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‎master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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‎master/_downloads/22c027ac1764bd1e50c7597f407c49bb/plot_bcic_iv_4_ecog_cropped.ipynb

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"from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode.training import TimeSeriesLoss\nfrom braindecode import EEGRegressor\nfrom braindecode.training import CroppedTimeSeriesEpochScoring\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 8\n\nregressor = EEGRegressor(\n model,\n cropped=True,\n aggregate_predictions=False,\n criterion=TimeSeriesLoss,\n criterion__loss_function=torch.nn.functional.mse_loss,\n optimizer=torch.optim.AdamW,\n train_split=predefined_split(valid_set),\n optimizer__lr=lr,\n optimizer__weight_decay=weight_decay,\n iterator_train__shuffle=True,\n batch_size=batch_size,\n callbacks=[\n (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n lower_is_better=False,\n on_train=True,\n name='r2_train')\n ),\n ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n lower_is_better=False,\n on_train=False,\n name='r2_valid')\n )\n ],\n device=device,\n)\nset_log_level(verbose='WARNING')"
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"from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode.training import TimeSeriesLoss\nfrom braindecode import EEGRegressor\nfrom braindecode.training import CroppedTimeSeriesEpochScoring\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 27 # only 27 examples in train set, otherwise set to 64\nn_epochs = 8\n\nregressor = EEGRegressor(\n model,\n cropped=True,\n aggregate_predictions=False,\n criterion=TimeSeriesLoss,\n criterion__loss_function=torch.nn.functional.mse_loss,\n optimizer=torch.optim.AdamW,\n train_split=predefined_split(valid_set),\n optimizer__lr=lr,\n optimizer__weight_decay=weight_decay,\n iterator_train__shuffle=True,\n batch_size=batch_size,\n callbacks=[\n (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n lower_is_better=False,\n on_train=True,\n name='r2_train')\n ),\n ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n lower_is_better=False,\n on_train=False,\n name='r2_valid')\n )\n ],\n device=device,\n)\nset_log_level(verbose='WARNING')"
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‎master/_downloads/3e0455c88adcaa2571230abb981020db/plot_relative_positioning.ipynb

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"import os\n\nfrom skorch.helper import predefined_split\nfrom skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 5e-3\nbatch_size = 512\nn_epochs = 25\nnum_workers = 0 if n_jobs <= 1 else n_jobs\n\ncp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)\nearly_stopping = EarlyStopping(patience=10)\ntrain_acc = EpochScoring(\n scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)\nvalid_acc = EpochScoring(\n scoring='accuracy', on_train=False, name='valid_acc',\n lower_is_better=False)\ncallbacks = [\n ('cp', cp),\n ('patience', early_stopping),\n ('train_acc', train_acc),\n ('valid_acc', valid_acc)\n]\n\nclf = EEGClassifier(\n model,\n criterion=torch.nn.BCEWithLogitsLoss,\n optimizer=torch.optim.Adam,\n max_epochs=n_epochs,\n iterator_train__shuffle=False,\n iterator_train__sampler=train_sampler,\n iterator_valid__sampler=valid_sampler,\n iterator_train__num_workers=num_workers,\n iterator_valid__num_workers=num_workers,\n train_split=predefined_split(splitted['valid']),\n optimizer__lr=lr,\n batch_size=batch_size,\n callbacks=callbacks,\n device=device\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(splitted['train'], y=None)\nclf.load_params(checkpoint=cp) # Load the model with the lowest valid_loss\n\nos.remove('./params.pt') # Delete parameters file"
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"import os\n\nfrom skorch.helper import predefined_split\nfrom skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 5e-3\nbatch_size = 128 # 512 if data large enough\nn_epochs = 25\nnum_workers = 0 if n_jobs <= 1 else n_jobs\n\ncp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)\nearly_stopping = EarlyStopping(patience=10)\ntrain_acc = EpochScoring(\n scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)\nvalid_acc = EpochScoring(\n scoring='accuracy', on_train=False, name='valid_acc',\n lower_is_better=False)\ncallbacks = [\n ('cp', cp),\n ('patience', early_stopping),\n ('train_acc', train_acc),\n ('valid_acc', valid_acc)\n]\n\nclf = EEGClassifier(\n model,\n criterion=torch.nn.BCEWithLogitsLoss,\n optimizer=torch.optim.Adam,\n max_epochs=n_epochs,\n iterator_train__shuffle=False,\n iterator_train__sampler=train_sampler,\n iterator_valid__sampler=valid_sampler,\n iterator_train__num_workers=num_workers,\n iterator_valid__num_workers=num_workers,\n train_split=predefined_split(splitted['valid']),\n optimizer__lr=lr,\n batch_size=batch_size,\n callbacks=callbacks,\n device=device\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(splitted['train'], y=None)\nclf.load_params(checkpoint=cp) # Load the model with the lowest valid_loss\n\nos.remove('./params.pt') # Delete parameters file"
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