C#编写TensorFlow人工智能应用 TensorFlowSharp

TensorFlowSharp入门使用C#编写TensorFlow人工智能应用学习。

TensorFlow简单介绍

TensorFlow 是谷歌的第二代机器学习系统，按照谷歌所说，在某些基准测试中，TensorFlow的表现比第一代的DistBelief快了2倍。

TensorFlow 内建深度学习的扩展支持，任何能够用计算流图形来表达的计算，都可以使用TensorFlow。任何基于梯度的机器学习算法都能够受益于TensorFlow的自动分化（auto-differentiation）。通过灵活的Python接口，要在TensorFlow中表达想法也会很容易。

TensorFlow 对于实际的产品也是很有意义的。将思路从桌面GPU训练无缝搬迁到手机中运行。

示例Python代码：

import tensorflow as tfimport numpy as np# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3x_data = np.random.rand(100).astype(np.float32)y_data = x_data * 0.1 + 0.3# Try to find values for W and b that compute y_data = W * x_data + b# (We know that W should be 0.1 and b 0.3, but TensorFlow will# figure that out for us.)W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))b = tf.Variable(tf.zeros([1]))y = W * x_data + b# Minimize the mean squared errors.loss = tf.reduce_mean(tf.square(y - y_data))optimizer = tf.train.GradientDescentOptimizer(0.5)train = optimizer.minimize(loss)# Before starting, initialize the variables.  We will 'run' this first.init = tf.global_variables_initializer()# Launch the graph.sess = tf.Session()sess.run(init)# Fit the line.for step in range(201):    sess.run(train)    if step % 20 == 0:        print(step, sess.run(W), sess.run(b))# Learns best fit is W: [0.1], b: [0.3]

 

使用TensorFlowSharp 

GitHub:https://github.com/migueldeicaza/TensorFlowSharp

官方源码库，该项目支持跨平台，使用Mono。

可以使用NuGet 安装TensorFlowSharp，如下：

Install-Package TensorFlowSharp

 

编写简单应用

使用VS2017新建一个.NET Framework 控制台应用 tensorflowdemo，接着添加TensorFlowSharp 引用。

TensorFlowSharp 包比较大，需要耐心等待。

然后在项目属性中生成->平台目标 改为 x64。

打开Program.cs 写入如下代码：

        static void Main(string[] args)        {            using (var session = new TFSession())            {                var graph = session.Graph;                Console.WriteLine(TFCore.Version);                var a = graph.Const(2);                var b = graph.Const(3);                Console.WriteLine("a=2 b=3");                // 两常量加                var addingResults = session.GetRunner().Run(graph.Add(a, b));                var addingResultValue = addingResults[0].GetValue();                Console.WriteLine("a+b={0}", addingResultValue);                // 两常量乘                var multiplyResults = session.GetRunner().Run(graph.Mul(a, b));                var multiplyResultValue = multiplyResults[0].GetValue();                Console.WriteLine("a*b={0}", multiplyResultValue);                var tft = new TFTensor(Encoding.UTF8.GetBytes($"Hello TensorFlow Version {TFCore.Version}! LineZero"));                var hello = graph.Const(tft);                var helloResults = session.GetRunner().Run(hello);                Console.WriteLine(Encoding.UTF8.GetString((byte[])helloResults[0].GetValue()));            }            Console.ReadKey();        }        

运行程序结果如下：

 

TensorFlow C# image recognition

图像识别示例体验

https://github.com/migueldeicaza/TensorFlowSharp/tree/master/Examples/ExampleInceptionInference

下面学习一个实际的人工智能应用，是非常简单的一个示例，图像识别。

新建一个 imagerecognition .NET Framework 控制台应用项目，接着添加TensorFlowSharp 引用。

然后在项目属性中生成->平台目标 改为 x64。

接着编写如下代码：

 

