Create 4.py

14/4.py

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+import numpy as np
+import torch
+import torch.nn.functional as F
+from matplotlib import pyplot as plt
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+# Super parameters ------------------------------------------------------------------------------------
+batch_size = 64
+learning_rate = 0.01
+momentum = 0.5
+EPOCH = 10
+
+# Prepare dataset ------------------------------------------------------------------------------------
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+)
+
+train_dataset = datasets.MNIST(
+    root="./data/mnist", train=True, download=True, transform=transform
+)
+test_dataset = datasets.MNIST(
+    root="./data/mnist", train=False, download=True, transform=transform
+)
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+
+# Design model using class ------------------------------------------------------------------------------
+class Net(torch.nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = torch.nn.Linear(
+            28 * 28, 512
+        )  # Flatten 28x28 input to a vector of 512 units
+        self.fc2 = torch.nn.Linear(512, 128)  # First hidden layer
+        self.dropout = torch.nn.Dropout(p=0.5)  # 添加 Dropout 层，丢弃概率为 0.5
+        self.fc3 = torch.nn.Linear(
+            128, 10
+        )  # Output layer with 10 units (one for each digit)
+
+    def forward(self, x):
+        x = x.view(-1, 28 * 28)  # Flatten the image
+        x = self.fc1(x)  # the first linear layer
+        x = F.relu(x)  # Activation
+        x = self.fc2(x)  # the second linear layer
+        x = F.relu(x)  # Activation
+        x = self.dropout(x)  # 在最后一个全连接层之前应用 Dropout
+        x = self.fc3(x)  # No activation on the final output layer (for classification)
+        return x
+
+
+model = Net()
+
+# Construct loss and optimizer ----------------------------------------------------------------------
+criterion = (
+    torch.nn.CrossEntropyLoss()
+)  # CrossEntropy loss for multi-class classification
+optimizer = torch.optim.SGD(
+    model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=0.01
+)  # SGD optimizer
+
+
+# Train and Test CLASS -----------------------------------------------------------------------------------
+def train(epoch):
+    running_loss = 0.0
+    running_total = 0
+    running_correct = 0
+    for batch_idx, data in enumerate(train_loader):
+        inputs, target = data
+        optimizer.zero_grad()
+
+        # forward + backward + update
+        outputs = model(inputs)
+        loss = criterion(outputs, target)
+
+        loss.backward()
+        optimizer.step()
+
+        # Accumulate loss and accuracy
+        running_loss += loss.item()
+        _, predicted = torch.max(outputs.data, dim=1)
+        running_total += target.shape[0]
+        running_correct += (predicted == target).sum().item()
+
+    # Print after each epoch
+    avg_loss = running_loss / len(train_loader)
+    avg_acc = 100 * running_correct / running_total
+    print(
+        "[%d / %d]: Training Loss: %.3f, Training Accuracy: %.2f %%"
+        % (epoch + 1, EPOCH, avg_loss, avg_acc)
+    )
+
+
+# ... existing code ...
+
+
+def add_noise(images, noise_level=0.5):
+    """
+    添加噪声函数
+    参数:
+        images: 输入数据 batch (torch.Tensor)
+        noise_level: 噪声强度
+    返回:
+        添加噪声后的数据
+    """
+    noise = noise_level * torch.randn_like(images)  # 生成随机噪声
+    noisy_images = images + noise  # 将噪声添加到数据中
+    noisy_images = torch.clamp(noisy_images, 0.0, 1.0)  # 限制值范围在 [0, 1]
+    return noisy_images
+
+
+def test(epoch, noise_level=0.2):
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for data in test_loader:
+            images, labels = data
+            # 添加噪声到测试数据
+            noisy_images = add_noise(images, noise_level=noise_level)
+            outputs = model(noisy_images)
+            _, predicted = torch.max(outputs.data, dim=1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+    acc = correct / total
+    print(
+        "[%d / %d]: Accuracy on test set with noise level %.2f after epoch %d: %.1f %%"
+        % (epoch + 1, EPOCH, noise_level, epoch + 1, 100 * acc)
+    )
+    return 100 * acc
+
+
+# Add EarlyStopping class after imports
+class EarlyStopping:
+    def __init__(self, patience=3, min_delta=0):
+        self.patience = patience
+        self.min_delta = min_delta
+        self.counter = 0
+        self.best_acc = None
+        self.should_stop = False
+
+    def __call__(self, acc):
+        if self.best_acc is None:
+            self.best_acc = acc
+        elif acc <= self.best_acc + self.min_delta:
+            self.counter += 1
+            if self.counter >= self.patience:
+                self.should_stop = True
+        else:
+            self.best_acc = acc
+            self.counter = 0
+
+
+if __name__ == "__main__":
+    acc_list_test_noisy = []
+    early_stopping = EarlyStopping(patience=3)
+
+    for epoch in range(EPOCH):
+        train(epoch)
+        acc_test_noisy = test(epoch, noise_level=0.8)
+        acc_list_test_noisy.append(acc_test_noisy)
+
+        # Early stopping check
+        early_stopping(acc_test_noisy)
+        if early_stopping.should_stop:
+            print(f"Early stopping triggered at epoch {epoch + 1}")
+            break
+
+    plt.plot(range(1, len(acc_list_test_noisy) + 1), acc_list_test_noisy)
+    plt.xlabel("Epoch")
+    plt.ylabel("Accuracy On Noisy Test Set")
+    plt.show()