some change to notebookdocs
Esse commit está contido em:
Robin Tibor Schirrmeister
@@ -447,7 +447,7 @@
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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": "3.6.2"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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@@ -359,7 +359,7 @@
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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": "3.6.2"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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@@ -346,7 +346,7 @@
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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": "3.6.2"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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@@ -50,7 +50,9 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import mne\n",
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@@ -93,7 +95,9 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@@ -195,7 +199,9 @@
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
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@@ -227,7 +233,9 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from braindecode.torch_ext.optimizers import AdamW\n",
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@@ -269,105 +277,105 @@
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"output_type": "stream",
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"text": [
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"Epoch 0\n",
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"Train Loss: 3.88829\n",
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"Train Loss: 3.79010\n",
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"Train Accuracy: 50.0%\n",
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"Valid Loss: 3.18595\n",
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"Valid Accuracy: 50.0%\n",
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"Valid Loss: 3.12765\n",
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"Valid Accuracy: 46.7%\n",
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"Epoch 1\n",
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"Train Loss: 1.33431\n",
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"Train Accuracy: 55.0%\n",
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"Valid Loss: 0.96900\n",
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"Valid Accuracy: 60.0%\n",
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"Train Loss: 1.91778\n",
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"Train Accuracy: 50.0%\n",
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"Valid Loss: 1.52058\n",
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"Valid Accuracy: 46.7%\n",
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"Epoch 2\n",
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"Train Loss: 0.60838\n",
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"Train Accuracy: 77.5%\n",
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"Valid Loss: 0.55685\n",
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"Valid Accuracy: 76.7%\n",
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"Train Loss: 1.09943\n",
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"Train Accuracy: 60.0%\n",
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"Valid Loss: 0.99602\n",
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"Valid Accuracy: 56.7%\n",
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"Epoch 3\n",
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"Train Loss: 0.49553\n",
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"Train Accuracy: 77.5%\n",
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"Valid Loss: 0.57463\n",
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"Valid Accuracy: 76.7%\n",
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"Train Loss: 0.73015\n",
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"Train Accuracy: 67.5%\n",
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"Valid Loss: 0.82321\n",
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"Valid Accuracy: 56.7%\n",
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"Epoch 4\n",
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"Train Loss: 0.46749\n",
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"Train Accuracy: 80.0%\n",
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"Valid Loss: 0.62036\n",
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"Valid Accuracy: 76.7%\n",
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"Train Loss: 0.55965\n",
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"Train Accuracy: 75.0%\n",
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"Valid Loss: 0.77549\n",
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"Valid Accuracy: 63.3%\n",
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"Epoch 5\n",
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"Train Loss: 0.36088\n",
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"Train Accuracy: 85.0%\n",
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"Valid Loss: 0.59054\n",
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"Valid Accuracy: 76.7%\n",
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"Train Loss: 0.38075\n",
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"Train Accuracy: 82.5%\n",
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"Valid Loss: 0.65490\n",
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"Valid Accuracy: 73.3%\n",
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"Epoch 6\n",
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"Train Loss: 0.26471\n",
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"Train Loss: 0.28886\n",
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"Train Accuracy: 90.0%\n",
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"Valid Loss: 0.55745\n",
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"Valid Accuracy: 76.7%\n",
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"Valid Loss: 0.59481\n",
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"Valid Accuracy: 73.3%\n",
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"Epoch 7\n",
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"Train Loss: 0.19023\n",
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"Train Accuracy: 95.0%\n",
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"Valid Loss: 0.53162\n",
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"Train Loss: 0.21871\n",
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"Train Accuracy: 97.5%\n",
