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Develop/two stage #12

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dcd1550
two stage training
LonSilent Oct 17, 2017
e6835c1
merge master
LonSilent Oct 17, 2017
eb6d301
Added 1) weighted option, 2) save ET and TT of ICE separately option,…
thwang1231 Oct 21, 2017
d039bb4
train function split
LonSilent Nov 9, 2017
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78 changes: 75 additions & 3 deletions ICE/ICE.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,11 @@ void ICE::LoadDict(unordered_map<string, unordered_map<string, double>>& graph)
vvnet.LoadDict(graph);
}

void ICE::SaveWeights(string model_name){
void ICE::LoadWeights(string filename){
vvnet.LoadWeights(filename, w_context);
}

void ICE::SaveVertexWeights(string model_name){

cout << "Save Model:" << endl;
ofstream model(model_name);
Expand All @@ -43,6 +47,66 @@ void ICE::SaveWeights(string model_name){
}
}

void ICE::SaveContextWeights(string model_name){

cout << "Save Model:" << endl;
ofstream model(model_name);
if (model)
{
//model << vvnet.MAX_vid << " " << dim << endl;
for (auto k: vvnet.keys)
{
if (vvnet.vertex[vvnet.kmap[k]].branch > 0)
{
model << k;
for (int d=0; d<dim; ++d)
model << " " << w_context[vvnet.kmap[k]][d];
model << endl;
}
}
cout << "\tSave to <" << model_name << ">" << endl;
}
else
{
cout << "\tfail to open file" << endl;
}
}

void ICE::SaveEntityWeights(string model_name){

cout << "Save Model:" << endl;
ofstream model(model_name);
int i = 0;
if (model)
{
//model << vvnet.MAX_vid << " " << dim << endl;
for (auto k: vvnet.keys)
{
// cout << k << endl;
if (i < vocab_count){
i++;
continue;
}
if (vvnet.vertex[vvnet.kmap[k]].branch > 0)
{
model << k;
for (int d=0; d<dim; ++d)
model << " " << w_vertex[vvnet.kmap[k]][d];
model << endl;
}
}
cout << "\tSave to <" << model_name << ">" << endl;
}
else
{
cout << "\tfail to open file" << endl;
}
}

int ICE::getVocabCount(){
vocab_count = vvnet.keys.size();
}

void ICE::Init(int dimension) {

cout << "Model Setting:" << endl;
Expand Down Expand Up @@ -70,7 +134,7 @@ void ICE::Init(int dimension) {
}


void ICE::Train(int sample_times, int negative_samples, double alpha, double alpha_min, int workers){
void ICE::Train(int sample_times, int negative_samples, double alpha, double alpha_min, int workers, int vocab_count, string mode){

omp_set_num_threads(workers);

Expand Down Expand Up @@ -103,7 +167,15 @@ void ICE::Train(int sample_times, int negative_samples, double alpha, double alp
{
v1 = vvnet.SourceSample();
v2 = vvnet.TargetSample(v1);
vvnet.UpdatePair(w_vertex, w_context, v1, v2, dim, negative_samples, _alpha);

if (mode == "stage1"){
vvnet.UpdatePair(w_vertex, w_context, v1, v2, dim, negative_samples, _alpha);
}
else{
if (v1 >= vocab_count){
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Collaborator

@chihming chihming Nov 7, 2017 •
edited
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if (v1 >= vocab_count)

what's the function of this condition? (you wanna sample a node with an offset vocab_count? if yes, I think we can use "v1+vocab_count" instead)

vvnet.UpdateVertex(w_vertex, w_context, v1, v2, dim, negative_samples, _alpha);
}
}

count++;
if (count % MONITOR == 0)
Expand Down
9 changes: 7 additions & 2 deletions ICE/ICE.h
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Original file line number Diff line number Diff line change
Expand Up @@ -18,18 +18,23 @@ class ICE {

// model parameters
int dim;
int vocab_count;
vector< vector<double> > w_vertex;
vector< vector<double> > w_context;

// data function
void LoadEdgeList(string);
void LoadWeights(string);
void LoadItemConceptList(string);
void LoadDict(unordered_map<string, unordered_map<string, double>>&);
void SaveWeights(string);
void SaveVertexWeights(string);
void SaveContextWeights(string);
void SaveEntityWeights(string);
int getVocabCount();

// model function
void Init(int);
void Train(int, int, double, double, int);
void Train(int, int, double, double, int, int, string);

};

