chore: model class

model.py

@@ -0,0 +1,29 @@
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)  # 32x32 -> 16x16
+        self.bn1 = nn.BatchNorm2d(32)
+        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)  # 16x16 -> 8x8
+        self.bn2 = nn.BatchNorm2d(64)
+        self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)  # 8x8 -> 4x4
+        self.bn3 = nn.BatchNorm2d(128)
+        self.pool = nn.MaxPool2d(stride=2, kernel_size=2)
+        self.fc1 = nn.Linear(128 * 4 * 4, 512)
+        self.fc2 = nn.Linear(512, 10)
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.bn1(self.conv1(x))))
+        x = self.pool(F.relu(self.bn2(self.conv2(x))))
+        x = self.pool(F.relu(self.bn3(self.conv3(x))))
+        x = x.view(x.size(0), -1)
+        x = self.dropout(x)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x