    class Program    {        static string dir, modelFile, labelsFile;        public static void Main(string[] args)        {            dir = "tmp";            List<string> files = Directory.GetFiles("img").ToList();            ModelFiles(dir);            var graph = new TFGraph();            // 从文件加载序列化的GraphDef            var model = File.ReadAllBytes(modelFile);            //导入GraphDef            graph.Import(model, "");            using (var session = new TFSession(graph))            {                var labels = File.ReadAllLines(labelsFile);                Console.WriteLine("TensorFlow图像识别 LineZero");                foreach (var file in files)                {                    // Run inference on the image files                    // For multiple images, session.Run() can be called in a loop (and                    // concurrently). Alternatively, images can be batched since the model                    // accepts batches of image data as input.                    var tensor = CreateTensorFromImageFile(file);                    var runner = session.GetRunner();                    runner.AddInput(graph["input"][0], tensor).Fetch(graph["output"][0]);                    var output = runner.Run();                    // output[0].Value() is a vector containing probabilities of                    // labels for each image in the "batch". The batch size was 1.                    // Find the most probably label index.                    var result = output[0];                    var rshape = result.Shape;                    if (result.NumDims != 2 || rshape[0] != 1)                    {                        var shape = "";                        foreach (var d in rshape)                        {                            shape += $"{d} ";                        }                        shape = shape.Trim();                        Console.WriteLine($"Error: expected to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape [{shape}]");                        Environment.Exit(1);                    }                    // You can get the data in two ways, as a multi-dimensional array, or arrays of arrays,                     // code can be nicer to read with one or the other, pick it based on how you want to process                    // it                    bool jagged = true;                    var bestIdx = 0;                    float p = 0, best = 0;                    if (jagged)                    {                        var probabilities = ((float[][])result.GetValue(jagged: true))[0];                        for (int i = 0; i < probabilities.Length; i++)                        {                            if (probabilities[i] > best)                            {                                bestIdx = i;                                best = probabilities[i];                            }                        }                    }                    else                    {                        var val = (float[,])result.GetValue(jagged: false);                        // Result is [1,N], flatten array                        for (int i = 0; i < val.GetLength(1); i++)                        {                            if (val[0, i] > best)                            {                                bestIdx = i;                                best = val[0, i];                            }                        }                    }                    Console.WriteLine($"{Path.GetFileName(file)} 最佳匹配: [{bestIdx}] {best * 100.0}% 标识为：{labels[bestIdx]}");                }            }            Console.ReadKey();        }        // Convert the image in filename to a Tensor suitable as input to the Inception model.        static TFTensor CreateTensorFromImageFile(string file)        {            var contents = File.ReadAllBytes(file);            // DecodeJpeg uses a scalar String-valued tensor as input.            var tensor = TFTensor.CreateString(contents);            TFGraph graph;            TFOutput input, output;            // Construct a graph to normalize the image            ConstructGraphToNormalizeImage(out graph, out input, out output);            // Execute that graph to normalize this one image            using (var session = new TFSession(graph))            {                var normalized = session.Run(                         inputs: new[] { input },                         inputValues: new[] { tensor },                         outputs: new[] { output });                return normalized[0];            }        }        // The inception model takes as input the image described by a Tensor in a very        // specific normalized format (a particular image size, shape of the input tensor,        // normalized pixel values etc.).        //        // This function constructs a graph of TensorFlow operations which takes as        // input a JPEG-encoded string and returns a tensor suitable as input to the        // inception model.        static void ConstructGraphToNormalizeImage(out TFGraph graph, out TFOutput input, out TFOutput output)        {            // Some constants specific to the pre-trained model at:            // https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip            //            // - The model was trained after with images scaled to 224x224 pixels.            // - The colors, represented as R, G, B in 1-byte each were converted to            //   float using (value - Mean)/Scale.            const int W = 224;            const int H = 224;            const float Mean = 117;            const float Scale = 1;            graph = new TFGraph();            input = graph.Placeholder(TFDataType.String);            output = graph.Div(                x: graph.Sub(                    x: graph.ResizeBilinear(                        images: graph.ExpandDims(                            input: graph.Cast(                                graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float),                            dim: graph.Const(0, "make_batch")),                        size: graph.Const(new int[] { W, H }, "size")),                    y: graph.Const(Mean, "mean")),                y: graph.Const(Scale, "scale"));        }        /// <summary>        /// 下载初始Graph和标签        /// </summary>        /// <param name="dir"></param>        static void ModelFiles(string dir)        {            string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";            modelFile = Path.Combine(dir, "tensorflow_inception_graph.pb");            labelsFile = Path.Combine(dir, "imagenet_comp_graph_label_strings.txt");            var zipfile = Path.Combine(dir, "inception5h.zip");            if (File.Exists(modelFile) && File.Exists(labelsFile))                return;            Directory.CreateDirectory(dir);            var wc = new WebClient();            wc.DownloadFile(url, zipfile);            ZipFile.ExtractToDirectory(zipfile, dir);            File.Delete(zipfile);        }    }

这里需要注意的是由于需要下载初始Graph和标签，而且是google的站点，所以得使用一些特殊手段。

最终我随便下载了几张图放到bin\Debug\img

 

 然后运行程序，首先确保bin\Debug\tmp文件夹下有tensorflow_inception_graph.pb及imagenet_comp_graph_label_strings.txt。

 

人工智能的魅力非常大，本文只是一个入门，复制上面的代码，你没法训练模型等等操作。所以道路还是很远，需一步一步来。

更多可以查看 https://github.com/migueldeicaza/TensorFlowSharp 及 https://github.com/tensorflow/models

参考文档：

TensorFlow 官网：https://www.tensorflow.org/get_started/

TensorFlow 中文社区：http://www.tensorfly.cn/

TensorFlow 官方文档中文版：http://wiki.jikexueyuan.com/project/tensorflow-zh/