import torch
import torch.nn as nn
import torch.nn.functional as F
# Define the model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)  # MNIST images are 28x28
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 10)  # There are 10 classes (0 through 9)

    def forward(self, x):
        x = x.view(x.shape[0], -1)  # Flatten the input
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.relu(self.fc3(x))
        return self.fc4(x)

class NetConv(nn.Module):
    def __init__(self):
        super(NetConv, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.fc1 = nn.Linear(64 * 5 * 5, 128) # Corrected
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features