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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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import matplotlib.pyplot as plt
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import os
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import seaborn as sns
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from sklearn.metrics import confusion_matrix
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plt.style.use('ggplot')
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def save_model(epochs, model, optimizer, criterion, model_path):
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"""
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Fungsi untuk menyimpan checkpoint model.
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"""
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print(f"Menyimpan model ke {model_path}")
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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torch.save({
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'epoch': epochs,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'loss': criterion,
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}, model_path)
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def save_plots(train_acc, valid_acc, train_loss, valid_loss, plot_path):
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"""
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Fungsi untuk menyimpan plot akurasi dan loss.
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"""
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print(f"Menyimpan plot ke {plot_path}")
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os.makedirs(os.path.dirname(plot_path), exist_ok=True)
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plt.figure(figsize=(10, 7))
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plt.plot(train_acc, color='green', linestyle='-', label='train accuracy')
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plt.plot(valid_acc, color='blue', linestyle='-', label='validation accuracy')
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plt.xlabel('Epochs')
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plt.ylabel('Accuracy')
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plt.legend()
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plt.savefig(f"{plot_path}/accuracy.png")
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plt.figure(figsize=(10, 7))
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plt.plot(train_loss, color='orange', linestyle='-', label='train loss')
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plt.plot(valid_loss, color='red', linestyle='-', label='validation loss')
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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plt.legend()
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plt.savefig(f"{plot_path}/loss.png")
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def save_confusion_matrix(y_true, y_pred, class_names, save_path):
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"""
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Menyimpan plot confusion matrix.
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"""
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cm = confusion_matrix(y_true, y_pred)
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plt.figure(figsize=(10, 8))
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sns.heatmap(
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cm,
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annot=True,
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fmt='d',
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cmap='Blues',
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xticklabels=class_names,
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yticklabels=class_names
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)
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plt.xlabel('Predicted Label')
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plt.ylabel('True Label')
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plt.title('Confusion Matrix of Best Validation Model')
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plt.savefig(save_path)
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print(f"Confusion matrix disimpan di {save_path}")
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class FocalLoss(nn.Module):
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"""
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Implementasi Focal Loss.
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"""
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def __init__(self, alpha=1, gamma=2, reduction='mean'):
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super(FocalLoss, self).__init__()
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self.alpha = alpha
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self.gamma = gamma
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self.reduction = reduction
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def forward(self, inputs, targets):
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ce_loss = F.cross_entropy(inputs, targets, reduction='none')
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pt = torch.exp(-ce_loss)
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focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss
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if self.reduction == 'mean':
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return focal_loss.mean()
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elif self.reduction == 'sum':
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return focal_loss.sum()
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else:
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return focal_loss |
