public static void FitMnistSimple()
{
var model = new Sequential();
model.Add(new Dense(512, activation: "relu", inputShape: new int[] { 784 }));
model.Add(new Dropout(0.2));
model.Add(new Dense(512, activation: "relu"));
model.Add(new Dropout(0.2));
model.Add(new Dense(10, activation: "softmax"));
var optimizer = new SGD(lr: 0.01);
model.Compile("categorical_crossentropy", optimizer, new string[] { "accuracy" });
var xtrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_xtrain.nda"), FileMode.Open));
var ytrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_ytrain.nda"), FileMode.Open));
xtrain = xtrain.Cast(DType.Float32);
xtrain = Ops.Div(null, xtrain, 255f);
ytrain = ytrain.Cast(DType.Float32);
model.Fit(xtrain, ytrain, batchSize: 128, epochs: 20);
var stream = new FileStream("c:/ttt/mnist-simple.model", FileMode.OpenOrCreate, FileAccess.Write);
stream.SetLength(0);
model.Save(stream);
}
public static void FitMnist()
{
var model = new Sequential();
model.Add(new Conv2D(32, kernelSize: new int[] { 3, 3 }, inputShape: new int[] { 28, 28, 1 }, activation: "relu"));
model.Add(new Conv2D(64, kernelSize: new int[] { 3, 3 }, activation: "relu"));
// model.Add(new MaxPooling1D(poolSize: 2));
model.Add(new MaxPooling2D(poolSize: new int[] { 2, 2 }));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(128, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(10, activation: "softmax"));
var optimizer = new SGD(lr: 0.01);
model.Compile("categorical_crossentropy", optimizer, new string[] { "accuracy" });
var xtrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_xtrain.nda"), FileMode.Open));
var ytrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_ytrain.nda"), FileMode.Open));
xtrain = xtrain.Cast(DType.Float32);
xtrain = Ops.Div(null, xtrain, 255f);
ytrain = ytrain.Cast(DType.Float32);
model.Fit(xtrain, ytrain, batchSize: 128, epochs: 12);
var stream = new FileStream("c:/ttt/mnist.model", FileMode.OpenOrCreate, FileAccess.Write);
stream.SetLength(0);
model.Save(stream);
}
static void PredictMnist(string modelPath, string xtestPath, string ytestPath = null)
{
var xtest = TensorUtils.Deserialize(File.OpenRead(xtestPath));
xtest = xtest.Cast(DType.Float32);
xtest = Ops.Div(null, xtest, 255f);
// xtest = xtest.Narrow(0, 0, 101);
var model = Sequential.Load(modelPath);
var result = model.Predict(xtest, batchSize: 32);
if (ytestPath == null)
{
return;
}
var ytest = TensorUtils.Deserialize(File.OpenRead(ytestPath));
ytest = ytest.Cast(DType.Float32);
// ytest = ytest.Narrow(0, 0, 101);
ytest = Ops.Argmax(null, ytest, 1).Squeeze();
var t = result.Narrow(0, 0, 11);
// Console.WriteLine(t.Format());
result = Ops.Argmax(null, result, 1).Squeeze();
t = result.Narrow(0, 0, 11);
// Console.WriteLine(t.Format());
double sum = 0.0;
for (var i = 0; i < ytest.Sizes[0]; ++i)
{
sum += (int)ytest.GetElementAsFloat(i) == (int)result.GetElementAsFloat(i) ? 1.0 : 0.0;
}
Console.WriteLine($"Accuracy: {sum / ytest.Sizes[0] * 100}%");
}
public void TestLoadMnist()
{
string path = "../../../datasets/nda_mnist/mnist_xtrain.nda";
var nda = TensorUtils.Deserialize(File.OpenRead(path));
CollectionAssert.AreEqual(nda.Sizes, new long[] { 60000, 28, 28 });
Assert.AreEqual(nda.GetElementAsFloat(1, 0, 0), 0f);
Assert.AreEqual(nda.GetElementAsFloat(0, 6, 9), 36f);
// Cast the array to float
nda = nda.Cast(DType.Float32);
CollectionAssert.AreEqual(nda.Sizes, new long[] { 60000, 28, 28 });
Assert.AreEqual(nda.GetElementAsFloat(1, 0, 0), 0f);
Assert.AreEqual(nda.GetElementAsFloat(0, 6, 9), 36f);
// Divide by 255
nda = Ops.Div(null, nda, 255f);
Assert.AreEqual(nda.GetElementAsFloat(1, 0, 0), 0f);
Assert.AreEqual(nda.GetElementAsFloat(0, 6, 9), 36f / 255f, float.Epsilon);
Assert.AreEqual(nda.GetElementAsFloat(0, 6, 9), 36.0 / 255, 0.000001);
}
