| from __future__ import annotations |
|
|
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
|
|
|
|
| class LinearSplineLayer(nn.Module): |
| def __init__( |
| self, |
| input_dimensions: int, |
| output_dimensions: int, |
| grid_points: int, |
| ) -> None: |
| super().__init__() |
| centers = torch.linspace(-1, 1, grid_points) |
| self.register_buffer("centers", centers) |
| self.spacing = float(centers[1] - centers[0]) |
| self.coefficients = nn.Parameter( |
| torch.randn(output_dimensions, input_dimensions, grid_points) * 0.05 |
| ) |
| self.base_weight = nn.Parameter( |
| torch.randn(output_dimensions, input_dimensions) * 0.1 |
| ) |
| self.bias = nn.Parameter(torch.zeros(output_dimensions)) |
|
|
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: |
| distance = torch.abs(inputs[:, :, None] - self.centers) |
| basis = F.relu(1 - distance / self.spacing) |
| spline = torch.einsum("big,oig->bo", basis, self.coefficients) |
| return spline + F.linear(inputs, self.base_weight, self.bias) |
|
|
|
|
| class SplineKAN(nn.Module): |
| def __init__(self) -> None: |
| super().__init__() |
| self.first = LinearSplineLayer(2, 16, 14) |
| self.second = LinearSplineLayer(16, 1, 8) |
|
|
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: |
| hidden = torch.tanh(self.first(inputs)) |
| return self.second(hidden) |
|
|
|
|
| class MatchedMLP(nn.Module): |
| def __init__(self) -> None: |
| super().__init__() |
| self.network = nn.Sequential( |
| nn.Linear(2, 32), |
| nn.Tanh(), |
| nn.Linear(32, 16), |
| nn.Tanh(), |
| nn.Linear(16, 1), |
| ) |
|
|
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: |
| return self.network(inputs) |
|
|
|
|
| def parameter_count(model: nn.Module) -> int: |
| return sum(parameter.numel() for parameter in model.parameters()) |
|
|
|
|
