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diff --git a/_downloads/6b06d7ed910de0ab89620b6507398d10/3_create_dataset.zip b/_downloads/6b06d7ed910de0ab89620b6507398d10/3_create_dataset.zip
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diff --git a/_downloads/8334499696025c0d54f384f86f5d7a67/1_understanding_basics.zip b/_downloads/8334499696025c0d54f384f86f5d7a67/1_understanding_basics.zip
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diff --git a/_downloads/e6f65f5b3784f09df47c98819866272d/2_complex_filtering.zip b/_downloads/e6f65f5b3784f09df47c98819866272d/2_complex_filtering.zip
index 3195011..08e9d08 100644
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diff --git a/_sources/auto_examples/1_understanding_basics.rst.txt b/_sources/auto_examples/1_understanding_basics.rst.txt
index db4ba88..c6e9c5e 100644
--- a/_sources/auto_examples/1_understanding_basics.rst.txt
+++ b/_sources/auto_examples/1_understanding_basics.rst.txt
@@ -352,9 +352,9 @@ In case you do not want to index using the filter sequence name, you can retriev
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.469 seconds)
+   **Total running time of the script:** (0 minutes 5.279 seconds)
 
-**Estimated memory usage:**  556 MB
+**Estimated memory usage:**  553 MB
 
 
 .. _sphx_glr_download_auto_examples_1_understanding_basics.py:
diff --git a/_sources/auto_examples/2_complex_filtering.rst.txt b/_sources/auto_examples/2_complex_filtering.rst.txt
index a4eb191..32b0a2a 100644
--- a/_sources/auto_examples/2_complex_filtering.rst.txt
+++ b/_sources/auto_examples/2_complex_filtering.rst.txt
@@ -461,9 +461,9 @@ This can be achieved in a more concise way by using the apply_filter_pipeline me
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.633 seconds)
+   **Total running time of the script:** (0 minutes 6.337 seconds)
 
-**Estimated memory usage:**  1313 MB
+**Estimated memory usage:**  1363 MB
 
 
 .. _sphx_glr_download_auto_examples_2_complex_filtering.py:
diff --git a/_sources/auto_examples/3_create_dataset.rst.txt b/_sources/auto_examples/3_create_dataset.rst.txt
index 9de08d7..d0e2b2b 100644
--- a/_sources/auto_examples/3_create_dataset.rst.txt
+++ b/_sources/auto_examples/3_create_dataset.rst.txt
@@ -103,8 +103,8 @@ In this example we will create a dataset that was used in our real-time paper [1
 
  .. code-block:: none
 
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@@ -137,10 +137,10 @@ Default dataset are also available. Here is an example of how to use the EMBCDat
 
  .. code-block:: none
 
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@@ -148,9 +148,9 @@ Default dataset are also available. Here is an example of how to use the EMBCDat
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 20.328 seconds)
+   **Total running time of the script:** (0 minutes 19.833 seconds)
 
-**Estimated memory usage:**  982 MB
+**Estimated memory usage:**  945 MB
 
 
 .. _sphx_glr_download_auto_examples_3_create_dataset.py:
diff --git a/_sources/auto_examples/4_train_model.rst.txt b/_sources/auto_examples/4_train_model.rst.txt
index 67d54f5..28ad261 100644
--- a/_sources/auto_examples/4_train_model.rst.txt
+++ b/_sources/auto_examples/4_train_model.rst.txt
@@ -103,13 +103,13 @@ Training the model
     /home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:512: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.
     /home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
 
