Thought-to-text-EEG/captum.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98abe12e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "from tqdm import tqdm\n",
    "import glob\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "from einops import rearrange\n",
    "from captum.attr import DeepLift, Saliency\n",
    "from captum.attr import visualization as viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd5442b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('bilab_10mfc_train.pkl', 'rb') as f:\n",
    "    mfc_train = pickle.load(f)\n",
    "with open('bilab_10mfc_test.pkl', 'rb') as f:\n",
    "    mfc_test = pickle.load(f)\n",
    "with open(\"data/bilabial/y_bilabial_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/bilabial/y_bilabial_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "498285d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01f70ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "trials = []\n",
    "for trial in mfc_train:\n",
    "    pic = np.zeros((7,9,10,11))\n",
    "    pic[0,2] = trial[3]\n",
    "    pic[0,3] = trial[0]\n",
    "    pic[0,4] = trial[1]\n",
    "    pic[0,5] = trial[2]\n",
    "    pic[0,6] = trial[4]\n",
    "    pic[1,:] = trial[5:14]\n",
    "    pic[2,:] = trial[14:23]\n",
    "    pic[3,:] = trial[23:32]\n",
    "    pic[4,:] = trial[32:41]\n",
    "    pic[5,:] = trial[41:50]\n",
    "    pic[6,0] = trial[50]\n",
    "    pic[6,1] = trial[51]\n",
    "    pic[6,2] = trial[52]\n",
    "    pic[6,3] = trial[58]\n",
    "    pic[6,4] = trial[53]\n",
    "    pic[6,5] = trial[60]\n",
    "    pic[6,6] = trial[54]\n",
    "    pic[6,7] = trial[55]\n",
    "    pic[6,8] = trial[56]\n",
    "    trials.append(pic)\n",
    "picture_data_train = np.array(trials)\n",
    "trials = []\n",
    "for trial in mfc_test:\n",
    "    pic = np.zeros((7,9,10,11))\n",
    "    pic[0,2] = trial[3]\n",
    "    pic[0,3] = trial[0]\n",
    "    pic[0,4] = trial[1]\n",
    "    pic[0,5] = trial[2]\n",
    "    pic[0,6] = trial[4]\n",
    "    pic[1,:] = trial[5:14]\n",
    "    pic[2,:] = trial[14:23]\n",
    "    pic[3,:] = trial[23:32]\n",
    "    pic[4,:] = trial[32:41]\n",
    "    pic[5,:] = trial[41:50]\n",
    "    pic[6,0] = trial[50]\n",
    "    pic[6,1] = trial[51]\n",
    "    pic[6,2] = trial[52]\n",
    "    pic[6,3] = trial[58]\n",
    "    pic[6,4] = trial[53]\n",
    "    pic[6,5] = trial[60]\n",
    "    pic[6,6] = trial[54]\n",
    "    pic[6,7] = trial[55]\n",
    "    pic[6,8] = trial[56]\n",
    "    trials.append(pic)  \n",
    "picture_data_test = np.array(trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6e5d06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 10, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = np.vstack((picture_data_train, picture_data_test))\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c0cd1a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = y__train + y__test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19f34131",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model\n",
    "\n",
    "class CNN_RNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(20, 16, 3)\n",
    "        #torch.nn.init.xavier_normal_(self.conv1.weight)\n",
    "        self.pool = nn.MaxPool2d(2, 1)\n",
    "        self.conv2 = nn.Conv2d(16, 32, 3)\n",
    "        #torch.nn.init.xavier_normal_(self.conv2.weight)\n",
    "        self.lstm = nn.LSTM(input_size=256, hidden_size=128, num_layers=2, batch_first=True)\n",
    "        self.fc = nn.Linear(128, 2)\n",
    "        #torch.nn.init.xavier_normal_(self.fc.weight)\n",
    "        self.batch1 = nn.BatchNorm2d(16)\n",
    "        self.batch2 = nn.BatchNorm2d(32)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.relu2 = nn.ReLU()\n",
    "    \n",
    "    \n",
    "    def forward(self, x):\n",
    "        hidden = torch.zeros(2, x.shape[0], 128), torch.zeros(2, x.shape[0], 128)\n",
    "        # (batch, heigth, width, feature, time)\n",
    "        #print(x.shape)\n",
    "        x = rearrange(x, 'batch heigth width feature time -> (batch time) feature heigth width')\n",
    "        #print(x.shape)\n",
    "        out = self.pool(self.relu1(self.batch1(self.conv1(x))))\n",
    "        #print(out.shape)\n",
    "        out = self.relu2(self.batch2(self.conv2(out)))\n",
    "        #print(out.shape)\n",
    "        out = rearrange(out, '(batch time) channel heigth width -> batch time (channel heigth width)', time=11)\n",
    "        #print(out.shape)\n",
    "        out, hidden = self.lstm(out, hidden) \n",
    "        out = out[:,-1,:]\n",
    "        out = self.fc(out)\n",
    "        return out\n",
    "    \n",
    "class FC(nn.Module):\n",
    "    def __init__(self, hidden1=500):\n",
    "        super(FC, self).__init__()\n",
    "        self.fc1 = nn.Linear(6820, hidden1)\n",
    "        torch.nn.init.xavier_normal(self.fc1.weight)\n",
    "        self.fc2 = nn.Linear(hidden1, 1)\n",
    "        torch.nn.init.xavier_normal(self.fc2.weight)\n",
    "        self.dropout = nn.Dropout(0.3)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 6820)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        #x = self.dropout(x)\n",
    "        x = F.sigmoid(self.fc2(x))\n",
    "        return x\n",
    "    \n",
    "class cnn3d(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv3d(20, 16, kernel_size=(3, 3, 3), padding=1)\n",
    "        self.conv2 = nn.Conv3d(16, 32, kernel_size=(3, 3, 3), padding=0)\n",
    "        self.pool = nn.MaxPool3d((2, 2, 2), stride=2)\n",
    "        self.fc1 = nn.Linear(192, 128)\n",
    "        self.fc2 = nn.Linear(128, 1)\n",
    "        self.drop = nn.Dropout(0.25)\n",
    "        self.batch1 = nn.BatchNorm3d(16)\n",
    "        self.batch2 = nn.BatchNorm3d(32)\n",
    "        self.batch3 = nn.BatchNorm1d(128)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = rearrange(x, 'n h w m t -> n m t h w')\n",
    "        out = self.pool(F.relu(self.batch1(self.conv1(x))))\n",
    "        out = F.relu(self.batch2(self.conv2(out)))\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.drop(F.relu(self.batch3(self.fc1(out))))\n",
    "        out = F.sigmoid(self.fc2(out))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fbe27fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 62, 20, 11) 1913\n"
     ]
    }
   ],
   "source": [
    "with open(\"data/normal_all_data.pkl\", \"rb\") as f:\n",
    "    all_data = pickle.load(f)\n",
    "with open(\"data/11_20mfc.pkl\", \"rb\") as f:\n",
    "    data = pickle.load(f)\n",
    "with open(\"data/all_label.pkl\", \"rb\") as f:\n",
    "    labels = pickle.load(f)\n",
    "with open(\"data/vowel_label.pkl\", \"rb\") as f:\n",
    "    vowel_label = pickle.load(f)\n",
    "with open(\"data/bilab_label.pkl\", \"rb\") as f:\n",
    "    bilab_label = pickle.load(f)\n",
    "with open(\"data/nasal_label.pkl\", \"rb\") as f:\n",
    "    nasal_label = pickle.load(f)\n",
    "with open(\"data/iy_label.pkl\", \"rb\") as f:\n",
    "    iy_label = pickle.load(f)\n",
    "with open(\"data/uw_label.pkl\", \"rb\") as f:\n",
    "    uw_label = pickle.load(f)\n",
    "\n",
    "print(all_data.shape, len(uw_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d79522e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "trials = []\n",
    "for trial in all_data:\n",
    "    pic = np.zeros((7,9,20,11))\n",
    "    pic[0,2] = trial[3]\n",
    "    pic[0,3] = trial[0]\n",
    "    pic[0,4] = trial[1]\n",
    "    pic[0,5] = trial[2]\n",
    "    pic[0,6] = trial[4]\n",
    "    pic[1,:] = trial[5:14]\n",
    "    pic[2,:] = trial[14:23]\n",
    "    pic[3,:] = trial[23:32]\n",
    "    pic[4,:] = trial[32:41]\n",
    "    pic[5,:] = trial[41:50]\n",
    "    pic[6,0] = trial[50]\n",
    "    pic[6,1] = trial[51]\n",
    "    pic[6,2] = trial[52]\n",
    "    pic[6,3] = trial[58]\n",
    "    pic[6,4] = trial[53]\n",
    "    pic[6,5] = trial[60]\n",
    "    pic[6,6] = trial[54]\n",
    "    pic[6,7] = trial[55]\n",
    "    pic[6,8] = trial[56]\n",
    "    trials.append(pic)\n",
    "picture_data_train = np.array(trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b2c4df69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 7, 9, 20, 11) 1913\n"
     ]
    }
   ],
   "source": [
    "dataset = picture_data_train\n",
    "labels = nasal_label\n",
    "print(dataset.shape, len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "ba72e9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 20, 11)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max = np.max(dataset, axis=(0,1,2,4), keepdims=True)\n",
    "Min = np.min(dataset, axis=(0,1,2,4), keepdims=True)\n",
    "dataset = (dataset-Min)/(Max-Min)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "45d5b88c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CNN_RNN().float()\n",
    "model.load_state_dict(torch.load('train/model0_acc.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "509b8e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = []\n",
    "for l in labels:\n",
    "    label.append(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "d2a30b5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1913, 7, 9, 20, 11])\n"
     ]
    }
   ],
   "source": [
    "sample = torch.from_numpy(dataset)\n",
    "#data = torch.rand((7,9,20,11))\n",
    "sample = sample.float()\n",
    "sample.requires_grad = True\n",
    "print(sample.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "75a7a952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNN_RNN(\n",
       "  (conv1): Conv2d(20, 16, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (lstm): LSTM(256, 128, num_layers=2, batch_first=True)\n",
       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (batch1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu1): ReLU()\n",
       "  (relu2): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "81b5c2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def attribute_image_features(algorithm, data, **kwargs):\n",
    "    model.zero_grad()\n",
    "    tensor_attributions = algorithm.attribute(data,\n",
    "                                              target=labels,\n",
    "                                              **kwargs\n",
    "                                             )\n",
    "    \n",
    "    return tensor_attributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "e1c1a53c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1913, 7, 9, 20, 11])\n"
     ]
    }
   ],
   "source": [
    "dl = DeepLift(model)\n",
    "#attr_dl = dl.attribute(data, labels)\n",
    "attr_dl = attribute_image_features(dl, sample, baselines=sample * 0)\n",
    "\n",
    "print(attr_dl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "05a94327",
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_dl = attr_dl.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "8d88cd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sal_nasal.pkl', 'wb') as f:\n",
    "    pickle.dump(attr_dl, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "4608f5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sal_vowel.pkl', 'rb') as f:\n",
    "    attr_dl = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "8a4afb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 20, 11)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr_dl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "184f5964",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 7, 9, 20, 11)\n",
      "(7, 9, 20)\n",
      "(20, 63)\n",
      "(array([ 2,  3,  9,  2, 16], dtype=int64), array([21, 32, 32, 22, 20], dtype=int64))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2520x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"for i in range(attr.shape[-1]):\\n    final = attr[:,:,i]\\n    plt.imshow(final, cmap='viridis', interpolation='nearest')\\n    plt.colorbar()\\n    plt.title('MFC'+str(i+1))\\n    plt.show()\""
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#origin = datashape[0,:,:,0,0]\n",
    "print(attr_dl.shape)\n",
    "attr = np.mean(attr_dl, axis=(0, 4))\n",
    "print(attr.shape)\n",
    "'''with open('vowel_electrode.pkl', 'wb') as f:\n",
    "    pickle.dump(attr, f)'''\n",
    "\n",
    "'''_ = viz.visualize_image_attr(final, original, method=\"blended_heat_map\",sign=\"all\",show_colorbar=True, \n",
    "                      title=\"Overlayed DeepLift\")'''\n",
    "'''plt.figure(figsize=(8, 8))\n",
    "plt.imshow(attr, cmap='viridis', interpolation='nearest')\n",
    "plt.colorbar()\n",
    "plt.title('electrode attribute')\n",
    "plt.show()'''\n",
    "#plt.savefig('electode_uw.png', dpi=300)\n",
    "\n",
    "'''plt.figure(figsize=(12, 5))\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor((0.95,0.95,0.95))\n",
    "plt.grid()\n",
    "plt.bar(x=range(20), height=attr, tick_label=range(20))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.title('MFCC attribute')\n",
    "plt.show()'''\n",
    "All=[]\n",
    "for i in range(20):\n",
    "    All.append(attr[:,:,i])\n",
    "All = np.stack(All)\n",
    "All = All.reshape((20,63))\n",
    "print(All.shape)\n",
    "print(np.unravel_index(np.argsort(All, axis=None)[-5:], All.shape))\n",
    "plt.figure(figsize=(35, 10))\n",
    "ax = plt.gca()\n",
    "ax.set_xticks(range(63))\n",
    "ax.set_yticks(range(20))\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.imshow(All, cmap='viridis', interpolation='nearest')\n",
    "plt.title('all features attribute', fontsize=20)\n",
    "cbar = plt.colorbar()\n",
    "for t in cbar.ax.get_yticklabels():\n",
    "     t.set_fontsize(16)\n",
    "plt.show()\n",
    "'''for i in range(attr.shape[-1]):\n",
    "    final = attr[:,:,i]\n",
    "    plt.imshow(final, cmap='viridis', interpolation='nearest')\n",
    "    plt.colorbar()\n",
    "    plt.title('MFC'+str(i+1))\n",
    "    plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a07ed621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3645833333333333\n"
     ]
    }
   ],
   "source": [
    "print(sum(y__test)/(len(y__test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c2174d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "class cnn(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(20, 16, 3)\n",
