braindecode.models.TimeDistributed#
- class braindecode.models.TimeDistributed(module)[source]#
Apply module on multiple windows.
Apply the provided module on a sequence of windows and return their concatenation. Useful with sequence-to-prediction models (e.g. sleep stager which must map a sequence of consecutive windows to the label of the middle window in the sequence).
- Parameters:
module (nn.Module) – Module to be applied to the input windows. Must accept an input of shape (batch_size, n_channels, n_times).
Methods
- forward(x)[source]#
- Parameters:
x (torch.Tensor) – Sequence of windows, of shape (batch_size, seq_len, n_channels, n_times).
- Returns:
Shape (batch_size, seq_len, output_size).
- Return type:
Examples using braindecode.models.TimeDistributed
#
![](../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png)
Sleep staging on the Sleep Physionet dataset using Chambon2018 network
Sleep staging on the Sleep Physionet dataset using Chambon2018 network
![](../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png)
Sleep staging on the Sleep Physionet dataset using Eldele2021
Sleep staging on the Sleep Physionet dataset using Eldele2021