Removed useless comments

Author: jaywyawhare

include/Layers/dense.h

@@ -22,13 +22,12 @@ typedef struct
     int input_size;
     int output_size;
 
-    // Add members for optimizers
-    float *rmsprop_cache_w; // Cache for RMSProp weights
-    float *rmsprop_cache_b; // Cache for RMSProp biases
-    float *adam_v_w;        // Adam first moment vector for weights
-    float *adam_v_b;        // Adam first moment vector for biases
-    float *adam_s_w;        // Adam second moment vector for weights
-    float *adam_s_b;        // Adam second moment vector for biases
+    float *rmsprop_cache_w;
+    float *rmsprop_cache_b;
+    float *adam_v_w;
+    float *adam_v_b;
+    float *adam_s_w;
+    float *adam_s_b;
 } DenseLayer;
 
 /**

include/Metrics/r2_score.h

@@ -11,4 +11,4 @@
  */
 float r2_score(const float *y_true, const float *y_pred, int size);
 
-#endif // R2_SCORE_H
+#endif 

src/Layers/dense.c

@@ -7,8 +7,6 @@
 #include "../../include/Core/memory_management.h"
 #include "../../include/Core/logging.h"
 
-
-
 /**
  * @brief Initializes a Dense Layer with random weights and biases.
  *
@@ -30,17 +28,14 @@ int initialize_dense(DenseLayer *layer, int input_size, int output_size)
         LOG_ERROR("Invalid input size (%d) or output size (%d)", input_size, output_size);
         return CM_INVALID_PARAMETER_ERROR;
     }
-    
-    // initialize struct members to NULL
-    // cm_safe_free can be called - even inadvertently - without crashing
+
     layer->weights = NULL;
     layer->biases = NULL;
     layer->adam_v_w = NULL;
     layer->adam_v_b = NULL;
     layer->adam_s_w = NULL;
     layer->adam_s_b = NULL;
 
-    // if we don't see this Log message, we had a prolem zero-ing out memory
     LOG_DEBUG("Initialized DenseLayer with input size (%d) and output size (%d)", input_size, output_size);
 
     layer->input_size = input_size;
@@ -66,7 +61,6 @@ int initialize_dense(DenseLayer *layer, int input_size, int output_size)
         layer->biases[i] = ((float)rand() / RAND_MAX) - 0.5;
     }
 
-    // Allocate and initialize Adam optimizer's moment vectors
     layer->adam_v_w = (float *)cm_safe_malloc(input_size * output_size * sizeof(float), __FILE__, __LINE__);
     layer->adam_v_b = (float *)cm_safe_malloc(output_size * sizeof(float), __FILE__, __LINE__);
     layer->adam_s_w = (float *)cm_safe_malloc(input_size * output_size * sizeof(float), __FILE__, __LINE__);
@@ -148,9 +142,7 @@ int backward_dense(DenseLayer *layer, float *input, float *output, float *d_outp
         d_input[i] = 0;
         for (int j = 0; j < layer->output_size; j++)
         {
-            // Breakpoint condition: Check for potential out-of-bounds access
-            // For GDB:
-            // break dense.c:108 if (i + j * layer->input_size) >= (layer->input_size * layer->output_size)
+
             d_input[i] += d_output[j] * layer->weights[i + j * layer->input_size];
         }
     }

