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01e81ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | from __future__ import annotations
import torch
from torch import nn
def mlp(outputs: int) -> nn.Sequential:
return nn.Sequential(
nn.Linear(2, 64),
nn.Tanh(),
nn.Linear(64, 64),
nn.Tanh(),
nn.Linear(64, outputs),
)
class HamiltonianNetwork(nn.Module):
def __init__(self) -> None:
super().__init__()
self.energy = mlp(1)
def forward(
self, states: torch.Tensor, *, create_graph: bool = True
) -> torch.Tensor:
if not states.requires_grad:
states = states.requires_grad_(True)
hamiltonian = self.energy(states).sum()
gradient = torch.autograd.grad(
hamiltonian,
states,
create_graph=create_graph,
)[0]
return torch.stack([gradient[:, 1], -gradient[:, 0]], dim=1)
class VectorFieldNetwork(nn.Module):
def __init__(self) -> None:
super().__init__()
self.network = mlp(2)
def forward(self, states: torch.Tensor) -> torch.Tensor:
return self.network(states)
def parameter_count(module: nn.Module) -> int:
return sum(parameter.numel() for parameter in module.parameters())
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