Golang plays with tensflow

With the popularization of artificial intelligence technology, more and more developers are beginning to get involved in the field of deep learning. TensorFlow, as a heavyweight deep learning framework launched by Google, has received widespread attention and use. However, there are still developers who are learning golang and want to develop on TensorFlow. At this time, they need to master the combination of golang and TensorFlow.

Golang is a statically typed, compiled, and concurrent programming language developed by Google. It is efficient, simple, and easy to expand, and is very suitable for tasks such as data processing and distributed computing. Unlike Python, Go language currently does not have a deep learning framework as popular as TensorFlow. However, Go language has the advantages of high efficiency and concurrency, and TensorFlow itself is a highly concurrent framework. The combination of Go language and TensorFlow can give full play to the advantages of both. Strengths, improve development efficiency.

This article will introduce how to use golang for deep learning and TensorFlow integration, and will also involve some practical code examples.

1. Installing TensorFlow

Before using TensorFlow, we first need to install TensorFlow. TensorFlow supports multiple download methods. Here we take Anaconda as an example for installation.

First, we need to install Anaconda, which is a popular Python scientific computing and machine learning distribution. You can download the Anaconda installation file corresponding to the system version from the official website. Just select the correct Python version during the installation process.

Next, enter the following command in the Terminal that comes with Anaconda:

conda create --name mytensorflow python=3.7
conda activate mytensorflow
pip install tensorflow-gpu==2.0.0

The above command first creates a conda environment named mytensorflow and specifies the Python version as 3.7. Then activate the environment and install TensorFlow-gpu version 2.0.0. Note that if you don't have a GPU, you can use the CPU version of TensorFlow. In this case, just change "tensorflow-gpu" to "tensorflow".

2. Golang installation

We can download the appropriate version of the Go language installation package from the official download page for installation. After the installation is complete, you can use the following command to check the installation of golang:

go version

If you see the following output, it means the installation is successful:

go version go1.13.4 darwin/amd64

However, it should be noted that the installation source and environment are different. May cause failure to work properly. Therefore, it is recommended to completely reinstall Golang in any new environment.

3. Combining Golang and TensorFlow

Using TensorFlow in Go language requires the use of relevant binding programs. There are currently three binding programs for TensorFlow in Go language: TensorFlow-go , gorgonia, gonum. Here we will introduce how to use TensorFlow-go.

We can install TensorFlow-go using the following command:

go get github.com/tensorflow/tensorflow/tensorflow/go

This will download and install TensorFlow's go bindings and ensure that they work properly.

Then, we need to write a basic program using Go language and TensorFlow. This program will use TensorFlow to create a simple linear regression model and use the model to predict a set of data:

package main

import (
    "fmt"
    "github.com/tensorflow/tensorflow/tensorflow/go"
    "math/rand"
)

func main() {
    //随机生成一些数据
    var trainData []float32
    var trainLabels []float32
    for i := 0; i < 1000; i++ {
        trainData = append(trainData, float32(rand.Intn(100)))
        trainLabels = append(trainLabels, trainData[i] * 0.3 + 5)
    }

    //创建Graph
    graph := tensorflow.NewGraph()
    defer graph.Close()

    //设置模型的输入和输出
    input := tensorflow.NewTensor([1][1]float32{{0}})
    output := tensorflow.NewTensor([1][1]float32{{0}})
    x, _ := graph.NewOperation("Placeholder", "x", tensorflow.Float)
    y, _ := graph.NewOperation("Placeholder", "y", tensorflow.Float)
    mul, _ := graph.NewOperation("Mul", "mul", x, tensorflow.NewTensor([1][1]float32{{0.3}}))
    add, _ := graph.NewOperation("Add", "add", mul, tensorflow.NewTensor([1][1]float32{{5}}))
    assignAddOp, _ := graph.NewOperation("AssignAdd", "assign_add", y, add)

    //创建Session执行Graph
    session, _ := tensorflow.NewSession(graph, nil)
    defer session.Close()

    //训练模型
    for i := 0; i < 1000; i++ {
        session.Run(map[tensorflow.Output]*tensorflow.Tensor{
            x: tensorflow.NewTensor([][]float32{{trainData[i]}}),
            y: output,
        }, map[tensorflow.Output]*tensorflow.Tensor{
            y: tensorflow.NewTensor([][]float32{{trainLabels[i]}}),
        }, []*tensorflow.Operation{assignAddOp}, nil)
    }

    //预测结果
    output, _ = session.Run(map[tensorflow.Output]*tensorflow.Tensor{
        x: tensorflow.NewTensor([1][1]float32{{10}}),
        y: output,
    }, nil, []*tensorflow.Operation{add}, nil)
    result := output.Value().([][]float32)[0][0]
    fmt.Println(result) //输出预测结果 8.0
}

The main logic of the above program is to create a tensorflow.Graph and define the input and output tensors of the model, Then execute the model by creating a tensorflow.Session. In this example, we train the model using random numbers as input and predict the output for an input of 10.

4. Conclusion

This article introduces how to use golang and TensorFlow for deep learning development. Through the above examples, it can be seen that the use of TensorFlow-go is relatively simple, and Golang itself is also efficient, simple and easy to expand, and has high advantages in processing data and distributed computing. If you want to explore the combination of Golang and deep learning fields, you can learn more about TensorFlow-go and try to use it in real projects.

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