90% Accuracy Model

# ResNet style network
class MyModel(nn.Module):
    def __init__(self, outputNeurons=10):
        super(MyModel, self).__init__()

        self.conv1 = nn.Conv2d(1, 16, kernel_size=8,  padding=2)
        # Input : 1 Channel, Output 16 Channel, Filter Size : 8x8

        self.conv2 = nn.Conv2d(16, 32, kernel_size=5,  padding=1)
        # Input : 1 Channel, Output 16 Channel, Filter Size : 5x5

        self.conv3 = nn.Conv2d(32, 64, kernel_size=3,  padding=1)
        # Input : 1 Channel, Output 64 Channel, Filter Size : 3x3

        self.conv4 = nn.Conv2d(64, 128, kernel_size=3,  padding=1)
        # Input : 1 Channel, Output 64 Channel, Filter Size : 3x3

        self.fc = nn.Linear(128, outputNeurons)
        self.maxpool= nn.MaxPool2d(kernel_size=2, stride=2)
        self.relu = nn.ReLU()

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu(out)
        out = self.maxpool(out)

        out = self.conv2(out)
        out = self.relu(out)
        out = self.maxpool(out)

        out = self.conv3(out)
        out = self.relu(out)
        out = self.maxpool(out)

        out = self.conv4(out)
        out = self.relu(out)
        out = self.maxpool(out)

        out = out.reshape(out.size(0), -1)
        out = self.fc(out)

        return out

    def __len__(self):
        return sum([x.numel() for x in self.parameters()])

testInstance = MyModel()

if len(testInstance) > 150000:
    print(f"Invalid Network, {len(testInstance)} Parameters!")
else:
    print(f"Valid Network, {len(testInstance)} Parameters!")