diff --git a/sage/trainer/utils.py b/sage/trainer/utils.py
index 32b4471..6104e59 100644
--- a/sage/trainer/utils.py
+++ b/sage/trainer/utils.py
@@ -162,10 +162,12 @@ def _get_norm_cf_reg(preds, target, root_dir, run_name) -> None:
     fig, ax = plt.subplots(figsize=(13, 6), ncols=2)
     labelsize = "large"
     titlesize = "xx-large"
+    cmap = "Blues"
 
     # Normalized Confusion Matrix
     cf = confusion_matrix(_preds, _target)
-    sns.heatmap(cf, annot=True, fmt="d", xticklabels=_target.categories, ax=ax[0])
+    sns.heatmap(cf, annot=True, fmt="d",
+                xticklabels=_target.categories, cmap=cmap, ax=ax[0])
     ax[0].set_xlabel("Target", size=labelsize)
     ax[0].set_yticklabels(_preds.categories, rotation=270)
     ax[0].set_ylabel("Prediction", size=labelsize)
@@ -173,7 +175,8 @@ def _get_norm_cf_reg(preds, target, root_dir, run_name) -> None:
     # 0-1 row-wise Noramlized Confusion.
     norm_cf = (cf.T / cf.sum(axis=1)).T
     norm_cf = np.nan_to_num(x=norm_cf, nan=0.0)
-    sns.heatmap(norm_cf, annot=True, fmt="0.2f", xticklabels=_target.categories, ax=ax[1])
+    sns.heatmap(norm_cf, annot=True, fmt="0.2f",
+                xticklabels=_target.categories, cmap=cmap, ax=ax[1])
     ax[1].set_xlabel("Target", size=labelsize)
     ax[1].set_yticklabels(_preds.categories, rotation=270)
     ax[1].set_ylabel("Prediction", size=labelsize)