完成实验2

Lab2/code/4.py

@@ -0,0 +1,102 @@
+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_4_1(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(in_features=28 * 28, out_features=512)
+        self.fc2 = nn.Linear(in_features=512, out_features=num_classes)
+        self.activate_fn = leaky_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)
+        return x
+    
+
+class Model_4_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=num_classes)
+        self.activate_fn = leaky_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)
+        return x
+    
+
+class Model_4_3(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(in_features=28 * 28, out_features=512)
+        self.fc2 = nn.Linear(in_features=512, out_features=512)
+        self.fc3 = nn.Linear(in_features=512, out_features=num_classes)
+        self.activate_fn = leaky_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_4_4(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=1024)
+        self.fc3 = nn.Linear(in_features=1024, out_features=num_classes)
+        self.activate_fn = leaky_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
+
+
+if __name__ == "__main__":
+    print("模型1开始训练，hidden_size=512，hidden_layer=1 ：")
+    train_loss_4_1, test_acc_4_1 = train_MNIST_CLS(Model=Model_4_1) # hidden_size=512, hidden_layer=1
+    print("模型2开始训练，hidden_size=1024，hidden_layer=1 ：")
+    train_loss_4_2, test_acc_4_2 = train_MNIST_CLS(Model=Model_4_2) # hidden_size=1024, hidden_layer=1
+    print("模型3开始训练，hidden_size=512，hidden_layer=2 ：")
+    train_loss_4_3, test_acc_4_3 = train_MNIST_CLS(Model=Model_4_3) # hidden_size=512, hidden_layer=2
+    print("模型4开始训练，hidden_size=1024，hidden_layer=2 ：")
+    train_loss_4_4, test_acc_4_4 = train_MNIST_CLS(Model=Model_4_4) # hidden_size=1024, hidden_layer=2
+