--- a
+++ b/ChronoNet.ipynb
@@ -0,0 +1,674 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mne\n",
+    "import numpy as np\n",
+    "import pyedflib\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.autograd import Variable\n",
+    "\n",
+    "from IPython.display import Image\n",
+    "from IPython.core.display import HTML \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {
+      "image/jpeg": {
+       "height": 500,
+       "width": 500
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename = \"../Electrode-placements-of-32-channels-according-to-the-international-10-20-system.jpeg\", width=500, height=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"../train/01_tcp_ar/00000005/s03_2010_10_02/00000005_s03_a00.edf\"\n",
+    "f = pyedflib.EdfReader(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = f.signals_in_file\n",
+    "signal_labels = f.getSignalLabels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'EEG FP1-REF',\n",
+       " 1: 'EEG FP2-REF',\n",
+       " 2: 'EEG F3-REF',\n",
+       " 3: 'EEG F4-REF',\n",
+       " 4: 'EEG C3-REF',\n",
+       " 5: 'EEG C4-REF',\n",
+       " 6: 'EEG P3-REF',\n",
+       " 7: 'EEG P4-REF',\n",
+       " 8: 'EEG O1-REF',\n",
+       " 9: 'EEG O2-REF',\n",
+       " 10: 'EEG F7-REF',\n",
+       " 11: 'EEG F8-REF',\n",
+       " 12: 'EEG T3-REF',\n",
+       " 13: 'EEG T4-REF',\n",
+       " 14: 'EEG T5-REF',\n",
+       " 15: 'EEG T6-REF',\n",
+       " 16: 'EEG A1-REF',\n",
+       " 17: 'EEG A2-REF',\n",
+       " 18: 'EEG FZ-REF',\n",
+       " 19: 'EEG CZ-REF',\n",
+       " 20: 'EEG PZ-REF',\n",
+       " 21: 'EEG ROC-REF',\n",
+       " 22: 'EEG LOC-REF',\n",
+       " 23: 'EEG EKG1-REF',\n",
+       " 24: 'EMG-REF',\n",
+       " 25: 'EEG 26-REF',\n",
+       " 26: 'EEG 27-REF',\n",
+       " 27: 'EEG 28-REF',\n",
+       " 28: 'EEG 29-REF',\n",
+       " 29: 'EEG 30-REF',\n",
+       " 30: 'EEG T1-REF',\n",
+       " 31: 'EEG T2-REF',\n",
+       " 32: 'PHOTIC-REF',\n",
+       " 33: 'IBI',\n",
+       " 34: 'BURSTS',\n",
+       " 35: 'SUPPR'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(zip(np.arange(0, len(signal_labels)), signal_labels))\n",
+    "\n",
+    "#We see that all the the system is based on a single reference electrode.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(33, 290500)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sigbufs = np.zeros((n-3, f.getNSamples()[0]))\n",
+    "print(sigbufs.shape)\n",
+    "for i in np.arange(n-3):\n",
+    "    sigbufs[i, :] = f.readSignal(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 12))\n",
+    "for i in range(n-3):\n",
+    "    plt.plot(np.arange(1, sigbufs.shape[1]+1), sigbufs[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = open(\"../train/01_tcp_ar/00000005/s03_2010_10_02/00000005_s03_a00.lbl\", \"r\")\n",
+    "labels = labels.read().split('\\n')\n",
+    "for i, element in enumerate(labels):\n",
+    "    if element == \"symbols[0] = {0: '(null)', 1: 'spsw', 2: 'gped', 3: 'pled', 4: 'eyem', 5: 'artf', 6: 'bckg', 7: 'seiz', 8: 'fnsz', 9: 'gnsz', 10: 'spsz', 11: 'cpsz', 12: 'absz', 13: 'tnsz', 14: 'cnsz', 15: 'tcsz', 16: 'atsz', 17: 'mysz'}\":\n",
+    "        print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['version = tse_v1.0.0', '', '0.0000 1162.0000 bckg 1.0000', '']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def load_eeg_data(path):\n",
+    "    f = pyedflib.EdfReader(path 'a00.edf')\n",
+    "    n = f.signals_in_file\n",
+    "    signal_labels = f.getSignalLabels()\n",
+    "    sigbufs = np.zeros((n-3, f.getNSamples()[0]))\n",
+    "    for i in np.arange(n-3):\n",
+    "        sigbufs[i, :] = f.readSignal(i)\n",
+    "    return sigbufs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(RNN, self).__init__()\n",
+    "        self.gru1 = nn.GRU(22, 32, num_layers = 1, batch_first = True)\n",
+    "        self.affine1 = nn.Linear(15000, 1875)\n",
+    "        self.gru2 = nn.GRU(32, 32, num_layers = 2, batch_first = True)\n",
+    "        self.affine2 = nn.Linear(1875, 1)\n",
+    "        self.gru3 = nn.GRU(32, 2, num_layers = 1, batch_first = True)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = x.contiguous().view(64, 15000, 22)\n",
