如何注册网站的名字,html表单制作,网络上哪里可以做推广,科技兴国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#xff0c;按照谷歌所说#xff0c;在某些基准测试中#xff0c;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… 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)# 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(a2 b3);// 两常量加var addingResults session.GetRunner().Run(graph.Add(a, b));var addingResultValue addingResults[0].GetValue();Console.WriteLine(ab{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;Liststring files Directory.GetFiles(img).ToList();ModelFiles(dir);var graph new TFGraph();// 从文件加载序列化的GraphDefvar model File.ReadAllBytes(modelFile);//导入GraphDefgraph.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// itbool 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 arrayfor (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 imageConstructGraphToNormalizeImage(out graph, out input, out output);// Execute that graph to normalize this one imageusing (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 namedir/paramstatic 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/
