diff options
Diffstat (limited to 'xor.py')
| -rw-r--r-- | xor.py | 51 |
1 files changed, 0 insertions, 51 deletions
@@ -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) |