实验2任务3code完成

Lab2/code/3.py

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+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 Model_3_1(nn.Module):
+    def __init__(self, num_classes):
+        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=256)
+        self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
+        self.activate_fn = relu
+
+    def forward(self, x: torch.Tensor):
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.activate_fn(x)
+
+        x = self.fc2(x)
+        x = self.activate_fn(x)
+
+        x = self.fc3(x)
+        x = self.activate_fn(x)
+        return x
+    
+
+class Model_3_2(nn.Module):
+    def __init__(self, num_classes):
+        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=256)
+        self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
+        self.activate_fn = sigmoid
+
+    def forward(self, x: torch.Tensor):
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.activate_fn(x)
+
+        x = self.fc2(x)
+        x = self.activate_fn(x)
+
+        x = self.fc3(x)
+        x = self.activate_fn(x)
+        return x
+    
+
+class Model_3_3(nn.Module):
+    def __init__(self, num_classes):
+        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=256)
+        self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
+        self.activate_fn = tanh
+
+    def forward(self, x: torch.Tensor):
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.activate_fn(x)
+
+        x = self.fc2(x)
+        x = self.activate_fn(x)
+
+        x = self.fc3(x)
+        x = self.activate_fn(x)
+        return x
+
+
+if __name__ == "__main__":
+    train_MNIST_CLS(Model=Model_3_1)
+    train_MNIST_CLS(Model=Model_3_2)
+    train_MNIST_CLS(Model=Model_3_3)