TensorFlowSharp入门使用C#编写TensorFlow人工智能应用 - LineZero_H5之家

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 tf import numpy as np # Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3 x_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) 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(); addingResults = session.GetRunner().Run(graph.Add(a, b)); var addingResultValue = addingResults[0].GetValue(); Console.WriteLine(, addingResultValue); multiplyResults = session.GetRunner().Run(graph.Mul(a, b)); var multiplyResultValue = multiplyResults[0].GetValue(); Console.WriteLine(, multiplyResultValue); )); 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; Main(string[] args) { dir = ; List<).ToList(); ModelFiles(dir); var graph = new TFGraph(); model = File.ReadAllBytes(modelFile); //导入GraphDef graph.Import(model, ""); using (var session = new TFSession(graph)) { var labels = File.ReadAllLines(labelsFile); Console.WriteLine(); 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 tensor = CreateTensorFromImageFile(file); var runner = session.GetRunner(); runner.AddInput(graph[][][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. result = output[0]; var rshape = result.Shape; if (result.NumDims != 2 || rshape[0] != 1) { var shape = ""; foreach (var d in rshape) { shape += $; } shape = shape.Trim(); Console.WriteLine($); 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 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); (int i = 0; i < val.GetLength(1); i++) { if (val[0, i] > best) { bestIdx = i; best = val[0, i]; } } } Console.WriteLine($); } } Console.ReadKey(); } TFTensor CreateTensorFromImageFile(string file) { var contents = File.ReadAllBytes(file); tensor = TFTensor.CreateString(contents); TFGraph graph; TFOutput input, output; // Construct a graph to normalize the image ConstructGraphToNormalizeImage(out graph, out input, out output); (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 ConstructGraphToNormalizeImage(out TFGraph graph, out TFOutput input, out TFOutput output) { // Some constants specific to the pre-trained model at: - 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 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()), size: graph.Const()), y: graph.Const(Mean, )), y: graph.Const(Scale, )); } 下载初始Graph和标签 ModelFiles(string dir) { ; modelFile = Path.Combine(dir, ); labelsFile = Path.Combine(dir, ); ); 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); } }