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linear.cc
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linear.cc
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//#include "util.h"
#include "itensor/all.h"
#include "mllib/mnist.h"
using namespace itensor;
using namespace mllib;
using std::vector;
using std::abs;
using std::min;
using std::string;
struct TImg
{
long l = 1000;
int y = -1;
Real dC = 0.;
Vector img;
TImg(long l_, int y_, Vector img_)
{
l = l_;
y = y_;
img = img_;
}
};
Real
cgrad(Vector & W,
vector<TImg> const& train,
Args const& args)
{
auto lambda = args.getReal("lambda",0.);
auto Npass = args.getInt("Npass");
int size = W.size();
int NT = train.size();
auto r = Vector(size);
for(auto& t : train)
{
auto Wt = W*t.img;
r += (t.y-Wt)*t.img;
}
r /= NT;
if(lambda != 0.) r = r - lambda*W;
auto C = 0.;
auto p = r;
for(auto pass : range1(Npass))
{
//Compute p*A*p
Real pAp = 0.;
for(auto& t : train)
{
auto pv = p*t.img;
pAp += pv*pv;
}
pAp /= NT;
pAp += lambda*(W*W);
auto a = (r*r)/pAp;
W = W + a*p;
C = 0.;
auto nr = Vector(size);
for(auto& t : train)
{
auto dW = (t.y-W*t.img);
nr += dW*t.img;
C += sqr(dW);
}
nr /= NT;
C /= NT;
if(lambda != 0.) nr = nr - lambda*W;
auto beta = (nr*nr)/(r*r);
r = nr;
C += lambda*(W*W);
printfln(" %d C = %.10f",pass,C);
if(fileExists("STOP"))
{
println("Found file STOP, exiting");
system("rm -f STOP");
return C;
}
p = r + beta*p;
}
return C;
}
int
main(int argc, char* argv[])
{
if(argc != 2) return printfln("Usage: %s inputfile",argv[0]),0;
auto in = InputGroup(argv[1],"input");
auto datadir = in.getString("datadir","/Users/mstoudenmire/software/tnml/mllib/MNIST");
auto Niter = in.getInt("Nlinear_iter",5000);
auto Ntrain = in.getInt("Ntrain",60000);
auto lambda = in.getReal("lambda",0.);
auto L = in.getInt("label");
auto d = 2;
auto dotest = in.getYesNo("dotest",false);
auto imgtype = dotest ? Test : Train;
int Nimg = 0;
print("Loading training data...");
auto traindata = readMNIST(datadir,Train,{"NT=",Ntrain});
auto testdata = readMNIST(datadir,Test);
println("done");
int N = traindata.front().data.size();
auto phi = [](Real x, int n) -> Real
{
return n==1 ? 1. : x/4.;
};
auto size = 1+N;
printfln("Vector size = %d",size);
auto setup = [&](vector<TImg> & set,
vector<MNISTData> const & data)
{
for(auto& img : data)
{
auto l = img.label;
auto y = (l==L) ? +1 : -1;
auto v = Vector(size);
int nn = 0;
v(nn++) = 1.;
for(auto j : range(N))
{
v(nn++) = phi(img[j],2);
}
set.emplace_back(l,y,v);
}
};
print("Setting up training images...");
auto train = vector<TImg>();
setup(train,traindata);
print("Setting up testing images...");
auto test = vector<TImg>();
setup(test,testdata);
println("done");
auto Vname = format("V%d",L);
Vector V;
if(fileExists(Vname))
{
println("Reading parameters from disk");
V = readFromFile<Vector>(Vname);
}
else
{
V = Vector(size);
randomize(V);
V /= norm(V);
}
Print(norm(V));
auto C = cgrad(V,train,{"Npass=",Niter,"lambda=",lambda});
auto evaluate = [&](vector<TImg> const& set)
{
auto T = set.size();
auto ncor = 0;
auto Cnl = 0.;
for(auto& t : set)
{
auto f = V*t.img;
if(f*t.y > 0.) ++ncor;
Cnl += sqr(f-t.y);
}
Cnl /= T;
auto ninc = T-ncor;
printfln("Percent correct = %.4f%%, #correct = %d/%d, #incorrect = %d/%d",
ncor*100./T,ncor,T,ninc,T);
auto Cl = lambda*(V*V);
printfln("C (= %.10f + %.10f) = %.10f",Cnl,Cl,Cnl+Cl);
};
println("Evaluating training set");
evaluate(train);
println("Evaluating testing set");
evaluate(test);
writeToFile(Vname,V);
SiteSet sites;
if(fileExists("sites"))
{
println("Reading previous site set from disk");
sites = readFromFile<SiteSet>("sites");
}
else
{
sites = SiteSet(N,d);
writeToFile("sites",sites);
}
//
// Make MPS version of V
//
auto W = MPS(sites);
auto M = 2;
auto links = vector<Index>(2+N);
for(auto j : range(2+N)) links.at(j) = Index(format("l%d",j),M,Link);
for(auto j : range1(N))
{
auto l = links.at(j-1);
auto r = links.at(j);
auto s = sites(j);
auto& A = W.Aref(j);
A = ITensor(l,s,r);
A.set(1,1,1,1.);
A.set(2,1,2,1.);
A.set(2,2,1,V(j));
}
auto l0 = links.at(0);
auto A0 = ITensor(l0);
A0.set(l0(1),V(0));
A0.set(l0(2),1.);
W.Aref(1) *= A0;
W.Aref(N) *= setElt(links.at(N)(1));
W.position(1);
Print(overlap(W,W));
Print(sqr(norm(V)));
writeToFile(format("W%d",L),W);
return 0;
}