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    Epoch 0:  62%|██████▏   | 56/91 [01:24<00:52,  0.66it/s, v_num=0, loss_step=43.00]
    Epoch 0:  62%|██████▏   | 56/91 [01:24<00:52,  0.66it/s, v_num=0, loss_step=43.80]
    Epoch 0:  63%|██████▎   | 57/91 [01:25<00:51,  0.66it/s, v_num=0, loss_step=43.80]
    Epoch 0:  63%|██████▎   | 57/91 [01:25<00:51,  0.66it/s, v_num=0, loss_step=43.80]
    Epoch 0:  64%|██████▎   | 58/91 [01:27<00:49,  0.66it/s, v_num=0, loss_step=43.80]
    Epoch 0:  64%|██████▎   | 58/91 [01:27<00:49,  0.66it/s, v_num=0, loss_step=41.80]
    Epoch 0:  65%|██████▍   | 59/91 [01:28<00:48,  0.66it/s, v_num=0, loss_step=41.80]
    Epoch 0:  65%|██████▍   | 59/91 [01:28<00:48,  0.66it/s, v_num=0, loss_step=41.90]
    Epoch 0:  66%|██████▌   | 60/91 [01:30<00:46,  0.66it/s, v_num=0, loss_step=41.90]
    Epoch 0:  66%|██████▌   | 60/91 [01:30<00:46,  0.66it/s, v_num=0, loss_step=41.80]
    Epoch 0:  67%|██████▋   | 61/91 [01:31<00:45,  0.66it/s, v_num=0, loss_step=41.80]
    Epoch 0:  67%|██████▋   | 61/91 [01:31<00:45,  0.66it/s, v_num=0, loss_step=41.70]
    Epoch 0:  68%|██████▊   | 62/91 [01:33<00:43,  0.66it/s, v_num=0, loss_step=41.70]
    Epoch 0:  68%|██████▊   | 62/91 [01:33<00:43,  0.66it/s, v_num=0, loss_step=41.40]
    Epoch 0:  69%|██████▉   | 63/91 [01:34<00:42,  0.66it/s, v_num=0, loss_step=41.40]
    Epoch 0:  69%|██████▉   | 63/91 [01:34<00:42,  0.66it/s, v_num=0, loss_step=41.50]
    Epoch 0:  70%|███████   | 64/91 [01:36<00:40,  0.66it/s, v_num=0, loss_step=41.50]
    Epoch 0:  70%|███████   | 64/91 [01:36<00:40,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  71%|███████▏  | 65/91 [01:37<00:39,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  71%|███████▏  | 65/91 [01:37<00:39,  0.66it/s, v_num=0, loss_step=40.50]
    Epoch 0:  73%|███████▎  | 66/91 [01:39<00:37,  0.66it/s, v_num=0, loss_step=40.50]
    Epoch 0:  73%|███████▎  | 66/91 [01:39<00:37,  0.66it/s, v_num=0, loss_step=39.60]
    Epoch 0:  74%|███████▎  | 67/91 [01:40<00:36,  0.66it/s, v_num=0, loss_step=39.60]
    Epoch 0:  74%|███████▎  | 67/91 [01:40<00:36,  0.66it/s, v_num=0, loss_step=40.70]
    Epoch 0:  75%|███████▍  | 68/91 [01:42<00:34,  0.66it/s, v_num=0, loss_step=40.70]
    Epoch 0:  75%|███████▍  | 68/91 [01:42<00:34,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  76%|███████▌  | 69/91 [01:43<00:33,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  76%|███████▌  | 69/91 [01:43<00:33,  0.66it/s, v_num=0, loss_step=38.50]
    Epoch 0:  77%|███████▋  | 70/91 [01:45<00:31,  0.66it/s, v_num=0, loss_step=38.50]
    Epoch 0:  77%|███████▋  | 70/91 [01:45<00:31,  0.66it/s, v_num=0, loss_step=39.00]
    Epoch 0:  78%|███████▊  | 71/91 [01:46<00:30,  0.66it/s, v_num=0, loss_step=39.00]
    Epoch 0:  78%|███████▊  | 71/91 [01:46<00:30,  0.66it/s, v_num=0, loss_step=38.90]
    Epoch 0:  79%|███████▉  | 72/91 [01:48<00:28,  0.66it/s, v_num=0, loss_step=38.90]
    Epoch 0:  79%|███████▉  | 72/91 [01:48<00:28,  0.66it/s, v_num=0, loss_step=39.40]
    Epoch 0:  80%|████████  | 73/91 [01:50<00:27,  0.66it/s, v_num=0, loss_step=39.40]
    Epoch 0:  80%|████████  | 73/91 [01:50<00:27,  0.66it/s, v_num=0, loss_step=38.90]
    Epoch 0:  81%|████████▏ | 74/91 [01:51<00:25,  0.66it/s, v_num=0, loss_step=38.90]
    Epoch 0:  81%|████████▏ | 74/91 [01:51<00:25,  0.66it/s, v_num=0, loss_step=38.50]
    Epoch 0:  82%|████████▏ | 75/91 [01:52<00:24,  0.66it/s, v_num=0, loss_step=38.50]
    Epoch 0:  82%|████████▏ | 75/91 [01:52<00:24,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  84%|████████▎ | 76/91 [01:54<00:22,  0.66it/s, v_num=0, loss_step=40.10]
    Epoch 0:  84%|████████▎ | 76/91 [01:54<00:22,  0.66it/s, v_num=0, loss_step=37.80]
    Epoch 0:  85%|████████▍ | 77/91 [01:55<00:21,  0.66it/s, v_num=0, loss_step=37.80]
    Epoch 0:  85%|████████▍ | 77/91 [01:56<00:21,  0.66it/s, v_num=0, loss_step=39.30]
    Epoch 0:  86%|████████▌ | 78/91 [01:57<00:19,  0.66it/s, v_num=0, loss_step=39.30]
    Epoch 0:  86%|████████▌ | 78/91 [01:57<00:19,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  87%|████████▋ | 79/91 [01:59<00:18,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  87%|████████▋ | 79/91 [01:59<00:18,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  88%|████████▊ | 80/91 [02:00<00:16,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  88%|████████▊ | 80/91 [02:00<00:16,  0.66it/s, v_num=0, loss_step=37.90]
    Epoch 0:  89%|████████▉ | 81/91 [02:02<00:15,  0.66it/s, v_num=0, loss_step=37.90]
    Epoch 0:  89%|████████▉ | 81/91 [02:02<00:15,  0.66it/s, v_num=0, loss_step=38.60]
    Epoch 0:  90%|█████████ | 82/91 [02:03<00:13,  0.66it/s, v_num=0, loss_step=38.60]
    Epoch 0:  90%|█████████ | 82/91 [02:03<00:13,  0.66it/s, v_num=0, loss_step=39.20]
    Epoch 0:  91%|█████████ | 83/91 [02:05<00:12,  0.66it/s, v_num=0, loss_step=39.20]
    Epoch 0:  91%|█████████ | 83/91 [02:05<00:12,  0.66it/s, v_num=0, loss_step=36.60]
    Epoch 0:  92%|█████████▏| 84/91 [02:06<00:10,  0.66it/s, v_num=0, loss_step=36.60]
    Epoch 0:  92%|█████████▏| 84/91 [02:06<00:10,  0.66it/s, v_num=0, loss_step=37.30]
    Epoch 0:  93%|█████████▎| 85/91 [02:08<00:09,  0.66it/s, v_num=0, loss_step=37.30]
    Epoch 0:  93%|█████████▎| 85/91 [02:08<00:09,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  95%|█████████▍| 86/91 [02:09<00:07,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0:  95%|█████████▍| 86/91 [02:09<00:07,  0.66it/s, v_num=0, loss_step=37.30]
    Epoch 0:  96%|█████████▌| 87/91 [02:11<00:06,  0.66it/s, v_num=0, loss_step=37.30]
    Epoch 0:  96%|█████████▌| 87/91 [02:11<00:06,  0.66it/s, v_num=0, loss_step=36.90]
    Epoch 0:  97%|█████████▋| 88/91 [02:12<00:04,  0.66it/s, v_num=0, loss_step=36.90]
    Epoch 0:  97%|█████████▋| 88/91 [02:12<00:04,  0.66it/s, v_num=0, loss_step=36.90]
    Epoch 0:  98%|█████████▊| 89/91 [02:14<00:03,  0.66it/s, v_num=0, loss_step=36.90]
    Epoch 0:  98%|█████████▊| 89/91 [02:14<00:03,  0.66it/s, v_num=0, loss_step=37.90]
    Epoch 0:  99%|█████████▉| 90/91 [02:15<00:01,  0.66it/s, v_num=0, loss_step=37.90]
    Epoch 0:  99%|█████████▉| 90/91 [02:15<00:01,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0: 100%|██████████| 91/91 [02:17<00:00,  0.66it/s, v_num=0, loss_step=38.40]
    Epoch 0: 100%|██████████| 91/91 [02:17<00:00,  0.66it/s, v_num=0, loss_step=38.00]
+
    Sanity Checking:   0%|          | 0/2 [00:00<?, ?it/s]
    Sanity Checking DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]
    Sanity Checking DataLoader 0:  50%|█████     | 1/2 [00:00<00:00,  2.24it/s]
    Sanity Checking DataLoader 0: 100%|██████████| 2/2 [00:00<00:00,  3.06it/s]
                                                                           

