added LICENSE

1 files changed, 53 insertions, 0 deletions

diff --git a/examples/fashion_mnist/fashion_mnist.py b/examples/fashion_mnist/fashion_mnist.py
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+++ b/examples/fashion_mnist/fashion_mnist.py
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+import torch, torch.nn as nn
+from torchvision.datasets import FashionMNIST
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+class FashionMNIST_MLP(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(6 * 6, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 2)
+        )
+    def forward(self, x):
+        return self.layers(x)
+
+transform = transforms.Compose([
+    transforms.Resize((6, 6)),
+    transforms.ToTensor(),
+])
+
+train_dataset = FashionMNIST('./', download=True, transform=transform, train=True)
+tshirts_trousers = [id for id, data in enumerate(train_dataset.targets) if data.item() == 0 or data.item() == 1]
+train_dataset = torch.utils.data.Subset(train_dataset, tshirts_trousers)
+
+trainloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
+
+def train_model(name: str, dim):
+    net = FashionMNIST_MLP(hidden_dim=dim)
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)
+
+    print(f"Training {name}...")
+    for epoch in range(100):
+        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) % 10 == 0:
+            print(f"  Epoch {epoch+1}, Loss: {loss.item():.4f}")
+    return net
+
+if __name__ == "__main__":
+    torch_net_a = train_model("Network A", 6).eval()
+    torch_net_b = train_model("Network B", 12).eval()
+
+    torch.onnx.export(torch_net_a, (torch.randn(1, 1, 6, 6),), "fashion_mnist_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 1, 6, 6),), "fashion_mnist_b.onnx")