public void TestReadAndFillDoesntChangeError()
{
var tests = new[]
{
new[] { new[] { 1f, 1f }, new[] { -1f } },
new[] { new[] { 1f, -1f }, new[] { 1f } },
new[] { new[] { -1f, 1f }, new[] { 1f } },
new[] { new[] { -1f, -1f }, new[] { -1f } }
};
var builder = new LayerBuilder();
var desc = builder.BuildDescription(2, new[]
{
new LayerBuilder.LayerSpec(2, "sum", "tanh"),
new LayerBuilder.LayerSpec(1, "sum", "tanh")
});
var net = Net.FromDescription(desc);
var train = net.GetTrainingFunction();
var weights = new float[net.NumberOfWeights];
var deltas = new float[net.NumberOfWeights];
net.FillWeights(weights);
var loss = 0f;
for (var i = 0; i < 15001; i++)
{
loss = 0;
foreach (var test in tests)
{
loss += train(test[0], test[1], weights, deltas);
for (var j = 0; j < weights.Length; j++)
{
weights[j] -= deltas[j] / 2f;
}
}
if (i % 300 == 0)
{
Console.WriteLine($"{i}, {loss}");
}
}
loss = 0;
foreach (var test in tests)
{
loss += train(test[0], test[1], weights, deltas);
}
Console.WriteLine(loss);
Console.WriteLine(string.Join(", ", weights));
net.ReadWeights(weights);
var newWeights = new float[net.NumberOfWeights];
net.FillWeights(newWeights);
Console.WriteLine(string.Join(", ", newWeights));
for (var i = 0; i < weights.Length; i++)
{
Assert.AreEqual(weights[i], newWeights[i]);
}
var newLoss = 0f;
foreach (var test in tests)
{
newLoss += train(test[0], test[1], newWeights, new float[net.NumberOfWeights]);
}
Console.WriteLine(newLoss);
Assert.AreEqual(loss, newLoss);
}
static void Main(string[] args)
{
var events = ReadEventFile("trainingEvents.csv");
var ins = events
.Where(e => (e.Open - e.NextLow) / e.Open > .1f)
.Select(evts => evts.GetInputArray());
Console.WriteLine($"Qualified Events: {ins.Count()}");
var unsupervisedTests = ins.Select(i => Tuple.Create(i, i));
var supervisedTests = events.Select(evt => Tuple.Create(evt.GetInputArray(), evt.GetOutputArray()));
var builder = new LayerBuilder();
var description = builder.BuildDescription(5, new[]
{
new LayerBuilder.LayerSpec(10, "sum", "softplus"),
new LayerBuilder.LayerSpec(10, "sum", "softplus"),
new LayerBuilder.LayerSpec(10, "sum", "tanh"),
new LayerBuilder.LayerSpec(10, "sum", "softplus"),
new LayerBuilder.LayerSpec(10, "sum", "softplus"),
new LayerBuilder.LayerSpec(5, "sum", null)
});
//var description = builder.BuildDescription(5, new[]
//{
// new LayerBuilder.LayerSpec(5, "sum", "softplus"),
// new LayerBuilder.LayerSpec(6, "sum", "tanh"),
// new LayerBuilder.LayerSpec(4, "sum", "softplus"),
// new LayerBuilder.LayerSpec(5, "sum", null)
//});
var net = Net.FromDescription(description);
var trainer = new SimpleTrainer();
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 5e-6f,
maxEpochs: 200,
learningRate: 0.75f);
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 5e-6f,
maxEpochs: 9500,
learningRate: 0.5f);
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-8f,
maxEpochs: 200,
learningRate: .25f);
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-8f,
maxEpochs: 200,
learningRate: .125f);
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-8f,
maxEpochs: 200,
learningRate: .0625f);
for (var i = 0; i < 5; i++)
{
var nextDescription = net.Description;
var firstSigmoidId = nextDescription.Nodes.First(n => n.Processor == "tanh").NodeId;
nextDescription.Nodes = nextDescription.Nodes.Where(n => n.NodeId != firstSigmoidId).ToArray();
foreach (var node in nextDescription.Nodes)
{
node.Inputs = node.Inputs
.Where(inp => inp.FromInputVector || inp.InputId != firstSigmoidId)
.ToArray();
}
net = Net.FromDescription(nextDescription);
Console.WriteLine($"Removed {i + 1} sigmoids");
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-6f,
maxEpochs: 400,
learningRate: 0.5f);
}
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-6f,
maxEpochs: 500,
learningRate: 0.5f);
trainer.Train(
net: net,
tests: unsupervisedTests,
desiredError: 1e-6f,
maxEpochs: 400,
learningRate: 0.25f);
var finalDescription = net.Description;
var outsRemoved = new[] { finalDescription.Outputs[1], finalDescription.Outputs[2], finalDescription.Outputs[3], finalDescription.Outputs[4] };
finalDescription.Nodes = finalDescription.Nodes
.Where(n => !outsRemoved.Contains(n.NodeId))
.ToArray();
finalDescription.Outputs = new[] { finalDescription.Outputs[0] };
var nextNet = Net.FromDescription(finalDescription);
trainer.Train(
net: nextNet,
tests: supervisedTests,
desiredError: 1e-8f,
maxEpochs: 20000,
learningRate: .25f);
trainer.Train(
net: nextNet,
tests: supervisedTests,
desiredError: 1e-8f,
maxEpochs: 20000,
learningRate: .125f);
trainer.Train(
net: nextNet,
tests: supervisedTests,
desiredError: 1e-8f,
maxEpochs: 2000,
learningRate: .0625f);
trainer.Train(
net: nextNet,
tests: supervisedTests,
desiredError: 1e-8f,
maxEpochs: 2000,
learningRate: .03125f);
var final = net.Description;
var finalText = JsonConvert.SerializeObject(final);
using (var writer = File.CreateText("out3.json"))
{
writer.Write(finalText);
}
Console.WriteLine();
Console.ReadLine();
}