would take pointers to two matrices as input, `x` and `y`. `x` will be the features of our data set (i.e., the independent variables) and `y` will represent what we are trying to predict (i.e., the dependent variable). I will show an example of these later in the article, but for now, let's assume that they take this form.
The actual implementation of backpropagation is shown below. **Note/Warning**, for clarity and simplicity, I'm going to create a handful of matrices as I carry out the backpropagation. For large data sets, you would likely want to optimize this to reduce the number of matrices in memory.
After training our neural net, we are going to want to use it to make predictions. To do this, we just need to feed some given `x` values forward through the network to produce an output. This looks similar to the first part of backpropagation. Except, here we are going to return the generated output.
Ok, we now have the building blocks that we will need to train and test our neural network. However, before we go off and try to run this, let's take a brief look at the data that I will be using to experiment with this neural net.
The data I'm going to use is a slightly transformed version of the popular [iris data set](https://archive.ics.uci.edu/ml/datasets/iris). This data set includes sets of four iris flower measurements (what will become our `x` values) along with a corresponding indication of iris species (what will become our `y` values). To utilize this data set with our neural net, I have slightly transformed the data set, such that the species values are represented by three binary columns (1 if the row corresponds to that species, 0 otherwise). I have also added a little bit of random noise to the measurement to try and confuse the neural net (because this problem is pretty easy to solve otherwise):
That gives us a trained neural net. We can then parse the test data into matrices `testInputs` and `testLabels` (I'll spare you these details as they are the same as above), use our `predict()`method to make predictions for the flower species, and then compare the predictions to the actual species. Calculating the predictions and accuracy looks like the following:
Woohoo! 97% accuracy isn't too shabby for our little from scratch neural net! Of course this number will vary due to the randomness in the model, but it does generally perform very nicely.
我希望这对你来说是有益的和有趣的。所有的代码和数据都是[可在这里](https://github.com/dwhitena/gophernet)，所以自己试试吧！ 此外，如果您对Go for ML / AI和Data Science感兴趣，我强烈建议：
I hope this was informative and interesting for you. All of the code and data is [available here](https://github.com/dwhitena/gophernet), so try it out yourself! Also, if you are interested in Go for ML/AI and Data Science in general, I highly recommend:
- Joining [Gophers Slack](https://invite.slack.golangbridge.org/), and participating in the #data-science channel (I'm @dwhitena there)
- Checking out all the great Go ML/AI/data tooling [here](https://github.com/gopherdata/resources/tree/master/tooling)
- Following the [GopherData blog/website](http://gopherdata.io/) for more interesting articles and community information
| [BUILDING A NEURAL NET FROM SCRATCH IN GO](https://www.datadan.io/building-a-neural-net-from-scratch-in-go/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | |
| [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | |
| [Practical Data Science in Python](https://radimrehurek.com/data_science_python/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | |
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| [BUILDING A NEURAL NET FROM SCRATCH IN GO](https://www.datadan.io/building-a-neural-net-from-scratch-in-go/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | 介绍用Go构建一个简单的神经网络 |
| [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | 神经网络的快速入门 |
| [Practical Data Science in Python](https://radimrehurek.com/data_science_python/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com) | 用垃圾邮件分类完整的介绍了建立机器学习模型过程（Python2) |