    Training: |          | 0/? [00:00<?, ?it/s]
    Training:   0%|          | 0/91 [00:00<?, ?it/s]
    Epoch 0:   0%|          | 0/91 [00:00<?, ?it/s] 
    Epoch 0:   1%|          | 1/91 [00:01<02:16,  0.66it/s]
    Epoch 0:   1%|          | 1/91 [00:01<02:16,  0.66it/s, v_num=0, loss_step=54.90]
    Epoch 0:   2%|▏         | 2/91 [00:03<02:13,  0.67it/s, v_num=0, loss_step=54.90]
    Epoch 0:   2%|▏         | 2/91 [00:03<02:13,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:   3%|▎         | 3/91 [00:04<02:11,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:   3%|▎         | 3/91 [00:04<02:11,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:   4%|▍         | 4/91 [00:05<02:10,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:   4%|▍         | 4/91 [00:05<02:10,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:   5%|▌         | 5/91 [00:07<02:08,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:   5%|▌         | 5/91 [00:07<02:08,  0.67it/s, v_num=0, loss_step=54.40]
    Epoch 0:   7%|▋         | 6/91 [00:08<02:07,  0.67it/s, v_num=0, loss_step=54.40]
    Epoch 0:   7%|▋         | 6/91 [00:08<02:07,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:   8%|▊         | 7/91 [00:10<02:05,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:   8%|▊         | 7/91 [00:10<02:05,  0.67it/s, v_num=0, loss_step=54.20]
    Epoch 0:   9%|▉         | 8/91 [00:11<02:04,  0.67it/s, v_num=0, loss_step=54.20]
    Epoch 0:   9%|▉         | 8/91 [00:11<02:04,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  10%|▉         | 9/91 [00:13<02:02,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  10%|▉         | 9/91 [00:13<02:02,  0.67it/s, v_num=0, loss_step=52.90]
    Epoch 0:  11%|█         | 10/91 [00:14<02:01,  0.67it/s, v_num=0, loss_step=52.90]
    Epoch 0:  11%|█         | 10/91 [00:14<02:01,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:  12%|█▏        | 11/91 [00:16<01:59,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:  12%|█▏        | 11/91 [00:16<01:59,  0.67it/s, v_num=0, loss_step=53.90]
    Epoch 0:  13%|█▎        | 12/91 [00:17<01:58,  0.67it/s, v_num=0, loss_step=53.90]
    Epoch 0:  13%|█▎        | 12/91 [00:17<01:58,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:  14%|█▍        | 13/91 [00:19<01:56,  0.67it/s, v_num=0, loss_step=53.80]
    Epoch 0:  14%|█▍        | 13/91 [00:19<01:56,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  15%|█▌        | 14/91 [00:20<01:55,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  15%|█▌        | 14/91 [00:20<01:55,  0.67it/s, v_num=0, loss_step=54.00]
    Epoch 0:  16%|█▋        | 15/91 [00:22<01:53,  0.67it/s, v_num=0, loss_step=54.00]
    Epoch 0:  16%|█▋        | 15/91 [00:22<01:53,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  18%|█▊        | 16/91 [00:23<01:52,  0.67it/s, v_num=0, loss_step=53.60]
    Epoch 0:  18%|█▊        | 16/91 [00:23<01:52,  0.67it/s, v_num=0, loss_step=52.80]
    Epoch 0:  19%|█▊        | 17/91 [00:25<01:50,  0.67it/s, v_num=0, loss_step=52.80]
    Epoch 0:  19%|█▊        | 17/91 [00:25<01:50,  0.67it/s, v_num=0, loss_step=53.50]
    Epoch 0:  20%|█▉        | 18/91 [00:27<01:49,  0.67it/s, v_num=0, loss_step=53.50]
    Epoch 0:  20%|█▉        | 18/91 [00:27<01:49,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:  21%|██        | 19/91 [00:28<01:47,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:  21%|██        | 19/91 [00:28<01:47,  0.67it/s, v_num=0, loss_step=52.70]
    Epoch 0:  22%|██▏       | 20/91 [00:29<01:46,  0.67it/s, v_num=0, loss_step=52.70]
    Epoch 0:  22%|██▏       | 20/91 [00:29<01:46,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:  23%|██▎       | 21/91 [00:31<01:44,  0.67it/s, v_num=0, loss_step=53.30]
    Epoch 0:  23%|██▎       | 21/91 [00:31<01:44,  0.67it/s, v_num=0, loss_step=53.20]
    Epoch 0:  24%|██▍       | 22/91 [00:32<01:43,  0.67it/s, v_num=0, loss_step=53.20]
    Epoch 0:  24%|██▍       | 22/91 [00:32<01:43,  0.67it/s, v_num=0, loss_step=52.30]
    Epoch 0:  25%|██▌       | 23/91 [00:34<01:41,  0.67it/s, v_num=0, loss_step=52.30]
    Epoch 0:  25%|██▌       | 23/91 [00:34<01:41,  0.67it/s, v_num=0, loss_step=53.20]
    Epoch 0:  26%|██▋       | 24/91 [00:35<01:40,  0.67it/s, v_num=0, loss_step=53.20]
    Epoch 0:  26%|██▋       | 24/91 [00:35<01:40,  0.67it/s, v_num=0, loss_step=53.40]
    Epoch 0:  27%|██▋       | 25/91 [00:37<01:38,  0.67it/s, v_num=0, loss_step=53.40]
    Epoch 0:  27%|██▋       | 25/91 [00:37<01:38,  0.67it/s, v_num=0, loss_step=52.90]
    Epoch 0:  29%|██▊       | 26/91 [00:38<01:37,  0.67it/s, v_num=0, loss_step=52.90]
    Epoch 0:  29%|██▊       | 26/91 [00:38<01:37,  0.67it/s, v_num=0, loss_step=52.40]
    Epoch 0:  30%|██▉       | 27/91 [00:40<01:35,  0.67it/s, v_num=0, loss_step=52.40]
    Epoch 0:  30%|██▉       | 27/91 [00:40<01:35,  0.67it/s, v_num=0, loss_step=52.60]
    Epoch 0:  31%|███       | 28/91 [00:41<01:34,  0.67it/s, v_num=0, loss_step=52.60]
    Epoch 0:  31%|███       | 28/91 [00:41<01:34,  0.67it/s, v_num=0, loss_step=52.70]
    Epoch 0:  32%|███▏      | 29/91 [00:43<01:32,  0.67it/s, v_num=0, loss_step=52.70]
    Epoch 0:  32%|███▏      | 29/91 [00:43<01:32,  0.67it/s, v_num=0, loss_step=52.60]
    Epoch 0:  33%|███▎      | 30/91 [00:44<01:31,  0.67it/s, v_num=0, loss_step=52.60]
    Epoch 0:  33%|███▎      | 30/91 [00:44<01:31,  0.67it/s, v_num=0, loss_step=52.10]
    Epoch 0:  34%|███▍      | 31/91 [00:46<01:29,  0.67it/s, v_num=0, loss_step=52.10]
    Epoch 0:  34%|███▍      | 31/91 [00:46<01:29,  0.67it/s, v_num=0, loss_step=52.00]
    Epoch 0:  35%|███▌      | 32/91 [00:47<01:28,  0.67it/s, v_num=0, loss_step=52.00]
    Epoch 0:  35%|███▌      | 32/91 [00:47<01:28,  0.67it/s, v_num=0, loss_step=51.50]
    Epoch 0:  36%|███▋      | 33/91 [00:49<01:26,  0.67it/s, v_num=0, loss_step=51.50]
    Epoch 0:  36%|███▋      | 33/91 [00:49<01:26,  0.67it/s, v_num=0, loss_step=52.40]
    Epoch 0:  37%|███▋      | 34/91 [00:50<01:25,  0.67it/s, v_num=0, loss_step=52.40]
    Epoch 0:  37%|███▋      | 34/91 [00:50<01:25,  0.67it/s, v_num=0, loss_step=51.90]
    Epoch 0:  38%|███▊      | 35/91 [00:52<01:23,  0.67it/s, v_num=0, loss_step=51.90]
    Epoch 0:  38%|███▊      | 35/91 [00:52<01:23,  0.67it/s, v_num=0, loss_step=51.20]
    Epoch 0:  40%|███▉      | 36/91 [00:53<01:22,  0.67it/s, v_num=0, loss_step=51.20]
    Epoch 0:  40%|███▉      | 36/91 [00:53<01:22,  0.67it/s, v_num=0, loss_step=50.70]
    Epoch 0:  41%|████      | 37/91 [00:55<01:20,  0.67it/s, v_num=0, loss_step=50.70]
    Epoch 0:  41%|████      | 37/91 [00:55<01:20,  0.67it/s, v_num=0, loss_step=50.70]
    Epoch 0:  42%|████▏     | 38/91 [00:56<01:19,  0.67it/s, v_num=0, loss_step=50.70]
    Epoch 0:  42%|████▏     | 38/91 [00:56<01:19,  0.67it/s, v_num=0, loss_step=49.60]
    Epoch 0:  43%|████▎     | 39/91 [00:58<01:17,  0.67it/s, v_num=0, loss_step=49.60]
    Epoch 0:  43%|████▎     | 39/91 [00:58<01:17,  0.67it/s, v_num=0, loss_step=49.40]
    Epoch 0:  44%|████▍     | 40/91 [00:59<01:16,  0.67it/s, v_num=0, loss_step=49.40]
    Epoch 0:  44%|████▍     | 40/91 [00:59<01:16,  0.67it/s, v_num=0, loss_step=48.90]
    Epoch 0:  45%|████▌     | 41/91 [01:01<01:14,  0.67it/s, v_num=0, loss_step=48.90]
    Epoch 0:  45%|████▌     | 41/91 [01:01<01:14,  0.67it/s, v_num=0, loss_step=48.90]
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    Epoch 0:  71%|███████▏  | 65/91 [01:37<00:38,  0.67it/s, v_num=0, loss_step=40.30]
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    Epoch 0:  73%|███████▎  | 66/91 [01:38<00:37,  0.67it/s, v_num=0, loss_step=39.40]
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    Epoch 0:  74%|███████▎  | 67/91 [01:39<00:35,  0.67it/s, v_num=0, loss_step=39.10]
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    Epoch 0:  75%|███████▍  | 68/91 [01:41<00:34,  0.67it/s, v_num=0, loss_step=39.50]
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    Epoch 0:  76%|███████▌  | 69/91 [01:43<00:32,  0.67it/s, v_num=0, loss_step=38.90]
    Epoch 0:  76%|███████▌  | 69/91 [01:43<00:32,  0.67it/s, v_num=0, loss_step=38.50]
    Epoch 0:  77%|███████▋  | 70/91 [01:44<00:31,  0.67it/s, v_num=0, loss_step=38.50]
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    Epoch 0:  78%|███████▊  | 71/91 [01:45<00:29,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  78%|███████▊  | 71/91 [01:45<00:29,  0.67it/s, v_num=0, loss_step=37.80]
    Epoch 0:  79%|███████▉  | 72/91 [01:47<00:28,  0.67it/s, v_num=0, loss_step=37.80]
    Epoch 0:  79%|███████▉  | 72/91 [01:47<00:28,  0.67it/s, v_num=0, loss_step=39.10]
    Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=39.10]
    Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=37.00]
    Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=37.00]
    Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=37.40]
    Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=37.40]
    Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=38.80]
    Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=38.80]
    Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=39.80]
    Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=39.80]
    Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=37.20]
    Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=37.20]
    Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=38.30]
    Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=38.30]
    Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=38.90]
    Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=38.90]
    Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=37.40]
    Epoch 0:  90%|█████████ | 82/91 [02:02<00:13,  0.67it/s, v_num=0, loss_step=37.40]
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    Epoch 0:  91%|█████████ | 83/91 [02:03<00:11,  0.67it/s, v_num=0, loss_step=38.10]
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    Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=37.30]
    Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=38.60]
    Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=38.60]
    Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=37.30]
    Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=37.30]
    Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=38.10]
    Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=38.10]
    Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=37.20]
    Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=37.20]
    Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=37.70]
    Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=36.80]
    Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=36.80]
    Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=38.20]
 
