public string TrainModel(IpfsJob job)
{
var tmpInput = Ipfs.Get <JToken>(job.input);
var tmpTarget = Ipfs.Get <JToken>(job.target);
var seq = CreateSequential(job.Model);
var inputData = tmpInput.SelectToken("data").ToObject <float[]>();
var inputShape = tmpInput.SelectToken("shape").ToObject <int[]>();
var inputTensor = controller.floatTensorFactory.Create(_data: inputData, _shape: inputShape, _autograd: true);
var targetData = tmpTarget.SelectToken("data").ToObject <float[]>();
var targetShape = tmpTarget.SelectToken("shape").ToObject <int[]>();
var targetTensor = controller.floatTensorFactory.Create(_data: targetData, _shape: targetShape, _autograd: true);
var grad = controller.floatTensorFactory.Create(_data: new float[] { 1, 1, 1, 1 },
_shape: new int[] { 4, 1 });
Loss loss;
switch (job.config.criterion)
{
case "mseloss":
loss = new MSELoss(this.controller);
break;
case "categorical_crossentropy":
loss = new CategoricalCrossEntropyLoss(this.controller);
break;
case "cross_entropy_loss":
loss = new CrossEntropyLoss(this.controller, 1); // TODO -- real value
break;
case "nll_loss":
loss = new NLLLoss(this.controller);
break;
default:
loss = new MSELoss(this.controller);
break;
}
var optimizer = new SGD(this.controller, seq.getParameters(), job.config.lr, 0, 0);
for (var i = 0; i < job.config.iters; ++i)
{
var pred = seq.Forward(inputTensor);
var l = loss.Forward(pred, targetTensor);
l.Backward();
// TODO -- better batch size
optimizer.Step(100, i);
}
var resultJob = new Ipfs();
var response = resultJob.Write(new IpfsJob(job.input, job.target, seq.GetConfig(), job.config));
return(response.Hash);
}
public float FitBatch(int batch_i)
{
if (((batch_i + 1) * _input_batch_offset) < _input_tensor_origin.Size)
{
last_input_buffer.Fill(_input_tensor_origin, starting_offset: batch_i * _input_batch_offset,
length_to_fill: _input_batch_offset);
last_target_buffer.Fill(_target_tensor_origin, starting_offset: batch_i * _target_batch_offset,
length_to_fill: _target_batch_offset);
var pred = Forward(last_input_buffer);
var loss = _criterion.Forward(pred, last_target_buffer);
if (cached_ones_grad_for_backprop == null || cached_ones_grad_for_backprop.Size != loss.Size)
{
cached_ones_grad_for_backprop = loss.createOnesTensorLike();
cached_ones_grad_for_backprop.Autograd = false;
}
loss.Backward(cached_ones_grad_for_backprop);
_optimizer.Step(this.last_input_buffer.Shape[0]);
return(loss.Data[0]);
}
else
{
return(0);
}
}
void Update()
{
CurveObject.DrawMesh();
if (Input.GetKey(KeyCode.Space))
{
(NewCurve, NewMomentum) = SGD.Step(CurrentCurve, Momentum);
CurrentCurve = NewCurve;
Momentum = NewMomentum;
CurveObject.UpdateMesh(CurrentCurve);
Debug.Log("Updated Knot");
List <Vector3> tangents = CurrentCurve.GetTangents();
}
}
