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b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html @@ -605,7 +605,7 @@

Preprocessingpreprocess(test_set, [Preprocessor("crop", tmin=0, tmax=24)], n_jobs=-1) - -plot bcic iv 4 ecog cropped

Total running time of the script: (2 minutes 14.551 seconds)

-

Estimated memory usage: 1420 MB

+plot bcic iv 4 ecog cropped

Total running time of the script: (1 minutes 2.701 seconds)

+

Estimated memory usage: 1144 MB

  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
-      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.8397
-      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6171
-      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6296
-      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.5932
+      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7543
+      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6015
+      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6000
+      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.5904
 
 <class 'braindecode.classifier.EEGClassifier'>[initialized](
   module_=============================================================================================================================================
@@ -850,8 +850,8 @@ 

Setting the data aug -

Total running time of the script: (0 minutes 17.932 seconds)

-

Estimated memory usage: 1128 MB

+

Total running time of the script: (0 minutes 17.887 seconds)

+

Estimated memory usage: 1078 MB

-
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f8c0edf5de0>] to the mne.io.Raw of an EEGWindowsDataset.
+
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f5857023ee0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
-/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f8c0edf5de0>] to the mne.io.Raw of an EEGWindowsDataset.
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f5857023ee0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
-/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f8c0edf5de0>] to the mne.io.Raw of an EEGWindowsDataset.
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f5857023ee0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
 
-<braindecode.datasets.base.BaseConcatDataset object at 0x7f8c0edf69e0>
+<braindecode.datasets.base.BaseConcatDataset object at 0x7f584227ee00>
 
@@ -892,31 +892,31 @@

Training
  epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
 -------  -----------  ------------  -----------  ------------  ----  ------
-      1       0.5234        0.7013       0.6680        0.6320     +  1.2006
-      2       0.5938        0.7149       0.4880        0.8358        0.7928
-      3       0.4922        1.0040       0.6440        0.6172     +  0.8014
-      4       0.5234        0.7031       0.6120        0.5990     +  0.8067
-      5       0.5391        0.6751       0.5920        0.6213        0.8067
-      6       0.6719        0.6227       0.5920        0.6263        0.7858
-      7       0.6562        0.6309       0.6240        0.6117        0.7974
-      8       0.6641        0.6272       0.6480        0.5950     +  0.8027
-      9       0.6328        0.6238       0.6680        0.5797     +  0.7876
-     10       0.6406        0.6177       0.6800        0.5746     +  0.7836
-     11       0.6250        0.6323       0.7040        0.5787        0.8291
-     12       0.6094        0.6281       0.6760        0.5772        0.8127
-     13       0.6328        0.6422       0.6880        0.5790        0.7865
-     14       0.6406        0.5920       0.6840        0.5765        0.7844
-     15       0.6562        0.6170       0.6920        0.5730     +  0.7901
-     16       0.7578        0.5608       0.6960        0.5676     +  0.8147
-     17       0.6875        0.5936       0.7120        0.5612     +  0.7903
-     18       0.7734        0.5472       0.7080        0.5500     +  0.8090
-     19       0.7656        0.5245       0.7120        0.5400     +  0.7924
-     20       0.6641        0.5641       0.7160        0.5333     +  0.8176
-     21       0.7422        0.5307       0.7200        0.5272     +  0.8060
-     22       0.7109        0.5499       0.7360        0.5211     +  0.8188
-     23       0.6250        0.6259       0.7400        0.5164     +  0.8079
-     24       0.7031        0.5712       0.7400        0.5120     +  0.8179
-     25       0.7109        0.5030       0.7280        0.5120        0.8226
+      1       0.5234        0.7013       0.6680        0.6320     +  1.1507
+      2       0.5938        0.7149       0.4880        0.8358        0.8320
+      3       0.4922        1.0040       0.6440        0.6172     +  0.8176
+      4       0.5234        0.7031       0.6120        0.5990     +  0.8006
+      5       0.5391        0.6751       0.5920        0.6213        0.8129
+      6       0.6719        0.6227       0.5920        0.6263        0.8188
+      7       0.6562        0.6309       0.6240        0.6117        0.8158
+      8       0.6641        0.6272       0.6480        0.5950     +  0.8141
+      9       0.6328        0.6238       0.6680        0.5797     +  0.7969
+     10       0.6406        0.6177       0.6800        0.5746     +  0.8103
+     11       0.6250        0.6323       0.7040        0.5787        0.8046
+     12       0.6094        0.6281       0.6760        0.5772        0.8033
+     13       0.6328        0.6422       0.6880        0.5790        0.7943
+     14       0.6406        0.5920       0.6840        0.5765        0.8053
+     15       0.6562        0.6170       0.6920        0.5730     +  0.8170
+     16       0.7578        0.5608       0.6960        0.5676     +  0.8004
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8037
+     18       0.7734        0.5472       0.7080        0.5500     +  0.7943
+     19       0.7656        0.5245       0.7120        0.5400     +  0.8059
+     20       0.6641        0.5641       0.7160        0.5333     +  0.8082
+     21       0.7422        0.5307       0.7200        0.5272     +  0.8068
+     22       0.7109        0.5499       0.7360        0.5211     +  0.7967
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8132
+     24       0.7031        0.5712       0.7400        0.5120     +  0.8109
+     25       0.7109        0.5030       0.7280        0.5120        0.8109
 /home/runner/.local/lib/python3.10/site-packages/skorch/net.py:2626: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
   return torch.load(f_name, map_location=map_location)
 
@@ -1102,7 +1102,7 @@

Using the learned re ax.legend()

-plot relative positioning
<matplotlib.legend.Legend object at 0x7f8c0ed4ac20>
+plot relative positioning
<matplotlib.legend.Legend object at 0x7f5842217d30>
 

We see that there is sleep stage-related structure in the embedding. A @@ -1159,8 +1159,8 @@

References -

Total running time of the script: (1 minutes 58.871 seconds)

-

Estimated memory usage: 723 MB

+

Total running time of the script: (2 minutes 10.989 seconds)

+

Estimated memory usage: 741 MB