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"Valid Loss: 0.52357\n",
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"Valid Accuracy: 83.3%\n",
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"Epoch 8\n",
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"Train Loss: 0.13684\n",
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"Train Loss: 0.16713\n",
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"Train Accuracy: 97.5%\n",
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"Valid Loss: 0.50510\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.44890\n",
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"Valid Accuracy: 86.7%\n",
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"Epoch 9\n",
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"Train Loss: 0.10682\n",
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"Train Loss: 0.13149\n",
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"Train Accuracy: 97.5%\n",
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"Valid Loss: 0.47980\n",
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"Valid Accuracy: 86.7%\n",
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"Valid Loss: 0.40492\n",
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"Valid Accuracy: 83.3%\n",
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"Epoch 10\n",
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"Train Loss: 0.08165\n",
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"Train Loss: 0.09581\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.45300\n",
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"Valid Accuracy: 86.7%\n",
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"Valid Loss: 0.36021\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 11\n",
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"Train Loss: 0.05927\n",
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"Train Loss: 0.07818\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.43593\n",
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"Valid Accuracy: 86.7%\n",
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"Valid Loss: 0.34625\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 12\n",
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"Train Loss: 0.04447\n",
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"Train Loss: 0.07454\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.43543\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.34489\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 13\n",
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"Train Loss: 0.03776\n",
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"Train Loss: 0.06694\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.43941\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.33878\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 14\n",
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"Train Loss: 0.03341\n",
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"Train Loss: 0.05971\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.43653\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.33128\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 15\n",
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"Train Loss: 0.02990\n",
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"Train Loss: 0.05269\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.43027\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.32202\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 16\n",
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"Train Loss: 0.02713\n",
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"Train Loss: 0.04354\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.42535\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.31063\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 17\n",
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"Train Loss: 0.02483\n",
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"Train Loss: 0.03759\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.42535\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.30314\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 18\n",
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"Train Loss: 0.02301\n",
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"Train Loss: 0.03401\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.42749\n",
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"Valid Accuracy: 83.3%\n",
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"Valid Loss: 0.29997\n",
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"Valid Accuracy: 90.0%\n",
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"Epoch 19\n",
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"Train Loss: 0.02164\n",
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"Train Loss: 0.03145\n",
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"Train Accuracy: 100.0%\n",
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"Valid Loss: 0.42855\n",
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"Valid Accuracy: 86.7%\n"
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"Valid Loss: 0.29764\n",
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"Valid Accuracy: 90.0%\n"
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]
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}
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],
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@@ -381,7 +389,7 @@
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"for i_epoch in range(20):\n",
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" # Set model to training mode\n",
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" model.train()\n",
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" for batch_X, batch_y in iterator.get_batches(train_set, shuffle=False):\n",
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" for batch_X, batch_y in iterator.get_batches(train_set, shuffle=True):\n",
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" net_in = np_to_var(batch_X)\n",
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" if cuda:\n",
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" net_in = net_in.cuda()\n",
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@@ -443,7 +451,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Eventually, we arrive at 86.7% accuracy, so 26 from 30 trials are correctly predicted, 4 more than for the trialwise decoding method."
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"Eventually, we arrive at 90.0% accuracy, so 27 from 30 trials are correctly predicted, 5 more than for the trialwise decoding method."