Expand Down
68 changes: 56 additions & 12 deletions ICE/main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,10 +21,18 @@ int main(int argc, char **argv){
printf("[ICE-CLI]\n");
printf("\tcommand line interface for ICE\n\n");
printf("Options:\n");
printf("\t-train <string>\n");
printf("\t\tTrain the Network data\n");
// printf("\t-train <string>\n");
// printf("\t\tTrain the Network data\n");
printf("\t-text <string>\n");
printf("\t\ttext-text network data\n");
printf("\t-entity <string>\n");
printf("\t\tentity-text network data\n");
printf("\t-textrep <string>\n");
printf("\t\tsave the word representation data\n");
printf("\t-textcontext <string>\n");
printf("\t\tsave the word context data\n");
printf("\t-save <string>\n");
printf("\t\tSave the representation data\n");
printf("\t\tSave the entity representation data\n");
printf("\t-save_times <int>\n");
printf("\t\tsave a model <int> times; default is 1\n");
printf("\t-dim <int>\n");
Expand All @@ -38,16 +46,21 @@ int main(int argc, char **argv){
printf("\t-alpha <float>\n");
printf("\t\tInit learning rate; default is 0.025\n");
printf("\nExample Usage:\n");
printf("\t./ice -train network.txt -save rep.txt -dim 64 -sample 10 -neg 5 -alpha 0.025 -thread 4\n\n");
printf("\t./ice -text ../data/word.txt -entity ../data/entity.txt -textrep ../data/text.embd -save ../data/result.txt -textcontext ../data/context.txt -dim 4 -sample 10 -neg 5 -alpha 0.025 -thread 4\n\n");
return 0;
}

char network_file[100], rep_file[100];
char text_file[100], text_rep_file[100], text_context_file[100], entity_file[100], result_file[100];
int dimensions=64, negative_samples=5, sample_times=10, save_times=1, threads=1;
double init_alpha=0.025;

if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(network_file, argv[i + 1]);
if ((i = ArgPos((char *)"-save", argc, argv)) > 0) strcpy(rep_file, argv[i + 1]);
// if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(network_file, argv[i + 1]);
// if ((i = ArgPos((char *)"-save", argc, argv)) > 0) strcpy(rep_file, argv[i + 1]);
if ((i = ArgPos((char *)"-text", argc, argv)) > 0) strcpy(text_file, argv[i + 1]);
if ((i = ArgPos((char *)"-entity", argc, argv)) > 0) strcpy(entity_file, argv[i + 1]);
if ((i = ArgPos((char *)"-textrep", argc, argv)) > 0) strcpy(text_rep_file, argv[i + 1]);
if ((i = ArgPos((char *)"-textcontext", argc, argv)) > 0) strcpy(text_context_file, argv[i + 1]);
if ((i = ArgPos((char *)"-save", argc, argv)) > 0) strcpy(result_file, argv[i + 1]);
if ((i = ArgPos((char *)"-save_times", argc, argv)) > 0) save_times = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-dim", argc, argv)) > 0) dimensions = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample_times = atoi(argv[i + 1]);
Expand All @@ -57,8 +70,9 @@ int main(int argc, char **argv){

ICE *ice;
ice = new ICE();
ice->LoadEdgeList(network_file);
ice->LoadEdgeList(text_file);
ice->Init(dimensions);
ice->getVocabCount();

int sub_sample_times = sample_times/save_times, current_sample_times=0;
double alpha_max=init_alpha, alpha_min=init_alpha;
Expand All @@ -68,16 +82,46 @@ int main(int argc, char **argv){
alpha_max = alpha_min;
alpha_min = init_alpha*((double)(save_times-i-1)/save_times);
if (alpha_min < init_alpha*0.0001) alpha_min = init_alpha*0.0001;
ice->Train(sub_sample_times, negative_samples, alpha_max, alpha_min, threads);
ice->Train(sub_sample_times, negative_samples, alpha_max, alpha_min, threads, ice->vocab_count, "stage1");

if (i==(save_times-1))
{
ice->SaveWeights(rep_file);
ice->SaveVertexWeights(text_rep_file);
ice->SaveContextWeights(text_context_file);
}
else
{
string sub_rep_file = rep_file + string(".") + to_string(current_sample_times);
ice->SaveWeights(sub_rep_file);
string sub_text_rep_file = text_rep_file + string(".") + to_string(current_sample_times);
ice->SaveVertexWeights(sub_text_rep_file);
string sub_text_context_file = text_context_file + string(".") + to_string(current_sample_times);
ice->SaveContextWeights(sub_text_context_file);

}
}

ice->LoadEdgeList(entity_file);
ice->Init(dimensions);
ice->LoadWeights(text_context_file);

sub_sample_times = sample_times/save_times, current_sample_times=0;
alpha_max=init_alpha, alpha_min=init_alpha;
for (int i=0; i<save_times; ++i)
{
current_sample_times += sub_sample_times;
alpha_max = alpha_min;
alpha_min = init_alpha*((double)(save_times-i-1)/save_times);
if (alpha_min < init_alpha*0.0001) alpha_min = init_alpha*0.0001;
ice->Train(sub_sample_times, negative_samples, alpha_max, alpha_min, threads, ice->vocab_count, "stage2");

if (i==(save_times-1))
{
ice->SaveEntityWeights(result_file);
}
else
{
string sub_result_file = result_file + string(".") + to_string(current_sample_times);
ice->SaveEntityWeights(sub_result_file);

}
}

Expand Down
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