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f771f94 | 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 | from __future__ import annotations
import torch
from torch import nn
class ConditionalNeuralProcess(nn.Module):
def __init__(self, representation_dimensions: int = 64) -> None:
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(2, 64),
nn.ReLU(),
nn.Linear(64, representation_dimensions),
nn.ReLU(),
)
self.decoder = nn.Sequential(
nn.Linear(representation_dimensions + 1, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 2),
)
def forward(
self,
context_x: torch.Tensor,
context_y: torch.Tensor,
target_x: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
pairs = torch.cat([context_x, context_y], dim=2)
representation = self.encoder(pairs).mean(dim=1)
expanded = representation[:, None].expand(-1, target_x.shape[1], -1)
output = self.decoder(torch.cat([target_x, expanded], dim=2))
mean = output[..., :1]
standard_deviation = 0.03 + 0.97 * torch.nn.functional.softplus(
output[..., 1:]
)
return mean, standard_deviation
def parameter_count(model: nn.Module) -> int:
return sum(parameter.numel() for parameter in model.parameters())
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