feedforward

allennlp.modules.feedforward

[SOURCE]

---

A feed-forward neural network.

FeedForward

class FeedForward(torch.nn.Module,  FromParams):
 | def __init__(
 |     self,
 |     input_dim: int,
 |     num_layers: int,
 |     hidden_dims: Union[int, List[int]],
 |     activations: Union[Activation, List[Activation]],
 |     dropout: Union[float, List[float]] = 0.0
 | ) -> None

This Module is a feed-forward neural network, just a sequence of Linear layers with activation functions in between.

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 Linear layers to apply to the input.
• hidden_dims : Union[int, List[int]]
  The output dimension of each of the Linear layers. If this is a single int, we use it for all Linear layers. If it is a List[int], len(hidden_dims) must be num_layers.
• activations : Union[Activation, List[Activation]]
  The activation function to use after each Linear layer. If this is a single function, we use it after all Linear layers. If it is a List[Activation], len(activations) must be num_layers. Activation must have torch.nn.Module type.
• dropout : Union[float, List[float]], optional (default = 0.0)
  If given, we will apply this amount of dropout after each layer. Semantics of float versus List[float] is the same as with other parameters.

Examples

FeedForward(124, 2, [64, 32], torch.nn.ReLU(), 0.2)
#> FeedForward(
#>   (_activations): ModuleList(
#>     (0): ReLU()
#>     (1): ReLU()
#>   )
#>   (_linear_layers): ModuleList(
#>     (0): Linear(in_features=124, out_features=64, bias=True)
#>     (1): Linear(in_features=64, out_features=32, bias=True)
#>   )
#>   (_dropout): ModuleList(
#>     (0): Dropout(p=0.2, inplace=False)
#>     (1): Dropout(p=0.2, inplace=False)
#>   )
#> )

get_output_dim

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

get_input_dim

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

forward

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