mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-12-23 06:41:49 +01:00
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
"""MLP feed forward stack in torch."""
|
|
|
|
from tml.projects.home.recap.model.config import MlpConfig
|
|
|
|
import torch
|
|
from absl import logging
|
|
|
|
|
|
def _init_weights(module):
|
|
if isinstance(module, torch.nn.Linear):
|
|
torch.nn.init.xavier_uniform_(module.weight)
|
|
torch.nn.init.constant_(module.bias, 0)
|
|
|
|
|
|
class Mlp(torch.nn.Module):
|
|
def __init__(self, in_features: int, mlp_config: MlpConfig):
|
|
super().__init__()
|
|
self._mlp_config = mlp_config
|
|
input_size = in_features
|
|
layer_sizes = mlp_config.layer_sizes
|
|
modules = []
|
|
for layer_size in layer_sizes[:-1]:
|
|
modules.append(torch.nn.Linear(input_size, layer_size, bias=True))
|
|
|
|
if mlp_config.batch_norm:
|
|
modules.append(
|
|
torch.nn.BatchNorm1d(
|
|
layer_size, affine=mlp_config.batch_norm.affine, momentum=mlp_config.batch_norm.momentum
|
|
)
|
|
)
|
|
|
|
modules.append(torch.nn.ReLU())
|
|
|
|
if mlp_config.dropout:
|
|
modules.append(torch.nn.Dropout(mlp_config.dropout.rate))
|
|
|
|
input_size = layer_size
|
|
modules.append(torch.nn.Linear(input_size, layer_sizes[-1], bias=True))
|
|
if mlp_config.final_layer_activation:
|
|
modules.append(torch.nn.ReLU())
|
|
self.layers = torch.nn.ModuleList(modules)
|
|
self.layers.apply(_init_weights)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
net = x
|
|
for i, layer in enumerate(self.layers):
|
|
net = layer(net)
|
|
if i == 1: # Share the first (widest?) set of activations for other applications.
|
|
shared_layer = net
|
|
return {"output": net, "shared_layer": shared_layer}
|
|
|
|
@property
|
|
def shared_size(self):
|
|
return self._mlp_config.layer_sizes[-1]
|
|
|
|
@property
|
|
def out_features(self):
|
|
return self._mlp_config.layer_sizes[-1]
|