Restablish deprecated docstring
@@ -20,7 +20,7 @@ class EEGInception(nn.Sequential):
""" EEG Inception for ERP-based classification
--> DEPERECATED <--
- THIS CLASS IS DEPRECATED AND WILL BE REMOVED IN THE NEXT RELEASE OF
+ THIS CLASS IS DEPRECATED AND WILL BE REMOVED IN THE RELEASE 0.9 OF
BRAINDECODE. PLEASE USE braindecode.models.EEGInceptionERP INSTEAD IN THE
FUTURE.
@@ -50,33 +50,28 @@ class EEGInception(nn.Sequential):
Number of EEG channels.
n_classes : int
Number of classes.
- input_window_samples : int, optional
- Size of the input, in number of sampels. Set to 128 (1s) as in
- [Santamaria2020]_.
- sfreq : float, optional
- EEG sampling frequency. Defaults to 128 as in [Santamaria2020]_.
- drop_prob : float, optional
- Dropout rate inside all the network. Defaults to 0.5 as in
- [Santamaria2020]_.
- scales_samples_s: list(float), optional
- Windows for inception block. Temporal scale (s) of the convolutions on
- each Inception module. This parameter determines the kernel sizes of
- the filters. Defaults to 0.5, 0.25, 0.125 seconds, as in
- [Santamaria2020]_.
- n_filters : int, optional
- Initial number of convolutional filters. Defaults to 8 as in
- [Santamaria2020]_.
- activation: nn.Module, optional
- Activation function. Defaults to ELU activation as in
- [Santamaria2020]_.
- batch_norm_alpha: float, optional
- Momentum for BatchNorm2d. Defaults to 0.01.
- depth_multiplier: int, optional
- Depth multiplier for the depthwise convolution. Defaults to 2 as in
- [Santamaria2020]_.
- pooling_sizes: list(int), optional
- Pooling sizes for the inception blocks. Defaults to 4, 2, 2 and 2, as
- in [Santamaria2020]_.
+ input_size_ms : int
+ Size of the input, in milliseconds. Set to 1000 in [Santamaria2020]_.
+ sfreq : float
+ EEG sampling frequency.
+ drop_prob : float
+ Dropout rate inside all the network.
+ scales_time: list(int)
+ Windows for inception block, must be a list with proportional values of
+ the input_size_ms.
+ According to the authors: temporal scale (ms) of the convolutions
+ on each Inception module.
+ This parameter determines the kernel sizes of the filters.
+ n_filters : int
+ Initial number of convolutional filters. Set to 8 in [Santamaria2020]_.
+ activation: nn.Module
+ Activation function, default: ELU activation.
+ batch_norm_alpha: float
+ Momentum for BatchNorm2d.
+ depth_multiplier: int
+ Depth multiplier for the depthwise convolution.
+ pooling_sizes: list(int)
+ Pooling sizes for the inception block.
References
----------
@@ -117,7 +112,7 @@ class EEGInception(nn.Sequential):
warn(
"The class EEGInception is deprecated and will be removed in the "
- "next release of braindecode. Please use "
+ "release 0.9 of braindecode. Please use "
"braindecode.models.EEGInceptionERP instead in the future.",
DeprecationWarning
)
