/// <summary> /// Runs the train on batch. /// </summary> /// <param name="i">The i.</param> /// <param name="x">The x.</param> /// <param name="y">The y.</param> private void RunTrainOnBatch(int i, SuperArray x, SuperArray y) { SuperArray pred = Forward(x); SuperArray lossVal = LossFn.Forward(pred, y); SuperArray grad = LossFn.Backward(pred, y).Reshape(-1, 1); lossVal = ApplyRegularizer(lossVal); var metricVal = MetricFn.Calc(pred, y); train_losses.Add(Ops.Mean(lossVal)); train_metrics.Add(Ops.Mean(metricVal)); Backward(grad); ApplyDeltaRegularizer(); foreach (var layer in Layers) { OptimizerFn.Update(i, layer); } pred.Dispose(); lossVal.Dispose(); grad.Dispose(); }
/// <summary> /// Runs the train on batch. /// </summary> /// <param name="i">The i.</param> /// <param name="x">The x.</param> /// <param name="y">The y.</param> private void RunTrainOnBatch(int i, Tensor x, Tensor y) { Tensor pred = Forward(x); Tensor lossVal = LossFn.Forward(pred, y); Tensor grad = LossFn.Backward(pred, y); lossVal = ApplyRegularizer(lossVal); var metricVal = MetricFn.Calc(pred, y); train_losses.Add(K.Mean(lossVal)); train_metrics.Add(K.Mean(metricVal)); Backward(grad); ApplyDeltaRegularizer(); foreach (var layer in Layers) { OptimizerFn.Update(i, layer); } pred.Dispose(); lossVal.Dispose(); grad.Dispose(); }
public void Fit(IFrameIter train, int epochs, int batchSize, IFrameIter val = null) { DateTime start = DateTime.Now; List <float> train_losses = new List <float>(); List <float> train_metrics = new List <float>(); List <float> val_losses = new List <float>(); List <float> val_metrics = new List <float>(); train.SetBatchSize(batchSize); for (int iteration = 1; iteration <= epochs; iteration++) { train.Reset(); while (train.Next()) { var(x, y) = train.GetBatch(); using (Variable pred = Forward(x)) using (Tensor lossVal = LossFn.Call(pred.Data, y)) using (Tensor grad = LossFn.CalcGrad(pred.Data, y)) using (Tensor reg_loss = ApplyRegularizer(lossVal)) { //var metricVal = MetricFn.Call(pred.Data, y); train_losses.Add(reg_loss.TVar().ToScalar().Evaluate()); //train_metrics.Add(metricVal.ToScalar().Evaluate()); Backward(grad); ApplyDeltaRegularizer(); foreach (var layer in Layers) { OptimizerFn.Update(iteration, layer); } } x.Dispose(); y.Dispose(); } if (val != null) { while (val.Next()) { var(x, y) = val.GetBatch(); var pred = Forward(x); var lossVal = LossFn.Call(pred.Data, y); var metricVal = MetricFn.Call(pred.Data, y); val_losses.Add(TOps.MeanF(lossVal)); val_metrics.Add(TOps.MeanF(metricVal)); } } Console.WriteLine("Epoch: {0}, Loss: {1}", iteration, train_losses.Average()); } }