    "        #torch.nn.init.xavier_normal_(self.conv1.weight)\n",
    "        self.pool = nn.MaxPool2d(2, 1)\n",
    "        self.conv2 = nn.Conv2d(16, 32, 3)\n",
    "        #torch.nn.init.xavier_normal_(self.conv2.weight)\n",
    "        self.fc1 = nn.Linear(256, 128)\n",
    "        self.fc2 = nn.Linear(128, 1)\n",
    "        #torch.nn.init.xavier_normal_(self.fc.weight)\n",
    "        self.batch1 = nn.BatchNorm2d(16)\n",
    "        self.batch2 = nn.BatchNorm2d(32)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        # (batch, heigth, width, feature)\n",
    "        #print(x.shape)\n",
    "        x = rearrange(x, 'batch heigth width feature -> batch feature heigth width')\n",
    "        #print(x.shape)\n",
    "        out = self.pool(F.relu(self.batch1(self.conv1(x))))\n",
    "        #print(out.shape)\n",
    "        out = F.relu(self.batch2(self.conv2(out)))\n",
    "        #print(out.shape)\n",
    "        out = rearrange(out, 'batch channel heigth width -> batch (channel heigth width)')\n",
    "        #print(out.shape)\n",
    "        out = F.relu(self.fc1(out))\n",
    "        out = F.sigmoid(self.fc2(out))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2c1032f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 20, 11)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('sal_nasal.pkl', 'rb') as f:\n",
    "    dataset = pickle.load(f)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a86f1e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 7, 9, 20) 1913\n"
     ]
    }
   ],
   "source": [
    "data = np.mean(dataset, axis=4)\n",
    "labels = nasal_label\n",
    "print(data.shape, len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "649e822c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1913"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = []\n",
    "for sample in data:\n",
    "    data1.append(sample)\n",
    "len(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "075addc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#config\n",
    "val_size = 0.15\n",
    "n_epochs = 100\n",
    "batch_size = 128\n",
    "print_every = 10\n",
    "k = 10\n",
    "skf=StratifiedKFold(n_splits=k, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "21e68d7f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------Fold 1---------------\n",
      "preparing dataloaders...\n",
      "(1913, 7, 9, 20)\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.698440),   val_acc = 0.3953488372093023\n",
      "validation acc increased (0.000000 ---> 0.395349)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\torch\\nn\\functional.py:1960: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 1: train loss = 0.6869507828848291,   l1loss = 0.13827660727062954,   train acc = 0.5616507297433316,\n",
      "val_loss = 0.71086893987286,     val_acc = 0.3953488372093023\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 2: train loss = 0.6654115830706062,   l1loss = 0.13782025073482934,   train acc = 0.6205334675390035,\n",
      "val_loss = 0.7223108600276386,     val_acc = 0.3953488372093023\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.430233)\n",
      "epoch 3: train loss = 0.6485615584738721,   l1loss = 0.13727900934057374,   train acc = 0.6396577755410167,\n",
      "val_loss = 0.7128145556117214,     val_acc = 0.5\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.430233 ---> 0.503876)\n",
      "validation loss decreased (0.698440 ---> 0.692173),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.503876 ---> 0.531008)\n",
      "epoch 4: train loss = 0.6330915434791266,   l1loss = 0.13658334071844982,   train acc = 0.6431806743834927,\n",
      "val_loss = 0.6788588637529418,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.692173 ---> 0.676339),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.531008 ---> 0.531008)\n",
      "validation loss decreased (0.676339 ---> 0.651838),   val_acc = 0.5387596899224806\n",
      "validation acc increased (0.531008 ---> 0.538760)\n",
      "epoch 5: train loss = 0.6172819093477204,   l1loss = 0.1356638338647419,   train acc = 0.6492199295420231,\n",
      "val_loss = 0.6452844336975453,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.651838 ---> 0.643711),   val_acc = 0.5387596899224806\n",
      "validation acc increased (0.538760 ---> 0.538760)\n",
      "validation loss decreased (0.643711 ---> 0.636844),   val_acc = 0.5426356589147286\n",
      "validation acc increased (0.538760 ---> 0.542636)\n",
      "epoch 6: train loss = 0.6055080684574033,   l1loss = 0.13447358302873286,   train acc = 0.6607951685958732,\n",
      "val_loss = 0.6349219661350398,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.636844 ---> 0.635237),   val_acc = 0.5426356589147286\n",
      "validation acc increased (0.542636 ---> 0.542636)\n",
      "validation loss decreased (0.635237 ---> 0.631333),   val_acc = 0.5348837209302325\n",
      "epoch 7: train loss = 0.5956864868448677,   l1loss = 0.1329548779029491,   train acc = 0.6623049823855058,\n",
      "val_loss = 0.6336472632811051,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.542636 ---> 0.542636)\n",
      "epoch 8: train loss = 0.5867841974778216,   l1loss = 0.13107547391942834,   train acc = 0.6673376950176145,\n",
      "val_loss = 0.6482481244922609,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.5799472323001541,   l1loss = 0.1288494519630628,   train acc = 0.6738802214393558,\n",
      "val_loss = 0.6537467005640961,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5720185998341638,   l1loss = 0.1262706755376906,   train acc = 0.6809260191243081,\n",
      "val_loss = 0.660815857177557,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5634220912340123,   l1loss = 0.12343447363715587,   train acc = 0.688475088072471,\n",
      "val_loss = 0.6865861693093943,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.5516697056160299,   l1loss = 0.12035546596169051,   train acc = 0.6985405133366884,\n",
      "val_loss = 0.6893619465273481,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5383751470057522,   l1loss = 0.11719332186942971,   train acc = 0.7075993960744842,\n",
      "val_loss = 0.7262142046477443,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5219575696154701,   l1loss = 0.11402586181603548,   train acc = 0.7166582788122798,\n",
      "val_loss = 0.8209437387165173,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5082864454930069,   l1loss = 0.11104010005781983,   train acc = 0.727730246602919,\n",
      "val_loss = 0.790199874907501,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.4823058645930432,   l1loss = 0.10828750046597213,   train acc = 0.7518872672370408,\n",
      "val_loss = 0.8347636539806691,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  17\n",
      "validation acc increased (0.542636 ---> 0.542636)\n",
      "epoch 17: train loss = 0.44870099950268694,   l1loss = 0.10601656717844926,   train acc = 0.7790639154504277,\n",
      "val_loss = 1.0818783419076787,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.4243430009905753,   l1loss = 0.10426574212390691,   train acc = 0.7911424257674887,\n",
      "val_loss = 0.8771759863047637,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  19\n",
      "validation acc increased (0.542636 ---> 0.562016)\n",
      "validation acc increased (0.562016 ---> 0.569767)\n",
      "epoch 19: train loss = 0.3871670792645165,   l1loss = 0.10277385458117702,   train acc = 0.8208354302969301,\n",
      "val_loss = 0.9480718586796014,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.3737996664698783,   l1loss = 0.1016839612097253,   train acc = 0.8102667337695018,\n",
      "val_loss = 1.400591679321703,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  21\n",
      "validation acc increased (0.569767 ---> 0.596899)\n",
      "epoch 21: train loss = 0.3521438902445516,   l1loss = 0.10036715933754027,   train acc = 0.8324106693507801,\n",
      "val_loss = 0.9109365958576054,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.319912125056991,   l1loss = 0.0992567646230473,   train acc = 0.8580775037745345,\n",
      "val_loss = 1.5151436608429103,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.3122329044474029,   l1loss = 0.09838688995634784,   train acc = 0.8575742325113236,\n",
      "val_loss = 3.091815226076012,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.28910951249612654,   l1loss = 0.09807651147628826,   train acc = 0.879214896829391,\n",
      "val_loss = 1.2312677035960116,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.2668642541332706,   l1loss = 0.09742016104909122,   train acc = 0.8842476094614997,\n",
      "val_loss = 1.9600338353667148,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.2474498063415874,   l1loss = 0.09673020170526284,   train acc = 0.8933064921992954,\n",
      "val_loss = 1.8068715759025988,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.1902359421446987,   l1loss = 0.09593981095727029,   train acc = 0.9315551082033215,\n",
      "val_loss = 1.3357789331628371,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.15691894562057232,   l1loss = 0.0954245226574892,   train acc = 0.9436336185203825,\n",
      "val_loss = 2.6843640092731444,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.1504599494989877,   l1loss = 0.09509522278868618,   train acc = 0.9431303472571716,\n",
      "val_loss = 2.4894338860068213,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.12966379567523306,   l1loss = 0.09448714830660015,   train acc = 0.9542023150478107,\n",
      "val_loss = 1.8916605646296065,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.15563943400926547,   l1loss = 0.09434211563092114,   train acc = 0.9471565173628586,\n",
      "val_loss = 4.939729660980461,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.16452017606474612,   l1loss = 0.09465074112097942,   train acc = 0.9406139909411173,\n",
      "val_loss = 3.636755084344583,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.12137126438525915,   l1loss = 0.09421853995242188,   train acc = 0.9592350276799195,\n",
      "val_loss = 3.1257032387016355,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.11817072889855236,   l1loss = 0.0940876242596418,   train acc = 0.9572219426270759,\n",
      "val_loss = 3.6307011426881304,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.09768068436092628,   l1loss = 0.09356706718073818,   train acc = 0.96527428283845,\n",
      "val_loss = 4.066986708678017,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.09975038621823992,   l1loss = 0.09309212238751408,   train acc = 0.9622546552591847,\n",
      "val_loss = 3.2775008031564163,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.08334749282366316,   l1loss = 0.092504221824137,   train acc = 0.9763462506290891,\n",
      "val_loss = 3.8003816779183093,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 38: train loss = 0.07791504205383123,   l1loss = 0.09236309195530733,   train acc = 0.9793658782083543,\n",
      "val_loss = 3.233410909194355,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.06921992062487432,   l1loss = 0.09197287779689495,   train acc = 0.9854051333668847,\n",
      "val_loss = 3.561995010505351,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.09455262519377947,   l1loss = 0.09194954407083131,   train acc = 0.9703069954705587,\n",
      "val_loss = 3.140381110104725,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.04864857653104365,   l1loss = 0.09150668548229776,   train acc = 0.9899345747357826,\n",
      "val_loss = 3.4616599368552365,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.03882463321757185,   l1loss = 0.09062009955703403,   train acc = 0.9924509310518369,\n",
      "val_loss = 3.4793882855149203,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.028278966656936192,   l1loss = 0.08959808966653936,   train acc = 0.995973829894313,\n",
      "val_loss = 3.8493388558543007,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.02344076073516177,   l1loss = 0.08865111783919288,   train acc = 0.9954705586311021,\n",
      "val_loss = 3.879825139115023,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.01661977870085159,   l1loss = 0.0877494524450055,   train acc = 0.9989934574735783,\n",
      "val_loss = 4.2667010209357095,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.013619207225413736,   l1loss = 0.086835880546817,   train acc = 0.9994967287367891,\n",
      "val_loss = 4.457068901653438,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.015068223528380243,   l1loss = 0.0860258729338166,   train acc = 1.0,\n",
      "val_loss = 3.7533517517727013,     val_acc = 0.5775193798449613\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]\n",
      "[0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666]\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
      "        1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
      "        1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 1, 1, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
      "        1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 1])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
      "        1, 1, 0, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
      "        0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
      "        1, 1, 0, 0, 1, 0, 1, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
      "        1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
      "        1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
      "        1, 0, 0, 0, 1, 0, 0, 0])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
      "        1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
      "        0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
      "        1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "        0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
      "        0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
      "        0, 0, 1, 0, 1, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "label =  tensor([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,\n",
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      "        1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
