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import torch
import torch.nn as nn
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader, TensorDataset
class Iris_MLP(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(4, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 3),
)
def forward(self, x):
return self.layers(x)
iris = load_iris()
scaler = StandardScaler()
X = scaler.fit_transform(iris.data).astype('float32') # pyright: ignore
y = iris.target.astype('int64') # pyright: ignore
dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
trainloader = DataLoader(dataset, batch_size=16, shuffle=True)
def train_model(name: str, dim):
net = Iris_MLP(hidden_dim=dim)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
print(f"Training {name} ({dim} neurons)...")
for epoch in range(200):
global loss
for data in trainloader:
inputs, targets = data
optimizer.zero_grad()
outputs = net(inputs)
loss = loss_fn(outputs, targets)
loss.backward()
optimizer.step()
if (epoch + 1) % 100 == 0:
print(f" Epoch {epoch+1}, Loss: {loss.item():.4f}")
return net
if __name__ == "__main__":
torch_net_a = train_model("Network A", 10).eval()
torch_net_b = Iris_MLP(hidden_dim=20).eval()
with torch.no_grad():
torch_net_b.layers[0].weight[:10].copy_(torch_net_a.layers[0].weight) # pyright: ignore
torch_net_b.layers[0].bias[:10].copy_(torch_net_a.layers[0].bias) # pyright: ignore
torch_net_b.layers[0].weight[10:].copy_(torch_net_a.layers[0].weight) # pyright: ignore
torch_net_b.layers[0].bias[10:].copy_(torch_net_a.layers[0].bias) # pyright: ignore
half_weights = torch_net_a.layers[2].weight / 2.0 # pyright: ignore
torch_net_b.layers[2].weight[:, :10].copy_(half_weights) # pyright: ignore
torch_net_b.layers[2].weight[:, 10:].copy_(half_weights) # pyright: ignore
torch_net_b.layers[2].bias.copy_(torch_net_a.layers[2].bias) # pyright: ignore
torch.onnx.export(torch_net_a, (torch.randn(1, 4),), "iris_a.onnx")
torch.onnx.export(torch_net_b, (torch.randn(1, 4),), "iris_b.onnx")
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