/// <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();
}
/// <summary>
/// Runs the epoch.
/// </summary>
/// <param name="iteration">The iteration.</param>
/// <param name="train">The train.</param>
/// <param name="val">The value.</param>
/// <returns></returns>
private int RunEpoch(int iteration, DataFrameIter train, DataFrameIter val = null)
{
train_losses.Clear();
train_metrics.Clear();
val_losses.Clear();
val_metrics.Clear();
train.Reset();
if (val != null)
{
val.Reset();
}
while (train.Next())
{
var(x, y) = train.GetBatch();
RunTrainOnBatch(iteration, x, y);
x.Dispose();
y.Dispose();
}
if (val != null)
{
while (val.Next())
{
var(x, y) = val.GetBatch();
var pred = Forward(x);
var lossVal = LossFn.Forward(pred, y);
var metricVal = MetricFn.Calc(pred, y);
val_losses.Add(Ops.Mean(lossVal));
val_metrics.Add(Ops.Mean(metricVal));
x.Dispose();
y.Dispose();
lossVal.Dispose();
metricVal.Dispose();
}
}
return(iteration);
}
