.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_plot_regression.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_regression.py:
Regression example on fake data
===============================
.. GENERATED FROM PYTHON SOURCE LINES 5-141
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:597: UserWarning: Meaning of `trial_stop_offset_samples`=0 has changed, use `None` to indicate end of trial/recording. Using `None`.
'Meaning of `trial_stop_offset_samples`=0 has changed, use `None` '
epoch train_loss train_neg_root_mean_squared_error valid_loss valid_neg_root_mean_squared_error lr dur
------- ------------ ----------------------------------- ------------ ----------------------------------- ------ ------
1 1088.8579 -28.2085 42.6341 -6.5295 0.0006 1.0773
2 1086.1705 -27.0311 23.0701 -4.8031 0.0003 0.7379
3 1084.8259 -27.0974 24.0774 -4.9069 0.0000 0.7279
|
.. code-block:: default
# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD-3
import numpy as np
import pandas as pd
import torch
from skorch.callbacks import LRScheduler
from skorch.helper import predefined_split
from braindecode import EEGRegressor
from braindecode.preprocessing import create_fixed_length_windows
from braindecode.datasets import BaseDataset, BaseConcatDataset
from braindecode.training.losses import CroppedLoss
from braindecode.models import Deep4Net
from braindecode.models import ShallowFBCSPNet
from braindecode.models.util import to_dense_prediction_model, get_output_shape
from braindecode.util import set_random_seeds, create_mne_dummy_raw
model_name = "shallow" # 'shallow' or 'deep'
n_epochs = 3
seed = 20200220
input_window_samples = 6000
batch_size = 64
cuda = torch.cuda.is_available()
device = 'cuda' if cuda else 'cpu'
if cuda:
torch.backends.cudnn.benchmark = True
n_chans = 21
# set to how many targets you want to regress (age -> 1, [x, y, z] -> 3)
n_classes = 1
set_random_seeds(seed=seed, cuda=cuda)
# initialize a model, transform to dense and move to gpu
if model_name == "shallow":
model = ShallowFBCSPNet(
in_chans=n_chans,
n_classes=n_classes,
input_window_samples=input_window_samples,
n_filters_time=40,
n_filters_spat=40,
final_conv_length=35,
)
optimizer_lr = 0.000625
optimizer_weight_decay = 0
elif model_name == "deep":
model = Deep4Net(
in_chans=n_chans,
n_classes=n_classes,
input_window_samples=input_window_samples,
n_filters_time=25,
n_filters_spat=25,
stride_before_pool=True,
n_filters_2=int(n_chans * 2),
n_filters_3=int(n_chans * (2 ** 2.0)),
n_filters_4=int(n_chans * (2 ** 3.0)),
final_conv_length=1,
)
optimizer_lr = 0.01
optimizer_weight_decay = 0.0005
else:
raise ValueError(f'{model_name} unknown')
new_model = torch.nn.Sequential()
for name, module_ in model.named_children():
if "softmax" in name:
continue
new_model.add_module(name, module_)
model = new_model
if cuda:
model.cuda()
to_dense_prediction_model(model)
n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq, fake_duration_s):
datasets = []
for i in range(n_fake_recs):
train_or_eval = "eval" if i == 0 else "train"
raw, save_fname = create_mne_dummy_raw(
n_channels=n_fake_chs, n_times=fake_duration_s * fake_sfreq,
sfreq=fake_sfreq, savedir=None)
target = np.random.randint(0, 100, n_classes)
if n_classes == 1:
target = target[0]
fake_descrition = pd.Series(
data=[target, train_or_eval],
index=["target", "session"])
base_ds = BaseDataset(raw, fake_descrition, target_name="target")
datasets.append(base_ds)
dataset = BaseConcatDataset(datasets)
return dataset
dataset = fake_regression_dataset(
n_fake_recs=5, n_fake_chs=21, fake_sfreq=100, fake_duration_s=60)
windows_dataset = create_fixed_length_windows(
dataset,
start_offset_samples=0,
stop_offset_samples=0,
window_size_samples=input_window_samples,
window_stride_samples=n_preds_per_input,
drop_last_window=False,
drop_bad_windows=True,
)
splits = windows_dataset.split("session")
train_set = splits["train"]
valid_set = splits["eval"]
regressor = EEGRegressor(
model,
cropped=True,
criterion=CroppedLoss,
criterion__loss_function=torch.nn.functional.mse_loss,
optimizer=torch.optim.AdamW,
train_split=predefined_split(valid_set),
optimizer__lr=optimizer_lr,
optimizer__weight_decay=optimizer_weight_decay,
iterator_train__shuffle=True,
batch_size=batch_size,
callbacks=[
"neg_root_mean_squared_error",
# seems n_epochs -1 leads to desired behavior of lr=0 after end of training?
("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
],
device=device,
)
regressor.fit(train_set, y=None, epochs=n_epochs)
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 3.546 seconds)
**Estimated memory usage:** 200 MB
.. _sphx_glr_download_auto_examples_plot_regression.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_regression.py <plot_regression.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_regression.ipynb <plot_regression.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
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