    Validation: |          | 0/? [00:00<?, ?it/s]
 
    Validation:   0%|          | 0/2 [00:00<?, ?it/s]
 
    Validation DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]
-
    Validation DataLoader 0:  50%|█████     | 1/2 [00:00<00:00,  2.41it/s]
-
    Validation DataLoader 0: 100%|██████████| 2/2 [00:00<00:00,  3.19it/s]
-
                                                                          
    Epoch 0: 100%|██████████| 91/91 [02:17<00:00,  0.66it/s, v_num=0, loss_step=38.00, val_loss=39.00]
    Epoch 0: 100%|██████████| 91/91 [02:17<00:00,  0.66it/s, v_num=0, loss_step=38.00, val_loss=39.00, loss_epoch=46.60]
    Epoch 0: 100%|██████████| 91/91 [02:17<00:00,  0.66it/s, v_num=0, loss_step=38.00, val_loss=39.00, loss_epoch=46.60]
+
    Validation DataLoader 0:  50%|█████     | 1/2 [00:00<00:00,  2.51it/s]
+
    Validation DataLoader 0: 100%|██████████| 2/2 [00:00<00:00,  3.30it/s]
+
                                                                          
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=38.20, val_loss=38.70]
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=38.20, val_loss=38.70, loss_epoch=46.10]
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=38.20, val_loss=38.70, loss_epoch=46.10]
 
 
 
@@ -117,9 +117,9 @@ Training the model
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (2 minutes 19.262 seconds)
+   **Total running time of the script:** (2 minutes 17.745 seconds)
 
-**Estimated memory usage:**  120 MB
+**Estimated memory usage:**  115 MB
 
 
 .. _sphx_glr_download_auto_examples_4_train_model.py:
diff --git a/_sources/auto_examples/sg_execution_times.rst.txt b/_sources/auto_examples/sg_execution_times.rst.txt
index 308f3b4..b2f3854 100644
--- a/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/_sources/auto_examples/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:51.692** total execution time for 4 files **from auto_examples**:
+**02:49.194** total execution time for 4 files **from auto_examples**:
 
 .. container::
 
@@ -33,14 +33,14 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_4_train_model.py` (``4_train_model.py``)
-     - 02:19.262
-     - 119.7
+     - 02:17.745
+     - 114.5
    * - :ref:`sphx_glr_auto_examples_3_create_dataset.py` (``3_create_dataset.py``)
-     - 00:20.328
-     - 982.5
+     - 00:19.833
+     - 945.4
    * - :ref:`sphx_glr_auto_examples_2_complex_filtering.py` (``2_complex_filtering.py``)
-     - 00:06.633
-     - 1313.3
+     - 00:06.337
+     - 1363.3
    * - :ref:`sphx_glr_auto_examples_1_understanding_basics.py` (``1_understanding_basics.py``)
-     - 00:05.469
-     - 555.9
+     - 00:05.279
+     - 553.4
diff --git a/_sources/sg_execution_times.rst.txt b/_sources/sg_execution_times.rst.txt
index e130795..8fe5756 100644
--- a/_sources/sg_execution_times.rst.txt
+++ b/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:51.692** total execution time for 4 files **from all galleries**:
+**02:49.194** total execution time for 4 files **from all galleries**:
 
 .. container::
 
@@ -33,14 +33,14 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_4_train_model.py` (``../../examples/4_train_model.py``)
-     - 02:19.262
-     - 119.7
+     - 02:17.745
+     - 114.5
    * - :ref:`sphx_glr_auto_examples_3_create_dataset.py` (``../../examples/3_create_dataset.py``)
-     - 00:20.328
-     - 982.5
+     - 00:19.833
+     - 945.4
    * - :ref:`sphx_glr_auto_examples_2_complex_filtering.py` (``../../examples/2_complex_filtering.py``)
-     - 00:06.633
-     - 1313.3
+     - 00:06.337
+     - 1363.3
    * - :ref:`sphx_glr_auto_examples_1_understanding_basics.py` (``../../examples/1_understanding_basics.py``)
-     - 00:05.469
-     - 555.9
+     - 00:05.279
+     - 553.4
diff --git a/auto_examples/1_understanding_basics.html b/auto_examples/1_understanding_basics.html
index 190f90b..649ac8c 100644
--- a/auto_examples/1_understanding_basics.html
+++ b/auto_examples/1_understanding_basics.html
@@ -482,8 +482,8 @@ <h2>Accessing the filtered data<a class="headerlink" href="#accessing-the-filter
 True
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.469 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  556 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.279 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  553 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-1-understanding-basics-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b3ef4e63c63f06a8c247e51688fd86a5/1_understanding_basics.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">1_understanding_basics.ipynb</span></code></a></p>
diff --git a/auto_examples/2_complex_filtering.html b/auto_examples/2_complex_filtering.html
index 24b4cfe..bad069e 100644
--- a/auto_examples/2_complex_filtering.html
+++ b/auto_examples/2_complex_filtering.html
@@ -575,8 +575,8 @@ <h2>Easier way of applying the filter pipeline<a class="headerlink" href="#easie
 --
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.633 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1313 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.337 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1363 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-2-complex-filtering-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/05bdb3140c2c6ae43d9ab96ca1aabb68/2_complex_filtering.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">2_complex_filtering.ipynb</span></code></a></p>
diff --git a/auto_examples/3_create_dataset.html b/auto_examples/3_create_dataset.html
index 4f57ddd..56fb246 100644
--- a/auto_examples/3_create_dataset.html
+++ b/auto_examples/3_create_dataset.html
@@ -360,30 +360,30 @@
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Filtering and splitting data:   0%|          | 0/2 [00:00&lt;?, ?it/s]
-Filtering and splitting data:  50%|█████     | 1/2 [00:02&lt;00:02,  2.19s/it]
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+Filtering and splitting data: 100%|██████████| 2/2 [00:04&lt;00:00,  2.20s/it]
+Filtering and splitting data: 100%|██████████| 2/2 [00:04&lt;00:00,  2.20s/it]
 
