I’m trying to build a model to classify MNIST.
I saw this website: https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist
And tried to copy what they did, besides the fact I only want 1 CNN and not 15.
But the results I get have very different accuracy, so I got to be missing something.
Here is my code:
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x)
Can you please help me convert?