mnist / model.py
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import torch
import torch.nn as nn
# 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, 64)
self.fc3 = 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))
return self.fc3(x)