braindecode.models.EEGModuleMixin#

Mixin class for all EEG models in braindecode.

Parameters:

n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.
n_chans (int) – Number of EEG channels.
chs_info (list of dict) – Information about each individual EEG channel. This should be filled with info["chs"]. Refer to mne.Info for more details.
n_times (int) – Number of time samples of the input window.
input_window_seconds (float) – Length of the input window in seconds.
sfreq (float) – Sampling frequency of the EEG recordings.
add_log_softmax (bool) – Whether to use log-softmax non-linearity as the output function. LogSoftmax final layer will be removed in the future. Please adjust your loss function accordingly (e.g. CrossEntropyLoss)! Check the documentation of the torch.nn loss functions: https://pytorch.org/docs/stable/nn.html#loss-functions.

Raises:

ValueError – If some input signal-related parameters are not specified: and can not be inferred.
FutureWarning – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.

Notes

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

Methods

forward(*args, **kwargs)[source]#

get_output_shape() → Tuple[int, ...][source]#

Returns shape of neural network output for batch size equal 1.

Returns:: output_shape – shape of the network output for batch_size==1 (1, …)
Return type:: Tuple[int, …]

get_torchinfo_statistics(col_names: Iterable[str] | None = ('input_size', 'output_size', 'num_params', 'kernel_size'), row_settings: Iterable[str] | None = ('var_names', 'depth')) → ModelStatistics[source]#

Generate table describing the model using torchinfo.summary.

Parameters:

col_names (tuple, optional) – Specify which columns to show in the output, see torchinfo for details, by default (“input_size”, “output_size”, “num_params”, “kernel_size”)
row_settings (tuple, optional) – Specify which features to show in a row, see torchinfo for details, by default (“var_names”, “depth”)

Returns:

ModelStatistics generated by torchinfo.summary.

Return type:

torchinfo.ModelStatistics

load_state_dict(state_dict, *args, **kwargs)[source]#

modules()[source]#

parameters()[source]#

to_dense_prediction_model(axis: Tuple[int, ...] | int = (2, 3)) → None[source]#

Transform a sequential model with strides to a model that outputs dense predictions by removing the strides and instead inserting dilations. Modifies model in-place.