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debug : nan, it's the learning rate too high,s try to find optimum: 0.001

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fanasina
2025-12-13 05:00:23 +01:00
parent 32207170f6
commit 3d54639d4c
18 changed files with 834 additions and 148 deletions
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neuron_t/test/is_good.c
+99 -16
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@@ -38,6 +38,12 @@ float df(float x){
return exp(-x)/ ((1+exp(-x)) * (1+exp(-x)));
}
float __id_(float x){
return x;
}
float d__id_(float x){
return 1;
}
TEST(init_One){
//endian=false;
@@ -94,7 +100,7 @@ TEST(data_set_from_file){
#define epsilon 0.0001
bool cond(float e, size_t nbreps){
if (nbreps > 20000) return true;
if (nbreps > 2000) return true;
if ((e<epsilon) && (e>-epsilon)) return true;
return false;
}
@@ -106,6 +112,7 @@ TEST(learning_first){
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,false,0,1,5000); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
//setup_networks_OneD_TYPE_FLOAT(&bn, (size_t[]){2,4,1},3,true,0,1,5000); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
setup_all_layers_functions_TYPE_FLOAT(bn,
tensorContractnProdThread_TYPE_FLOAT,
@@ -115,11 +122,23 @@ TEST(learning_first){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
setup_all_layers_params_TYPE_FLOAT(bn, 5/*5*/, 3/*1*/ , 0.5);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
LOG("%s","setup done\n");
print_neurons_msg_TYPE_FLOAT(bn,"bn before ");
size_t reps = learning_online_neurons_TYPE_FLOAT(bn,ds,cond);
print_neurons_msg_TYPE_FLOAT(bn,"bn after ");
//char msg[256];
for(size_t i=0; i<ds->size; ++i){
@@ -136,16 +155,16 @@ TEST(learning_first){
*/
}
LOG("reps = %ld error=%f\n",reps, error_out_TYPE_FLOAT(bn));
free_data_set_TYPE_FLOAT(ds);
free_neurons_TYPE_FLOAT(bn);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(learning_second_PRINT){
TEST(learning_second_PRINT){ endian=false;
bool rec_randomizeInitWeight = randomizeInitWeight;
randomizeInitWeight =false;
@@ -162,8 +181,15 @@ TEST(learning_second_PRINT){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 3 , 0.1);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.4);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -214,8 +240,16 @@ TEST(learning_withconfig2){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
setup_all_layers_params_TYPE_FLOAT(bn, 1, 1 , 0.5);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -230,6 +264,8 @@ TEST(learning_withconfig2){
free_data_set_TYPE_FLOAT(ds);
free_neurons_TYPE_FLOAT(bn);
free_config_layers(pconf);
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
@@ -255,8 +291,15 @@ TEST(learning_cloneuroneset){
f,
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
setup_all_layers_params_TYPE_FLOAT(bn, 1, 1 , 0.5);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
//print_neurons_msg_TYPE_FLOAT(bn,"before create clones");
cloneuronset_TYPE_FLOAT *clnrnst = create_cloneuronset_from_base_conf_TYPE_FLOAT(bn, pconf, 3);
@@ -308,7 +351,14 @@ TEST(learning_cloneuroneset_LEARN_RATE){
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);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
//print_neurons_msg_TYPE_FLOAT(bn,"before create clones");
cloneuronset_TYPE_FLOAT *clnrnst = create_cloneuronset_from_base_conf_TYPE_FLOAT(bn, pconf, 3);
@@ -331,7 +381,6 @@ TEST(learning_cloneuroneset_LEARN_RATE){
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
}
TEST(copy_weight_in_neurons){
@@ -356,7 +405,14 @@ TEST(copy_weight_in_neurons){
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
@@ -396,6 +452,7 @@ TEST(copy_weight_in_neurons){
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
free_config_layers(pconf);
}
@@ -435,7 +492,14 @@ TEST(Extract_weight_in_neurons){
df);
setup_all_layers_params_TYPE_FLOAT(cpyn, 5, 1 , 0.1);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, cpyn, weight_in, ".ff_bn_weight_in__toExtract.txt")
// copy_weight_in_neurons_TYPE_FLOAT(cpyn, bn);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in__toExtract___exp.txt")
@@ -463,6 +527,8 @@ TEST(Extract_weight_in_neurons){
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
free_config_layers(pconf);
}
@@ -492,7 +558,14 @@ TEST(Extract_EXPORT_weight_in_neurons){
df);
setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 , 0.1);
neurons_TYPE_FLOAT *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=__id_;
ttmp->d_f_act=d__id_;
}
ttmp=ttmp->next_layer;
}
size_t reps = 1;// learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in__toCMP__.txt")
@@ -534,6 +607,8 @@ TEST(Extract_EXPORT_weight_in_neurons){
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
free_config_layers(pconf);
}
@@ -596,7 +671,14 @@ TEST(Extract_EXPORT_weight_in_neurons_double){
doubledf);
setup_all_layers_params_TYPE_DOUBLE(cpyn, 5, 1 , 0.1);
neurons_TYPE_DOUBLE *ttmp=bn;
while(ttmp){
if(ttmp->next_layer == NULL){
ttmp->f_act=id_TYPE_DOUBLE;
ttmp->d_f_act=d_id_TYPE_DOUBLE;
}
ttmp=ttmp->next_layer;
}
// EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_DOUBLE, cpyn, weight_in, ".ff_bn_weight_in__toExtract.txt")
// copy_weight_in_neurons_TYPE_DOUBLE(cpyn, bn);
@@ -623,6 +705,7 @@ TEST(Extract_EXPORT_weight_in_neurons_double){
LOG("reps = %ld\n",reps);
randomizeInitWeight = rec_randomizeInitWeight;
free_config_layers(pconf);
}