maxout

allennlp.modules.maxout

[SOURCE]

---

A maxout neural network.

Maxout

class Maxout(torch.nn.Module,  FromParams):
 | def __init__(
 |     self,
 |     input_dim: int,
 |     num_layers: int,
 |     output_dims: Union[int, Sequence[int]],
 |     pool_sizes: Union[int, Sequence[int]],
 |     dropout: Union[float, Sequence[float]] = 0.0
 | ) -> None

This Module is a maxout neural network.

Parameters

• input_dim : int
  The dimensionality of the input. We assume the input has shape (batch_size, input_dim).
• num_layers : int
  The number of maxout layers to apply to the input.
• output_dims : Union[int, Sequence[int]]
  The output dimension of each of the maxout layers. If this is a single int, we use it for all maxout layers. If it is a Sequence[int], len(output_dims) must be num_layers.
• pool_sizes : Union[int, Sequence[int]]
  The size of max-pools. If this is a single int, we use it for all maxout layers. If it is a Sequence[int], len(pool_sizes) must be num_layers.
• dropout : Union[float, Sequence[float]], optional (default = 0.0)
  If given, we will apply this amount of dropout after each layer. Semantics of float versus Sequence[float] is the same as with other parameters.

get_output_dim

class Maxout(torch.nn.Module,  FromParams):
 | ...
 | def get_output_dim(self)

get_input_dim

class Maxout(torch.nn.Module,  FromParams):
 | ...
 | def get_input_dim(self)

forward

class Maxout(torch.nn.Module,  FromParams):
 | ...
 | def forward(self, inputs: torch.Tensor) -> torch.Tensor