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]
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},
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{
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@@ -462,14 +470,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test Loss: 0.43439\n",
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"Test Loss: 0.42105\n",
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"Test Accuracy: 85.0%\n"
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]
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}
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@@ -0,0 +1,318 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"from matplotlib import pyplot as plt\n",
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"from matplotlib import cm\n",
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"%matplotlib inline\n",
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"%config InlineBackend.figure_format = 'png'\n",
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"matplotlib.rcParams['figure.figsize'] = (12.0, 4.0)\n",
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"matplotlib.rcParams['font.size'] = 7\n",
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"\n",
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"import matplotlib.lines as mlines\n",
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"import seaborn\n",
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"seaborn.set_style('darkgrid')\n",
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"import logging\n",
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"import importlib\n",
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"importlib.reload(logging) # see https://stackoverflow.com/a/21475297/1469195\n",
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"log = logging.getLogger()\n",
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"log.setLevel('DEBUG')\n",
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"import sys\n",
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"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
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" level=logging.DEBUG, stream=sys.stdout)\n",
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"seaborn.set_palette('colorblind')\n",
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"import os\n",
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"# add the repo itself\n",
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"os.sys.path.insert(0, '/home/schirrmr/code/explaining/reversible//')\n",
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"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from braindecode.datasets.bbci import BBCIDataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"cnt = BBCIDataset('/data/schirrmr/schirrmr/HGD-public/reduced/train/8.mat', load_sensor_names=['C3', 'C4', 'Cz']).load()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In trialwise decoding, you forward trials through your network and get one prediction per trial. Then you train the network using the loss between the predictions and the labels for the trials.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(5,5))\n",
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"data = cnt.get_data()[0,100:201]\n",
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"plt.plot(data - np.mean(data))\n",
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"ax = plt.gca()\n",
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"plt.plot([0,0],[-20,20], color='black')\n",
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"plt.plot([100,100],[-20,20], color='black')\n",
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"points = [[0, -20], [50, -80], [100, -20]]\n",
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"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
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"ax.add_artist(polygon)\n",
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"plt.ylim(-100,22)\n",
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"plt.text(50,30, \"Trial\", fontsize=20, ha='center')\n",
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"plt.text(50,-40, \"Network\", fontsize=20, ha='center')\n",
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"plt.plot(50,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
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"plt.text(45,-80, \"Prediction\", fontsize=20, ha='right', va='center')\n",
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"plt.plot(50,-95, ls='', marker='o')\n",
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"plt.text(45,-95, \"Target\", fontsize=20, ha='right', va='center')\n",
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"plt.axis('off')\n",
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"\n",
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"plt.plot([50,65],[-80, -87.5], color='black')\n",
