func (b *BatchRGradienter) runBatch(rv autofunc.RVector, rgrad autofunc.RGradient,
grad autofunc.Gradient, s sgd.SampleSet) {
if s.Len() == 0 {
return
}
sampleCount := s.Len()
firstSample := s.GetSample(0).(VectorSample)
inputSize := len(firstSample.Input)
outputSize := len(firstSample.Output)
inVec := make(linalg.Vector, sampleCount*inputSize)
outVec := make(linalg.Vector, sampleCount*outputSize)
for i := 0; i < s.Len(); i++ {
sample := s.GetSample(i)
vs := sample.(VectorSample)
copy(inVec[i*inputSize:], vs.Input)
copy(outVec[i*outputSize:], vs.Output)
}
inVar := &autofunc.Variable{inVec}
if rgrad != nil {
rVar := autofunc.NewRVariable(inVar, rv)
result := b.Learner.BatchR(rv, rVar, sampleCount)
cost := b.CostFunc.CostR(rv, outVec, result)
cost.PropagateRGradient(linalg.Vector{1}, linalg.Vector{0},
rgrad, grad)
} else {
result := b.Learner.Batch(inVar, sampleCount)
cost := b.CostFunc.Cost(outVec, result)
cost.PropagateGradient(linalg.Vector{1}, grad)
}
}
func (b *SingleRGradienter) RGradient(rv autofunc.RVector, s sgd.SampleSet) (autofunc.Gradient,
autofunc.RGradient) {
if b.gradCache == nil {
b.gradCache = autofunc.NewGradient(b.Learner.Parameters())
} else {
b.gradCache.Zero()
}
if b.rgradCache == nil {
b.rgradCache = autofunc.NewRGradient(b.Learner.Parameters())
} else {
b.rgradCache.Zero()
}
for i := 0; i < s.Len(); i++ {
sample := s.GetSample(i)
vs := sample.(VectorSample)
output := vs.Output
inVar := &autofunc.Variable{vs.Input}
rVar := autofunc.NewRVariable(inVar, rv)
result := b.Learner.ApplyR(rv, rVar)
cost := b.CostFunc.CostR(rv, output, result)
cost.PropagateRGradient(linalg.Vector{1}, linalg.Vector{0},
b.rgradCache, b.gradCache)
}
return b.gradCache, b.rgradCache
}
func manualNetworkSeq(rv autofunc.RVector, f autofunc.RFunc, start *autofunc.Variable,
ins [][]*autofunc.Variable, stateSize int) (out, outR [][]linalg.Vector) {
out = make([][]linalg.Vector, len(ins))
outR = make([][]linalg.Vector, len(ins))
for seqIdx, inSeq := range ins {
var state autofunc.RResult = autofunc.NewRVariable(start, rv)
for _, in := range inSeq {
inR := rv[in]
packedIn := append(linalg.Vector{}, in.Output()...)
packedIn = append(packedIn, state.Output()...)
packedInR := append(linalg.Vector{}, inR...)
packedInR = append(packedInR, state.ROutput()...)
stepOut := f.ApplyR(rv, &autofunc.RVariable{
Variable: &autofunc.Variable{Vector: packedIn},
ROutputVec: packedInR,
})
outSize := len(stepOut.Output()) - stateSize
out[seqIdx] = append(out[seqIdx], stepOut.Output()[:outSize])
outR[seqIdx] = append(outR[seqIdx], stepOut.ROutput()[:outSize])
state = &autofunc.RVariable{
Variable: &autofunc.Variable{Vector: stepOut.Output()[outSize:]},
ROutputVec: stepOut.ROutput()[outSize:],
}
}
}
return
}
func TestMaxPoolingBatchR(t *testing.T) {
layer := &MaxPoolingLayer{
XSpan: 5,
YSpan: 4,
InputWidth: 17,
InputHeight: 19,
InputDepth: 3,
}
n := 3
batchInput := make(linalg.Vector, n*layer.InputWidth*layer.InputHeight*layer.InputDepth)
for i := range batchInput {
batchInput[i] = rand.NormFloat64()
}
batchRes := &autofunc.Variable{Vector: batchInput}
rVec := autofunc.RVector{
batchRes: make(linalg.Vector, len(batchInput)),
}
for i := range rVec[batchRes] {
rVec[batchRes][i] = rand.NormFloat64()
}
testRBatcher(t, rVec, layer, autofunc.NewRVariable(batchRes, rVec),
n, []*autofunc.Variable{batchRes})
}
func TestConvLayerBatchR(t *testing.T) {
layer := &ConvLayer{
FilterCount: 3,
FilterWidth: 2,
FilterHeight: 4,
Stride: 2,
InputHeight: 17,
InputWidth: 19,
InputDepth: 5,
}
layer.Randomize()
n := 3
batchInput := make(linalg.Vector, n*layer.InputWidth*layer.InputHeight*layer.InputDepth)
for i := range batchInput {