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      "        0, 1, 1, 1, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
      "        0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
      "        0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
      "        0, 1, 0, 0, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,\n",
      "        0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
      "        0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 1, 0, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
      "        0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]\n",
      "label =  tensor([0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n",
      "        1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
      "        1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
      "        0, 0, 0, 0, 1, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1,\n",
      "        1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
      "        1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n",
      "        0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
      "        1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
      "        0, 1, 0, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1])\n",
      "\t [0.6673376950176145]\n",
      "[1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1]\n",
      "[1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0]\n",
      "full train results:\n",
      "\t [0.59375]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t [0.9949672873678913]\n",
      "[1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1]\n",
      "[0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "best accs results:\n",
      "\t [0.59375]\n",
      "\t [0.8037242073477604]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]\n",
      "-----------------------------Fold 2---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([67, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.693442),   val_acc = 0.437984496124031\n",
      "validation acc increased (0.000000 ---> 0.437984)\n",
      "epoch 1: train loss = 0.7009523889089279,   l1loss = 0.13856501084980288,   train acc = 0.4914400805639476,\n",
      "val_loss = 0.7006198250970175,     val_acc = 0.3992248062015504\n",
      "\n",
      "epoch:  2\n",
      "epoch 2: train loss = 0.6762631435888892,   l1loss = 0.13815073134735872,   train acc = 0.607754279959718,\n",
      "val_loss = 0.7034130031748336,     val_acc = 0.4108527131782946\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.437984 ---> 0.492248)\n",
      "epoch 3: train loss = 0.6556627195767406,   l1loss = 0.13765930575726615,   train acc = 0.6299093655589124,\n",
      "val_loss = 0.6913710462954618,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  4\n",
      "validation loss decreased (0.693442 ---> 0.689858),   val_acc = 0.5116279069767442\n",
      "validation acc increased (0.492248 ---> 0.511628)\n",
      "validation loss decreased (0.689858 ---> 0.666388),   val_acc = 0.562015503875969\n",
      "validation acc increased (0.511628 ---> 0.562016)\n",
      "epoch 4: train loss = 0.6381383219514368,   l1loss = 0.13702793940076416,   train acc = 0.6374622356495468,\n",
      "val_loss = 0.6515760264655416,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.666388 ---> 0.648785),   val_acc = 0.5775193798449613\n",
      "validation acc increased (0.562016 ---> 0.577519)\n",
      "validation loss decreased (0.648785 ---> 0.622154),   val_acc = 0.6085271317829457\n",
      "validation acc increased (0.577519 ---> 0.608527)\n",
      "epoch 5: train loss = 0.6235407856776035,   l1loss = 0.13619495138961143,   train acc = 0.6414904330312186,\n",
      "val_loss = 0.6117342756700146,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.622154 ---> 0.609880),   val_acc = 0.6085271317829457\n",
      "validation acc increased (0.608527 ---> 0.608527)\n",
      "validation loss decreased (0.609880 ---> 0.601803),   val_acc = 0.6124031007751938\n",
      "validation acc increased (0.608527 ---> 0.612403)\n",
      "epoch 6: train loss = 0.6092025198244977,   l1loss = 0.13509638041768068,   train acc = 0.649043303121853,\n",
      "val_loss = 0.597300708062889,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.601803 ---> 0.597826),   val_acc = 0.6085271317829457\n",
      "validation loss decreased (0.597826 ---> 0.594267),   val_acc = 0.6085271317829457\n",
      "epoch 7: train loss = 0.5984585170539364,   l1loss = 0.13366857624246034,   train acc = 0.6560926485397784,\n",
      "val_loss = 0.5917771292287249,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.594267 ---> 0.591831),   val_acc = 0.6124031007751938\n",
      "validation acc increased (0.612403 ---> 0.612403)\n",
      "validation loss decreased (0.591831 ---> 0.585725),   val_acc = 0.6124031007751938\n",
      "validation acc increased (0.612403 ---> 0.612403)\n",
      "epoch 8: train loss = 0.5909301571375414,   l1loss = 0.13186680199156356,   train acc = 0.6591137965760322,\n",
      "val_loss = 0.5863324623237285,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  9\n",
      "validation loss decreased (0.585725 ---> 0.582507),   val_acc = 0.6046511627906976\n",
      "epoch 9: train loss = 0.5829210489057942,   l1loss = 0.12968964685666237,   train acc = 0.6636455186304129,\n",
      "val_loss = 0.5822689447681918,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  10\n",
      "validation loss decreased (0.582507 ---> 0.582276),   val_acc = 0.6124031007751938\n",
      "validation acc increased (0.612403 ---> 0.612403)\n",
      "validation acc increased (0.612403 ---> 0.612403)\n",
      "epoch 10: train loss = 0.575366021463158,   l1loss = 0.12713940261533974,   train acc = 0.6717019133937563,\n",
      "val_loss = 0.5861175392949304,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5670345990439316,   l1loss = 0.12424162386617392,   train acc = 0.6782477341389728,\n",
      "val_loss = 0.588328478872314,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.5552928300062335,   l1loss = 0.1211286605770014,   train acc = 0.6888217522658611,\n",
      "val_loss = 0.5963715642921684,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5412835172055831,   l1loss = 0.11785069293335966,   train acc = 0.6978851963746223,\n",
      "val_loss = 0.6184392266495283,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5228479005178777,   l1loss = 0.1145535701471273,   train acc = 0.7235649546827795,\n",
      "val_loss = 0.6210187313168548,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5053458828460294,   l1loss = 0.11146547943953901,   train acc = 0.7240684793554885,\n",
      "val_loss = 0.6982138563496197,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.48290507022707846,   l1loss = 0.10866142951199656,   train acc = 0.7452165156092648,\n",
      "val_loss = 0.6597347740055055,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.4580553521562559,   l1loss = 0.10626861951262329,   train acc = 0.7678751258811681,\n",
      "val_loss = 0.6594978039578874,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.42439078243237366,   l1loss = 0.10417765665570534,   train acc = 0.7809667673716012,\n",
      "val_loss = 0.8379030893015307,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.4111362245207707,   l1loss = 0.10249360746067217,   train acc = 0.7990936555891238,\n",
      "val_loss = 0.7785166943258093,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.3810017136828777,   l1loss = 0.10114079596022704,   train acc = 0.8227593152064451,\n",
      "val_loss = 1.1790409819042498,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3430937575129464,   l1loss = 0.09981364857364157,   train acc = 0.8423967774420946,\n",
      "val_loss = 2.173583840214929,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.3137253760571206,   l1loss = 0.09864820635991034,   train acc = 0.8600201409869084,\n",
      "val_loss = 1.0921057191236998,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.2892607558830027,   l1loss = 0.09758137509391868,   train acc = 0.8746223564954683,\n",
      "val_loss = 3.513900268909543,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.26403892289355685,   l1loss = 0.09678075590132466,   train acc = 0.8856998992950654,\n",
      "val_loss = 2.315718826740374,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.2428219530577626,   l1loss = 0.09631269205312834,   train acc = 0.9033232628398792,\n",
      "val_loss = 1.3863746112631272,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.23106987370705195,   l1loss = 0.0956531882075985,   train acc = 0.9078549848942599,\n",
      "val_loss = 3.2332160620726356,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.24449666369657622,   l1loss = 0.09556730201867412,   train acc = 0.8942598187311178,\n",
      "val_loss = 1.645380326943804,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.2848308266169595,   l1loss = 0.09535873357923129,   train acc = 0.8690835850956697,\n",
      "val_loss = 1.1068245080091648,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 29: train loss = 0.21259009558988842,   l1loss = 0.09493379782573934,   train acc = 0.9154078549848943,\n",
      "val_loss = 1.3550787757533465,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.17986376305961416,   l1loss = 0.0943629225155138,   train acc = 0.93202416918429,\n",
      "val_loss = 3.399730042824269,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.1377756273278297,   l1loss = 0.09381341631829078,   train acc = 0.9587109768378651,\n",
      "val_loss = 1.4636259065117947,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.15627087596409991,   l1loss = 0.09361198741083539,   train acc = 0.9370594159113796,\n",
      "val_loss = 2.010465736427144,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.21454838335454043,   l1loss = 0.09376692507052109,   train acc = 0.904833836858006,\n",
      "val_loss = 3.555855639821258,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.1433841212908667,   l1loss = 0.09360743821237502,   train acc = 0.9516616314199395,\n",
      "val_loss = 2.7130774370459623,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  35\n",
      "validation acc increased (0.612403 ---> 0.620155)\n",
      "epoch 35: train loss = 0.09808508254970308,   l1loss = 0.09324432826954553,   train acc = 0.9712990936555891,\n",
      "val_loss = 2.107295219288316,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.09397425880695037,   l1loss = 0.09265048068095551,   train acc = 0.9707955689828801,\n",
      "val_loss = 2.3170659689940223,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.07624247869756166,   l1loss = 0.09214130119764793,   train acc = 0.9788519637462235,\n",
      "val_loss = 2.4360666565015885,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.08142898870258773,   l1loss = 0.09167566879728409,   train acc = 0.972306143001007,\n",
      "val_loss = 3.0442896406779916,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.05564044175476346,   l1loss = 0.09119880020858538,   train acc = 0.9914400805639476,\n",
      "val_loss = 3.227812499038933,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  40\n",
      "validation acc increased (0.620155 ---> 0.627907)\n",
      "epoch 40: train loss = 0.047954427309086674,   l1loss = 0.09070139868261831,   train acc = 0.9894259818731118,\n",
      "val_loss = 2.6779214356296746,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.0805706052561661,   l1loss = 0.09069800027247522,   train acc = 0.972306143001007,\n",
      "val_loss = 3.348146224206732,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.120132422931905,   l1loss = 0.09100891725504387,   train acc = 0.9561933534743202,\n",
      "val_loss = 3.8875221688618034,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  43\n",
      "validation acc increased (0.627907 ---> 0.627907)\n",
      "epoch 43: train loss = 0.08399603084435035,   l1loss = 0.09161909364261416,   train acc = 0.972809667673716,\n",
      "val_loss = 3.6390174350073172,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.08419710318187452,   l1loss = 0.09146056805192404,   train acc = 0.9788519637462235,\n",
      "val_loss = 3.222931582105252,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.06798528590261156,   l1loss = 0.09109198656176992,   train acc = 0.9783484390735147,\n",
      "val_loss = 4.210132499073827,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.04261230672114085,   l1loss = 0.09061521228706729,   train acc = 0.9924471299093656,\n",
      "val_loss = 3.675780127214831,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.030354493255431558,   l1loss = 0.09003987193317692,   train acc = 0.9969788519637462,\n",
      "val_loss = 3.3861651013987935,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.02647563922973978,   l1loss = 0.08927071901846749,   train acc = 0.9974823766364552,\n",
      "val_loss = 3.631073840828829,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.021390184417764645,   l1loss = 0.08851830925406046,   train acc = 0.9984894259818731,\n",
      "val_loss = 3.7224400486623823,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.019483359864456175,   l1loss = 0.08783999447826173,   train acc = 0.998992950654582,\n",
      "val_loss = 3.5347149741742037,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.01563782924795589,   l1loss = 0.08718703778728858,   train acc = 0.998992950654582,\n",
      "val_loss = 3.792635572049045,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.015153491843307967,   l1loss = 0.0865459504189328,   train acc = 0.999496475327291,\n",
      "val_loss = 3.765355708987214,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.014703800348416797,   l1loss = 0.08596928344152245,   train acc = 1.0,\n",
      "val_loss = 3.4551806544610697,     val_acc = 0.6085271317829457\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]\n",
      "[1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666]\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
      "        1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
      "        1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n",
