actually take advantadge of using namespace std;

examples/slm_basic_train_ex.cpp

@@ -154,7 +154,7 @@ int main(int argc, char** argv)
         // ----------------------------------------------------------------------------------------
         if (parser.option("train"))
         {
-            std::cout << "=== TRAIN MODE ===\n";
+            cout << "=== TRAIN MODE ===\n";
 
             // 1) Prepare training data (simple approach)
             // We will store characters from shakespeare_text into a vector
@@ -163,7 +163,7 @@ int main(int argc, char** argv)
             auto full_tokens = char_based_tokenize(shakespeare_text);
             if (full_tokens.empty())
             {
-                std::cerr << "ERROR: The Shakespeare text is empty. Please provide a valid training text.\n";
+                cerr << "ERROR: The Shakespeare text is empty. Please provide a valid training text.\n";
                 return 0;
             }
 
@@ -173,13 +173,13 @@ int main(int argc, char** argv)
                 : 0;
 
             // Display the size of the training text and the number of sequences
-            std::cout << "Training text size: " << full_tokens.size() << " characters\n";
-            std::cout << "Maximum number of sequences: " << max_sequences << "\n";
+            cout << "Training text size: " << full_tokens.size() << " characters\n";
+            cout << "Maximum number of sequences: " << max_sequences << "\n";
 
             // Check if the text is too short
             if (max_sequences == 0)
             {
-                std::cerr << "ERROR: The Shakespeare text is too short for training. It must contain at least "
+                cerr << "ERROR: The Shakespeare text is too short for training. It must contain at least "
                     << (max_seq_len + 1) << " characters.\n";
                 return 0;
             }
@@ -203,7 +203,7 @@ int main(int argc, char** argv)
             // Shuffle samples and labels if the --shuffle option is enabled
             if (parser.option("shuffle"))
             {
-                std::cout << "Shuffling training sequences and labels...\n";
+                cout << "Shuffling training sequences and labels...\n";
                 shuffle_samples_and_labels(samples, labels);
             }
 
@@ -232,41 +232,41 @@ int main(int argc, char** argv)
                 if (predicted[i] == labels[i])
                     correct++;
             double accuracy = (double)correct / labels.size();
-            std::cout << "Training accuracy (on this sample set): " << accuracy << "\n";
+            cout << "Training accuracy (on this sample set): " << accuracy << "\n";
 
             // 7) Save the model
             net.clean();
             serialize(model_file) << net;
-            std::cout << "Model saved to " << model_file << "\n";
+            cout << "Model saved to " << model_file << "\n";
         }
 
         // ----------------------------------------------------------------------------------------
         // Generate mode
         // ----------------------------------------------------------------------------------------
         if (parser.option("generate"))
         {
-            std::cout << "=== GENERATE MODE ===\n";
+            cout << "=== GENERATE MODE ===\n";
             // 1) Load the trained model
             using net_infer = my_transformer_cfg::network_type<false>;
             net_infer net;
             if (file_exists(model_file))
             {
                 deserialize(model_file) >> net;
-                std::cout << "Loaded model from " << model_file << "\n";
+                cout << "Loaded model from " << model_file << "\n";
             }
             else
             {
-                std::cerr << "Error: model file not found. Please run --train first.\n";
+                cerr << "Error: model file not found. Please run --train first.\n";
                 return 0;
             }
-            std::cout << my_transformer_cfg::model_info::describe() << std::endl;
-            std::cout << "Model parameters: " << count_parameters(net) << std::endl << std::endl;
+            cout << my_transformer_cfg::model_info::describe() << endl;
+            cout << "Model parameters: " << count_parameters(net) << endl << endl;
 
             // 2) Get the prompt from the included slm_data.h
             std::string prompt_text = shakespeare_prompt;
             if (prompt_text.empty())
             {
-                std::cerr << "No prompt found in slm_data.h.\n";
+                cerr << "No prompt found in slm_data.h.\n";
                 return 0;
             }
             // If prompt is longer than max_seq_len, we keep only the first window
@@ -287,7 +287,7 @@ int main(int argc, char** argv)
                     input_seq(i, 0) = PAD_TOKEN;
             }
 
-            std::cout << "\nInitial prompt:\n" << prompt_text << " (...)\n\n\nGenerated text:\n" << prompt_text;
+            cout << "\nInitial prompt:\n" << prompt_text << " (...)\n\n\nGenerated text:\n" << prompt_text;
 
             // 3) Generate new text
             // We'll predict one character at a time, then shift the window
@@ -296,22 +296,22 @@ int main(int argc, char** argv)
                 const int next_char = net(input_seq); // single inference
 
                 // Print the generated character
-                std::cout << static_cast<char>(std::min(next_char, MAX_TOKEN_ID)) << std::flush;
+                cout << static_cast<char>(std::min(next_char, MAX_TOKEN_ID)) << flush;
 
                 // Shift left by 1
                 for (long i = 0; i < max_seq_len - 1; ++i)
                     input_seq(i, 0) = input_seq(i + 1, 0);
                 input_seq(max_seq_len - 1, 0) = std::min(next_char, MAX_TOKEN_ID);
             }
 
-            std::cout << "\n\n(end of generation)\n";
+            cout << "\n\n(end of generation)\n";
         }
 
         return 0;
     }
-    catch (std::exception& e)
+    catch (exception& e)
     {
-        std::cerr << "Exception thrown: " << e.what() << std::endl;
+        cerr << "Exception thrown: " << e.what() << endl;
         return 1;
     }
 }