diff --git a/src/learn/learn.cpp b/src/learn/learn.cpp
index 6257d920..c9313575 100644
--- a/src/learn/learn.cpp
+++ b/src/learn/learn.cpp
@@ -141,7 +141,7 @@ namespace Learner
             void print(const std::string& prefix, ostream& s) const
             {
                 s
-                    << "INFO: "
+                    << "--> "
                     << prefix << "_cross_entropy_eval = " << cross_entropy_eval / count
                     << " , " << prefix << "_cross_entropy_win = " << cross_entropy_win / count
                     << " , " << prefix << "_entropy_eval = " << entropy_eval / count
@@ -722,7 +722,7 @@ namespace Learner
 
         if (psv.size() && test_loss_sum.count > 0.0)
         {
-            cout << "INFO: norm = " << sum_norm
+            cout << "--> norm = " << sum_norm
                 << " , move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%"
                 << endl;
 
diff --git a/src/nnue/trainer/trainer_clipped_relu.h b/src/nnue/trainer/trainer_clipped_relu.h
index 35503493..d1dd738b 100644
--- a/src/nnue/trainer/trainer_clipped_relu.h
+++ b/src/nnue/trainer/trainer_clipped_relu.h
@@ -99,8 +99,13 @@ namespace Eval::NNUE {
             const auto smallest_max_activation = *std::min_element(
                 std::begin(max_activations_), std::end(max_activations_));
 
-            std::cout << "INFO: largest min activation = " << largest_min_activation
-                      << ", smallest max activation = " << smallest_max_activation
+            std::cout << "INFO (check_health):"
+                      << " layer = " << LayerType::kLayerIndex
+                      << " , name = " << LayerType::get_name()
+                      << std::endl;
+
+            std::cout << "--> largest min activation = " << largest_min_activation
+                      << " , smallest max activation = " << smallest_max_activation
                       << std::endl;
 
             std::fill(std::begin(min_activations_), std::end(min_activations_),
diff --git a/src/nnue/trainer/trainer_feature_transformer.h b/src/nnue/trainer/trainer_feature_transformer.h
index 2311fc0c..dbfe18a2 100644
--- a/src/nnue/trainer/trainer_feature_transformer.h
+++ b/src/nnue/trainer/trainer_feature_transformer.h
@@ -330,25 +330,32 @@ namespace Eval::NNUE {
 
         // Check if there are any problems with learning
         void check_health() {
-            std::cout << "INFO: observed " << observed_features.count()
-                      << " (out of " << kInputDimensions << ") features" << std::endl;
+            std::cout << "INFO (check_health):"
+                      << " layer = " << LayerType::kLayerIndex
+                      << " , name = " << LayerType::get_name()
+                      << std::endl;
+
+            std::cout << "--> observed " << observed_features.count()
+                      << " (out of " << kInputDimensions << ") features"
+                      << std::endl;
 
             constexpr LearnFloatType kPreActivationLimit =
                 std::numeric_limits<typename LayerType::WeightType>::max() /
                 kWeightScale;
 
-            std::cout << "INFO: (min, max) of pre-activations = "
+            std::cout << "--> (min, max) of pre-activations = "
                       << min_pre_activation_ << ", "
                       << max_pre_activation_ << " (limit = "
-                      << kPreActivationLimit << ")" << std::endl;
+                      << kPreActivationLimit << ")"
+                      << std::endl;
 
             const auto largest_min_activation = *std::max_element(
                 std::begin(min_activations_), std::end(min_activations_));
             const auto smallest_max_activation = *std::min_element(
                 std::begin(max_activations_), std::end(max_activations_));
 
-            std::cout << "INFO: largest min activation = " << largest_min_activation
-                      << ", smallest max activation = " << smallest_max_activation
+            std::cout << "--> largest min activation = " << largest_min_activation
+                      << " , smallest max activation = " << smallest_max_activation
                       << std::endl;
 
             std::fill(std::begin(min_activations_), std::end(min_activations_),