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5f70dab | 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 48 49 50 51 52 53 | from __future__ import annotations
import torch
from torch import nn
class HighwayNetwork(nn.Module):
def __init__(self, width: int = 32, layers: int = 8) -> None:
super().__init__()
self.input = nn.Linear(64, width)
self.transforms = nn.ModuleList([nn.Linear(width, width) for _ in range(layers)])
self.gates = nn.ModuleList([nn.Linear(width, width) for _ in range(layers)])
self.output = nn.Linear(width, 10)
for gate in self.gates:
nn.init.constant_(gate.bias, -2.0)
def forward(
self,
pixels: torch.Tensor,
*,
return_gates: bool = False,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
hidden = torch.tanh(self.input(pixels))
gate_values = []
for transform, gate in zip(self.transforms, self.gates, strict=True):
transformed = torch.tanh(transform(hidden))
carry = torch.sigmoid(gate(hidden))
hidden = carry * transformed + (1 - carry) * hidden
gate_values.append(carry)
logits = self.output(hidden)
if return_gates:
return logits, torch.stack(gate_values, dim=1)
return logits
class PlainDeepNetwork(nn.Module):
def __init__(self, width: int = 32, layers: int = 16) -> None:
super().__init__()
self.input = nn.Linear(64, width)
self.layers = nn.ModuleList([nn.Linear(width, width) for _ in range(layers)])
self.output = nn.Linear(width, 10)
def forward(self, pixels: torch.Tensor) -> torch.Tensor:
hidden = torch.tanh(self.input(pixels))
for layer in self.layers:
hidden = torch.tanh(layer(hidden))
return self.output(hidden)
def parameter_count(model: nn.Module) -> int:
return sum(parameter.numel() for parameter in model.parameters())
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