 Augmenting with [WaveletDecomposition (WaveletDecomposition)]:   0%|          | 0/317 [00:00&lt;?, ?it/s]
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-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  33%|███▎      | 105/317 [00:00&lt;00:01, 171.72it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  39%|███▉      | 123/317 [00:00&lt;00:01, 167.82it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  44%|████▍     | 140/317 [00:00&lt;00:01, 161.48it/s]
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-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  60%|██████    | 191/317 [00:01&lt;00:00, 160.48it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  66%|██████▌   | 208/317 [00:01&lt;00:00, 160.22it/s]
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-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  76%|███████▋  | 242/317 [00:01&lt;00:00, 160.92it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  82%|████████▏ | 259/317 [00:02&lt;00:01, 55.53it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  87%|████████▋ | 276/317 [00:02&lt;00:00, 69.54it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  92%|█████████▏| 293/317 [00:02&lt;00:00, 84.54it/s]
-Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  98%|█████████▊| 311/317 [00:02&lt;00:00, 100.50it/s]
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+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:   5%|▌         | 16/317 [00:00&lt;00:01, 156.00it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  10%|█         | 33/317 [00:00&lt;00:01, 163.98it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  16%|█▌        | 51/317 [00:00&lt;00:01, 167.84it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  22%|██▏       | 69/317 [00:00&lt;00:01, 169.72it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  27%|██▋       | 87/317 [00:00&lt;00:01, 170.70it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  33%|███▎      | 105/317 [00:00&lt;00:01, 171.53it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  39%|███▉      | 123/317 [00:00&lt;00:01, 167.73it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  44%|████▍     | 140/317 [00:00&lt;00:01, 163.34it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  50%|████▉     | 157/317 [00:00&lt;00:00, 163.32it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  55%|█████▍    | 174/317 [00:01&lt;00:00, 163.28it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  60%|██████    | 191/317 [00:01&lt;00:00, 163.78it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  66%|██████▌   | 208/317 [00:01&lt;00:00, 163.98it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  71%|███████   | 225/317 [00:01&lt;00:00, 163.68it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  76%|███████▋  | 242/317 [00:01&lt;00:00, 163.86it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  82%|████████▏ | 259/317 [00:02&lt;00:01, 56.67it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  87%|████████▋ | 277/317 [00:02&lt;00:00, 71.76it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  93%|█████████▎| 295/317 [00:02&lt;00:00, 87.56it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  99%|█████████▊| 313/317 [00:02&lt;00:00, 103.13it/s]
+Augmenting with [WaveletDecomposition (WaveletDecomposition)]: 100%|██████████| 317/317 [00:02&lt;00:00, 123.98it/s]
 </pre></div>
 </div>
 <p>Default dataset are also available. Here is an example of how to use the EMBCDataset used in <a class="footnote-reference brackets" href="#id4" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
@@ -406,78 +406,76 @@
 </pre></div>
 </div>
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 Filtering and splitting data: 100%|██████████| 2/2 [00:02&lt;00:00,  1.46s/it]
 Filtering and splitting data: 100%|██████████| 2/2 [00:02&lt;00:00,  1.46s/it]
 
 Augmenting with [GaussianNoise (GaussianNoise)]:   0%|          | 0/317 [00:00&lt;?, ?it/s]
 Augmenting with [GaussianNoise (GaussianNoise)]:   0%|          | 1/317 [00:01&lt;08:16,  1.57s/it]
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-Augmenting with [GaussianNoise (GaussianNoise)]:  11%|█         | 34/317 [00:01&lt;00:09, 30.30it/s]
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-Augmenting with [GaussianNoise (GaussianNoise)]:  37%|███▋      | 117/317 [00:02&lt;00:01, 113.59it/s]
-Augmenting with [GaussianNoise (GaussianNoise)]:  42%|████▏     | 133/317 [00:02&lt;00:01, 124.77it/s]
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-Augmenting with [GaussianNoise (GaussianNoise)]:  57%|█████▋    | 181/317 [00:02&lt;00:00, 140.30it/s]
-Augmenting with [GaussianNoise (GaussianNoise)]:  62%|██████▏   | 197/317 [00:02&lt;00:00, 144.08it/s]
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-Augmenting with [GaussianNoise (GaussianNoise)]:  87%|████████▋ | 277/317 [00:03&lt;00:00, 151.86it/s]
-Augmenting with [GaussianNoise (GaussianNoise)]:  92%|█████████▏| 293/317 [00:03&lt;00:00, 151.99it/s]
-Augmenting with [GaussianNoise (GaussianNoise)]:  97%|█████████▋| 309/317 [00:03&lt;00:00, 152.15it/s]
-Augmenting with [GaussianNoise (GaussianNoise)]: 100%|██████████| 317/317 [00:03&lt;00:00, 87.95it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:   7%|▋         | 21/317 [00:01&lt;00:17, 17.19it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  13%|█▎        | 41/317 [00:01&lt;00:07, 36.39it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  19%|█▉        | 61/317 [00:01&lt;00:04, 57.55it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  26%|██▌       | 81/317 [00:01&lt;00:02, 79.49it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  32%|███▏      | 101/317 [00:02&lt;00:02, 100.85it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  38%|███▊      | 121/317 [00:02&lt;00:01, 120.40it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  44%|████▍     | 141/317 [00:02&lt;00:01, 137.48it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  51%|█████     | 161/317 [00:02&lt;00:01, 151.16it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  57%|█████▋    | 180/317 [00:02&lt;00:00, 160.03it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  63%|██████▎   | 199/317 [00:02&lt;00:00, 167.13it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  69%|██████▉   | 218/317 [00:02&lt;00:00, 171.91it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  75%|███████▍  | 237/317 [00:02&lt;00:00, 173.53it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  81%|████████  | 256/317 [00:02&lt;00:00, 176.23it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  87%|████████▋ | 275/317 [00:03&lt;00:00, 179.00it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  93%|█████████▎| 294/317 [00:03&lt;00:00, 180.87it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]:  99%|█████████▊| 313/317 [00:03&lt;00:00, 182.19it/s]
+Augmenting with [GaussianNoise (GaussianNoise)]: 100%|██████████| 317/317 [00:03&lt;00:00, 97.58it/s]
 
 Augmenting with [MagnitudeWarping (MagnitudeWarping)]:   0%|          | 0/317 [00:00&lt;?, ?it/s]
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+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:   6%|▌         | 19/317 [00:00&lt;00:01, 182.20it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  12%|█▏        | 39/317 [00:00&lt;00:01, 189.95it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  19%|█▊        | 59/317 [00:00&lt;00:01, 191.90it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  25%|██▍       | 79/317 [00:00&lt;00:01, 186.82it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  31%|███       | 99/317 [00:00&lt;00:01, 190.10it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  38%|███▊      | 119/317 [00:00&lt;00:01, 192.51it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  44%|████▍     | 139/317 [00:00&lt;00:00, 194.72it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  50%|█████     | 159/317 [00:00&lt;00:00, 195.86it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  56%|█████▋    | 179/317 [00:00&lt;00:00, 196.89it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  63%|██████▎   | 199/317 [00:01&lt;00:00, 195.15it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  69%|██████▉   | 219/317 [00:01&lt;00:00, 192.20it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  75%|███████▌  | 239/317 [00:01&lt;00:00, 189.95it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  82%|████████▏ | 259/317 [00:01&lt;00:00, 188.55it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  88%|████████▊ | 278/317 [00:01&lt;00:00, 187.50it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]:  94%|█████████▎| 297/317 [00:01&lt;00:00, 186.68it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]: 100%|█████████▉| 316/317 [00:01&lt;00:00, 186.36it/s]
+Augmenting with [MagnitudeWarping (MagnitudeWarping)]: 100%|██████████| 317/317 [00:01&lt;00:00, 190.11it/s]
 
