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import torch, torch.nn as nn
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
from torchvision import transforms
class MNIST_MLP(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.flatten = nn.Flatten()
self.layers = nn.Sequential(
nn.Linear(784, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 10),
)
def forward(self, x):
x = self.flatten(x)
return self.layers(x)
class MNIST_Linear(nn.Module):
def __init__(self, weight, bias):
super().__init__()
self.flatten = nn.Flatten()
self.fc = nn.Linear(784, 10)
self.fc.weight.data = weight
self.fc.bias.data = bias
def forward(self, x):
x = self.flatten(x)
return self.fc(x)
train_dataset = MNIST('./', download=True, transform=transforms.ToTensor(), train=True)
trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
def train_model(name: str, dim):
net = MNIST_MLP(hidden_dim=dim)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.5e-4)
print(f"Training {name} ({dim} neurons)...")
for epoch in range(10):
global loss
for data in trainloader:
inputs, targets = data
optimizer.zero_grad()
outputs = net(inputs)
loss = loss_fn(outputs, targets)
loss.backward()
optimizer.step()
print(f" Epoch {epoch+1}, Loss: {loss.item():.4f}")
return net
if __name__ == "__main__":
torch_net_a = train_model("Base Network", 6).eval()
with torch.no_grad():
torch_net_a.layers[0].weight.fill_(0.01) # pyright: ignore
torch_net_a.layers[0].bias.fill_(5.0) # pyright: ignore
W1 = torch_net_a.layers[0].weight.data
b1 = torch_net_a.layers[0].bias.data
W2 = torch_net_a.layers[2].weight.data
b2 = torch_net_a.layers[2].bias.data
W_collapsed = torch.matmul(W2, W1) # pyright: ignore
b_collapsed = torch.matmul(W2, b1) + b2 # pyright: ignore
torch_net_b = MNIST_Linear(W_collapsed, b_collapsed).eval()
torch.onnx.export(torch_net_a, (torch.randn(1, 28, 28),), "mnist_a.onnx")
torch.onnx.export(torch_net_b, (torch.randn(1, 28, 28),), "mnist_b.onnx")
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