created class

1 files changed, 40 insertions, 0 deletions

diff --git a/xor/xor.py b/xor/xor.py
new file mode 100644
index 0000000..ebc5477
--- /dev/null
+++ b/xor/xor.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn as nn
+import torch.onnx
+
+class xor_mlp(nn.Module):
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(2, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, 1)
+        )
+    def forward(self, x):
+        return self.layers(x)
+
+def train_model(name: str, dim):
+    X = torch.tensor([[0,0], [0,1], [1,0], [1,1]], dtype=torch.float32)
+    Y = torch.tensor([[0], [1], [1], [0]], dtype=torch.float32)
+
+    net = xor_mlp(hidden_dim=dim)
+    loss_fn = nn.MSELoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=0.1)
+
+    print(f"Training {name}...")
+    for epoch in range(1000):
+        optimizer.zero_grad()
+        out = net(X)
+        loss = loss_fn(out, Y)
+        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", 8).eval()
+    torch_net_b = train_model("Network B", 16).eval()
+
+    torch.onnx.export(torch_net_a, (torch.randn(1, 2),), "xor_a.onnx")
+    torch.onnx.export(torch_net_b, (torch.randn(1, 2),), "xor_b.onnx")