static void Main(string[] args)
{
// 创建图
var g = new TFGraph();
// 定义常量
var a = g.Const(2);
var b = g.Const(3);
// 加法和乘法运算
var add = g.Add(a, b);
var mul = g.Mul(a, b);
// 创建会话
var sess = new TFSession(g);
// 计算加法
var result1 = sess.GetRunner().Run(add).GetValue();
Console.WriteLine("a+b={0}", result1);
// 计算乘法
var result2 = sess.GetRunner().Run(mul).GetValue();
Console.WriteLine("a*b={0}", result2);
// 关闭会话
sess.CloseSession();
}
public static void SVD(float[,] covMat, out float[] s, out float[,] v)
{
TFShape shape = new TFShape(covMat.GetLength(0), covMat.GetLength(1));
var reshaped = covMat.Reshape();
var inputTensor = TFTensor.FromBuffer(shape, reshaped, 0, reshaped.Length);
TFGraph svdGraph = new TFGraph();
TFOutput input = svdGraph.Placeholder(TFDataType.Float, shape);
var svdResult = (ValueTuple <TFOutput, TFOutput, TFOutput>)svdGraph.Svd(input, true);
var sess = new TFSession(svdGraph);
var runner = sess.GetRunner();
runner.AddInput(input, inputTensor);
runner.Fetch(svdResult.Item1);
runner.Fetch(svdResult.Item2);
TFTensor[] results = runner.Run();
s = (float[])results[0].GetValue();
v = (float[, ])results[1].GetValue();
TFStatus temp = new TFStatus();
sess.CloseSession(temp);
sess.DeleteSession(temp);
}
public void Dispose()
{
Console.WriteLine("Finalizing TF model resources");
session.CloseSession();
session.Dispose();
Console.WriteLine("TF resources closed");
}
public void TestCleanup()
{
if (s != null)
{
s.CloseSession();
s.Dispose();
s = null;
}
if (g != null)
{
g.Dispose();
g = null;
}
}
static void Main(string[] args)
{
var g = new TFGraph();
var sess = new TFSession(g);
Program.Demo(g, sess);
Program.First();
Program.placeholder(g, sess);
Program.Variable(g, sess);
Program.InitVariable(g, sess);
sess.CloseSession();
Console.ReadKey();
}
private void Dispose(bool disposing)
{
// Ensure that the Session is not null and it's handle is not Zero, as it may have already been disposed/finalized.
// Technically we shouldn't be calling this if disposing == false, since we're running in finalizer
// and the GC doesn't guarantee ordering of finalization of managed objects, but we have to make sure
// that the Session is closed before deleting our temporary directory.
try
{
if (Session?.Handle != IntPtr.Zero)
{
Session.CloseSession();
Session.Dispose();
}
}
finally
{
if (!string.IsNullOrEmpty(_savedModelPath) && _isTemporarySavedModel)
{
TensorFlowUtils.DeleteFolderWithRetries(_host, _savedModelPath);
}
}
}
static void KNN()
{
//取得数据
var mnist = Mnist.Load();
//拿5000个训练数据，200个测试数据
const int trainCount = 5000;
const int testCount = 200;
//获得的数据有两个
//一个是图片，它们都是28*28的
//一个是one-hot的标签，它们都是1*10的
(var trainingImages, var trainingLabels) = mnist.GetTrainReader().NextBatch(trainCount);
(var testImages, var testLabels) = mnist.GetTestReader().NextBatch(testCount);
Console.WriteLine($"MNIST 1NN");
//建立一个图表示计算任务
using (var graph = new TFGraph())
{
var session = new TFSession(graph);
//用来feed数据的占位符。trainingInput表示N张用来进行训练的图片,N是一个变量，所以这里使用-1
TFOutput trainingInput = graph.Placeholder(TFDataType.Float, new TFShape(-1, 784));
//xte表示一张用来测试的图片
TFOutput xte = graph.Placeholder(TFDataType.Float, new TFShape(784));
//计算这两张图片的L1距离。这很简单，实际上就是把784个数字逐对相减，然后取绝对值，最后加起来变成一个总和
var distance = graph.ReduceSum(graph.Abs(graph.Sub(trainingInput, xte)), axis: graph.Const(1));
//这里只是用了最近的那个数据
//也就是说，最近的那个数据是什么，那pred（预测值）就是什么
TFOutput pred = graph.ArgMin(distance, graph.Const(0));
var accuracy = 0f;
//开始循环进行计算，循环trainCount次
for (int i = 0; i < testCount; i++)
{
var runner = session.GetRunner();
//每次，对一张新的测试图，计算它和trainCount张训练图的距离，并获得最近的那张
var result = runner.Fetch(pred).Fetch(distance)
//trainCount张训练图（数据是trainingImages）
.AddInput(trainingInput, trainingImages)
//testCount张测试图（数据是从testImages中拿出来的）
.AddInput(xte, Extract(testImages, i))
.Run();
//最近的点的序号
var nn_index = (int)(long)result[0].GetValue();
//从trainingLabels中找到答案（这是预测值）
var prediction = ArgMax(trainingLabels, nn_index);
//正确答案位于testLabels[i]中
var real = ArgMax(testLabels, i);
//PrintImage(testImages, i);
Console.WriteLine($"测试 {i}: " +
$"预测: {prediction} " +
$"正确答案: {real} (最近的点的序号={nn_index})");
//Console.WriteLine(testImages);
if (prediction == real)
{
accuracy += 1f / testCount;
}
}
Console.WriteLine("准确率: " + accuracy);
session.CloseSession();
}
}
public void ReleaseClassifier()
{
session.CloseSession();
}