batchInput[i] = rand.NormFloat64()
}
batchRes := &autofunc.Variable{Vector: batchInput}
params := []*autofunc.Variable{batchRes, layer.Biases, layer.FilterVar}
rVec := autofunc.RVector{}
for _, param := range params {
vec := make(linalg.Vector, len(param.Vector))
for i := range vec {
vec[i] = rand.NormFloat64()
}
rVec[param] = vec
}
testRBatcher(t, rVec, layer, autofunc.NewRVariable(batchRes, rVec), n, params)
}
func (d *DropoutLayer) ApplyR(v autofunc.RVector, in autofunc.RResult) autofunc.RResult {
if d.Training {
mask := d.dropoutMask(len(in.Output()))
maskVar := autofunc.NewRVariable(mask, v)
return autofunc.MulR(in, maskVar)
} else {
return autofunc.ScaleR(in, d.KeepProbability)
}
}
// StartRState is like StartState but with an RState.
func (l *LSTM) StartRState(rv autofunc.RVector) RState {
rVar := autofunc.NewRVariable(l.initState, rv)
return lstmRState{
Internal: l.initState.Vector[:len(l.initState.Vector)/2],
InternalR: rVar.ROutputVec[:len(l.initState.Vector)/2],
Output: l.initState.Vector[len(l.initState.Vector)/2:],
OutputR: rVar.ROutputVec[len(l.initState.Vector)/2:],
}
}
func (r *RegularizingCost) CostR(v autofunc.RVector, a linalg.Vector,
x autofunc.RResult) autofunc.RResult {
regFunc := autofunc.SquaredNorm{}
cost := r.CostFunc.CostR(v, a, x)
for _, variable := range r.Variables {
norm := regFunc.ApplyR(v, autofunc.NewRVariable(variable, v))
cost = autofunc.AddR(cost, autofunc.ScaleR(norm, r.Penalty))
}
return cost
}
func (_ SigmoidCECost) CostR(v autofunc.RVector, x linalg.Vector,
a autofunc.RResult) autofunc.RResult {
logsig := autofunc.LogSigmoid{}
log := logsig.ApplyR(v, a)
invLog := logsig.ApplyR(v, autofunc.ScaleR(a, -1))
xVar := autofunc.NewRVariable(&autofunc.Variable{x}, v)
oneMinusX := autofunc.AddScalerR(autofunc.ScaleR(xVar, -1), 1)
sums := autofunc.AddR(autofunc.MulR(xVar, log), autofunc.MulR(oneMinusX, invLog))
return autofunc.ScaleR(autofunc.SumAllR(sums), -1)
}
func (b *BlockChecker) testNilUpstreamR(t *testing.T) {
t.Run("Nil Upstream R", func(t *testing.T) {
out := b.B.ApplyBlockR(b.RV, []rnn.RState{b.B.StartRState(b.RV)},
[]autofunc.RResult{autofunc.NewRVariable(b.Input[0][0], b.RV)})
g1 := autofunc.NewGradient(b.Vars)
rg1 := autofunc.NewRGradient(b.Vars)
initLen1 := len(g1)
c := len(out.PropagateRGradient(nil, nil, nil, rg1, g1))
if c != 1 {
t.Errorf("expected %d downstream states, got %d", 1, c)
}
g2 := autofunc.NewGradient(b.Vars)
rg2 := autofunc.NewRGradient(b.Vars)
initLen2 := len(g2)
zeroUpstream := make([]linalg.Vector, len(out.Outputs()))
for i, x := range out.Outputs() {
zeroUpstream[i] = make(linalg.Vector, len(x))
}
nilStateUpstream := make([]rnn.RStateGrad, len(out.RStates()))
c = len(out.PropagateRGradient(zeroUpstream, zeroUpstream, nilStateUpstream, rg2, g2))
if c != 1 {
t.Errorf("expected %d downstream states, got %d", 1, c)
}
if len(g1) != initLen1 {
t.Errorf("all nil gradient length changed from %d to %d", initLen1, len(g1))
}
if len(rg1) != initLen1 {
t.Errorf("all nil r-gradient length changed from %d to %d", initLen1, len(rg1))
}
if len(g2) != initLen2 {
t.Errorf("non-nil gradient length changed from %d to %d", initLen2, len(g2))
}
if len(rg2) != initLen2 {
t.Errorf("non-nil r-gradient length changed from %d to %d", initLen2, len(rg2))
}
for i, variable := range b.Vars {
val1 := g1[variable]
val2 := g2[variable]
if !b.vecsEqual(val1, val2) {
t.Errorf("gradients for var %d don't match: %v and %v", i, val1, val2)
}
val1 = rg1[variable]
val2 = rg2[variable]
if !b.vecsEqual(val1, val2) {
t.Errorf("r-gradients for var %d don't match: %v and %v", i, val1, val2)
}
}
})
}
// StartRState is like StartState but with an RState.
