C# 編寫 TensorFlow 人工智能應用

時間 2019-11-19

標籤 c# 編寫 tensorflow 人工智能應用欄目 C# 简体版

原文原文鏈接

TensorFlowSharp入門使用C#編寫TensorFlow人工智能應用學習。git

TensorFlow簡單介紹github

TensorFlow 是谷歌的第二代機器學習系統，按照谷歌所說，在某些基準測試中，TensorFlow的表現比第一代的DistBelief快了2倍。算法

TensorFlow 內建深度學習的擴展支持，任何可以用計算流圖形來表達的計算，均可以使用TensorFlow。api

任何基於梯度的機器學習算法都可以受益於TensorFlow的自動分化（auto-differentiation）。經過靈活的Python接口，要在TensorFlow中表達想法也會很容易。session

TensorFlow 對於實際的產品也是頗有意義的。將思路從桌面GPU訓練無縫搬遷到手機中運行。dom

示例Python代碼：機器學習

import tensorflow as tfoop

import numpy as np學習

# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3測試

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

官方源碼庫，該項目支持跨平臺，使用Mono。

能夠使用NuGet 安裝TensorFlowSharp，以下：

Install-Package TensorFlowSharp

編寫簡單應用

使用VS2017新建一個.NET Framework 控制檯應用 tensorflowdemo，接着添加TensorFlowSharp 引用。

TensorFlowSharp 包比較大，須要耐心等待。

而後在項目屬性中生成->平臺目標改成 x64。

打開Program.cs 寫入以下代碼：

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");

// 兩常量加

var addingResults = session.GetRunner().Run(graph.Add(a, b));

var addingResultValue = addingResults[0].GetValue();

Console.WriteLine("a+b={0}", addingResultValue);

// 兩常量乘

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();

}

運行程序結果以下：

TensorFlow C# image recognition

圖像識別示例體驗

https://github.com/migueldeicaza/TensorFlowSharp/tree/master/Examples/ExampleInceptionInference

下面學習一個實際的人工智能應用，是很是簡單的一個示例，圖像識別。

新建一個 imagerecognition .NET Framework 控制檯應用項目，接着添加TensorFlowSharp 引用。

而後在項目屬性中生成->平臺目標改成 x64。

接着編寫以下代碼：

class Program

{

static string dir, modelFile, labelsFile;

public static void Main(string[] args)

{

dir = "tmp";

List<string> files = Directory.GetFiles("img").ToList();

ModelFiles(dir);

var graph = new TFGraph();

// 從文件加載序列化的GraphDef

var model = File.ReadAllBytes(modelFile);

//導入GraphDef

graph.Import(model, "");

using (var session = new TFSession(graph))

{

var labels = File.ReadAllLines(labelsFile);

Console.WriteLine("TensorFlow圖像識別 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}% 標識爲：{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"));

}

/// <summary>

/// 下載初始Graph和標籤

/// </summary>

/// <param name="dir"></param>

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);

}

}