TensorFlowSharp入門使用C#編寫TensorFlow人工智慧應用

知識 05-23

TensorFlow簡單介紹

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。
TensorFlow 內建深度學習的擴展支持，任何能夠用計算流圖形來表達的計算，都可以使用TensorFlow。任何基於梯度的機器學習演算法都能夠受益於TensorFlow的自動分化（auto-differentiation）。通過靈活的Python介面，要在TensorFlow中表達想法也會很容易。

TensorFlow 對於實際的產品也是很有意義的。將思路從桌面GPU訓練無縫搬遷到手機中運行。
示例Python代碼：

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

官方源碼庫，該項目支持跨平台，使用Mono。

可以使用NuGet 安裝TensorFlowSharp，如下：

Install-Package TensorFlowSharp

編寫簡單應用

使用VS2017新建一個.NET Framework 控制台應用 tensorflowdemo，接著添加TensorFlowSharp 引用。

TensorFlowSharp 包比較大，需要耐心等待。

然後在項目屬性中生成->平台目標改為 x64。

打開Program.cs 寫入如下代碼：

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;
}

運行程序結果如下：

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慧應用，是非常簡單的一個示例，圖像識別。

新建一個 imagerecognition .NET Framework 控制台應用項目，接著添加TensorFlowSharp 引用。

然後在項目屬性中生成->平台目標改為 x64。

接著編寫如下代碼：

class Program
{
static string dir, modelFile, labelsFile;
public static void Main(string[] args)
{
dir = "tmp";
List files = Directory.GetFiles("img").ToList;
ModelFiles(dir);
var graph = new TFGraph;
// 從文件載入序列化的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.

// 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}% 標識為：{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"));
}

///

/// 下載初始Graph和標籤
///

/// 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);
}
}

View Code

這裡需要注意的是由於需要下載初始Graph和標籤，而且是google的站點，所以得使用一些特殊手段。

最終我隨便下載了幾張圖放到binDebugimg

TensorFlowSharp入門使用C#編寫TensorFlow人工智慧應用