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"plt.plot([50,65],[-95, -87.5], color='black')\n",
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"plt.text(67,-87.5, \"Loss\", fontsize=20, ha='left', va='center')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(5,5))\n",
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"data = cnt.get_data()[0,100:201]\n",
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"plt.plot(data - np.mean(data))\n",
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"ax = plt.gca()\n",
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"plt.plot([0,0],[-20,20], color='black')\n",
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"plt.plot([100,100],[-20,20], color='black')\n",
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"points = [[0, -20], [25, -80], [50, -20]]\n",
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"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
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"ax.add_artist(polygon)\n",
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"points = [[3, -20], [28, -80], [53, -20]]\n",
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"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
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"ax.add_artist(polygon)\n",
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"points = [[6, -20], [31, -80], [56, -20]]\n",
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"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
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"ax.add_artist(polygon)\n",
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"plt.plot(25,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
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"plt.plot(28,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
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"plt.plot(31,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
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"plt.ylim(-100,22)\n",
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"plt.text(50,35, \"Trial\", fontsize=20, ha='center')\n",
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"plt.text(28,-38, \"Network\", fontsize=16, ha='center')\n",
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"plt.text(20,-80, \"Predictions\", fontsize=20, ha='right', va='center')\n",
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"plt.plot(28,-95, ls='', marker='o')\n",
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"plt.text(20,-95, \"Target\", fontsize=20, ha='right', va='center')\n",
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"\n",
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"# from https://stackoverflow.com/a/20308475/1469195\n",
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"def range_brace(x_min, x_max, mid=0.75, \n",
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" beta1=50.0, beta2=100.0, height=1, \n",
|
||||
" initial_divisions=11, resolution_factor=1.5):\n",
|
||||
" NP = np\n",
|
||||
" # determine x0 adaptively values using second derivitive\n",
|
||||
" # could be replaced with less snazzy:\n",
|
||||
" # x0 = NP.arange(0, 0.5, .001)\n",
|
||||
" x0 = NP.array(())\n",
|
||||
" tmpx = NP.linspace(0, 0.5, initial_divisions)\n",
|
||||
" tmp = beta1**2 * (NP.exp(beta1*tmpx)) * (1-NP.exp(beta1*tmpx)) / NP.power((1+NP.exp(beta1*tmpx)),3)\n",
|
||||
" tmp += beta2**2 * (NP.exp(beta2*(tmpx-0.5))) * (1-NP.exp(beta2*(tmpx-0.5))) / NP.power((1+NP.exp(beta2*(tmpx-0.5))),3)\n",
|
||||
" for i in range(0, len(tmpx)-1):\n",
|
||||
" t = int(NP.ceil(resolution_factor*max(NP.abs(tmp[i:i+2]))/float(initial_divisions)))\n",
|
||||
" x0 = NP.append(x0, NP.linspace(tmpx[i],tmpx[i+1],t))\n",
|
||||
" x0 = NP.sort(NP.unique(x0)) # sort and remove dups\n",
|
||||
" # half brace using sum of two logistic functions\n",
|
||||
" y0 = mid*2*((1/(1.+NP.exp(-1*beta1*x0)))-0.5)\n",
|
||||
" y0 += (1-mid)*2*(1/(1.+NP.exp(-1*beta2*(x0-0.5))))\n",
|
||||
" # concat and scale x\n",
|
||||
" x = NP.concatenate((x0, 1-x0[::-1])) * float((x_max-x_min)) + x_min\n",
|
||||
" y = NP.concatenate((y0, y0[::-1])) * float(height)\n",
|
||||
" return (x,y)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"x,y = range_brace(0, 50, height=6)\n",
|
||||
"ax.plot(x, y-20,'-')\n",
|
||||
"\n",
|
||||
"ax.annotate('Crop', xy=(25, -10), xycoords='data', \n",
|
||||
" fontsize=13, ha='center', va='bottom', color=seaborn.color_palette()[2],\n",
|
||||
" bbox=dict(boxstyle='square', fc='white', ec='None',))\n",
|
||||
"\n",
|
||||
"x,y = range_brace(0, 56, height=6, mid=0.5, beta1=50)\n",
|
||||
"ax.plot(x, y+10,'-', color=seaborn.color_palette()[2])\n",
|
||||
"plt.plot([56,56], [-20,10], ls='--', color=seaborn.color_palette()[2])\n",
|
||||
"ax.annotate('Supercrop', xy=(28, 20), xycoords='data', \n",
|
||||
" fontsize=13, ha='center', va='bottom', color=seaborn.color_palette()[2],\n",
|
||||
