Source code for flambe.nn.softmax

# type: ignore[override]

from typing import Optional

import torch
from torch import nn

from flambe.nn.mlp import MLPEncoder
from flambe.nn.module import Module

[docs]class SoftmaxLayer(Module): """Implement an SoftmaxLayer module. Can be used to form a classifier out of any encoder. Note: by default takes the log_softmax so that it can be fed to the NLLLoss module. You can disable this behavior through the `take_log` argument. """ def __init__(self, input_size: int, output_size: int, mlp_layers: int = 1, mlp_dropout: float = 0., mlp_hidden_activation: Optional[nn.Module] = None, take_log: bool = True) -> None: """Initialize the SoftmaxLayer. Parameters ---------- input_size : int Input size of the decoder, usually the hidden size of some encoder. output_size : int The output dimension, usually the number of target labels mlp_layers : int The number of layers in the MLP mlp_dropout: float, optional Dropout to be used before each MLP layer mlp_hidden_activation: nn.Module, optional Any PyTorch activation layer, defaults to None take_log: bool, optional If ``True``, compute the LogSoftmax to be fed in NLLLoss. Defaults to ``False``. """ super().__init__() softmax = nn.LogSoftmax(dim=-1) if take_log else nn.Softmax() self.mlp = MLPEncoder(input_size=input_size, output_size=output_size, n_layers=mlp_layers, dropout=mlp_dropout, hidden_activation=mlp_hidden_activation, output_activation=softmax)
[docs] def forward(self, data: torch.Tensor) -> torch.Tensor: """Performs a forward pass through the network. Parameters ---------- data: torch.Tensor input to the model of shape (*, input_size) Returns ------- output: torch.Tensor output of the model of shape (*, output_size) """ return self.mlp(data)