func costFuncDeriv(c neuralnet.CostFunc, expected, actual linalg.Vector) linalg.Vector {
variable := &autofunc.Variable{Vector: actual}
result := make(linalg.Vector, len(actual))
res := c.Cost(expected, variable)
res.PropagateGradient([]float64{1}, autofunc.Gradient{variable: result})
return result
}
// TotalCostBlock runs an rnn.Block on a set of Samples
// and evaluates the total output cost.
//
// The batchSize specifies how many samples to run in
// batches while computing the cost.
func TotalCostBlock(b rnn.Block, batchSize int, s sgd.SampleSet, c neuralnet.CostFunc) float64 {
runner := &rnn.Runner{Block: b}
var cost float64
for i := 0; i < s.Len(); i += batchSize {
var inSeqs, outSeqs [][]linalg.Vector
for j := i; j < i+batchSize && j < s.Len(); j++ {
seq := s.GetSample(j).(Sample)
inSeqs = append(inSeqs, seq.Inputs)
outSeqs = append(outSeqs, seq.Outputs)
}
output := runner.RunAll(inSeqs)
for j, outSeq := range outSeqs {
for t, actual := range output[j] {
expected := outSeq[t]
actualVar := &autofunc.Variable{Vector: actual}
cost += c.Cost(expected, actualVar).Output()[0]
}
}
}
return cost
}
// TotalCostSeqFunc runs a seqfunc.RFunc on a set of
// Samples and evaluates the total output cost.
//
// The batchSize specifies how many samples to run in
// batches while computing the cost.
func TotalCostSeqFunc(f seqfunc.RFunc, batchSize int, s sgd.SampleSet,
c neuralnet.CostFunc) float64 {
var totalCost float64
for i := 0; i < s.Len(); i += batchSize {
var inSeqs [][]linalg.Vector
var outSeqs [][]linalg.Vector
for j := i; j < i+batchSize && j < s.Len(); j++ {
seq := s.GetSample(j).(Sample)
inSeqs = append(inSeqs, seq.Inputs)
outSeqs = append(outSeqs, seq.Outputs)
}
output := f.ApplySeqs(seqfunc.ConstResult(inSeqs))
for j, actualSeq := range output.OutputSeqs() {
expectedSeq := outSeqs[j]
for k, actual := range actualSeq {
expected := expectedSeq[k]
actualVar := &autofunc.Variable{Vector: actual}
totalCost += c.Cost(expected, actualVar).Output()[0]
}
}
}
return totalCost
}
