1 files changed, 23 insertions, 7 deletions

diff --git a/training/lr.py b/training/lr.py
index 3cdf994..c765150 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -51,7 +51,7 @@ class LRFinder():
         num_train_batches: int = 1,
         num_val_batches: int = math.inf,
         smooth_f: float = 0.05,
-        diverge_th: int = 5
+        diverge_th: int = 5,
     ):
         best_loss = None
         best_acc = None
@@ -157,10 +157,6 @@ class LRFinder():
             # self.model.load_state_dict(self.model_state)
             # self.optimizer.load_state_dict(self.optimizer_state)
 
-            if loss > diverge_th * best_loss:
-                print("Stopping early, the loss has diverged")
-                break
-
         fig, ax_loss = plt.subplots()
         ax_acc = ax_loss.twinx()
 
@@ -186,14 +182,21 @@ class LRFinder():
             accs = accs[skip_start:-skip_end]
 
         try:
-            min_grad_idx = (np.gradient(np.array(losses))).argmin()
+            min_grad_idx = np.gradient(np.array(losses)).argmin()
+        except ValueError:
+            print(
+                "Failed to compute the gradients, there might not be enough points."
+            )
+
+        try:
+            max_val_idx = np.array(accs).argmax()
         except ValueError:
             print(
                 "Failed to compute the gradients, there might not be enough points."
             )
 
         if min_grad_idx is not None:
-            print("Suggested LR: {:.2E}".format(lrs[min_grad_idx]))
+            print("Suggested LR (loss): {:.2E}".format(lrs[min_grad_idx]))
             ax_loss.scatter(
                 lrs[min_grad_idx],
                 losses[min_grad_idx],
@@ -205,6 +208,19 @@ class LRFinder():
             )
             ax_loss.legend()
 
+        if max_val_idx is not None:
+            print("Suggested LR (acc): {:.2E}".format(lrs[max_val_idx]))
+            ax_acc.scatter(
+                lrs[max_val_idx],
+                accs[max_val_idx],
+                s=75,
+                marker="o",
+                color="blue",
+                zorder=3,
+                label="maximum",
+            )
+            ax_acc.legend()
+
 
 def get_exponential_schedule(optimizer, end_lr: float, num_epochs: int, last_epoch: int = -1):
     def lr_lambda(base_lr: float, current_epoch: int):