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TensorFlowSharp-TensorFlow的.Net Api类库

  • 授权协议:MIT
  • 开发厂商:未知
  • 开发语言:csharp
  • 收录时间:2020-03-20
  • 操作系统:Windows,Linux,OS X
  • 软件作者:migueldeicaza
  • 开源标签: tensorflow

软件介绍

TensorFlowSharp是一个TensorFlowd的.Net Api类库,通过Mono支持跨平台"

TensorFlow是谷歌公司2015年年底开源的内部使用的深度学习框架,也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。

TensorFlowSharp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 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"));        }        ///         /// 下载初始Graph和标签        ///         ///        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);        }    }

View Code

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/

 

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