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