Create model.py

model.py

@@ -0,0 +1,27 @@
+import torch.nn as nn
+
+class AlexNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(64, 192, kernel_size=3, padding=1), nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(0.5),
+            nn.Linear(256 * 4 * 4, 4096), nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(4096, 4096), nn.ReLU(),
+            nn.Linear(4096, 10),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        return self.classifier(x)