      "        0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
      "        1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
      "        0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
      "        1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
      "        1, 0, 0, 0, 1, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
      "        0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        1, 0, 1, 1, 0, 1, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
      "        0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
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      "        0, 0, 1, 1, 1, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
      "        1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "        1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
      "        0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
      "        1, 0, 0, 0, 0, 1, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,\n",
      "        0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,\n",
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      "        1, 1, 1, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
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      "        0, 0, 1, 1, 0, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
      "        1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1,\n",
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      "        0, 1, 0, 1, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
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      "        1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
      "        1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
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      "        1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
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      "        0, 0, 1, 1, 1, 1, 1, 0])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
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      "        1, 0, 1, 1, 1, 1, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output =  [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,\n",
      "        0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
      "        1, 1, 1, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
      "        0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
      "        0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
      "        1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
      "        1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653]\n",
      "[1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0]\n",
      "[0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666]\n",
      "\t [0.9949672873678913, 0.9974823766364552]\n",
      "[1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1]\n",
      "[1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625]\n",
      "\t [0.8037242073477604, 0.8670694864048338]\n",
      "[0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1]\n",
      "-----------------------------Fold 3---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([66, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.694551),   val_acc = 0.3643410852713178\n",
      "validation acc increased (0.000000 ---> 0.364341)\n",
      "validation acc increased (0.364341 ---> 0.364341)\n",
      "epoch 1: train loss = 0.6969524957183608,   l1loss = 0.1381219726307948,   train acc = 0.5654135338345865,\n",
      "val_loss = 0.7013817683670872,     val_acc = 0.3643410852713178\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.364341 ---> 0.364341)\n",
      "validation acc increased (0.364341 ---> 0.364341)\n",
      "epoch 2: train loss = 0.6680379967940481,   l1loss = 0.1377297131937548,   train acc = 0.6476190476190476,\n",
      "val_loss = 0.7116472231325253,     val_acc = 0.3643410852713178\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.364341 ---> 0.364341)\n",
      "validation acc increased (0.364341 ---> 0.395349)\n",
      "epoch 3: train loss = 0.6447332068194721,   l1loss = 0.1372586890644298,   train acc = 0.656641604010025,\n",
      "val_loss = 0.7110485115716624,     val_acc = 0.42248062015503873\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.395349 ---> 0.426357)\n",
      "validation acc increased (0.426357 ---> 0.480620)\n",
      "epoch 4: train loss = 0.6260023004130313,   l1loss = 0.1366433581687454,   train acc = 0.6551378446115288,\n",
      "val_loss = 0.6881170402201571,     val_acc = 0.4883720930232558\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.694551 ---> 0.686318),   val_acc = 0.49224806201550386\n",
      "validation acc increased (0.480620 ---> 0.492248)\n",
      "validation loss decreased (0.686318 ---> 0.665753),   val_acc = 0.49612403100775193\n",
      "validation acc increased (0.492248 ---> 0.496124)\n",
      "epoch 5: train loss = 0.6107026895783599,   l1loss = 0.13582232229990468,   train acc = 0.6636591478696742,\n",
      "val_loss = 0.6599389855251756,     val_acc = 0.5\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.665753 ---> 0.659603),   val_acc = 0.49612403100775193\n",
      "validation acc increased (0.496124 ---> 0.496124)\n",
      "validation loss decreased (0.659603 ---> 0.657801),   val_acc = 0.49612403100775193\n",
      "validation acc increased (0.496124 ---> 0.496124)\n",
      "epoch 6: train loss = 0.5962554595524208,   l1loss = 0.13474145999229642,   train acc = 0.6731829573934837,\n",
      "val_loss = 0.6608283524365388,     val_acc = 0.4883720930232558\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.5844666071702962,   l1loss = 0.13333625524563897,   train acc = 0.6807017543859649,\n",
      "val_loss = 0.6693935172502384,     val_acc = 0.5\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.496124 ---> 0.500000)\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "epoch 8: train loss = 0.5756398552342465,   l1loss = 0.13154963165297545,   train acc = 0.6832080200501253,\n",
      "val_loss = 0.6796527236931084,     val_acc = 0.49612403100775193\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.5669913336149135,   l1loss = 0.12937707920421035,   train acc = 0.6907268170426065,\n",
      "val_loss = 0.691935848820117,     val_acc = 0.49612403100775193\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5586488504756364,   l1loss = 0.1268213682380834,   train acc = 0.6952380952380952,\n",
      "val_loss = 0.7135535478591919,     val_acc = 0.49612403100775193\n",
      "\n",
      "epoch:  11\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "epoch 11: train loss = 0.547169718527256,   l1loss = 0.12398374975474556,   train acc = 0.7007518796992481,\n",
      "val_loss = 0.762653996778089,     val_acc = 0.5\n",
      "\n",
      "epoch:  12\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "epoch 12: train loss = 0.5339804249001028,   l1loss = 0.12088241919613721,   train acc = 0.7132832080200501,\n",
      "val_loss = 0.748571188874947,     val_acc = 0.49224806201550386\n",
      "\n",
      "epoch:  13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 13: train loss = 0.520279674302964,   l1loss = 0.11765421265572833,   train acc = 0.7127819548872181,\n",
      "val_loss = 0.8685980189678281,     val_acc = 0.49224806201550386\n",
      "\n",
      "epoch:  14\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "epoch 14: train loss = 0.5055005631948772,   l1loss = 0.11437528439631738,   train acc = 0.7298245614035088,\n",
      "val_loss = 1.0051480910574744,     val_acc = 0.5\n",
      "\n",
      "epoch:  15\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "epoch 15: train loss = 0.48474925627983306,   l1loss = 0.11120888095601161,   train acc = 0.7518796992481203,\n",
      "val_loss = 0.7568337228871131,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  16\n",
      "validation acc increased (0.500000 ---> 0.500000)\n",
      "validation acc increased (0.500000 ---> 0.635659)\n",
      "epoch 16: train loss = 0.47231552145236116,   l1loss = 0.10832016670091409,   train acc = 0.7543859649122807,\n",
      "val_loss = 0.8228741385215936,     val_acc = 0.5\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.4447837758333163,   l1loss = 0.10566314655661882,   train acc = 0.7729323308270677,\n",
      "val_loss = 0.7647813073424405,     val_acc = 0.5\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.4166216857750015,   l1loss = 0.10333552387423982,   train acc = 0.793984962406015,\n",
      "val_loss = 1.1814187527164932,     val_acc = 0.49612403100775193\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.4013006369571638,   l1loss = 0.1015192489128065,   train acc = 0.7984962406015037,\n",
      "val_loss = 1.049088865287544,     val_acc = 0.5077519379844961\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.38358660407532424,   l1loss = 0.10005261027872712,   train acc = 0.8030075187969925,\n",
      "val_loss = 0.9182334223458933,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3649053702826488,   l1loss = 0.09899659350626451,   train acc = 0.8180451127819549,\n",
      "val_loss = 1.0533229842666507,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.3366708062645188,   l1loss = 0.09801618972219023,   train acc = 0.837593984962406,\n",
      "val_loss = 1.0070983062880907,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.30011755532788154,   l1loss = 0.097101370052885,   train acc = 0.8736842105263158,\n",
      "val_loss = 1.9908723738766456,     val_acc = 0.5\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.2893361300005949,   l1loss = 0.09641273515789132,   train acc = 0.8671679197994987,\n",
      "val_loss = 4.135273530501728,     val_acc = 0.46124031007751937\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.2674656217408957,   l1loss = 0.09561551359288376,   train acc = 0.87468671679198,\n",
      "val_loss = 3.555174014365026,     val_acc = 0.4689922480620155\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.25737038730529316,   l1loss = 0.09514911164690677,   train acc = 0.8927318295739348,\n",
      "val_loss = 1.4680318331071573,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.19562739423641884,   l1loss = 0.09470970924560887,   train acc = 0.9263157894736842,\n",
      "val_loss = 1.4795843288879986,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.21110729103100329,   l1loss = 0.09441207906506713,   train acc = 0.9132832080200501,\n",
      "val_loss = 2.8698120837987857,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.2995036028082807,   l1loss = 0.09422946139087056,   train acc = 0.8756892230576441,\n",
      "val_loss = 1.910594179854519,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.29151671891821956,   l1loss = 0.09452762886097557,   train acc = 0.8696741854636592,\n",
      "val_loss = 2.842216599819272,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.24369466431756368,   l1loss = 0.09408135806632818,   train acc = 0.8897243107769424,\n",
      "val_loss = 1.2342111413302117,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.177748836042887,   l1loss = 0.09305703179131175,   train acc = 0.9273182957393483,\n",
      "val_loss = 1.935332564247209,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.1304508766285459,   l1loss = 0.09235037882674607,   train acc = 0.9604010025062657,\n",
      "val_loss = 1.6885591052299322,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.1040880726020139,   l1loss = 0.09180786986846971,   train acc = 0.968421052631579,\n",
      "val_loss = 1.8644648585901704,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.11854673838406278,   l1loss = 0.09142765101036034,   train acc = 0.9508771929824561,\n",
      "val_loss = 4.638857999517503,     val_acc = 0.49612403100775193\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.10307383279789958,   l1loss = 0.09096615721557971,   train acc = 0.9664160401002506,\n",
      "val_loss = 2.192981764212135,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.09253215106731669,   l1loss = 0.09045285032805345,   train acc = 0.9654135338345865,\n",
      "val_loss = 1.9693481386169907,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.08359189911519077,   l1loss = 0.09000530017721922,   train acc = 0.9764411027568922,\n",
      "val_loss = 2.151853931042575,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.06199482042613185,   l1loss = 0.08960390809484592,   train acc = 0.9824561403508771,\n",
      "val_loss = 2.2035991822102274,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.07736399561951035,   l1loss = 0.08935502176744896,   train acc = 0.9709273182957393,\n",
      "val_loss = 2.0890348573872286,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.07827905084106856,   l1loss = 0.08891892515701758,   train acc = 0.9724310776942355,\n",
      "val_loss = 4.512386254561964,     val_acc = 0.5038759689922481\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.046724244664635574,   l1loss = 0.08847408577762451,   train acc = 0.9899749373433584,\n",
      "val_loss = 2.2274649688439774,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.04698878815747742,   l1loss = 0.08795604082874786,   train acc = 0.9904761904761905,\n",
      "val_loss = 3.3725712779880492,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.044069078596388186,   l1loss = 0.08755256260621518,   train acc = 0.9884711779448622,\n",
      "val_loss = 2.6858827642692154,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.03322243625134753,   l1loss = 0.08706357124679369,   train acc = 0.993483709273183,\n",
      "val_loss = 3.174153364906015,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.0319087140971706,   l1loss = 0.08656990248383137,   train acc = 0.9949874686716792,\n",
      "val_loss = 3.085727780595306,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.025276731471892886,   l1loss = 0.0862220882547828,   train acc = 0.9979949874686717,\n",
      "val_loss = 2.4670777454856756,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.03616303299976172,   l1loss = 0.08582206025234142,   train acc = 0.9909774436090225,\n",
      "val_loss = 2.59104063915623,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.022464091186983545,   l1loss = 0.08538708544165867,   train acc = 0.9984962406015038,\n",
