allennlp.modules.input_variational_dropout

class allennlp.modules.input_variational_dropout.InputVariationalDropout(p=0.5, inplace=False)[source]

Bases: torch.nn.modules.dropout.Dropout

Apply the dropout technique in Gal and Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning” (https://arxiv.org/abs/1506.02142) to a 3D tensor.

This module accepts a 3D tensor of shape (batch_size, num_timesteps, embedding_dim) and samples a single dropout mask of shape (batch_size, embedding_dim) and applies it to every time step.

forward(input_tensor)[source]

Apply dropout to input tensor.

Parameters:
input_tensor: ``torch.FloatTensor``

A tensor of shape (batch_size, num_timesteps, embedding_dim)

Returns:
output: ``torch.FloatTensor``

A tensor of shape (batch_size, num_timesteps, embedding_dim) with dropout applied.