Lab 1 CIFAR10 initial done

lab1-pytorch-cifar10/alexnet.py

@@ -0,0 +1,39 @@
+import torch.nn as nn
+
+
+CIFAR10_NUM_CLASSES = 10
+
+
+class AlexNet(nn.Module):
+    def __init__(self, /, num_classes: int):
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2),
+            nn.Conv2d(64, 192, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(256 * 2 * 2, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Linear(4096, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), 256 * 2 * 2)
+        x = self.classifier(x)
+        return x