      "val_loss = 2.7770460093206215,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.01979252443389785,   l1loss = 0.08483099960593651,   train acc = 0.9984962406015038,\n",
      "val_loss = 2.9248241084490636,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.015581658314493365,   l1loss = 0.08423851138368287,   train acc = 0.9994987468671679,\n",
      "val_loss = 2.912791069625884,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.014949842383688254,   l1loss = 0.08364893044146679,   train acc = 1.0,\n",
      "val_loss = 2.928498700261116,     val_acc = 0.5891472868217055\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1]\n",
      "[0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334]\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
      "        1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
      "        0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 1, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
      "        0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
      "        1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
      "        1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
      "        0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
      "        0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
      "        1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
      "        0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
      "        0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
      "        1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
      "        1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
      "        0, 0, 0, 1, 0, 1, 0, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
      "        0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        0, 0, 0, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "        1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
      "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
      "        1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
      "        1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 0, 0, 0, 1, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
      "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        0, 1, 0, 0, 0, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
      "        0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
      "        1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
      "        1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "        1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
      "        0, 0, 1, 0, 0, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 1, 0, 0, 0, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
      "        1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 0, 1, 1, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
      "        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 1, 0])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0]\n",
      "[1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667]\n",
      "[0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1]\n",
      "[1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915]\n",
      "[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1]\n",
      "-----------------------------Fold 4---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([75, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.683370),   val_acc = 0.6666666666666666\n",
      "validation acc increased (0.000000 ---> 0.666667)\n",
      "epoch 1: train loss = 0.6846315076727115,   l1loss = 0.13866792857171056,   train acc = 0.5518962075848304,\n",
      "val_loss = 0.7129394467486891,     val_acc = 0.3333333333333333\n",
      "\n",
      "epoch:  2\n",
      "epoch 2: train loss = 0.6627097059628683,   l1loss = 0.13824024070760685,   train acc = 0.6147704590818364,\n",
      "val_loss = 0.7328977205956629,     val_acc = 0.34108527131782945\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.6461570380690569,   l1loss = 0.13772706633913304,   train acc = 0.6382235528942116,\n",
      "val_loss = 0.7231047513873078,     val_acc = 0.46124031007751937\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6311211969086272,   l1loss = 0.13707065026084345,   train acc = 0.6482035928143712,\n",
      "val_loss = 0.6812371556148973,     val_acc = 0.5\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.683370 ---> 0.677676),   val_acc = 0.5116279069767442\n",
      "validation loss decreased (0.677676 ---> 0.642738),   val_acc = 0.5271317829457365\n",
      "epoch 5: train loss = 0.6152418267703104,   l1loss = 0.1361936211526513,   train acc = 0.6596806387225549,\n",
      "val_loss = 0.6299803982409395,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.642738 ---> 0.628161),   val_acc = 0.5310077519379846\n",
      "validation loss decreased (0.628161 ---> 0.618165),   val_acc = 0.5271317829457365\n",
      "epoch 6: train loss = 0.6014872402726057,   l1loss = 0.1350108287529555,   train acc = 0.6641716566866267,\n",
      "val_loss = 0.6179831277492435,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.618165 ---> 0.617216),   val_acc = 0.5271317829457365\n",
      "validation loss decreased (0.617216 ---> 0.614287),   val_acc = 0.5193798449612403\n",
      "epoch 7: train loss = 0.5905261646487756,   l1loss = 0.1334880442319516,   train acc = 0.6711576846307385,\n",
      "val_loss = 0.6132445769716602,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.614287 ---> 0.612300),   val_acc = 0.5193798449612403\n",
      "validation loss decreased (0.612300 ---> 0.611189),   val_acc = 0.5271317829457365\n",
      "epoch 8: train loss = 0.5812796850404339,   l1loss = 0.13158048356245616,   train acc = 0.6781437125748503,\n",
      "val_loss = 0.6176805500836335,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.5716513974699907,   l1loss = 0.12926983946335768,   train acc = 0.687125748502994,\n",
      "val_loss = 0.6314459124276804,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5630688287778767,   l1loss = 0.12655608326256157,   train acc = 0.6976047904191617,\n",
      "val_loss = 0.6434678920479708,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5509181600844789,   l1loss = 0.12356667217618215,   train acc = 0.7065868263473054,\n",
      "val_loss = 0.6670152029787847,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.5384525743549218,   l1loss = 0.12038559576648913,   train acc = 0.7180638722554891,\n",
      "val_loss = 0.7214980153150337,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5237833831957477,   l1loss = 0.11706482975961205,   train acc = 0.7250499001996008,\n",
      "val_loss = 0.642303595550416,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.49995520455156733,   l1loss = 0.11376702059766727,   train acc = 0.7380239520958084,\n",
      "val_loss = 0.7001118160957513,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.4798619277225045,   l1loss = 0.11074621205082434,   train acc = 0.751996007984032,\n",
      "val_loss = 0.6432332724563835,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.450496327377365,   l1loss = 0.10813732176067349,   train acc = 0.7639720558882236,\n",
      "val_loss = 0.6254766467929811,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.42610586504498404,   l1loss = 0.1061172228076025,   train acc = 0.7869261477045908,\n",
      "val_loss = 0.8937256091324858,     val_acc = 0.6666666666666666\n",
      "\n",
      "epoch:  18\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "epoch 18: train loss = 0.4125394836633267,   l1loss = 0.10447883528625655,   train acc = 0.7999001996007984,\n",
      "val_loss = 1.3210663388865862,     val_acc = 0.6666666666666666\n",
      "\n",
      "epoch:  19\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "epoch 19: train loss = 0.36414110577273034,   l1loss = 0.10303020968051728,   train acc = 0.8313373253493014,\n",
      "val_loss = 1.6127898642006366,     val_acc = 0.6666666666666666\n",
      "\n",
      "epoch:  20\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "epoch 20: train loss = 0.3496626854061842,   l1loss = 0.10169063459434909,   train acc = 0.8383233532934131,\n",
      "val_loss = 1.0074581005141254,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.32243358922337817,   l1loss = 0.10068233941724439,   train acc = 0.8502994011976048,\n",
      "val_loss = 0.9281984200311262,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.31491396836415975,   l1loss = 0.09971560896216276,   train acc = 0.8507984031936128,\n",
      "val_loss = 1.5095452051754146,     val_acc = 0.6589147286821705\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.29515863696258227,   l1loss = 0.09860645926105761,   train acc = 0.874251497005988,\n",
      "val_loss = 0.887832756074824,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.24699413461123637,   l1loss = 0.09801728431574123,   train acc = 0.8917165668662674,\n",
      "val_loss = 1.674672379974247,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.255139184093285,   l1loss = 0.09737496454261259,   train acc = 0.8782435129740519,\n",
      "val_loss = 0.9508669986281302,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.21437893561022486,   l1loss = 0.09652904412168228,   train acc = 0.9131736526946108,\n",
      "val_loss = 2.068134845689286,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  27\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "epoch 27: train loss = 0.23168334174536898,   l1loss = 0.09616398560013362,   train acc = 0.905688622754491,\n",
      "val_loss = 1.2540726026361302,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 28: train loss = 0.19921719834595145,   l1loss = 0.09572086222990306,   train acc = 0.9171656686626747,\n",
      "val_loss = 1.3161783601886543,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.17246595051831115,   l1loss = 0.09554041276672881,   train acc = 0.937624750499002,\n",
      "val_loss = 3.398609378820582,     val_acc = 0.6589147286821705\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.19262273220007053,   l1loss = 0.09535936463973717,   train acc = 0.9166666666666666,\n",
      "val_loss = 3.561779273572818,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.13978799877171508,   l1loss = 0.09458633950965371,   train acc = 0.9545908183632734,\n",
      "val_loss = 3.981414251124248,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.13305879046698055,   l1loss = 0.09435142839918594,   train acc = 0.9491017964071856,\n",
      "val_loss = 1.737026299617087,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.15574632034806196,   l1loss = 0.09403544885967068,   train acc = 0.9406187624750499,\n",
      "val_loss = 3.2701686964940735,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.12506161606359387,   l1loss = 0.09349807686434534,   train acc = 0.9545908183632734,\n",
      "val_loss = 1.6769787791336692,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.0993102289914728,   l1loss = 0.0933545645988154,   train acc = 0.9715568862275449,\n",
      "val_loss = 2.9960278762403383,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.1006979171684878,   l1loss = 0.09339527790239471,   train acc = 0.9645708582834331,\n",
      "val_loss = 3.6225777300753337,     val_acc = 0.6589147286821705\n",
      "\n",
      "epoch:  37\n",
      "validation acc increased (0.666667 ---> 0.666667)\n",
      "epoch 37: train loss = 0.20696525356012904,   l1loss = 0.09307418661024756,   train acc = 0.9436127744510978,\n",
      "val_loss = 3.3196238436440164,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.16276606584261516,   l1loss = 0.094022158347442,   train acc = 0.9416167664670658,\n",
      "val_loss = 1.4645525244779365,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.13700719901961006,   l1loss = 0.09361297870705465,   train acc = 0.9500998003992016,\n",
      "val_loss = 1.8845138701126558,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.10037804332500447,   l1loss = 0.09339838315864761,   train acc = 0.9655688622754491,\n",
      "val_loss = 2.1819226367529048,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.06094860051295715,   l1loss = 0.09271240844103153,   train acc = 0.9860279441117764,\n",
      "val_loss = 2.0809006154999254,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.058166408848143863,   l1loss = 0.09190802524487178,   train acc = 0.9840319361277445,\n",
      "val_loss = 3.20259105789569,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.04276541528290141,   l1loss = 0.09118846676009382,   train acc = 0.9955089820359282,\n",
      "val_loss = 2.6667797657870507,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.03647657026520033,   l1loss = 0.09060218744351717,   train acc = 0.9945109780439122,\n",
      "val_loss = 2.7449685954308327,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.03098047074830223,   l1loss = 0.09009029960144542,   train acc = 0.9965069860279441,\n",
      "val_loss = 3.183011844176655,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.031913511436469776,   l1loss = 0.08947685778795364,   train acc = 0.9945109780439122,\n",
      "val_loss = 3.353361038852108,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.02452494901513863,   l1loss = 0.08888289796378085,   train acc = 0.9970059880239521,\n",
      "val_loss = 2.6891871090083157,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.05274008282882249,   l1loss = 0.08846809942565279,   train acc = 0.9810379241516967,\n",
      "val_loss = 4.0638800591461415,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.048649915469858696,   l1loss = 0.08838037115906527,   train acc = 0.9865269461077845,\n",
      "val_loss = 3.1181407630905627,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.051724245299598175,   l1loss = 0.08826220857942414,   train acc = 0.9810379241516967,\n",
      "val_loss = 2.9276230390681777,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.03112137069304784,   l1loss = 0.08755336800021325,   train acc = 0.9915169660678643,\n",
      "val_loss = 3.8795810669891595,     val_acc = 0.6744186046511628\n",
      "\n",