func (b *BatcherBlock) StartRState(rv autofunc.RVector) RState {
if b.Start != nil {
rVar := autofunc.NewRVariable(b.Start, rv)
return VecRState{
State: rVar.Output(),
RState: rVar.ROutput(),
}
}
zero := make(linalg.Vector, b.StateSize)
return VecRState{
State: zero,
RState: zero,
}
}
func (_ CrossEntropyCost) CostR(v autofunc.RVector, x linalg.Vector,
a autofunc.RResult) autofunc.RResult {
return autofunc.PoolR(a, func(a autofunc.RResult) autofunc.RResult {
xVar := autofunc.NewRVariable(&autofunc.Variable{x}, autofunc.RVector{})
logA := autofunc.Log{}.ApplyR(v, a)
oneMinusA := autofunc.AddScalerR(autofunc.ScaleR(a, -1), 1)
oneMinusX := autofunc.AddScalerR(autofunc.ScaleR(xVar, -1), 1)
log1A := autofunc.Log{}.ApplyR(v, oneMinusA)
errorVec := autofunc.AddR(autofunc.MulR(xVar, logA),
autofunc.MulR(oneMinusX, log1A))
return autofunc.ScaleR(autofunc.SumAllR(errorVec), -1)
})
}
func (l *lstmGate) BatchR(rv autofunc.RVector, in autofunc.RResult, n int) autofunc.RResult {
if l.Peephole == nil {
return l.Activation.ApplyR(rv, l.Dense.BatchR(rv, in, n))
}
return autofunc.PoolR(in, func(in autofunc.RResult) autofunc.RResult {
vecSize := len(in.Output()) / n
var weightedInputs []autofunc.RResult
var peepholed []autofunc.RResult
peephole := autofunc.NewRVariable(l.Peephole, rv)
for i := 0; i < n; i++ {
start := vecSize * i
weightedEnd := start + vecSize - len(l.Peephole.Vector)
weightedInputs = append(weightedInputs, autofunc.SliceR(in, start, weightedEnd))
peepholeMe := autofunc.SliceR(in, weightedEnd, (i+1)*vecSize)
peepholed = append(peepholed, autofunc.MulR(peephole, peepholeMe))
}
weighted := l.Dense.BatchR(rv, autofunc.ConcatR(weightedInputs...), n)
joinedPeep := autofunc.ConcatR(peepholed...)
return l.Activation.ApplyR(rv, autofunc.AddR(joinedPeep, weighted))
})
}
func (_ MeanSquaredCost) CostR(v autofunc.RVector, a linalg.Vector,
x autofunc.RResult) autofunc.RResult {
aVar := &autofunc.Variable{a.Copy().Scale(-1)}
aVarR := autofunc.NewRVariable(aVar, v)
return autofunc.SquaredNorm{}.ApplyR(v, autofunc.AddR(aVarR, x))
}
func (_ DotCost) CostR(v autofunc.RVector, x linalg.Vector,
a autofunc.RResult) autofunc.RResult {
xVar := autofunc.NewRVariable(&autofunc.Variable{x}, v)
return autofunc.ScaleR(autofunc.SumAllR(autofunc.MulR(xVar, a)), -1)
}
// StartStateR is like StartState but with r-operators.
func (g *GRU) StartRState(rv autofunc.RVector) RState {
resVar := autofunc.NewRVariable(g.initState, rv)
return VecRState{State: resVar.Output(), RState: resVar.ROutput()}
}
