继续使用分步骤保存了的ckpt文件

这个貌似脱离不了tensorflow框架，而且生成的ckpt文件比较大，发布到生产环境的时候，还得把python的算法文件一起搞上去，如何和其他程序交互，可能还得自己去写服务。估计很少有人这么做，貌似性能也很一般。

使用tensorflow Serving

tf Serving貌似是大家都比较推崇的方法。需要编译tfServing，然后把模型导出来。直接执行tf Serving的进程，就可以对外提供服务了。具体调用的时候，还得自己写客户端，使用人gRPC去调用Serving，然后再对外提供服务，听上去比较麻烦。而且我今天没太多的时间去研究gRPC，网络上关于客户端很多都是用python写的，我感觉自己的python水平比较菜，没信心能写好。所以这个方式就先没研究。

生产.pb文件，然后写程序去调用.pb文件

生成了.pb文件以后，就可以被程序去直接调用，传入参数，然后就可以传出来参数，而且生成的.pb文件非常的小。而我又有比较丰富的.net开发经验。在想，是否可以用C#来解析.pb文件，然后做一个.net core的对外服务的API，这样貌似更加高效，关键是自己熟悉这款的开发，不用花费太多的时间去摸索。、

具体的思路

使用.net下面的TensorFlow框架tensorflowSharp（貌似还是没脱离了框架）.去调用pb文件，然后做成.net core web API 对外提供服务。

具体的实现

直接上代码，非常简单，本身设计到tensorflowsharp的地方非常的少

            var graph = new TFGraph();
            //重点是下面的这句，把训练好的pb文件给读出来字节，然后导入
            var model = File.ReadAllBytes(model_file);
            graph.Import(model);
                Console.WriteLine("请输入一个图片的地址");
                var src = Console.ReadLine();
                var tensor = ImageUtil.CreateTensorFromImageFile(src);
                
                using (var sess = new TFSession(graph))
                {
                    var runner = sess.GetRunner();
                    runner.AddInput(graph["Cast_1"][0], tensor);
                    var r = runner.Run(graph.softmax(graph["softmax_linear/softmax_linear"][0]));
                    var v = (float[,])r.GetValue();
                    Console.WriteLine(v[0,0]);
                    Console.WriteLine(v[0, 1]);
                }

ImageUtil这个类库是tensorflowSharp官方的例子中一个把图片转成tensor的类库，我直接copy过来了，根据我的网络，修改了几个参数。

public static class ImageUtil
    {
        public static TFTensor CreateTensorFromImageFile(byte[] contents, TFDataType destinationDataType = TFDataType.Float)
        {
            var tensor = TFTensor.CreateString(contents);
            TFOutput input, output;
            // Construct a graph to normalize the image
            using (var graph = ConstructGraphToNormalizeImage(out input, out output, destinationDataType))
            {
                // 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];
                }
            }
        }
        // Convert the image in filename to a Tensor suitable as input to the Inception model.
        public static TFTensor CreateTensorFromImageFile(string file, TFDataType destinationDataType = TFDataType.Float)
        {
            var contents = File.ReadAllBytes(file);
            // DecodeJpeg uses a scalar String-valued tensor as input.
            var tensor = TFTensor.CreateString(contents);
            TFOutput input, output;
            // Construct a graph to normalize the image
            using (var graph = ConstructGraphToNormalizeImage(out input, out output, destinationDataType))
            {
                // 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.
        private static TFGraph ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.Float)
        {
            // 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 = 128;
            const int H = 128;
            const float Mean = 0;
            const float Scale = 1f;
            var graph = new TFGraph();
            input = graph.Placeholder(TFDataType.String);
            output = graph.Cast(
                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")), destinationDataType);
            return graph;
        }
    }