added MNIST, changed cache and parser

1 files changed, 45 insertions, 0 deletions

diff --git a/examples/mnist/mnist.py b/examples/mnist/mnist.py
new file mode 100644
index 0000000..0a81878
--- /dev/null
+++ b/examples/mnist/mnist.py
@@ -0,0 +1,45 @@
+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.layers = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(28 * 28, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 10),
+        )
+    def forward(self, 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_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(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, 28, 28),), "mnist_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 28, 28),), "mnist_b.onnx")