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‎stable/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎

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‎stable/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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"version": "3.12.11"
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}
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},
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"nbformat": 4,

‎stable/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb‎

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},
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"outputs": [],
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"source": [
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"# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport time\nfrom itertools import product\n\nimport mne\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\n\nfrom braindecode.datasets import TUHAbnormal\nfrom braindecode.models import Deep4Net, ShallowFBCSPNet\nfrom braindecode.preprocessing import create_fixed_length_windows\n\nmne.set_log_level(\"WARNING\") # avoid messages every time a window is extracted"
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"# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport time\nfrom itertools import product\n\nimport mne\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\n\nfrom braindecode.datasets import TUHAbnormal\nfrom braindecode.models import Deep4Net, ShallowFBCSPNet\nfrom braindecode.preprocessing import create_fixed_length_windows\nfrom braindecode.util import set_random_seeds\n\nmne.set_log_level(\"WARNING\") # avoid messages every time a window is extracted"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"N_JOBS = 8\ntorch.backends.cudnn.benchmark = True # Enables automatic algorithm optimizations\ntorch.set_num_threads(N_JOBS) # Sets the available number of threads"
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"N_JOBS = 8\ntorch.backends.cudnn.benchmark = True # Enables automatic algorithm optimizations\ntorch.set_num_threads(N_JOBS) # Sets the available number of threads\ncuda = torch.cuda.is_available()\nseed = 20240205\nset_random_seeds(seed=seed, cuda=cuda)"
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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"version": "3.12.11"
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}
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"nbformat": 4,

‎stable/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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"version": "3.12.11"
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}
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"nbformat": 4,

‎stable/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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"version": "3.12.11"
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}
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"nbformat": 4,

‎stable/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py‎

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# ~~~~~~~~~~~~~~~
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#
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# We extract 30-s windows to be used in the classification task.
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# The :class:`braindecode.models.SleepStagerEldele2021` model takes a
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# The :class:`braindecode.models.AttnSleep` model takes a
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# single channel as input. Here, the Fpz-Cz channel is used as it
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# was found to give better performance than using the Pz-Oz channel
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import torch
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from torch import nn
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from braindecode.models import SleepStagerEldele2021
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from braindecode.models import AttnSleep
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from braindecode.modules import TimeDistributed
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from braindecode.util import set_random_seeds
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# Extract number of channels and time steps from dataset
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n_channels, input_size_samples = train_set[0][0].shape
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feat_extractor = SleepStagerEldele2021(
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sfreq,
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feat_extractor = AttnSleep(
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sfreq=sfreq,
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n_outputs=n_classes,
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n_times=input_size_samples,
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return_feats=True,

‎stable/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb‎

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‎stable/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb‎

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}
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‎stable/_downloads/2b3354037141fa0033174f982871d566/plot_relative_positioning.zip‎

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‎stable/_downloads/36393305d195c3207520572a3b677a05/plot_basic_training_epochs.zip‎

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