Upload model.py

model.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+import math
+
+class CNN(nn.Module):
+    def __init__(self, num_channel=1, num_classes=10, num_pixel=28):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            num_channel, 32, kernel_size=5, padding=0, stride=1, bias=True
+        )
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=0, stride=1, bias=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=(2, 2))
+        self.act = nn.ReLU(inplace=True)
+
+        ###
+        ### X_out = floor{ 1 + (X_in + 2*padding - dilation*(kernel_size-1) - 1)/stride }
+        ###
+        X = num_pixel
+        X = math.floor(1 + (X + 2 * 0 - 1 * (5 - 1) - 1) / 1)
+        X = X / 2
+        X = math.floor(1 + (X + 2 * 0 - 1 * (5 - 1) - 1) / 1)
+        X = X / 2
+        X = int(X)
+
+        self.fc1 = nn.Linear(64 * X * X, 512)
+        self.fc2 = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        x = self.act(self.conv1(x))
+        x = self.maxpool(x)
+        x = self.act(self.conv2(x))
+        x = self.maxpool(x)
+        x = torch.flatten(x, 1)
+        x = self.act(self.fc1(x))
+        x = self.fc2(x)
+        return x
+        
+def get_model():
+    return CNN
+
+