test/Core/test_logging.c

@@ -5,48 +5,44 @@
 
 /**
  * @brief Test the logging system
- * 
+ *
  * @return int 0 if all tests pass, non-zero otherwise
  */
-int main() {
+int main()
+{
     printf("Running logging tests...\n");
-    
-    // Test setting log level
+
     set_log_level(LOG_LEVEL_DEBUG);
-    
-    // Test different log levels
+
     LOG_DEBUG("This is a debug message: %d", 42);
     LOG_INFO("This is an info message: %s", "hello");
     LOG_WARNING("This is a warning message: %.2f", 3.14);
     LOG_ERROR("This is an error message: %c", 'X');
-    
-    // Test changing log level
+
     printf("\nChanging log level to INFO (DEBUG messages should not appear):\n");
     set_log_level(LOG_LEVEL_INFO);
-    
+
     LOG_DEBUG("This debug message should NOT appear");
     LOG_INFO("This info message should appear");
     LOG_WARNING("This warning message should appear");
     LOG_ERROR("This error message should appear");
-    
-    // Test changing log level to WARNING
+
     printf("\nChanging log level to WARNING (DEBUG and INFO messages should not appear):\n");
     set_log_level(LOG_LEVEL_WARNING);
-    
+
     LOG_DEBUG("This debug message should NOT appear");
     LOG_INFO("This info message should NOT appear");
     LOG_WARNING("This warning message should appear");
     LOG_ERROR("This error message should appear");
-    
-    // Test changing log level to ERROR
+
     printf("\nChanging log level to ERROR (only ERROR messages should appear):\n");
     set_log_level(LOG_LEVEL_ERROR);
-    
+
     LOG_DEBUG("This debug message should NOT appear");
     LOG_INFO("This info message should NOT appear");
     LOG_WARNING("This warning message should NOT appear");
     LOG_ERROR("This error message should appear");
-    
+
     printf("\nLogging tests completed successfully!\n");
     return 0;
 }

test/Core/test_training.c

@@ -6,75 +6,65 @@
 #include "../../include/Core/error_codes.h"
 #include "../../include/Core/logging.h"
 
-void test_large_layer_sizes() {
+void test_large_layer_sizes()
+{
     set_log_level(LOG_LEVEL_DEBUG);
-    // Create a network with very large layer sizes
+
     NeuralNetwork *network = create_neural_network(1000);
-    
-    // Add layers with increasing sizes to test buffer allocation
+
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 1000, 2000, 0.0f, 0, 0);
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 2000, 500, 0.0f, 0, 0);
-    
-    // Test forward pass with large input
+
     float *input = (float *)cm_safe_malloc(1000 * sizeof(float), __FILE__, __LINE__);
     float *output = (float *)cm_safe_malloc(500 * sizeof(float), __FILE__, __LINE__);
-    
-    // Fill input with test data
-    for (int i = 0; i < 1000; i++) input[i] = 0.1f;
-    
-    // This would segfault without the bug fixes
+
+    for (int i = 0; i < 1000; i++)
+        input[i] = 0.1f;
+
     CM_Error error = forward_pass(network, input, output, 1000, 500, 0);
-    
-    // Assert no error
+
     assert(error == CM_SUCCESS);
-    
-    // Clean up
+
     free_neural_network(network);
     cm_safe_free((void **)&input);
     cm_safe_free((void **)&output);
 }
 
-void test_mismatched_layer_sizes() {
-    // Create network with input size 10
+void test_mismatched_layer_sizes()
+{
+
     NeuralNetwork *network = create_neural_network(10);
-    
-    // Add layers with different input/output sizes
+
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 10, 20, 0.0f, 0, 0);
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 20, 5, 0.0f, 0, 0);
-    
-    // Try to run forward pass with input size 15 (mismatch)
+
     float input[15] = {0};
     float output[5] = {0};
-    
-    // This should return an error code, not segfault
+
     CM_Error error = forward_pass(network, input, output, 15, 5, 0);
-    
-    // Assert proper error handling
+
     assert(error == CM_INVALID_LAYER_DIMENSIONS_ERROR);
-    
+
     free_neural_network(network);
 }
 
-void test_optimizer_memory() {
-    // Create network
+void test_optimizer_memory()
+{
+
     NeuralNetwork *network = create_neural_network(10);
-    
-    // Add layers
+
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 10, 100, 0.0f, 0, 0);
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 100, 50, 0.0f, 0, 0);
-    
-    // Build with Adam optimizer (requires memory allocation)
+
     CM_Error error = build_network(network, OPTIMIZER_ADAM, 0.01f, LOSS_MSE, 0.0f, 0.0f);
     assert(error == CM_SUCCESS);
-    
-    // Initialize optimizer params (would segfault without your fixes)
+
     error = initialize_optimizer_params(network);
     assert(error == CM_SUCCESS);
-    
+
     free_neural_network(network);
 }
 
-
 int main()
 {
     printf("Testing Neural Network Training\n");