 Augmenting with [WaveletDecomposition (WaveletDecomposition)]:   0%|          | 0/317 [00:00&lt;?, ?it/s]
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+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  57%|█████▋    | 180/317 [00:01&lt;00:00, 147.85it/s]
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+Augmenting with [WaveletDecomposition (WaveletDecomposition)]:  66%|██████▌   | 210/317 [00:01&lt;00:00, 148.24it/s]
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-<p><strong>Estimated memory usage:</strong>  982 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 19.833 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  945 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-3-create-dataset-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6327ef2079176d026ee8af6cfdbdbd63/3_create_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">3_create_dataset.ipynb</span></code></a></p>
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-Epoch 0:  74%|███████▎  | 67/91 [01:40&lt;00:36,  0.66it/s, v_num=0, loss_step=40.70]
-Epoch 0:  75%|███████▍  | 68/91 [01:42&lt;00:34,  0.66it/s, v_num=0, loss_step=40.70]
-Epoch 0:  75%|███████▍  | 68/91 [01:42&lt;00:34,  0.66it/s, v_num=0, loss_step=40.10]
-Epoch 0:  76%|███████▌  | 69/91 [01:43&lt;00:33,  0.66it/s, v_num=0, loss_step=40.10]
-Epoch 0:  76%|███████▌  | 69/91 [01:43&lt;00:33,  0.66it/s, v_num=0, loss_step=38.50]
-Epoch 0:  77%|███████▋  | 70/91 [01:45&lt;00:31,  0.66it/s, v_num=0, loss_step=38.50]
-Epoch 0:  77%|███████▋  | 70/91 [01:45&lt;00:31,  0.66it/s, v_num=0, loss_step=39.00]
-Epoch 0:  78%|███████▊  | 71/91 [01:46&lt;00:30,  0.66it/s, v_num=0, loss_step=39.00]
-Epoch 0:  78%|███████▊  | 71/91 [01:46&lt;00:30,  0.66it/s, v_num=0, loss_step=38.90]
-Epoch 0:  79%|███████▉  | 72/91 [01:48&lt;00:28,  0.66it/s, v_num=0, loss_step=38.90]
-Epoch 0:  79%|███████▉  | 72/91 [01:48&lt;00:28,  0.66it/s, v_num=0, loss_step=39.40]
-Epoch 0:  80%|████████  | 73/91 [01:50&lt;00:27,  0.66it/s, v_num=0, loss_step=39.40]
-Epoch 0:  80%|████████  | 73/91 [01:50&lt;00:27,  0.66it/s, v_num=0, loss_step=38.90]
-Epoch 0:  81%|████████▏ | 74/91 [01:51&lt;00:25,  0.66it/s, v_num=0, loss_step=38.90]
-Epoch 0:  81%|████████▏ | 74/91 [01:51&lt;00:25,  0.66it/s, v_num=0, loss_step=38.50]
-Epoch 0:  82%|████████▏ | 75/91 [01:52&lt;00:24,  0.66it/s, v_num=0, loss_step=38.50]
-Epoch 0:  82%|████████▏ | 75/91 [01:52&lt;00:24,  0.66it/s, v_num=0, loss_step=40.10]
-Epoch 0:  84%|████████▎ | 76/91 [01:54&lt;00:22,  0.66it/s, v_num=0, loss_step=40.10]
-Epoch 0:  84%|████████▎ | 76/91 [01:54&lt;00:22,  0.66it/s, v_num=0, loss_step=37.80]
-Epoch 0:  85%|████████▍ | 77/91 [01:55&lt;00:21,  0.66it/s, v_num=0, loss_step=37.80]
-Epoch 0:  85%|████████▍ | 77/91 [01:56&lt;00:21,  0.66it/s, v_num=0, loss_step=39.30]
-Epoch 0:  86%|████████▌ | 78/91 [01:57&lt;00:19,  0.66it/s, v_num=0, loss_step=39.30]
-Epoch 0:  86%|████████▌ | 78/91 [01:57&lt;00:19,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  87%|████████▋ | 79/91 [01:59&lt;00:18,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  87%|████████▋ | 79/91 [01:59&lt;00:18,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  88%|████████▊ | 80/91 [02:00&lt;00:16,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  88%|████████▊ | 80/91 [02:00&lt;00:16,  0.66it/s, v_num=0, loss_step=37.90]
-Epoch 0:  89%|████████▉ | 81/91 [02:02&lt;00:15,  0.66it/s, v_num=0, loss_step=37.90]
-Epoch 0:  89%|████████▉ | 81/91 [02:02&lt;00:15,  0.66it/s, v_num=0, loss_step=38.60]
-Epoch 0:  90%|█████████ | 82/91 [02:03&lt;00:13,  0.66it/s, v_num=0, loss_step=38.60]
-Epoch 0:  90%|█████████ | 82/91 [02:03&lt;00:13,  0.66it/s, v_num=0, loss_step=39.20]
-Epoch 0:  91%|█████████ | 83/91 [02:05&lt;00:12,  0.66it/s, v_num=0, loss_step=39.20]
-Epoch 0:  91%|█████████ | 83/91 [02:05&lt;00:12,  0.66it/s, v_num=0, loss_step=36.60]
-Epoch 0:  92%|█████████▏| 84/91 [02:06&lt;00:10,  0.66it/s, v_num=0, loss_step=36.60]
-Epoch 0:  92%|█████████▏| 84/91 [02:06&lt;00:10,  0.66it/s, v_num=0, loss_step=37.30]
-Epoch 0:  93%|█████████▎| 85/91 [02:08&lt;00:09,  0.66it/s, v_num=0, loss_step=37.30]
-Epoch 0:  93%|█████████▎| 85/91 [02:08&lt;00:09,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  95%|█████████▍| 86/91 [02:09&lt;00:07,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0:  95%|█████████▍| 86/91 [02:09&lt;00:07,  0.66it/s, v_num=0, loss_step=37.30]
-Epoch 0:  96%|█████████▌| 87/91 [02:11&lt;00:06,  0.66it/s, v_num=0, loss_step=37.30]
-Epoch 0:  96%|█████████▌| 87/91 [02:11&lt;00:06,  0.66it/s, v_num=0, loss_step=36.90]
-Epoch 0:  97%|█████████▋| 88/91 [02:12&lt;00:04,  0.66it/s, v_num=0, loss_step=36.90]
-Epoch 0:  97%|█████████▋| 88/91 [02:12&lt;00:04,  0.66it/s, v_num=0, loss_step=36.90]
-Epoch 0:  98%|█████████▊| 89/91 [02:14&lt;00:03,  0.66it/s, v_num=0, loss_step=36.90]
-Epoch 0:  98%|█████████▊| 89/91 [02:14&lt;00:03,  0.66it/s, v_num=0, loss_step=37.90]
-Epoch 0:  99%|█████████▉| 90/91 [02:15&lt;00:01,  0.66it/s, v_num=0, loss_step=37.90]
-Epoch 0:  99%|█████████▉| 90/91 [02:15&lt;00:01,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0: 100%|██████████| 91/91 [02:17&lt;00:00,  0.66it/s, v_num=0, loss_step=38.40]
-Epoch 0: 100%|██████████| 91/91 [02:17&lt;00:00,  0.66it/s, v_num=0, loss_step=38.00]
+Epoch 0:   1%|          | 1/91 [00:01&lt;02:16,  0.66it/s]
+Epoch 0:   1%|          | 1/91 [00:01&lt;02:16,  0.66it/s, v_num=0, loss_step=54.90]
+Epoch 0:   2%|▏         | 2/91 [00:03&lt;02:13,  0.67it/s, v_num=0, loss_step=54.90]
+Epoch 0:   2%|▏         | 2/91 [00:03&lt;02:13,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:   3%|▎         | 3/91 [00:04&lt;02:11,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:   3%|▎         | 3/91 [00:04&lt;02:11,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:   4%|▍         | 4/91 [00:05&lt;02:10,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:   4%|▍         | 4/91 [00:05&lt;02:10,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:   5%|▌         | 5/91 [00:07&lt;02:08,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:   5%|▌         | 5/91 [00:07&lt;02:08,  0.67it/s, v_num=0, loss_step=54.40]
+Epoch 0:   7%|▋         | 6/91 [00:08&lt;02:07,  0.67it/s, v_num=0, loss_step=54.40]
+Epoch 0:   7%|▋         | 6/91 [00:08&lt;02:07,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:   8%|▊         | 7/91 [00:10&lt;02:05,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:   8%|▊         | 7/91 [00:10&lt;02:05,  0.67it/s, v_num=0, loss_step=54.20]
+Epoch 0:   9%|▉         | 8/91 [00:11&lt;02:04,  0.67it/s, v_num=0, loss_step=54.20]
+Epoch 0:   9%|▉         | 8/91 [00:11&lt;02:04,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  10%|▉         | 9/91 [00:13&lt;02:02,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  10%|▉         | 9/91 [00:13&lt;02:02,  0.67it/s, v_num=0, loss_step=52.90]
+Epoch 0:  11%|█         | 10/91 [00:14&lt;02:01,  0.67it/s, v_num=0, loss_step=52.90]
+Epoch 0:  11%|█         | 10/91 [00:14&lt;02:01,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:  12%|█▏        | 11/91 [00:16&lt;01:59,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:  12%|█▏        | 11/91 [00:16&lt;01:59,  0.67it/s, v_num=0, loss_step=53.90]
