237 lines
7.8 KiB
Python
237 lines
7.8 KiB
Python
import os
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import polars as pl
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import Dataset, DataLoader, random_split
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from concurrent.futures import ProcessPoolExecutor
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from tqdm import tqdm
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# --- CONFIGURATION ---
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BASE_PATH = "asl_kaggle"
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TARGET_FRAMES = 22
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LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
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HANDS = list(range(468, 543))
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SELECTED_INDICES = LIPS + HANDS
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NUM_FEATS = len(SELECTED_INDICES) * 3
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# Training hyperparameters
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BATCH_SIZE = 32
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EPOCHS = 50
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LEARNING_RATE = 0.001
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TRAIN_SPLIT = 0.8
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CHECKPOINT_DIR = "checkpoints"
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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# --- DATA PROCESSING ---
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def load_kaggle_metadata(base_path):
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return pl.read_csv(os.path.join(base_path, "train.csv"))
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def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
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parquet_path = os.path.join(base_path, path)
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df = pl.read_parquet(parquet_path)
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anchors = (
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df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
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.select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
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)
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processed = (
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df.join(anchors, on="frame", how="left")
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.with_columns([
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(pl.col("x") - pl.col("nx")).fill_null(0.0),
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(pl.col("y") - pl.col("ny")).fill_null(0.0),
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(pl.col("z") - pl.col("nz")).fill_null(0.0),
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])
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.sort(["frame", "type", "landmark_index"])
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)
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raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
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reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
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curr_len = reduced_tensor.shape[0]
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indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
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return reduced_tensor[indices]
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# --- DATASET CLASS ---
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class ASLDataset(Dataset):
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def __init__(self, tensors, labels):
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self.tensors = tensors
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self.labels = labels
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def __len__(self):
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return len(self.tensors)
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def __getitem__(self, idx):
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return self.tensors[idx], self.labels[idx]
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# --- MODEL ARCHITECTURE ---
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class ASLClassifier(nn.Module):
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def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
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super().__init__()
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self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm1d(256)
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self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm1d(512)
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self.pool = nn.MaxPool1d(2)
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self.dropout = nn.Dropout(0.5)
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self.fc = nn.Linear(512, num_classes)
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def forward(self, x):
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x = x.view(x.shape[0], x.shape[1], -1)
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x = x.transpose(1, 2)
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x = F.relu(self.bn1(self.conv1(x)))
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x = self.pool(x)
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x = F.relu(self.bn2(self.conv2(x)))
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x = self.pool(x)
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x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
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x = self.dropout(x)
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return self.fc(x)
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# --- TRAINING FUNCTIONS ---
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def train_epoch(model, dataloader, criterion, optimizer, device):
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model.train()
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running_loss = 0.0
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correct = 0
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total = 0
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progress_bar = tqdm(dataloader, desc="Training")
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for inputs, labels in progress_bar:
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inputs, labels = inputs.to(device), labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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progress_bar.set_postfix({
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'loss': running_loss / (progress_bar.n + 1),
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'acc': 100 * correct / total
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})
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epoch_loss = running_loss / len(dataloader)
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epoch_acc = 100 * correct / total
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return epoch_loss, epoch_acc
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def validate(model, dataloader, criterion, device):
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model.eval()
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running_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for inputs, labels in tqdm(dataloader, desc="Validation"):
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inputs, labels = inputs.to(device), labels.to(device)
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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running_loss += loss.item()
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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val_loss = running_loss / len(dataloader)
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val_acc = 100 * correct / total
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return val_loss, val_acc
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def save_checkpoint(model, optimizer, epoch, train_loss, val_loss, val_acc, checkpoint_dir):
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os.makedirs(checkpoint_dir, exist_ok=True)
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checkpoint = {
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'epoch': epoch,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_loss,
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'val_loss': val_loss,
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'val_acc': val_acc,
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}
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path = os.path.join(checkpoint_dir, f'checkpoint_epoch_{epoch}.pt')
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torch.save(checkpoint, path)
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print(f"Checkpoint saved: {path}")
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# --- EXECUTION ---
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if __name__ == "__main__":
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# Load metadata
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asl_data = load_kaggle_metadata(BASE_PATH)
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# Create label mapping
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unique_signs = sorted(asl_data["sign"].unique().to_list())
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label_to_idx = {sign: idx for idx, sign in enumerate(unique_signs)}
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labels = torch.tensor([label_to_idx[sign] for sign in asl_data["sign"].to_list()])
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print(f"Number of classes: {len(unique_signs)}")
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# Process data in parallel
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paths = asl_data["path"].to_list()
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print(f"Processing {len(paths)} files in parallel...")
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with ProcessPoolExecutor() as executor:
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results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))
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dataset_tensor = torch.tensor(np.array(results), dtype=torch.float32)
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print(f"Final Tensor Shape: {dataset_tensor.shape}")
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# Create dataset and split
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full_dataset = ASLDataset(dataset_tensor, labels)
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train_size = int(TRAIN_SPLIT * len(full_dataset))
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val_size = len(full_dataset) - train_size
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train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
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train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
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val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
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print(f"Train samples: {train_size}, Validation samples: {val_size}")
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# Initialize model, loss, optimizer
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model = ASLClassifier(num_classes=len(unique_signs)).to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5)
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# Training loop
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best_val_acc = 0.0
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print("\n" + "=" * 50)
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print("Starting Training")
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print("=" * 50 + "\n")
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for epoch in range(EPOCHS):
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print(f"\nEpoch [{epoch + 1}/{EPOCHS}]")
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print("-" * 50)
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train_loss, train_acc = train_epoch(model, train_loader, criterion, optimizer, device)
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val_loss, val_acc = validate(model, val_loader, criterion, device)
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scheduler.step(val_loss)
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print(f"\nEpoch {epoch + 1} Summary:")
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print(f" Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}%")
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print(f" Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.2f}%")
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print(f" Learning Rate: {optimizer.param_groups[0]['lr']:.6f}")
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# Save checkpoint if validation accuracy improved
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if val_acc > best_val_acc:
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best_val_acc = val_acc
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save_checkpoint(model, optimizer, epoch + 1, train_loss, val_loss, val_acc, CHECKPOINT_DIR)
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print(f" ✓ New best validation accuracy: {best_val_acc:.2f}%")
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print("\n" + "=" * 50)
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print("Training Complete!")
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print(f"Best Validation Accuracy: {best_val_acc:.2f}%")
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print("=" * 50) |