" bbox=dict(boxstyle='square', fc='white', ec='None',))\n",
|
||||
"x,y = range_brace(25, 31, height=3.5, mid=0.5, beta1=50)\n",
|
||||
"ax.plot(x, -y-82,'-', color=seaborn.color_palette()[2])\n",
|
||||
"plt.plot(28,-88, ls='', marker='o', color=seaborn.color_palette()[2])\n",
|
||||
"\n",
|
||||
"plt.plot([28,40],[-88, -91.5], color='black')\n",
|
||||
"plt.plot([28,40],[-95, -91.5], color='black')\n",
|
||||
"plt.text(42,-91.5, \"Loss\", fontsize=20, ha='left', va='center')\n",
|
||||
"plt.axis('off')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"import matplotlib.transforms as mtrans\n",
|
||||
"from matplotlib.text import TextPath\n",
|
||||
"from matplotlib.patches import PathPatch\n",
|
||||
"def curly(x,y, size, scale, rotation, ax=None):\n",
|
||||
" tp = TextPath((0, 0), \"}\", size=size,)\n",
|
||||
" #trans = mtrans.Affine2D().scale(1, scale) + mtrans.Affine2D().rotate_deg(rotation) + \\\n",
|
||||
" # mtrans.Affine2D().translate(x,y) + ax.transData\n",
|
||||
" trans = mtrans.Affine2D().rotate_deg(rotation) + mtrans.Affine2D().translate(x,y) + ax.transData\n",
|
||||
" pp = PathPatch(tp, lw=0, fc=\"k\", transform=trans)\n",
|
||||
" ax.add_artist(pp)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(5,5))\n",
|
||||
"data = cnt.get_data()[0,100:201]\n",
|
||||
"plt.plot(data - np.mean(data))\n",
|
||||
"ax = plt.gca()\n",
|
||||
"plt.plot([0,0],[-20,20], color='black')\n",
|
||||
"plt.plot([100,100],[-20,20], color='black')\n",
|
||||
"points = [[0, -20], [25, -80], [50, -20]]\n",
|
||||
"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
|
||||
"ax.add_artist(polygon)\n",
|
||||
"points = [[3, -20], [28, -80], [53, -20]]\n",
|
||||
"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
|
||||
"ax.add_artist(polygon)\n",
|
||||
"points = [[6, -20], [31, -80], [56, -20]]\n",
|
||||
"polygon = plt.Polygon(points, facecolor=seaborn.color_palette()[2] + (0.2,), edgecolor=seaborn.color_palette()[2])\n",
|
||||
"ax.add_artist(polygon)\n",
|
||||
"plt.plot(25,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
|
||||
"plt.plot(28,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
|
||||
"plt.plot(31,-80, ls='', marker='o', color=seaborn.color_palette()[2])\n",
|
||||
"plt.ylim(-100,22)\n",
|
||||
"plt.text(50,30, \"Trial\", fontsize=20, ha='center')\n",
|
||||
"plt.text(20,-80, \"Predictions\", fontsize=20, ha='right', va='center')\n",
|
||||
"plt.plot(25,-95, ls='', marker='o')\n",
|
||||
"plt.text(20,-95, \"Target\", fontsize=20, ha='right', va='center')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Here is the label and arrow code of interest\n",
|
||||
"\n",
|
||||
"ax.annotate('SDL', xy=(0.5, 0.90), xytext=(0.5, 1.00), xycoords='data', \n",
|
||||
" fontsize=13, ha='center', va='bottom',\n",
|
||||
" bbox=dict(boxstyle='square', fc='white'),\n",
|
||||
" arrowprops=dict(arrowstyle='-[, widthB=7.0, lengthB=1.5', lw=2.0))\n",
|
||||
"\"\"\"\n",
|
||||
"plt.annotate(r\"$\\}$\",fontsize=46,\n",
|
||||
" xy=(0.27, 0.77), xycoords='figure fraction',\n",
|
||||
" rotation=90)\"\"\"\n",
|
||||
"\n",
|
||||
"import matplotlib.transforms as mtrans\n",
|
||||
"from matplotlib.text import TextPath\n",
|
||||
"from matplotlib.patches import PathPatch\n",
|
||||
"def curly(x,y, size, scale, rotation, ax=None):\n",
|
||||
" tp = TextPath((0, 0), \"}\", size=size)\n",
|
||||
" #trans = mtrans.Affine2D().scale(1, scale) + mtrans.Affine2D().rotate_deg(rotation) + \\\n",
|
||||
" # mtrans.Affine2D().translate(x,y) + ax.transData\n",
|
||||
" trans = ax.transData + mtrans.Affine2D().translate(x,y) \n",
|
||||
" pp = PathPatch(tp, lw=0, fc=\"k\", transform=trans)\n",
|
||||
" ax.add_artist(pp)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"curly(10,-20,20, 0.5, 90, ax)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.text(45,-80, \"Prediction\", fontsize=20, ha='right', va='center')\n",
|
||||
"plt.plot(50,-95, ls='', marker='o')\n",
|
||||
"plt.text(45,-95, \"Target\", fontsize=20, ha='right', va='center')\n",
|
||||
"plt.axis('off')\n",
|
||||
"\n",
|
||||
"plt.plot([50,65],[-80, -87.5], color='black')\n",
|
||||
"plt.plot([50,65],[-95, -87.5], color='black')\n",
|
||||
"plt.text(67,-87.5, \"Loss\", fontsize=20, ha='left', va='center')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.plot(cnt.get_data()[0,100:201])\n",
|
||||
"plt.axvline(x=0, color='black')\n",
|
||||
"plt.axvline(x=100, color='black')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -92,7 +92,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import mne\n",
|
||||
@@ -226,7 +228,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.torch_ext.optimizers import AdamW\n",
|
||||
|
||||
@@ -174,7 +174,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.torch_ext.optimizers import AdamW\n",
|
||||
@@ -219,7 +221,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
|
||||
@@ -303,7 +307,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
|
||||
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