Update app.py

app.py

@@ -28,26 +28,7 @@ XCnn_train, XCnn_test, y_train, y_test = train_test_split(XCnn, y, test_size=0.2
 
 from PIL import Image
 import torchvision.transforms as transforms
-class Cnn(nn.Module):
-    def __init__(self, dropout=0.5):
-        super(Cnn, self).__init__()
-        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
-        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
-        self.conv2_drop = nn.Dropout2d(p=dropout)
-        self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height
-        self.fc2 = nn.Linear(100, 10)
-        self.fc1_drop = nn.Dropout(p=dropout)
 
-    def forward(self, x):
-        x = torch.relu(F.max_pool2d(self.conv1(x), 2))
-        x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
-
-        # flatten over channel, height and width = 1600
-        x = x.view(-1, x.size(1) * x.size(2) * x.size(3))
-
-        x = torch.relu(self.fc1_drop(self.fc1(x)))
-        x = torch.softmax(self.fc2(x), dim=-1)
-        return x
 torch.manual_seed(0)