public static void Run()
{
int dataPointCount = 1000;
int inputSize = 4570;
int outputSize = 3;
var random = new Random(0);
var inputDataset = new double[dataPointCount][];
var outputDataset = new double[dataPointCount][];
for (int i = 0; i < dataPointCount; i++)
{
inputDataset[i] = new double[inputSize];
outputDataset[i] = new double[outputSize];
for (int j = 0; j < inputSize; j++)
{
inputDataset[i][j] = random.NextDouble();
}
outputDataset[i][random.Next() % outputSize] = 1;
}
TFModel tfModel = new TFModel(inputSize, outputSize, 1, 1, "tfModel.pb", random, false);
tfModel.fit(inputDataset, outputDataset);
var predictions = tfModel.predict(inputDataset);
byte[] persisted = tfModel.PersistModelDefinition();
tfModel.SaveModelMetadataGraph();
TFModel model2 = new TFModel(inputSize, outputSize, 1, 1, persisted, random, true);
var predictions2 = model2.predict(inputDataset);
int sameFirst = 0;
for (int i = 0; i < predictions.Length; i++)
{
for (int j = 0; j < predictions[i].Length; j++)
{
Console.WriteLine(predictions[i][j] + " " + predictions2[i][j]);
if (Math.Abs(predictions[i][j] - predictions2[i][j]) >= Math.Pow(10, -15))
{
Console.WriteLine("err");
}
else
{
sameFirst++;
}
}
}
Console.WriteLine("FINISH");
Console.ReadLine();
}
public static void Run()
{
int dataPointCount = 1000;
int inputSize = 4570;
int outputSize = 3;
var random = new Random(0);
var inputDataset = new double[dataPointCount][];
var outputDataset = new double[dataPointCount][];
for (int i = 0; i < dataPointCount; i++)
{
inputDataset[i] = new double[inputSize];
outputDataset[i] = new double[outputSize];
for (int j = 0; j < inputSize; j++)
{
inputDataset[i][j] = random.NextDouble();
}
outputDataset[i][random.Next() % outputSize] = 1;
}
TFModel tfModel = new TFModel(inputSize, outputSize, 1, 1, "tfModel.pb", random, false);
var sw = new Stopwatch();
for (int i = 0; i < 100; i++)
{
sw.Reset();
sw.Start();
double[][] output = tfModel.predict(inputDataset);
sw.Stop();
Console.WriteLine("Predicting all at once ms: " + sw.ElapsedMilliseconds);
sw.Reset();
sw.Start();
foreach (var entry in inputDataset)
{
double[] prediction = tfModel.predict(entry);
}
sw.Stop();
Console.WriteLine("Predicting one by one ms: " + sw.ElapsedMilliseconds);
sw.Reset();
sw.Start();
tfModel.fit(inputDataset, outputDataset);
sw.Stop();
Console.WriteLine("Training ... well this is based on number of iterations in model ms: " + sw.ElapsedMilliseconds);
sw.Reset();
}
}