Implemented find_lr

utils/plotting.py

@@ -30,3 +30,27 @@ def plot_loss(training_epoch_losses, validation_epoch_losses, plot_path):
     plt.grid(True)
     plt.savefig(plot_path)
     plt.close()
+
+def plot_lr_vs_loss(log_lrs, losses, plot_path):
+    """
+    Plots the learning rate vs loss.
+
+    Args:
+        log_lrs (list): List of log learning rates.
+        losses (list): List of losses.
+        plot_path (str): Path to save the plot.
+
+    Returns:
+        None
+    """
+    plt.figure(figsize=(10, 5))
+
+    plt.plot(log_lrs, losses, label="Learning Rate vs Loss", marker='o')
+
+    plt.title("Learning Rate vs Loss")
+    plt.xlabel("Log Learning Rate")
+    plt.ylabel("Loss")
+    plt.legend()
+    plt.grid(True)
+    plt.savefig(plot_path)
+    plt.close()

utils/training.py

@@ -0,0 +1,51 @@
+import math
+import torch
+
+def find_lr(model, train_loader, criterion, optimizer_class, optimizer_params, init_value=1e-8, final_value=10, beta=0.98, device=None):
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    num = len(train_loader) - 1
+    if num <= 0:
+        raise ValueError("The training loader must contain more than one batch to compute the learning rate range test.")
+
+    lr = init_value
+    optimizer_params['lr'] = lr
+    optimizer = optimizer_class(model.parameters(), **optimizer_params)
+
+    mult = (final_value / init_value) ** (1 / num)
+    avg_loss = 0.
+    best_loss = float('inf')
+    batch_num = 0
+    losses = []
+    log_lrs = []
+
+    for data in train_loader:
+        batch_num += 1
+        inputs, targets = data
+        inputs, targets = inputs.to(device), targets.to(device)
+
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+
+        avg_loss = beta * avg_loss + (1 - beta) * loss.item()
+        smoothed_loss = avg_loss / (1 - beta**batch_num)
+
+        if batch_num > 1 and smoothed_loss > 4 * best_loss:
+            break
+
+        if smoothed_loss < best_loss or batch_num == 1:
+            best_loss = smoothed_loss
+
+        losses.append(smoothed_loss)
+        log_lrs.append(math.log10(lr))
+
+        loss.backward()
+        optimizer.step()
+
+        lr *= mult
+        optimizer.param_groups[0]['lr'] = lr
+
+    return log_lrs, losses