训练了很久的Tf模型，终于要到生产环境中去考研一番了。今天花费了一些时间去研究tf的模型如何在生产环境中去使用。大概整理了这些方法。
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 Servingtf 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
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; } }搞定