Trying to fix nan output of RL by using relu with upperbound

neuron_t/src/neuron_t/neuron_t.c

@@ -768,6 +768,7 @@ neurons_##type * calculate_output_by_network_neurons_##type(neurons_##type *base
     if(tmp->next_layer==NULL){\
       /*print_tensor_msg_##type(tmp->output,"retult");*/\
       *output_link = tmp->output;\
+      \
       return tmp;\
     }\
     tmp = tmp->next_layer;\

neuron_t/test/is_good.c

@@ -18,6 +18,7 @@
 
 //#include "permutation_t/permutation_t.h"
 #include "neuron_t/neuron_t.h"
+#include "neuron_t/nneuron_t_file.h"
 
 #define VALGRIND_ 1
 
@@ -135,7 +136,6 @@ TEST(learning_first){
     */
   }
   
-
   free_data_set_TYPE_FLOAT(ds);
   free_neurons_TYPE_FLOAT(bn); 
 
@@ -145,7 +145,7 @@ TEST(learning_first){
 
 
 
-TEST(learning_second){
+TEST(learning_second_PRINT){
    bool rec_randomizeInitWeight = randomizeInitWeight;
    randomizeInitWeight =false;
   
@@ -184,6 +184,9 @@ TEST(learning_second){
   }
  
 
+  PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, bn, input, " bn input");
+  PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, bn, output, " bn output");
+  PRINT_ATTRIBUTE_TENS_IN_ALL_LAYERS(TYPE_FLOAT, bn, bias, " bn bias");
 
   free_data_set_TYPE_FLOAT(ds);
   free_neurons_TYPE_FLOAT(bn); 
@@ -382,6 +385,8 @@ TEST(copy_weight_in_neurons){
     sprintf(msg," output copy %ld ",i);
     print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
   }
+
+  EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in.txt")
  
 
 
@@ -395,6 +400,73 @@ TEST(copy_weight_in_neurons){
 
 
 
+TEST(Extract_weight_in_neurons){
+   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 ;
+  neurons_TYPE_FLOAT *cpyn=NULL;
+  //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_networks_alloutputs_config_TYPE_FLOAT(&cpyn, 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);
+
+
+  size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
+  EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, bn, weight_in, ".ff_bn_weight_in__toExtract.txt")
+  
+  setup_all_layers_functions_TYPE_FLOAT(cpyn,
+    tensorContractnProdThread_TYPE_FLOAT,
+    tensorProdThread_TYPE_FLOAT,
+    DL,
+    L,
+    f,
+    df);
+
+  setup_all_layers_params_TYPE_FLOAT(cpyn, 5, 1 ,  0.1);
+
+  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);
+ 
+  char msg[256];
+  tensor_TYPE_FLOAT * linked_tens = NULL;
+  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]);
+ //   print_predict_by_network_with_error_neurons_TYPE_FLOAT(cpyn,ds->input[i],ds->target[i]);
+    calculate_output_by_network_neurons_TYPE_FLOAT(bn,ds->input[i],&linked_tens);
+    sprintf(msg," output base %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
+    calculate_output_by_network_neurons_TYPE_FLOAT(cpyn,ds->input[i],&linked_tens);
+    sprintf(msg," output copy %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
+  }
+
+ 
+  EXPORT_TO_FILE_TENSOR_ATTRIBUTE_IN_NNEURONS(TYPE_FLOAT, cpyn, weight_in, ".ff_bn_weight_in__exportedCPYfromExtract.txt")
+
+
+  free_data_set_TYPE_FLOAT(ds);
+  free_neurons_TYPE_FLOAT(bn); 
+  free_neurons_TYPE_FLOAT(cpyn); 
+
+  LOG("reps = %ld\n",reps);
+  randomizeInitWeight = rec_randomizeInitWeight;
+}
+
+
+