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class ResidualWithLayerDropout(torch.nn.Module):
 | def __init__(self, undecayed_dropout_prob: float = 0.5) -> None

A residual connection with the layer dropout technique Deep Networks with Stochastic Depth.

This module accepts the input and output of a layer, decides whether this layer should be stochastically dropped, returns either the input or output + input. During testing, it will re-calibrate the outputs of this layer by the expected number of times it participates in training.


class ResidualWithLayerDropout(torch.nn.Module):
 | ...
 | def forward(
 |     self,
 |     layer_input: torch.Tensor,
 |     layer_output: torch.Tensor,
 |     layer_index: int = None,
 |     total_layers: int = None
 | ) -> torch.Tensor

Apply dropout to this layer, for this whole mini-batch. dropout_prob = layer_index / total_layers * undecayed_dropout_prob if layer_idx and total_layers is specified, else it will use the undecayed_dropout_prob directly.


layer_input torch.FloatTensor required The input tensor of this layer. layer_output torch.FloatTensor required The output tensor of this layer, with the same shape as the layer_input. layer_index int The layer index, starting from 1. This is used to calcuate the dropout prob together with the total_layers parameter. total_layers int The total number of layers.


  • output : torch.FloatTensor
    A tensor with the same shape as layer_input and layer_output.