+    "        x, _ = self.gru1(x)\n",
+    "        x = x.contiguous().view(64, 32, 15000)\n",
+    "        x = F.elu(self.affine1(x))\n",
+    "        x = x.contiguous().view(64, 1875, 32)\n",
+    "        x, _ = self.gru2(x)\n",
+    "        x = x.contiguous().view(64, 32, 1875)\n",
+    "        x = F.elu(self.affine2(x))\n",
+    "        x = x.contiguous().view(64, 1, 32)\n",
+    "        x, _ = self.gru3(x)\n",
+    "        x = torch.squeeze(x, dim = 1)\n",
+    "        x = F.softmax(x)\n",
+    "        return x\n",
+    "    \n",
+    "class CRNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(CRNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv1d(22, 32, 4, stride = 2, padding = 1)\n",
+    "        self.conv2 = nn.Conv1d(32, 32, 4, stride = 2, padding = 1)\n",
+    "        self.conv3 = nn.Conv1d(32, 32, 4, stride = 2, padding = 1)\n",
+    "        self.gru1 = nn.GRU(32, 32, num_layers = 3, batch_first = True)\n",
+    "        self.affine1 = nn.Linear(1875, 1)\n",
+    "        self.gru2 = nn.GRU(32, 2, batch_first = True)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = x.contiguous().view(64, 22, 15000)\n",
+    "        x = F.elu(self.conv1(x))\n",
+    "        x = F.elu(self.conv2(x))\n",
+    "        x = F.elu(self.conv3(x))\n",
+    "        x = x.contiguous().view(64, 1875, 32)\n",
+    "        x, _ = self.gru1(x)\n",
+    "        x = x.contiguous().view(64, 32, 1875)\n",
+    "        x = F.elu(self.affine1(x))\n",
+    "        x = x.contiguous().view(64, 1, 32)\n",
+    "        x, _ = self.gru2(x)\n",
+    "        x = torch.squeeze(x, dim = 1)\n",
+    "        x = F.softmax(x)\n",
+    "        return x\n",
+    "    \n",
+    "    \n",
+    "class ICRNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ICRNN, self).__init__()\n",
+    "        self.inception1 = Inception(22)\n",
+    "        self.inception2 = Inception(96)\n",
+    "        self.inception3 = Inception(96)\n",
+    "        self.gru1 = nn.GRU(96, 32, num_layers = 3, batch_first = True)\n",
+    "        self.affine1 = nn.Linear(1875, 1)\n",
+    "        self.gru2 = nn.GRU(32, 2, batch_first = True)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = x.contiguous().view(64, 22, 15000)\n",
+    "        x = self.inception1(x)\n",
+    "        x = self.inception2(x)\n",
+    "        x = self.inception3(x)\n",
+    "        x = x.contiguous().view(64, 1875, 96)\n",
+    "        x, _ = self.gru1(x)\n",
+    "        x = x.contiguous().view(64, 32, 1875)\n",
+    "        x = self.affine1(x)\n",
+    "        x = x.contiguous().view(64, 1, 32)\n",
+    "        x, _ = self.gru2(x)\n",
+    "        x = torch.squeeze(x, dim = 1)\n",
+    "        x = F.softmax(x)\n",
+    "        return x\n",
+    "    \n",
+    "    \n",
+    "class Inception(nn.Module):\n",
+    "    def __init__(self, in_channels):\n",
+    "        super(Inception, self).__init__()\n",
+    "        self.conv1 = nn.Conv1d(in_channels, 32, 4, stride = 2, padding = 1)\n",
+    "        self.conv2 = nn.Conv1d(in_channels, 32, 2, stride = 2)\n",
+    "        self.conv3 = nn.Conv1d(in_channels, 32, 8, stride = 2, padding = 3)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x1 = F.elu(self.conv1(x))\n",
+    "        x2 = F.elu(self.conv2(x))\n",
+    "        x3 = F.elu(self.conv3(x))\n",
+    "        cat = [x1, x2, x3]\n",
+    "        x = torch.cat(cat, dim=1)\n",
+    "        return x        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ICRNN()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = torch.randn(64, 15000, 22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:73: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([64, 2])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = model(a)\n",
+    "output.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DCRNN(nn.Module): #Densely connected convolutional recurrent neural network\n",
+    "    def __init__(self):\n",
+    "        super(DCRNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv1d(22, 32, 4, stride = 2, padding = 1)\n",
+    "        self.conv2 = nn.Conv1d(32, 32, 4, stride = 2, padding = 1)\n",
