Add model for 3x3 sample size

src/models.py

@@ -29,6 +29,35 @@ class singlePhotonNet_250909(nn.Module):
         x = self.fc(x)
         return x
 
+class singlePhotonNet_251020(nn.Module):
+    '''
+    Smaller input size (3x3)
+    '''
+    def weight_init(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.constant_(m.bias, 0)
+
+    def __init__(self):
+        super(singlePhotonNet_251020, self).__init__()
+        self.conv1 = nn.Conv2d(1, 5, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(5, 10, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(10, 20, kernel_size=3, padding=1)
+        self.fc = nn.Linear(20*3*3, 2)
+    
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
 class doublePhotonNet_250909(nn.Module):
     def __init__(self):
         super(doublePhotonNet_250909, self).__init__()