/// <summary>
/// Generates output predictions for the input samples. Computation is done in batches.
/// </summary>
/// <param name="x">The input data frame to run prediction.</param>
/// <param name="batch_size">Size of the batch.</param>
/// <returns></returns>
public DataFrame Predict(DataFrame x, int batch_size)
{
DataFrameIter dataFrameIter = new DataFrameIter(x);
List <float> predictions = new List <float>();
dataFrameIter.SetBatchSize(batch_size);
while (dataFrameIter.Next())
{
var data = dataFrameIter.GetBatchX();
SuperArray output = data;
foreach (var layer in Layers)
{
if (layer.SkipPred)
{
continue;
}
layer.Forward(output);
output = layer.Output;
}
predictions.AddRange(output.List <float>());
}
DataFrame result = new DataFrame();
result.Load(predictions.ToArray());
return(result);
}
/// <summary>
/// Generates output predictions for the input samples. Computation is done in batches.
/// </summary>
/// <param name="x">The input data frame to run prediction.</param>
/// <param name="batch_size">Size of the batch.</param>
/// <returns></returns>
public Tensor Predict(DataFrame x, int batch_size)
{
DataFrameIter dataFrameIter = new DataFrameIter(x);
List <float> predictions = new List <float>();
dataFrameIter.SetBatchSize(batch_size);
long[] outshape = null;
while (dataFrameIter.Next())
{
var data = dataFrameIter.GetBatchX();
Tensor output = data;
foreach (var layer in Layers)
{
if (layer.SkipPred)
{
continue;
}
layer.Forward(output);
output = layer.Output;
}
predictions.AddRange(output.ToArray().Cast <float>());
}
return(K.CreateVariable(predictions.ToArray(), outshape));
}
/// <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);
}