+    "        self.conv3 = nn.Conv1d(32, 32, 4, stride = 2, padding = 1)\n",
+    "        self.gru1 = nn.GRU(32, 32, num_layers = 3, batch_first = True)\n",
+    "        self.affine1 = nn.Linear(1875, 1)\n",
+    "        self.gru2 = nn.GRU(32, 32, batch_first = True)\n",
+    "        self.gru3 = nn.GRU(64, 32, batch_first = True)\n",
+    "        self.gru4 = nn.GRU(96, 32, batch_first = True)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = x.contiguous().view(64, 22, 15000)\n",
+    "        x = F.elu(self.conv1(x))\n",
+    "        x = F.elu(self.conv2(x))\n",
+    "        x = F.elu(self.conv3(x))\n",
+    "        x = x.contiguous().view(64, 1875, 32)\n",
+    "        x, _ = self.gru1(x)\n",
+    "        x_res = x\n",
+    "        x, _ = self.gru2(x)\n",
+    "        x_res2 = x\n",
+    "        x_cat1 = torch.cat([x_res, x], dim = 2)\n",
+    "        x, _ = self.gru3(x_cat1)\n",
+    "        x = torch.cat([x_res, x_res2, x], dim = 2)\n",
+    "        x = x.contiguous().view(64, 96, 1875)\n",
+    "        x = F.elu(self.affine1(x))\n",
+    "        x = x.contiguous().view(64, 1, 96)\n",
+    "        x, _ = self.gru4(x)\n",
+    "        x = torch.squeeze(x, dim = 1)\n",
+    "        x = F.softmax(x)\n",
+    "        return x\n",
+    "    \n",
+    "    \n",
+    "class ChronoNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ChronoNet, self).__init__()\n",
+    "        self.inception1 = Inception(22)\n",
+    "        self.inception2 = Inception(96)\n",
+    "        self.inception3 = Inception(96)\n",
+    "        self.gru1 = nn.GRU(96, 32, num_layers = 1, batch_first = True)\n",
+    "        self.affine1 = nn.Linear(1875, 1)\n",
+    "        self.gru2 = nn.GRU(32, 32, batch_first = True)\n",
+    "        self.gru3 = nn.GRU(64, 32, batch_first = True)\n",
+    "        self.gru4 = nn.GRU(96, 2, batch_first = True)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = x.contiguous().view(64, 22, 15000)\n",
+    "        x = self.inception1(x)\n",
+    "        x = self.inception2(x)\n",
+    "        x = self.inception3(x)\n",
+    "        x = x.contiguous().view(64, 1875, 96)\n",
+    "        x, _ = self.gru1(x)\n",
+    "        x_res = x\n",
+    "        x, _ = self.gru2(x)\n",
+    "        x_res2 = x\n",
+    "        x_cat1 = torch.cat([x_res, x], dim = 2)\n",
+    "        x, _ = self.gru3(x_cat1)\n",
+    "        x = torch.cat([x_res, x_res2, x], dim = 2)\n",
+    "        x = x.contiguous().view(64, 96, 1875)\n",
+    "        x = F.elu(self.affine1(x))\n",
+    "        x = x.contiguous().view(64, 1, 96)\n",
+    "        x, _ = self.gru4(x)\n",
+    "        x = torch.squeeze(x, dim = 1)\n",
+    "        x = F.softmax(x)\n",
+    "        return x\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dcrnn = ChronoNet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:65: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([64, 2])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = torch.randn(64, 15000, 22)\n",
+    "output = dcrnn(a)\n",
+    "output.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data.dataset import Dataset\n",
+    "from torch.utils.data.dataloader import DataLoader\n",
+    "\n",
+    "class EEGDataset(Dataset):\n",
+    "    def __init__(self, cycle, time=60):\n",
+    "        self.time = time\n",
+    "        if self.time is None:\n",
+    "            self.concat = True\n",
+    "        \n",
+    "    def __getitem__(self, idx):\n",
+    "        sample = self.pairs[idx]\n",
+    "        initial = self.load_eeg_data(sample[0])\n",
+    "        print(initial.shape)\n",
+    "        eeg = self.concatenate(initial)\n",
+    "        print(sample[1])\n",
+    "        label = self.get_label(sample[1])\n",
+    "        sample = {'eeg': eeg, 'label': label}\n",
+    "        if self.transform is not None:\n",
+    "            sample = self.transform(sample)\n",
+    "        return sample\n",
+    "\n",
+    "    def get_pairs(self, cycle):\n",
+    "        import os\n",
+    "        edfs, tses = [], []\n",
+    "        for root, dirs, files in os.walk(\"../\" + cycle + '/', topdown=False):\n",
+    "            for name in files:\n",
+    "                if name[-3:]=='edf':\n",