+Epoch 0:  13%|█▎        | 12/91 [00:17&lt;01:58,  0.67it/s, v_num=0, loss_step=53.90]
+Epoch 0:  13%|█▎        | 12/91 [00:17&lt;01:58,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:  14%|█▍        | 13/91 [00:19&lt;01:56,  0.67it/s, v_num=0, loss_step=53.80]
+Epoch 0:  14%|█▍        | 13/91 [00:19&lt;01:56,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  15%|█▌        | 14/91 [00:20&lt;01:55,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  15%|█▌        | 14/91 [00:20&lt;01:55,  0.67it/s, v_num=0, loss_step=54.00]
+Epoch 0:  16%|█▋        | 15/91 [00:22&lt;01:53,  0.67it/s, v_num=0, loss_step=54.00]
+Epoch 0:  16%|█▋        | 15/91 [00:22&lt;01:53,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  18%|█▊        | 16/91 [00:23&lt;01:52,  0.67it/s, v_num=0, loss_step=53.60]
+Epoch 0:  18%|█▊        | 16/91 [00:23&lt;01:52,  0.67it/s, v_num=0, loss_step=52.80]
+Epoch 0:  19%|█▊        | 17/91 [00:25&lt;01:50,  0.67it/s, v_num=0, loss_step=52.80]
+Epoch 0:  19%|█▊        | 17/91 [00:25&lt;01:50,  0.67it/s, v_num=0, loss_step=53.50]
+Epoch 0:  20%|█▉        | 18/91 [00:27&lt;01:49,  0.67it/s, v_num=0, loss_step=53.50]
+Epoch 0:  20%|█▉        | 18/91 [00:27&lt;01:49,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:  21%|██        | 19/91 [00:28&lt;01:47,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:  21%|██        | 19/91 [00:28&lt;01:47,  0.67it/s, v_num=0, loss_step=52.70]
+Epoch 0:  22%|██▏       | 20/91 [00:29&lt;01:46,  0.67it/s, v_num=0, loss_step=52.70]
+Epoch 0:  22%|██▏       | 20/91 [00:29&lt;01:46,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:  23%|██▎       | 21/91 [00:31&lt;01:44,  0.67it/s, v_num=0, loss_step=53.30]
+Epoch 0:  23%|██▎       | 21/91 [00:31&lt;01:44,  0.67it/s, v_num=0, loss_step=53.20]
+Epoch 0:  24%|██▍       | 22/91 [00:32&lt;01:43,  0.67it/s, v_num=0, loss_step=53.20]
+Epoch 0:  24%|██▍       | 22/91 [00:32&lt;01:43,  0.67it/s, v_num=0, loss_step=52.30]
+Epoch 0:  25%|██▌       | 23/91 [00:34&lt;01:41,  0.67it/s, v_num=0, loss_step=52.30]
+Epoch 0:  25%|██▌       | 23/91 [00:34&lt;01:41,  0.67it/s, v_num=0, loss_step=53.20]
+Epoch 0:  26%|██▋       | 24/91 [00:35&lt;01:40,  0.67it/s, v_num=0, loss_step=53.20]
+Epoch 0:  26%|██▋       | 24/91 [00:35&lt;01:40,  0.67it/s, v_num=0, loss_step=53.40]
+Epoch 0:  27%|██▋       | 25/91 [00:37&lt;01:38,  0.67it/s, v_num=0, loss_step=53.40]
+Epoch 0:  27%|██▋       | 25/91 [00:37&lt;01:38,  0.67it/s, v_num=0, loss_step=52.90]
+Epoch 0:  29%|██▊       | 26/91 [00:38&lt;01:37,  0.67it/s, v_num=0, loss_step=52.90]
+Epoch 0:  29%|██▊       | 26/91 [00:38&lt;01:37,  0.67it/s, v_num=0, loss_step=52.40]
+Epoch 0:  30%|██▉       | 27/91 [00:40&lt;01:35,  0.67it/s, v_num=0, loss_step=52.40]
+Epoch 0:  30%|██▉       | 27/91 [00:40&lt;01:35,  0.67it/s, v_num=0, loss_step=52.60]
+Epoch 0:  31%|███       | 28/91 [00:41&lt;01:34,  0.67it/s, v_num=0, loss_step=52.60]
+Epoch 0:  31%|███       | 28/91 [00:41&lt;01:34,  0.67it/s, v_num=0, loss_step=52.70]
+Epoch 0:  32%|███▏      | 29/91 [00:43&lt;01:32,  0.67it/s, v_num=0, loss_step=52.70]
+Epoch 0:  32%|███▏      | 29/91 [00:43&lt;01:32,  0.67it/s, v_num=0, loss_step=52.60]
+Epoch 0:  33%|███▎      | 30/91 [00:44&lt;01:31,  0.67it/s, v_num=0, loss_step=52.60]
+Epoch 0:  33%|███▎      | 30/91 [00:44&lt;01:31,  0.67it/s, v_num=0, loss_step=52.10]
+Epoch 0:  34%|███▍      | 31/91 [00:46&lt;01:29,  0.67it/s, v_num=0, loss_step=52.10]
+Epoch 0:  34%|███▍      | 31/91 [00:46&lt;01:29,  0.67it/s, v_num=0, loss_step=52.00]
+Epoch 0:  35%|███▌      | 32/91 [00:47&lt;01:28,  0.67it/s, v_num=0, loss_step=52.00]
+Epoch 0:  35%|███▌      | 32/91 [00:47&lt;01:28,  0.67it/s, v_num=0, loss_step=51.50]
+Epoch 0:  36%|███▋      | 33/91 [00:49&lt;01:26,  0.67it/s, v_num=0, loss_step=51.50]
+Epoch 0:  36%|███▋      | 33/91 [00:49&lt;01:26,  0.67it/s, v_num=0, loss_step=52.40]
+Epoch 0:  37%|███▋      | 34/91 [00:50&lt;01:25,  0.67it/s, v_num=0, loss_step=52.40]
+Epoch 0:  37%|███▋      | 34/91 [00:50&lt;01:25,  0.67it/s, v_num=0, loss_step=51.90]
+Epoch 0:  38%|███▊      | 35/91 [00:52&lt;01:23,  0.67it/s, v_num=0, loss_step=51.90]
+Epoch 0:  38%|███▊      | 35/91 [00:52&lt;01:23,  0.67it/s, v_num=0, loss_step=51.20]
+Epoch 0:  40%|███▉      | 36/91 [00:53&lt;01:22,  0.67it/s, v_num=0, loss_step=51.20]
+Epoch 0:  40%|███▉      | 36/91 [00:53&lt;01:22,  0.67it/s, v_num=0, loss_step=50.70]
+Epoch 0:  41%|████      | 37/91 [00:55&lt;01:20,  0.67it/s, v_num=0, loss_step=50.70]
+Epoch 0:  41%|████      | 37/91 [00:55&lt;01:20,  0.67it/s, v_num=0, loss_step=50.70]
+Epoch 0:  42%|████▏     | 38/91 [00:56&lt;01:19,  0.67it/s, v_num=0, loss_step=50.70]
+Epoch 0:  42%|████▏     | 38/91 [00:56&lt;01:19,  0.67it/s, v_num=0, loss_step=49.60]
+Epoch 0:  43%|████▎     | 39/91 [00:58&lt;01:17,  0.67it/s, v_num=0, loss_step=49.60]
+Epoch 0:  43%|████▎     | 39/91 [00:58&lt;01:17,  0.67it/s, v_num=0, loss_step=49.40]
+Epoch 0:  44%|████▍     | 40/91 [00:59&lt;01:16,  0.67it/s, v_num=0, loss_step=49.40]
+Epoch 0:  44%|████▍     | 40/91 [00:59&lt;01:16,  0.67it/s, v_num=0, loss_step=48.90]
+Epoch 0:  45%|████▌     | 41/91 [01:01&lt;01:14,  0.67it/s, v_num=0, loss_step=48.90]
+Epoch 0:  45%|████▌     | 41/91 [01:01&lt;01:14,  0.67it/s, v_num=0, loss_step=48.90]
+Epoch 0:  46%|████▌     | 42/91 [01:02&lt;01:13,  0.67it/s, v_num=0, loss_step=48.90]
+Epoch 0:  46%|████▌     | 42/91 [01:02&lt;01:13,  0.67it/s, v_num=0, loss_step=48.10]
+Epoch 0:  47%|████▋     | 43/91 [01:04&lt;01:11,  0.67it/s, v_num=0, loss_step=48.10]
+Epoch 0:  47%|████▋     | 43/91 [01:04&lt;01:11,  0.67it/s, v_num=0, loss_step=49.10]
+Epoch 0:  48%|████▊     | 44/91 [01:05&lt;01:10,  0.67it/s, v_num=0, loss_step=49.10]
+Epoch 0:  48%|████▊     | 44/91 [01:05&lt;01:10,  0.67it/s, v_num=0, loss_step=47.10]
+Epoch 0:  49%|████▉     | 45/91 [01:07&lt;01:08,  0.67it/s, v_num=0, loss_step=47.10]
+Epoch 0:  49%|████▉     | 45/91 [01:07&lt;01:08,  0.67it/s, v_num=0, loss_step=46.90]
+Epoch 0:  51%|█████     | 46/91 [01:08&lt;01:07,  0.67it/s, v_num=0, loss_step=46.90]
+Epoch 0:  51%|█████     | 46/91 [01:08&lt;01:07,  0.67it/s, v_num=0, loss_step=46.50]
+Epoch 0:  52%|█████▏    | 47/91 [01:10&lt;01:05,  0.67it/s, v_num=0, loss_step=46.50]
+Epoch 0:  52%|█████▏    | 47/91 [01:10&lt;01:05,  0.67it/s, v_num=0, loss_step=46.50]
+Epoch 0:  53%|█████▎    | 48/91 [01:11&lt;01:04,  0.67it/s, v_num=0, loss_step=46.50]
+Epoch 0:  53%|█████▎    | 48/91 [01:11&lt;01:04,  0.67it/s, v_num=0, loss_step=45.50]
+Epoch 0:  54%|█████▍    | 49/91 [01:13&lt;01:02,  0.67it/s, v_num=0, loss_step=45.50]
+Epoch 0:  54%|█████▍    | 49/91 [01:13&lt;01:02,  0.67it/s, v_num=0, loss_step=44.80]
+Epoch 0:  55%|█████▍    | 50/91 [01:14&lt;01:01,  0.67it/s, v_num=0, loss_step=44.80]
+Epoch 0:  55%|█████▍    | 50/91 [01:14&lt;01:01,  0.67it/s, v_num=0, loss_step=45.90]
+Epoch 0:  56%|█████▌    | 51/91 [01:16&lt;00:59,  0.67it/s, v_num=0, loss_step=45.90]
+Epoch 0:  56%|█████▌    | 51/91 [01:16&lt;00:59,  0.67it/s, v_num=0, loss_step=44.20]
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 Validation: |          | 0/? [00:00&lt;?, ?it/s]
 