      "epoch:  52\n",
      "validation acc increased (0.666667 ---> 0.678295)\n",
      "epoch 52: train loss = 0.019947556626356527,   l1loss = 0.08692338591266772,   train acc = 0.998003992015968,\n",
      "val_loss = 2.994923972091529,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.016029105731797312,   l1loss = 0.08628454102489524,   train acc = 1.0,\n",
      "val_loss = 3.339036170826402,     val_acc = 0.5930232558139535\n",
      "\n",
      "!!! overfitted !!!\n",
      "[0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1]\n",
      "[0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613]\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,\n",
      "        0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0,\n",
      "        0, 0, 0, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 1, 1, 1, 0])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,\n",
      "        0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
      "        0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
      "        1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
      "        1, 0, 0, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
      "        1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
      "        0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 0])\n",
      "output =  [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
      "        0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
      "        0, 1, 0, 1, 0, 1, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
      "        0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
      "        1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
      "        1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
      "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
      "        0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 0, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,\n",
      "        0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
      "        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
      "        1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,\n",
      "        0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
      "        1, 1, 0, 1, 0, 0, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0]\n",
      "label =  tensor([1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
      "        0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 0, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
      "        0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
      "        0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
      "        1, 0, 1, 0, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0,\n",
      "        0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
      "        0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
      "        0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 0, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1]\n",
      "[1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291]\n",
      "[1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1]\n",
      "[1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824]\n",
      "[0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1]\n",
      "-----------------------------Fold 5---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([84, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.711296),   val_acc = 0.36046511627906974\n",
      "validation acc increased (0.000000 ---> 0.360465)\n",
      "validation acc increased (0.360465 ---> 0.360465)\n",
      "epoch 1: train loss = 0.6870066553995976,   l1loss = 0.13839889542723394,   train acc = 0.5608412618928392,\n",
      "val_loss = 0.7490144917207171,     val_acc = 0.36046511627906974\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.360465 ---> 0.360465)\n",
      "validation acc increased (0.360465 ---> 0.360465)\n",
      "epoch 2: train loss = 0.6565808814110371,   l1loss = 0.13801056421961136,   train acc = 0.629444166249374,\n",
      "val_loss = 0.765150977659595,     val_acc = 0.36046511627906974\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.360465 ---> 0.360465)\n",
      "validation acc increased (0.360465 ---> 0.375969)\n",
      "epoch 3: train loss = 0.6360432937793512,   l1loss = 0.13752871652543694,   train acc = 0.6459689534301453,\n",
      "val_loss = 0.7466173269027887,     val_acc = 0.42248062015503873\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.375969 ---> 0.434109)\n",
      "validation acc increased (0.434109 ---> 0.492248)\n",
      "epoch 4: train loss = 0.622315076511385,   l1loss = 0.13690243397256882,   train acc = 0.6439659489233851,\n",
      "val_loss = 0.6967740377714468,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.711296 ---> 0.693885),   val_acc = 0.5116279069767442\n",
      "validation acc increased (0.492248 ---> 0.511628)\n",
      "validation loss decreased (0.693885 ---> 0.664049),   val_acc = 0.5116279069767442\n",
      "validation acc increased (0.511628 ---> 0.511628)\n",
      "epoch 5: train loss = 0.6108160970147753,   l1loss = 0.13607064582702214,   train acc = 0.6539809714571858,\n",
      "val_loss = 0.6510321973830231,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.664049 ---> 0.649357),   val_acc = 0.5155038759689923\n",
      "validation acc increased (0.511628 ---> 0.515504)\n",
      "validation loss decreased (0.649357 ---> 0.643178),   val_acc = 0.5271317829457365\n",
      "validation acc increased (0.515504 ---> 0.527132)\n",
      "epoch 6: train loss = 0.598267633048188,   l1loss = 0.13498037214063083,   train acc = 0.6675012518778167,\n",
      "val_loss = 0.6432933178982994,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.643178 ---> 0.642807),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.527132 ---> 0.531008)\n",
      "validation loss decreased (0.642807 ---> 0.641224),   val_acc = 0.5271317829457365\n",
      "epoch 7: train loss = 0.5881321117707234,   l1loss = 0.13360911191375363,   train acc = 0.671507260891337,\n",
      "val_loss = 0.6395671737286471,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.641224 ---> 0.639952),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.531008 ---> 0.531008)\n",
      "validation loss decreased (0.639952 ---> 0.637798),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.531008 ---> 0.531008)\n",
      "epoch 8: train loss = 0.5801561990557877,   l1loss = 0.13193928131355187,   train acc = 0.6760140210315473,\n",
      "val_loss = 0.6369561816370765,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  9\n",
      "validation loss decreased (0.637798 ---> 0.637255),   val_acc = 0.5271317829457365\n",
      "validation loss decreased (0.637255 ---> 0.634658),   val_acc = 0.5348837209302325\n",
      "validation acc increased (0.531008 ---> 0.534884)\n",
      "epoch 9: train loss = 0.5717252453208029,   l1loss = 0.1299565265819796,   train acc = 0.6815222834251377,\n",
      "val_loss = 0.637611803403774,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.534884 ---> 0.534884)\n",
      "epoch 10: train loss = 0.5626098037840548,   l1loss = 0.12765505166727123,   train acc = 0.6920380570856285,\n",
      "val_loss = 0.656070447707361,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  11\n",
      "validation acc increased (0.534884 ---> 0.534884)\n",
      "epoch 11: train loss = 0.5535151342123105,   l1loss = 0.12510810005602147,   train acc = 0.6955433149724587,\n",
      "val_loss = 0.6719910063484843,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.5409535019172091,   l1loss = 0.1223971828236303,   train acc = 0.7090635953930896,\n",
      "val_loss = 0.7268788130708443,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  13\n",
      "validation acc increased (0.534884 ---> 0.538760)\n",
      "epoch 13: train loss = 0.5258218407123519,   l1loss = 0.11959482016089444,   train acc = 0.7250876314471708,\n",
      "val_loss = 0.6585291873577029,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5086835484893906,   l1loss = 0.1168519536823288,   train acc = 0.7336004006009014,\n",
      "val_loss = 0.6720465666563936,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  15\n",
      "validation acc increased (0.538760 ---> 0.538760)\n",
      "epoch 15: train loss = 0.48642194915784853,   l1loss = 0.11419245105238635,   train acc = 0.756634952428643,\n",
      "val_loss = 0.6040798605874528,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  16\n",
      "validation loss decreased (0.634658 ---> 0.629792),   val_acc = 0.5581395348837209\n",
      "validation acc increased (0.538760 ---> 0.558140)\n",
      "epoch 16: train loss = 0.4577500793297528,   l1loss = 0.11169700107397768,   train acc = 0.7791687531296946,\n",
      "val_loss = 0.9656664484231047,     val_acc = 0.5\n",
      "\n",
      "epoch:  17\n",
      "validation acc increased (0.558140 ---> 0.596899)\n",
      "epoch 17: train loss = 0.42826039845130415,   l1loss = 0.10948925794782673,   train acc = 0.7926890335503255,\n",
      "val_loss = 0.8292172735975695,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.39249560578680065,   l1loss = 0.10765625636429564,   train acc = 0.8232348522784176,\n",
      "val_loss = 0.8668296665184257,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  19\n",
      "validation acc increased (0.596899 ---> 0.643411)\n",
      "epoch 19: train loss = 0.3674749865732494,   l1loss = 0.10613021715677078,   train acc = 0.8312468703054582,\n",
      "val_loss = 1.4543597975731357,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.347654858826994,   l1loss = 0.10473605919523005,   train acc = 0.8357536304456685,\n",
      "val_loss = 2.475660421223156,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3268627472441974,   l1loss = 0.10326415842094001,   train acc = 0.85678517776665,\n",
      "val_loss = 0.8104829621869464,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 22: train loss = 0.35131114540723307,   l1loss = 0.10210657698411493,   train acc = 0.8307461191787682,\n",
      "val_loss = 1.7831758935322133,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.3259180520974103,   l1loss = 0.10085294403146969,   train acc = 0.8487731597396094,\n",
      "val_loss = 1.6435921807275262,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.2837501486421049,   l1loss = 0.09979496815306697,   train acc = 0.871807711567351,\n",
      "val_loss = 2.8983472193683344,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  25\n",
      "validation acc increased (0.643411 ---> 0.643411)\n",
      "epoch 25: train loss = 0.24991583432414619,   l1loss = 0.09866795351968506,   train acc = 0.8953430145217827,\n",
      "val_loss = 1.3234346984892853,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.23671170943787412,   l1loss = 0.09776017363437367,   train acc = 0.901352028042063,\n",
      "val_loss = 1.7247427341549895,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.233533194886486,   l1loss = 0.09673886006706407,   train acc = 0.9018527791687532,\n",
      "val_loss = 1.1423584624778393,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.20981159080967166,   l1loss = 0.09592169590768183,   train acc = 0.9148723084626941,\n",
      "val_loss = 5.035286263902058,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  29\n",
      "validation acc increased (0.643411 ---> 0.651163)\n",
      "epoch 29: train loss = 0.18721183538287653,   l1loss = 0.09532376013520842,   train acc = 0.9303955933900852,\n",
      "val_loss = 1.7854539109754932,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.21521277558552843,   l1loss = 0.09483509715208126,   train acc = 0.9048572859288934,\n",
      "val_loss = 2.0886588960654975,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.16117875964479203,   l1loss = 0.09430676077492069,   train acc = 0.9409113670505759,\n",
      "val_loss = 1.3893336746119713,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.18729717622832412,   l1loss = 0.0941365569135996,   train acc = 0.9243865798698047,\n",
      "val_loss = 5.178411402443583,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.15735476534546883,   l1loss = 0.0937146420292575,   train acc = 0.9419128693039559,\n",
      "val_loss = 2.165926408398059,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.13561190845343132,   l1loss = 0.09294714458924744,   train acc = 0.9489233850776164,\n",
      "val_loss = 2.862183544986932,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.11557932168946722,   l1loss = 0.09219135233594229,   train acc = 0.9584376564847271,\n",
      "val_loss = 3.1061169494109504,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.11341282006019225,   l1loss = 0.09179794447730646,   train acc = 0.9614421632448673,\n",
      "val_loss = 2.973599456843122,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.13424175273688602,   l1loss = 0.09140255465230526,   train acc = 0.9439158738107161,\n",
      "val_loss = 1.4640861449315565,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.09078391378350896,   l1loss = 0.09077151980051472,   train acc = 0.9699549323985979,\n",
      "val_loss = 1.923348656458448,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.1380670597001679,   l1loss = 0.09045900325731569,   train acc = 0.9444166249374061,\n",
      "val_loss = 3.3878318514934804,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.12143602777733227,   l1loss = 0.09066373266514743,   train acc = 0.9579369053580371,\n",
      "val_loss = 2.142883213915566,     val_acc = 0.5038759689922481\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.11649664227641339,   l1loss = 0.09069241947854945,   train acc = 0.9584376564847271,\n",
      "val_loss = 2.8424580928891205,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.11953155570288092,   l1loss = 0.09055208012230348,   train acc = 0.9534301452178268,\n",
      "val_loss = 3.844284035438715,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.1391850914218513,   l1loss = 0.09037555076282623,   train acc = 0.9514271407110666,\n",
      "val_loss = 3.0129795486843864,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.11035888379791348,   l1loss = 0.09016522343569418,   train acc = 0.9589384076114171,\n",
