neural_network.py

import torch.nn as nn 
import torch.nn.functional as F

class MNISTNetwork(nn.Module): 
    
    def __init__(self): 
        super().__init__()
        self.layer1 = nn.Linear(784, 128)
        self.layer2 = nn.Linear(128, 64)
        self.layer3 = nn.Linear(64, 32)
        self.layer4 = nn.Linear(32, 10)
    
    def forward(self, x):
        x = F.relu(self.layer1(x))
        x = F.relu(self.layer2(x))
        x = F.relu(self.layer3(x))
        x = self.layer4(x)
        return F.log_softmax(x, dim=1)



# class MNISTNetwork(nn.Module):
#     def __init__(self):
#         super().__init__()
#         self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
#         self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
#         self.fc1 = nn.Linear(64*7*7, 1024)
#         self.fc2 = nn.Linear(1024, 10)

#     def forward(self, x):
#         x = nn.functional.relu(self.conv1(x))
#         x = nn.functional.max_pool2d(x, 2)
#         x = nn.functional.relu(self.conv2(x))
#         x = nn.functional.max_pool2d(x, 2)
#         x = x.view(-1, 64*7*7)
#         x = nn.functional.relu(self.fc1(x))
#         x = nn.functional.dropout(x, training=self.training)
#         x = self.fc2(x)
#         return nn.functional.log_softmax(x, dim=1)