+    "                    edfs.append(os.path.join(root, name))\n",
+    "                if name[-3:]=='tse':\n",
+    "                    tses.append(os.path.join(root, name))\n",
+    "        edfs = sorted(edfs)\n",
+    "        tses = sorted(tses)\n",
+    "        pairs = list(zip(edfs, tses))\n",
+    "        self.pairs = pairs\n",
+    "    \n",
+    "    def concatenate(self, eeg):\n",
+    "        time = self.time*250\n",
+    "        return eeg[:, :time]\n",
+    "\n",
+    "    def set_montage(self, eeg):\n",
+    "        montaged = np.zeros_like(eeg)\n",
+    "        montaged[0] += eeg[0] - eeg[10]\n",
+    "        montaged[1] += eeg[10] - eeg[12]\n",
+    "        montaged[2] += eeg[12] - eeg[14]\n",
+    "        montaged[3] += eeg[14] - eeg[8]\n",
+    "        montaged[4] += eeg[0] - eeg[2]\n",
+    "        montaged[5] += eeg[2] - eeg[4]\n",
+    "        montaged[6] += eeg[4] - eeg[6]\n",
+    "        montaged[7] += eeg[6] - eeg[8]\n",
+    "        montaged[8] += eeg[1] - eeg[3]\n",
+    "        montaged[9] += eeg[3] - eeg[5]\n",
+    "        montaged[10] += eeg[5] - eeg[7]\n",
+    "        montaged[11] += eeg[7] - eeg[9]\n",
+    "        montaged[12] += eeg[1] - eeg[11]\n",
+    "        montaged[13] += eeg[11] - eeg[13]\n",
+    "        montaged[14] += eeg[13] - eeg[15]\n",
+    "        montaged[15] += eeg[15] - eeg[9]\n",
+    "        montaged[16] += eeg[16] - eeg[12]\n",
+    "        montaged[17] += eeg[12] - eeg[4]\n",
+    "        montaged[18] += eeg[4] - eeg[19]\n",
+    "        montaged[19] += eeg[19] - eeg[5]\n",
+    "        montaged[20] += eeg[5] - eeg[13]\n",
+    "        montaged[21] += eeg[13] - eeg[17]\n",
+    "        return montage\n",
+    "        \n",
+    "    def load_eeg_data(self, path):\n",
+    "        f = pyedflib.EdfReader(path)\n",
+    "        n = f.signals_in_file\n",
+    "        signal_labels = f.getSignalLabels()\n",
+    "        for i, element in enumerate(reversed(signal_labels)):\n",
+    "            if element[:6]=='EEG T2':\n",
+    "                n = len(signal_labels) - i - 1\n",
+    "        sigbufs = np.zeros((n, f.getNSamples()[0]))\n",
+    "        for i in np.arange(n):\n",
+    "            sigbufs[i, :] = f.readSignal(i)\n",
+    "        return sigbufs\n",
+    "    \n",
+    "    def get_label(self, tse):\n",
+    "        tse_labels = open(\"../train/02_tcp_le/00002936/s05_2003_01_07/00002936_s05_a01.tse\", \"r\")\n",
+    "        tse_labels = tse_labels.read()\n",
+    "        tse_labels = tse_labels.split('\\n')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dict(zip(np.arange(0, len(signal_labels)), signal_labels))\n",
+    "data = EEGDataset('train')\n",
+    "data.get_pairs('train')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(31, 290500)\n",
+      "../train/01_tcp_ar/00000005/s03_2010_10_02/00000005_s03_a00.tse\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'EEGDataset' object has no attribute 'get_label'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-6ced2d9344b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-36-62cf6a615535>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0meeg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'eeg'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0meeg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'label'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'EEGDataset' object has no attribute 'get_label'"
+     ]
+    }
+   ],
+   "source": [
+    "data.__getitem__(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NotImplementedError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-99f4eb8ad523>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0meeg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meeg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    311\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m             \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataset.py\u001b[0m in \u001b[0;36m__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNotImplementedError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "for i, element in enumerate(data_loader):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}