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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 Deep_MNIST_Block(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 1)
self.fc2 = nn.Linear(1, 10, bias=False)
def forward(self, x_s):
x, s = x_s
z = torch.relu(self.fc1(x))
ds = self.fc2(z)
return (x, s + ds)
class Deep_MNIST_MLP(nn.Module):
def __init__(self, num_blocks):
super().__init__()
self.flatten = nn.Flatten()
self.blocks = nn.Sequential(*[Deep_MNIST_Block() for _ in range(num_blocks)])
self.final_bias = nn.Parameter(torch.zeros(10))
def forward(self, x):
x = self.flatten(x)
s = torch.zeros(x.shape[0], 10, device=x.device)
_, final_s = self.blocks((x, s))
return final_s + self.final_bias
class Wide_MNIST_MLP(nn.Module):
def __init__(self, deep_net):
super().__init__()
self.flatten = nn.Flatten()
num_neurons = len(deep_net.blocks)
self.layers = nn.Sequential(
nn.Linear(784, num_neurons),
nn.ReLU(),
nn.Linear(num_neurons, 10),
)
with torch.no_grad():
w1_all = []
b1_all = []
w2_all = []
for block in deep_net.blocks:
w1_all.append(block.fc1.weight.data)
b1_all.append(block.fc1.bias.data)
w2_all.append(block.fc2.weight.data)
self.layers[0].weight.copy_(torch.cat(w1_all, dim=0)) # pyright: ignore
self.layers[0].bias.copy_(torch.cat(b1_all, dim=0)) # pyright: ignore
self.layers[2].weight.copy_(torch.cat(w2_all, dim=1)) # pyright: ignore
self.layers[2].bias.copy_(deep_net.final_bias.data) # pyright: ignore
def forward(self, x):
x = self.flatten(x)
return self.layers(x)
train_dataset = MNIST('./', download=True, transform=transforms.ToTensor(), train=True)
trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
def train_deep_model(name: str, num_blocks):
net = Deep_MNIST_MLP(num_blocks=num_blocks)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)
print(f"Training {name} ({num_blocks} blocks)...")
for epoch in range(10):
global loss
for inputs, targets in trainloader:
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_deep_model("Deep Network", 8).eval()
torch_net_b = Wide_MNIST_MLP(torch_net_a).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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