func main() {
rand.Seed(time.Now().UnixNano())
sampleSet := sgd.SliceSampleSet{}
for i := 0; i < TrainingCount; i++ {
inSeq, outSeq := genEvenOddSeq(rand.Intn(MaxSeqLen-MinSeqLen) + MinSeqLen)
sampleSet = append(sampleSet, seqtoseq.Sample{
Inputs: inSeq,
Outputs: outSeq,
})
}
outNet := neuralnet.Network{
&neuralnet.DenseLayer{
InputCount: HiddenSize,
OutputCount: 2,
},
}
outNet.Randomize()
outBlock := rnn.NewNetworkBlock(outNet, 0)
lstm := rnn.NewLSTM(2, HiddenSize)
net := rnn.StackedBlock{lstm, outBlock}
gradienter := &sgd.RMSProp{
Gradienter: &seqtoseq.Gradienter{
SeqFunc: &rnn.BlockSeqFunc{B: net},
Learner: net,
CostFunc: neuralnet.SigmoidCECost{},
MaxLanes: 1,
},
}
sgd.SGD(gradienter, sampleSet, StepSize, Epochs, BatchSize)
outNet = append(outNet, neuralnet.Sigmoid{})
var scoreSum float64
var scoreTotal float64
for i := 0; i < TestingCount; i++ {
size := rand.Intn(MaxSeqLen-MinSeqLen) + MinSeqLen
ins, outs := genEvenOddSeq(size)
score := runTestSample(ins, outs, net)
scoreSum += score
scoreTotal += 1
}
fmt.Println("Testing success rate:", scoreSum/scoreTotal)
}
func TestLSTM(t *testing.T) {
b := rnn.NewLSTM(4, 2)
NewChecker4In(b, b).FullCheck(t)
}