created class

1 files changed, 0 insertions, 51 deletions

diff --git a/xor.py b/xor.py
deleted file mode 100644
index 82a16b8..0000000
--- a/xor.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.onnx
-import nneq
-
-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()
-
-    onnx_net_a = torch.onnx.export(torch_net_a, (torch.randn(1, 2),), verbose=False).model_proto # type: ignore
-    onnx_net_b = torch.onnx.export(torch_net_b, (torch.randn(1, 2),), verbose=False).model_proto # type: ignore
-
-    z3_net_a = nneq.net(onnx_net_a)
-    z3_net_b = nneq.net(onnx_net_b)
-
-    print("")
-    nneq.strict_equivalence(z3_net_a, z3_net_b)
-    print("")
-    nneq.epsilon_equivalence(z3_net_a, z3_net_b, 0.1)
-    print("")
-    nneq.argmax_equivalence(z3_net_a, z3_net_b)