      "val_loss = 2.5781839115675145,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.09197489871015295,   l1loss = 0.08964754919753291,   train acc = 0.9669504256384577,\n",
      "val_loss = 2.4371593719304996,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.0579324397978988,   l1loss = 0.08879983621758106,   train acc = 0.9859789684526791,\n",
      "val_loss = 2.2613389057706494,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.0461813348439197,   l1loss = 0.08810112920473621,   train acc = 0.9864797195793691,\n",
      "val_loss = 3.979649772939756,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.03998827998063298,   l1loss = 0.0878447880879843,   train acc = 0.9929894842263395,\n",
      "val_loss = 5.175023500309434,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.04049086723706098,   l1loss = 0.08717421617995039,   train acc = 0.9934902353530295,\n",
      "val_loss = 3.0130414075629655,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  50\n",
      "validation acc increased (0.651163 ---> 0.655039)\n",
      "epoch 50: train loss = 0.03793664645537936,   l1loss = 0.08660745584537939,   train acc = 0.9914872308462694,\n",
      "val_loss = 2.4097132146820543,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.034598207665522455,   l1loss = 0.0860587003667413,   train acc = 0.9924887330996495,\n",
      "val_loss = 2.3104088583657907,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.029838849054010557,   l1loss = 0.08562057309736892,   train acc = 0.9964947421131698,\n",
      "val_loss = 5.1767294721085895,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.023908328355425706,   l1loss = 0.08518237430689989,   train acc = 0.9964947421131698,\n",
      "val_loss = 3.105967559093653,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  54\n",
      "epoch 54: train loss = 0.023712221452846013,   l1loss = 0.08467714752280002,   train acc = 0.9959939909864797,\n",
      "val_loss = 2.627571163944496,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  55\n",
      "epoch 55: train loss = 0.02119624899009137,   l1loss = 0.084207254313175,   train acc = 0.9979969954932398,\n",
      "val_loss = 3.670362725738407,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  56\n",
      "epoch 56: train loss = 0.02971064273695171,   l1loss = 0.08382122342847381,   train acc = 0.9934902353530295,\n",
      "val_loss = 4.768853254883384,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  57\n",
      "epoch 57: train loss = 0.026361721558016006,   l1loss = 0.08356228320447576,   train acc = 0.9959939909864797,\n",
      "val_loss = 3.9932817562605982,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  58\n",
      "epoch 58: train loss = 0.018343641952941223,   l1loss = 0.08313654303461179,   train acc = 0.9984977466199298,\n",
      "val_loss = 2.416079253147811,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  59\n",
      "epoch 59: train loss = 0.0166573378317614,   l1loss = 0.08268285322604206,   train acc = 0.9984977466199298,\n",
      "val_loss = 3.361119598383408,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  60\n",
      "epoch 60: train loss = 0.018886090959545875,   l1loss = 0.08225083055649032,   train acc = 0.9979969954932398,\n",
      "val_loss = 4.036783265050992,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  61\n",
      "epoch 61: train loss = 0.015808429777935618,   l1loss = 0.08185882754082911,   train acc = 0.99899849774662,\n",
      "val_loss = 2.6130907313768255,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  62\n",
      "epoch 62: train loss = 0.014593462708313345,   l1loss = 0.08146914296527474,   train acc = 0.99899849774662,\n",
      "val_loss = 2.4723181946333064,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  63\n",
      "epoch 63: train loss = 0.016414859970592235,   l1loss = 0.08114846524188801,   train acc = 0.9979969954932398,\n",
      "val_loss = 3.099476133206094,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  64\n",
      "epoch 64: train loss = 0.018514045871398748,   l1loss = 0.08086007390251504,   train acc = 0.9969954932398598,\n",
      "val_loss = 4.376173152480015,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  65\n",
      "epoch 65: train loss = 0.0247556148073601,   l1loss = 0.08061432335889512,   train acc = 0.9954932398597897,\n",
      "val_loss = 2.659250307452771,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  66\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 66: train loss = 0.020378888247494526,   l1loss = 0.08030128979688891,   train acc = 0.9954932398597897,\n",
      "val_loss = 3.7070143610932105,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  67\n",
      "epoch 67: train loss = 0.012092748920829163,   l1loss = 0.07994780938701147,   train acc = 0.99899849774662,\n",
      "val_loss = 3.0426118189050246,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  68\n",
      "epoch 68: train loss = 0.011227833565080171,   l1loss = 0.07961520851852301,   train acc = 1.0,\n",
      "val_loss = 2.48265065576217,     val_acc = 0.6201550387596899\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1]\n",
      "[0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471]\n",
      "output =  [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0,\n",
      "        1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1,\n",
      "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
      "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 0, 1, 1, 1, 0, 1, 0])\n",
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      "label =  tensor([1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
      "        0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1,\n",
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      "        1, 0, 0, 1, 0, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
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      "        1, 0, 1, 0, 0, 0, 0, 0])\n",
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      "label =  tensor([1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "        1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
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      "        1, 1, 0, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
      "        1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
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      "        1, 1, 0, 0, 1, 1, 0, 1])\n",
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      "label =  tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
      "        1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
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      "        1, 1, 0, 0, 1, 0, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
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      "        0, 1, 0, 1, 0, 0, 1, 1])\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "label =  tensor([1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
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      "        0, 1, 0, 1, 0, 0, 0, 1])\n",
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      "label =  tensor([1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
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      "        0, 1, 1, 0, 1, 1, 0, 0])\n",
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      "label =  tensor([0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
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      "        1, 1, 1, 0, 0, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
      "        1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 0, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
      "        0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
      "        1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "        0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
      "        0, 1, 0, 1, 0, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
      "        0, 0, 1, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        0, 1, 1, 1, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708]\n",
      "[0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331]\n",
      "[0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]\n",
      "[1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082]\n",
      "[0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1]\n",
      "-----------------------------Fold 6---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([77, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.691554),   val_acc = 0.6511627906976745\n",
      "validation acc increased (0.000000 ---> 0.651163)\n",
      "validation loss decreased (0.691554 ---> 0.690849),   val_acc = 0.6511627906976745\n",
      "validation acc increased (0.651163 ---> 0.651163)\n",
      "epoch 1: train loss = 0.6948271975517273,   l1loss = 0.13827931582927705,   train acc = 0.4485,\n",
      "val_loss = 0.6928248095882031,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  2\n",
      "epoch 2: train loss = 0.672557541847229,   l1loss = 0.137843541264534,   train acc = 0.589,\n",
      "val_loss = 0.6987676075262617,     val_acc = 0.3643410852713178\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.6535676913261413,   l1loss = 0.13731942212581635,   train acc = 0.636,\n",
      "val_loss = 0.6965285705965619,     val_acc = 0.4728682170542636\n",
      "\n",
      "epoch:  4\n",
      "validation loss decreased (0.690849 ---> 0.677476),   val_acc = 0.4883720930232558\n",
      "epoch 4: train loss = 0.6372407693862915,   l1loss = 0.13664585983753205,   train acc = 0.65,\n",
      "val_loss = 0.6638829569483913,     val_acc = 0.5038759689922481\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.677476 ---> 0.661241),   val_acc = 0.5\n",
      "validation loss decreased (0.661241 ---> 0.639020),   val_acc = 0.5116279069767442\n",
      "epoch 5: train loss = 0.6211547412872315,   l1loss = 0.13576398611068727,   train acc = 0.6585,\n",
      "val_loss = 0.6298631182474683,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.639020 ---> 0.628438),   val_acc = 0.5116279069767442\n",
      "validation loss decreased (0.628438 ---> 0.621201),   val_acc = 0.5077519379844961\n",
      "epoch 6: train loss = 0.6071342725753784,   l1loss = 0.1346161276102066,   train acc = 0.661,\n",
      "val_loss = 0.6213997648667919,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.621201 ---> 0.621017),   val_acc = 0.5193798449612403\n",
      "epoch 7: train loss = 0.5958632040023804,   l1loss = 0.13315785372257233,   train acc = 0.6715,\n",
      "val_loss = 0.6230281388112741,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.5865020289421081,   l1loss = 0.13134343349933625,   train acc = 0.6755,\n",
      "val_loss = 0.6317427439810058,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.578163959980011,   l1loss = 0.12913685393333435,   train acc = 0.683,\n",
      "val_loss = 0.6439248508261156,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5690374765396118,   l1loss = 0.12657931053638458,   train acc = 0.687,\n",
      "val_loss = 0.6405774336452632,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5578733282089233,   l1loss = 0.12375886642932891,   train acc = 0.6915,\n",
      "val_loss = 0.6485154487827952,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.547102264881134,   l1loss = 0.12075013303756714,   train acc = 0.7035,\n",
      "val_loss = 0.5815267077712125,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  13\n",
      "validation loss decreased (0.621017 ---> 0.578556),   val_acc = 0.6201550387596899\n",
      "epoch 13: train loss = 0.5293013830184936,   l1loss = 0.11762005990743638,   train acc = 0.715,\n",
      "val_loss = 0.622708785210469,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5110176424980164,   l1loss = 0.11452219623327255,   train acc = 0.736,\n",
      "val_loss = 0.5994160191495289,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  15\n",
      "validation acc increased (0.651163 ---> 0.651163)\n",
      "epoch 15: train loss = 0.47669880533218384,   l1loss = 0.11159368234872818,   train acc = 0.7645,\n",
      "val_loss = 0.7053935582092566,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  16\n",
      "validation acc increased (0.651163 ---> 0.655039)\n",
      "epoch 16: train loss = 0.4439049696922302,   l1loss = 0.10900945609807969,   train acc = 0.7855,\n",
      "val_loss = 0.8016450599182484,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.4089318103790283,   l1loss = 0.10682100701332092,   train acc = 0.808,\n",
      "val_loss = 1.34718641343476,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  18\n",
      "validation acc increased (0.655039 ---> 0.658915)\n",
      "epoch 18: train loss = 0.38175707817077636,   l1loss = 0.10503107064962387,   train acc = 0.819,\n",
      "val_loss = 1.51498044184012,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.3507974019050598,   l1loss = 0.10347136110067368,   train acc = 0.843,\n",
      "val_loss = 1.8761492145153904,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.3973570125102997,   l1loss = 0.10228700506687165,   train acc = 0.7975,\n",
      "val_loss = 3.359361020169517,     val_acc = 0.6550387596899225\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3766449837684631,   l1loss = 0.10100592476129532,   train acc = 0.8235,\n",
      "val_loss = 0.8532644796741101,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.30714254689216614,   l1loss = 0.09982918077707291,   train acc = 0.868,\n",
      "val_loss = 4.533395921132823,     val_acc = 0.4689922480620155\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.2730036299228668,   l1loss = 0.09869706439971924,   train acc = 0.8785,\n",
      "val_loss = 1.673116033391435,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.252084813952446,   l1loss = 0.09814584302902221,   train acc = 0.894,\n",
      "val_loss = 2.988402714100919,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.2553533761501312,   l1loss = 0.09725052165985107,   train acc = 0.8885,\n",
      "val_loss = 4.158237235490666,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.22806144893169403,   l1loss = 0.09632847493886948,   train acc = 0.9095,\n",
