public int[] Argmax(IWeightTensor w, int dim)
{
int[] idx = null;
WeightTensor m = w as WeightTensor;
using (Tensor argMaxT = Ops.Argmax(null, m.TWeight, dim))
{
float[] res = new float[argMaxT.ElementCount()];
argMaxT.CopyToArray(res);
idx = new int[res.Length];
for (int i = 0; i < res.Length; i++)
{
idx[i] = (int)res[i];
}
}
return(idx);
}
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}%");
}
/// <summary>
/// Argmaxes the specified dimension.
/// </summary>
/// <param name="dimension">The dimension.</param>
/// <returns>TVar.</returns>
public Variable Argmax(int dimension)
{
return(new Variable(new UnaryTensorExpression(this.Expression, (result, src) => Ops.Argmax(result, src, dimension))));
}
