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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 |