完成实验三

Lab3/code/1.2.py

@@ -1,37 +1,14 @@
-import time
 import numpy as np
 import torch
-from torch.nn.functional import *
-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 MNIST_CLS_Model(nn.Module):
-    def __init__(self, num_classes, dropout_rate=0.5):
-        super().__init__()
-        self.flatten = nn.Flatten()
-        self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
-        self.fc2 = nn.Linear(in_features=1024, out_features=num_classes)
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward(self, x: torch.Tensor):
-        x = self.flatten(x)
-        x = torch.relu(self.fc1(x))
-        x = self.dropout(x)
-        x = self.fc2(x)
-        return x
 
 if __name__ == "__main__":
     learning_rate = 8e-2
-    num_epochs = 10
+    num_epochs = 101
     for i in np.arange(3):
         dropout_rate = 0.1 + 0.4 * i
         model = MNIST_CLS_Model(num_classes=10, dropout_rate=dropout_rate)
         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
         print(f"dropout_rate={dropout_rate}")
-        train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)
+        train_loss, test_acc = train_MNIST_CLS(model, optimizer, num_epochs=num_epochs)