@@ -545,18 +545,18 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
 
 Validation DataLoader 0:   0%|          | 0/2 [00:00&lt;?, ?it/s]
 
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-Validation DataLoader 0: 100%|██████████| 2/2 [00:00&lt;00:00,  3.19it/s]
+Validation DataLoader 0: 100%|██████████| 2/2 [00:00&lt;00:00,  3.30it/s]
 
 
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+Epoch 0: 100%|██████████| 91/91 [02:16&lt;00:00,  0.67it/s, v_num=0, loss_step=38.20, val_loss=38.70]
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+Epoch 0: 100%|██████████| 91/91 [02:16&lt;00:00,  0.67it/s, v_num=0, loss_step=38.20, val_loss=38.70, loss_epoch=46.10]
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 <tr class="row-even"><td><p><a class="reference internal" href="4_train_model.html#sphx-glr-auto-examples-4-train-model-py"><span class="std std-ref">Training a deep learning model</span></a> (<code class="docutils literal notranslate"><span class="pre">4_train_model.py</span></code>)</p></td>
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-<td><p>00:05.469</p></td>
-<td><p>555.9</p></td>
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diff --git a/searchindex.js b/searchindex.js
index 0f178ce..00ecec7 100644
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\ No newline at end of file
diff --git a/sg_execution_times.html b/sg_execution_times.html
index 17b55c5..6b5f984 100644
--- a/sg_execution_times.html
+++ b/sg_execution_times.html
@@ -285,7 +285,7 @@
         <article role="main" id="furo-main-content">
           <section id="computation-times">
 <span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>02:51.692</strong> total execution time for 4 files <strong>from all galleries</strong>:</p>
+<p><strong>02:49.194</strong> total execution time for 4 files <strong>from all galleries</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -308,20 +308,20 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/4_train_model.html#sphx-glr-auto-examples-4-train-model-py"><span class="std std-ref">Training a deep learning model</span></a> (<code class="docutils literal notranslate"><span class="pre">../../examples/4_train_model.py</span></code>)</p></td>
-<td><p>02:19.262</p></td>
-<td><p>119.7</p></td>
+<td><p>02:17.745</p></td>
+<td><p>114.5</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/3_create_dataset.html#sphx-glr-auto-examples-3-create-dataset-py"><span class="std std-ref">Creating a dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../../examples/3_create_dataset.py</span></code>)</p></td>
-<td><p>00:20.328</p></td>
-<td><p>982.5</p></td>
+<td><p>00:19.833</p></td>
+<td><p>945.4</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/2_complex_filtering.html#sphx-glr-auto-examples-2-complex-filtering-py"><span class="std std-ref">Complex filtering</span></a> (<code class="docutils literal notranslate"><span class="pre">../../examples/2_complex_filtering.py</span></code>)</p></td>
-<td><p>00:06.633</p></td>
-<td><p>1313.3</p></td>
+<td><p>00:06.337</p></td>
+<td><p>1363.3</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/1_understanding_basics.html#sphx-glr-auto-examples-1-understanding-basics-py"><span class="std std-ref">Package basics</span></a> (<code class="docutils literal notranslate"><span class="pre">../../examples/1_understanding_basics.py</span></code>)</p></td>
-<td><p>00:05.469</p></td>
-<td><p>555.9</p></td>
+<td><p>00:05.279</p></td>
+<td><p>553.4</p></td>
 </tr>
 </tbody>
 </table>