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change setup neurons and add update learning rates

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fanasina
2024-03-22 10:15:21 +01:00
parent 19d7e03fa7
commit 3911456abc
7 changed files with 498 additions and 72 deletions
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neuron_t/test/is_good.c
+122 -11
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@@ -40,7 +40,7 @@ float df(float x){
TEST(init_One){
//endian=false;
neurons_TYPE_FLOAT *bn=NULL, *tmp=NULL, *ttmp=NULL;
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){3,5,2},3);
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){3,5,2},3,false,0,1,5000);
init_in_out_all_networks_OneD_TYPE_FLOAT(bn,(float[]){1.2,0.5,1.3},3,(float[]){0.1,0.8},2);
@@ -92,17 +92,18 @@ TEST(data_set_from_file){
#define epsilon 0.0001
bool cond(float e, size_t nbreps){
if (nbreps > 1) return true;
if (nbreps > 20000) return true;
if ((e<epsilon) && (e>-epsilon)) return true;
return false;
}
TEST(learning_first){
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
print_data_set_msg_TYPE_FLOAT(ds,"data");
neurons_TYPE_FLOAT *bn=NULL, *tmp ;
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){2,4,1},3); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){2,4,1},3,false,0,1,5000); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
@@ -112,7 +113,7 @@ TEST(learning_first){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.5);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
size_t reps = learning_online_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -120,7 +121,8 @@ TEST(learning_first){
//char msg[256];
for(size_t i=0; i<ds->size; ++i){
print_predict_by_network_neurons_TYPE_FLOAT(bn,ds->input[i]);
print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
//print_predict_by_network_neurons_TYPE_FLOAT(bn,ds->input[i]);
/*sprintf(msg, "data set [%ld]",i);
init_copy_in_out_networks_from_tensors_TYPE_FLOAT(bn, ds->input[i],ds->target[i]);\
tmp=bn->next_layer;\
@@ -137,16 +139,19 @@ TEST(learning_first){
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(learning_second){
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
// print_data_set_msg_TYPE_FLOAT(ds,"data");
neurons_TYPE_FLOAT *bn=NULL, *tmp ;
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){2,4,1},3); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){2,4,1},3,false,0,1,5000); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
@@ -156,7 +161,7 @@ TEST(learning_second){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.5);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 3 , 0.1);
size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -164,7 +169,8 @@ TEST(learning_second){
char msg[256];
for(size_t i=0; i<ds->size; ++i){
print_predict_by_network_neurons_TYPE_FLOAT(bn,ds->input[i]);
print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
//print_predict_by_network_neurons_TYPE_FLOAT(bn,ds->input[i]);
/*sprintf(msg, "data set [%ld]",i);
init_copy_in_out_networks_from_tensors_TYPE_FLOAT(bn, ds->input[i],ds->target[i]);\
tmp=bn->next_layer;\
@@ -182,15 +188,19 @@ TEST(learning_second){
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(learning_withconfig2){
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
// print_data_set_msg_TYPE_FLOAT(ds,"data");
config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){2,4,1}); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
neurons_TYPE_FLOAT *bn=NULL, *tmp ;
setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);
//setup_networks_alloutputs_config_GLOBAL_rdm01_TYPE_FLOAT(setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);bn,pconf);
setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf,false,0,1,5000);
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
@@ -200,7 +210,7 @@ TEST(learning_withconfig2){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.5);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -218,9 +228,110 @@ TEST(learning_withconfig2){
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(learning_cloneuroneset){
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
// print_data_set_msg_TYPE_FLOAT(ds,"data");
config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){2,4,1}); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
neurons_TYPE_FLOAT *bn=NULL, *tmp ;
//setup_networks_alloutputs_config_GLOBAL_rdm01_TYPE_FLOAT(setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);bn,pconf);
setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf,false,0,1,5000);
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
tensorProdThread_TYPE_FLOAT,
DL,
L,
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
//print_neurons_msg_TYPE_FLOAT(bn,"before create clones");
cloneuronset_TYPE_FLOAT *clnrnst = create_cloneuronset_from_base_conf_TYPE_FLOAT(bn, pconf, 3);
// size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
size_t reps = learning_cloneuronset_TYPE_FLOAT(clnrnst, ds,cond);
char msg[256];
for(size_t i=0; i<ds->size; ++i){
print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
}
free_cloneuronset_TYPE_FLOAT(clnrnst);
free_data_set_TYPE_FLOAT(ds);
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(learning_cloneuroneset_LEARN_RATE){
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
// print_data_set_msg_TYPE_FLOAT(ds,"data");
config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){2,4,1}); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
neurons_TYPE_FLOAT *bn=NULL, *tmp ;
//setup_networks_alloutputs_config_GLOBAL_rdm01_TYPE_FLOAT(setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);bn,pconf);
setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf,false,0,1,5000);
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
tensorProdThread_TYPE_FLOAT,
DL,
L,
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.4);
float initRate=0.6;
float decayRate=0.85; /* halving*/
size_t dropRate = 100;
// setup_learning_rate_params_neurons_TYPE_FLOAT(bn, initRate, decayRate, dropRate, time_based_update_learning_rate_TYPE_FLOAT);
setup_learning_rate_params_neurons_TYPE_FLOAT(bn, initRate, decayRate, dropRate, step_based_update_learning_rate_TYPE_FLOAT);
//print_neurons_msg_TYPE_FLOAT(bn,"before create clones");
cloneuronset_TYPE_FLOAT *clnrnst = create_cloneuronset_from_base_conf_TYPE_FLOAT(bn, pconf, 3);
// size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
size_t reps = learning_cloneuronset_TYPE_FLOAT(clnrnst, ds,cond);
char msg[256];
for(size_t i=0; i<ds->size; ++i){
print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
}
free_cloneuronset_TYPE_FLOAT(clnrnst);
free_data_set_TYPE_FLOAT(ds);
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
int main(int argc, char **argv){