func main() {
runtime.GOMAXPROCS(runtime.NumCPU() - 2)
gopath := os.Getenv("GOPATH")
path := filepath.Join(gopath, "prof", "github.com", "reggo", "reggo", "nnet")
nInputs := 10
nOutputs := 3
nLayers := 2
nNeurons := 50
nSamples := 1000000
nRuns := 50
config := &profile.Config{
CPUProfile: true,
ProfilePath: path,
}
defer profile.Start(config).Stop()
net, err := nnet.NewSimpleTrainer(nInputs, nOutputs, nLayers, nNeurons, nnet.Linear{})
if err != nil {
log.Fatal(err)
}
// Generate some random data
inputs := mat64.NewDense(nSamples, nInputs, nil)
outputs := mat64.NewDense(nSamples, nOutputs, nil)
for i := 0; i < nSamples; i++ {
for j := 0; j < nInputs; j++ {
inputs.Set(i, j, rand.Float64())
}
for j := 0; j < nOutputs; j++ {
outputs.Set(i, j, rand.Float64())
}
}
// Create trainer
prob := train.NewBatchGradBased(net, true, inputs, outputs, nil, nil, nil)
nParameters := net.NumParameters()
parameters := make([]float64, nParameters)
derivative := make([]float64, nParameters)
for i := 0; i < nRuns; i++ {
net.RandomizeParameters()
net.Parameters(parameters)
prob.ObjGrad(parameters, derivative)
fmt.Println(floats.Sum(derivative))
}
}
// TestDeriv uses finite difference to test that the prediction from Deriv
// is correct, and tests that computing the loss in parallel works properly
// Only does finite difference for the first nTest to save time
func TestDeriv(t *testing.T, trainable DerivTester, inputs, trueOutputs common.RowMatrix, name string) {
// Set the parameters to something random
trainable.RandomizeParameters()
// Compute the loss and derivative
losser := loss.SquaredDistance{}
regularizer := regularize.TwoNorm{}
batchGrad := train.NewBatchGradBased(trainable, true, inputs, trueOutputs, nil, losser, regularizer)
derivative := make([]float64, trainable.NumParameters())
parameters := trainable.Parameters(nil)
// Don't need to check loss, because if predict is right and losser is right then loss must be correct
_ = batchGrad.ObjGrad(parameters, derivative)
fdDerivative := make([]float64, trainable.NumParameters())
wg := &sync.WaitGroup{}
wg.Add(trainable.NumParameters())
for i := 0; i < trainable.NumParameters(); i++ {
go func(i int) {
newParameters := make([]float64, trainable.NumParameters())
tmpDerivative := make([]float64, trainable.NumParameters())
copy(newParameters, parameters)
newParameters[i] += fdStep
loss1 := batchGrad.ObjGrad(newParameters, tmpDerivative)
newParameters[i] -= 2 * fdStep
loss2 := batchGrad.ObjGrad(newParameters, tmpDerivative)
newParameters[i] += fdStep
fdDerivative[i] = (loss1 - loss2) / (2 * fdStep)
wg.Done()
}(i)
}
wg.Wait()
if !floats.EqualApprox(derivative, fdDerivative, 1e-6) {
t.Errorf("%v: deriv doesn't match: Finite Difference: %v, Analytic: %v", name, fdDerivative, derivative)
}
}
// TestLinearsolveAndDeriv compares the optimal weights found from gradient-based optimization with those found
// from computing a linear solve
func TestLinearsolveAndDeriv(t *testing.T, linear train.LinearTrainable, inputs, trueOutputs common.RowMatrix, name string) {
// Compare with no weights
rows, cols := trueOutputs.Dims()
predOutLinear := mat64.NewDense(rows, cols, nil)
parametersLinearSolve := train.LinearSolve(linear, nil, inputs, trueOutputs, nil, nil)
linear.SetParameters(parametersLinearSolve)
linear.Predictor().PredictBatch(inputs, predOutLinear)
//fmt.Println("Pred out linear", predOutLinear)
linear.RandomizeParameters()
parameters := linear.Parameters(nil)
batch := train.NewBatchGradBased(linear, true, inputs, trueOutputs, nil, loss.SquaredDistance{}, regularize.None{})
problem := batch
settings := multivariate.DefaultSettings()
settings.GradAbsTol = 1e-11
//settings. = 0
result, err := multivariate.OptimizeGrad(problem, parameters, settings, nil)
if err != nil {
t.Errorf("Error training: %v", err)
}
parametersDeriv := result.Loc
deriv := make([]float64, linear.NumParameters())
loss1 := batch.ObjGrad(parametersDeriv, deriv)
linear.SetParameters(parametersDeriv)
predOutDeriv := mat64.NewDense(rows, cols, nil)
linear.Predictor().PredictBatch(inputs, predOutDeriv)
linear.RandomizeParameters()
init2 := linear.Parameters(nil)
batch2 := train.NewBatchGradBased(linear, true, inputs, trueOutputs, nil, loss.SquaredDistance{}, regularize.None{})
problem2 := batch2
result2, err := multivariate.OptimizeGrad(problem2, init2, settings, nil)
parametersDeriv2 := result2.Loc
//fmt.Println("starting deriv2 loss")
deriv2 := make([]float64, linear.NumParameters())
loss2 := batch2.ObjGrad(parametersDeriv2, deriv2)
//fmt.Println("starting derivlin loss")
derivlinear := make([]float64, linear.NumParameters())
lossLin := batch2.ObjGrad(parametersLinearSolve, derivlinear)
_ = loss1
_ = loss2
_ = lossLin
/*
fmt.Println("param deriv 1 =", parametersDeriv)
fmt.Println("param deriv2 =", parametersDeriv2)
fmt.Println("linear params =", parametersLinearSolve)
fmt.Println("deriv1 loss =", loss1)
fmt.Println("deriv2 loss =", loss2)
fmt.Println("lin loss =", lossLin)
fmt.Println("deriv =", deriv)
fmt.Println("deriv2 =", deriv2)
fmt.Println("linderiv =", derivlinear)
//fmt.Println("Pred out deriv", predOutDeriv)
*/
/*
for i := 0; i < rows; i++ {
fmt.Println(predOutLinear.RowView(i), predOutBatch.RowView(i))
}
*/
if !floats.EqualApprox(parametersLinearSolve, parametersDeriv, 1e-8) {
t.Errorf("Parameters don't match for gradient based and linear solve.")
//for i := range parametersDeriv {
// fmt.Printf("index %v: Deriv = %v, linsolve = %v, diff = %v\n", i, parametersDeriv[i], parametersLinearSolve[i], parametersDeriv[i]-parametersLinearSolve[i])
//}
}
}
