完成实验二前两个任务；修改gitignore屏蔽.pyc文件

Lab2/code/2.1.py

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+import time
+import numpy as np
+import torch
+from torch.nn.functional import one_hot, softmax
+from torch.utils.data import Dataset, DataLoader
+from torch import nn
+from torchvision import datasets, transforms
+from tqdm import tqdm
+from utils import *
+
+import ipdb
+
+
+class Model_2_1(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(in_features=500, out_features=1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.sigmoid(x)
+        return x
+    
+
+class My_Regression_Dataset(Dataset):
+    def __init__(self, train=True):
+        data_size = 7000 if train else 3000
+        np.random.seed(0)
+        x = np.random.random((data_size, 500)) * 0.005
+        noise = np.random.randn(data_size) * 1e-7
+        y = 0.028 - 0.0056 * x.sum(axis=1) + noise
+        y = y.reshape(-1, 1)
+        self.data = [[x[i], y[i]] for i in range(x.shape[0])]
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        x, y = self.data[index]
+        x = torch.FloatTensor(x)
+        y = torch.FloatTensor(y)
+        return x, y
+
+if __name__ == "__main__":
+    learning_rate = 5
+    num_epochs = 10
+    batch_size = 512
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+    train_regression_dataset = My_Regression_Dataset(train=True)
+    test_regression_dataset = My_Regression_Dataset(train=False)
+    train_dataloader = DataLoader(
+        dataset=train_regression_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=14,
+        pin_memory=True,
+    )
+    test_dataloader = DataLoader(
+        dataset=test_regression_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=14,
+        pin_memory=True,
+    )
+
+    model = Model_2_1().to(device)
+    criterion = nn.BCELoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+
+    for epoch in range(num_epochs):
+        model.train()
+        total_epoch_loss = 0
+        start_time = time.time()
+        for index, (x, targets) in tqdm(enumerate(train_dataloader), total=len(train_dataloader)):
+            optimizer.zero_grad()
+            
+            x = x.to(device)
+            targets = targets.to(device)
+            
+            y_pred = model(x)
+            
+            loss = criterion(y_pred, targets)
+            total_epoch_loss += loss.item()
+
+            loss.backward()
+            optimizer.step()
+
+        end_time = time.time()
+        train_time = end_time - start_time
+
+        model.eval()
+        with torch.no_grad():
+            total_epoch_acc = 0
+            start_time = time.time()
+            for index, (x, targets) in tqdm(enumerate(test_dataloader), total=len(test_dataloader)):
+                x = x.to(device)
+                targets = targets.to(device)
+                
+                y_pred = model(x)
+                total_epoch_acc += (1 - torch.abs(y_pred - targets) / torch.abs(targets)).sum().item()
+                
+            end_time = time.time()
+            test_time = end_time - start_time
+            
+        avg_epoch_acc = total_epoch_acc / len(test_regression_dataset)
+        print(
+            f"Epoch [{epoch + 1}/{num_epochs}],",
+            f"Train Loss: {total_epoch_loss},",
+            f"Used Time: {train_time * 1000:.3f}ms,",
+            f"Test Acc: {avg_epoch_acc * 100:.3f}%,",
+            f"Used Time: {test_time * 1000:.3f}ms",
+        )