      "val_loss = 1.619036548709598,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.20052375197410582,   l1loss = 0.0954943853020668,   train acc = 0.9185,\n",
      "val_loss = 1.5913666817106942,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.19252619695663453,   l1loss = 0.09492724579572677,   train acc = 0.92,\n",
      "val_loss = 2.3363786150318706,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.19474881601333618,   l1loss = 0.09441870188713074,   train acc = 0.9265,\n",
      "val_loss = 3.430836339329564,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.21158030927181243,   l1loss = 0.09452930903434753,   train acc = 0.907,\n",
      "val_loss = 8.181018297062364,     val_acc = 0.4496124031007752\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.15486617839336395,   l1loss = 0.0938582792878151,   train acc = 0.9455,\n",
      "val_loss = 1.8784414095471995,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.12229575896263123,   l1loss = 0.09318001997470855,   train acc = 0.96,\n",
      "val_loss = 5.165876115015311,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.11896066904067994,   l1loss = 0.09262535566091537,   train acc = 0.962,\n",
      "val_loss = 3.3326926933702574,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  34\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 34: train loss = 0.1430732717514038,   l1loss = 0.09208029317855836,   train acc = 0.947,\n",
      "val_loss = 3.7897773609461005,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.10793969309329987,   l1loss = 0.09178146147727967,   train acc = 0.9635,\n",
      "val_loss = 3.7946822282897408,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.1086569909453392,   l1loss = 0.09210293853282929,   train acc = 0.964,\n",
      "val_loss = 3.237369472117618,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.09874134624004365,   l1loss = 0.09163604235649109,   train acc = 0.959,\n",
      "val_loss = 10.602950192237085,     val_acc = 0.4573643410852713\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.10966753149032593,   l1loss = 0.09086926692724227,   train acc = 0.957,\n",
      "val_loss = 6.996053880499315,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.08259383499622346,   l1loss = 0.09028866285085678,   train acc = 0.977,\n",
      "val_loss = 2.8690995652546256,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.07870484691858291,   l1loss = 0.08984918028116226,   train acc = 0.971,\n",
      "val_loss = 2.2550083565157513,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.08104722368717193,   l1loss = 0.08948963952064515,   train acc = 0.9775,\n",
      "val_loss = 2.7512668971867527,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.061936515539884565,   l1loss = 0.08921354949474335,   train acc = 0.9805,\n",
      "val_loss = 3.8835250869277838,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.04977551221847534,   l1loss = 0.08895368778705597,   train acc = 0.9865,\n",
      "val_loss = 3.912529797517052,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.03967177218198776,   l1loss = 0.08825107955932618,   train acc = 0.9905,\n",
      "val_loss = 3.231899913891341,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.048251408934593204,   l1loss = 0.08825813591480255,   train acc = 0.9865,\n",
      "val_loss = 3.3065699320430904,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.04396019262075424,   l1loss = 0.0874557437300682,   train acc = 0.987,\n",
      "val_loss = 2.2364588347024488,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.02501585677266121,   l1loss = 0.08677183699607849,   train acc = 0.998,\n",
      "val_loss = 3.7413160227989968,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.024205303311347962,   l1loss = 0.08615065145492554,   train acc = 0.9965,\n",
      "val_loss = 4.811114255772081,     val_acc = 0.6434108527131783\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.018718741066753863,   l1loss = 0.0853805913925171,   train acc = 0.9985,\n",
      "val_loss = 3.1942446878714157,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.01704993227124214,   l1loss = 0.0846337440609932,   train acc = 0.9975,\n",
      "val_loss = 2.7979257494904273,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.029849595367908478,   l1loss = 0.08401931124925613,   train acc = 0.9905,\n",
      "val_loss = 3.343561839687732,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.02539513537287712,   l1loss = 0.08352306187152862,   train acc = 0.995,\n",
      "val_loss = 4.478786712469057,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  53\n",
      "validation acc increased (0.658915 ---> 0.658915)\n",
      "epoch 53: train loss = 0.02735452988743782,   l1loss = 0.08342606168985367,   train acc = 0.9925,\n",
      "val_loss = 3.296960321271272,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  54\n",
      "epoch 54: train loss = 0.03272683323174715,   l1loss = 0.08329540795087814,   train acc = 0.994,\n",
      "val_loss = 17.770323502000913,     val_acc = 0.5\n",
      "\n",
      "epoch:  55\n",
      "epoch 55: train loss = 0.029803431689739226,   l1loss = 0.08343729907274246,   train acc = 0.996,\n",
      "val_loss = 7.326728362445683,     val_acc = 0.6472868217054264\n",
      "\n",
      "epoch:  56\n",
      "epoch 56: train loss = 0.02619129529595375,   l1loss = 0.08289635759592057,   train acc = 0.996,\n",
      "val_loss = 3.209116557714089,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  57\n",
      "epoch 57: train loss = 0.023176433131098746,   l1loss = 0.082220523416996,   train acc = 0.9955,\n",
      "val_loss = 3.3258880940518636,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  58\n",
      "epoch 58: train loss = 0.014486284106969833,   l1loss = 0.08156805974245071,   train acc = 0.9995,\n",
      "val_loss = 4.771162561667982,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  59\n",
      "epoch 59: train loss = 0.018196347802877426,   l1loss = 0.08102932274341583,   train acc = 0.9965,\n",
      "val_loss = 3.726594481357308,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  60\n",
      "epoch 60: train loss = 0.012295396901667118,   l1loss = 0.0804902771115303,   train acc = 0.9995,\n",
      "val_loss = 3.2824748639073915,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  61\n",
      "epoch 61: train loss = 0.014604092702269554,   l1loss = 0.08002961206436157,   train acc = 0.9985,\n",
      "val_loss = 3.5945028610412004,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  62\n",
      "epoch 62: train loss = 0.011242052119225264,   l1loss = 0.07957109451293945,   train acc = 1.0,\n",
      "val_loss = 4.122840467349503,     val_acc = 0.6395348837209303\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1]\n",
      "[1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471, 0.6073298429319371]\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
      "        1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
      "        1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
      "        0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 1, 1, 0, 0, 1])\n",
      "output =  [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0]\n",
      "label =  tensor([0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
      "        1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
      "        1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 0, 1, 1, 0, 0, 0, 0])\n",
      "output =  [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]\n",
      "label =  tensor([0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
      "        0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
      "        0, 1, 1, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
      "        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 0, 0, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
      "        1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
      "        0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 0])\n",
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      "label =  tensor([1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
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      "        0, 0, 0, 1, 0, 1, 0, 1])\n",
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      "label =  tensor([0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
      "        1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
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      "label =  tensor([0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
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      "        0, 0, 1, 1, 1, 1, 1, 0])\n",
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      "label =  tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
      "        1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
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      "label =  tensor([1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
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      "        1, 0, 1, 0, 0, 0, 0, 1])\n",
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      "label =  tensor([0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
      "        1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
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      "label =  tensor([0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
      "        1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
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      "        0, 1, 1, 1, 0, 1, 0, 1])\n",
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      "label =  tensor([0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
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      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
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      "        1, 1, 0, 1, 0, 0, 0, 1])\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "label =  tensor([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1,\n",
      "        0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
      "        0, 1, 0, 1, 0, 1, 0, 1])\n",
      "output =  [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
      "        0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
      "        1, 1, 1, 1, 0, 1, 1, 0])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708, 0.7]\n",
      "[1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1]\n",
      "[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822, 0.6492146596858639]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331, 0.638]\n",
      "[0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1]\n",
      "[0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964, 0.6387434554973822]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082, 0.519]\n",
      "[0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0]\n",
      "-----------------------------Fold 7---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([80, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.698563),   val_acc = 0.3488372093023256\n",
      "validation acc increased (0.000000 ---> 0.348837)\n",
      "validation acc increased (0.348837 ---> 0.348837)\n",
      "epoch 1: train loss = 0.6735606533417041,   l1loss = 0.13814079643100097,   train acc = 0.6146926536731634,\n",
      "val_loss = 0.7325391187224277,     val_acc = 0.3488372093023256\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.348837 ---> 0.348837)\n",
      "validation acc increased (0.348837 ---> 0.348837)\n",
      "epoch 2: train loss = 0.6563243215111481,   l1loss = 0.13766097700012023,   train acc = 0.6366816591704147,\n",
      "val_loss = 0.7539332402768986,     val_acc = 0.3488372093023256\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.348837 ---> 0.348837)\n",
      "validation acc increased (0.348837 ---> 0.379845)\n",
      "epoch 3: train loss = 0.6414987822880094,   l1loss = 0.13709656230692027,   train acc = 0.6391804097951025,\n",
      "val_loss = 0.7422034661899242,     val_acc = 0.43410852713178294\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.379845 ---> 0.441860)\n",
      "validation acc increased (0.441860 ---> 0.500000)\n",
      "epoch 4: train loss = 0.6271164830120607,   l1loss = 0.13637894998187725,   train acc = 0.6456771614192903,\n",
      "val_loss = 0.68995659388313,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.698563 ---> 0.686751),   val_acc = 0.5271317829457365\n",
      "validation acc increased (0.500000 ---> 0.527132)\n",
      "validation loss decreased (0.686751 ---> 0.653364),   val_acc = 0.5387596899224806\n",
      "validation acc increased (0.527132 ---> 0.538760)\n",
      "epoch 5: train loss = 0.6123478256542048,   l1loss = 0.13543122615860678,   train acc = 0.6561719140429785,\n",
      "val_loss = 0.6406823590744374,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.653364 ---> 0.639413),   val_acc = 0.5426356589147286\n",
      "validation acc increased (0.538760 ---> 0.542636)\n",
      "validation loss decreased (0.639413 ---> 0.626855),   val_acc = 0.5426356589147286\n",
      "validation acc increased (0.542636 ---> 0.542636)\n",
      "epoch 6: train loss = 0.5990132123216995,   l1loss = 0.1341949236550848,   train acc = 0.6686656671664168,\n",
      "val_loss = 0.6231413797814717,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.626855 ---> 0.624045),   val_acc = 0.5503875968992248\n",
      "validation acc increased (0.542636 ---> 0.550388)\n",
      "validation loss decreased (0.624045 ---> 0.620769),   val_acc = 0.5426356589147286\n",
      "epoch 7: train loss = 0.5881006238402158,   l1loss = 0.13262152082767562,   train acc = 0.6771614192903548,\n",
      "val_loss = 0.6177397381312163,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.620769 ---> 0.617393),   val_acc = 0.5503875968992248\n",
      "validation acc increased (0.550388 ---> 0.550388)\n",
      "validation acc increased (0.550388 ---> 0.554264)\n",
      "epoch 8: train loss = 0.5790939586332952,   l1loss = 0.13068488615563606,   train acc = 0.6836581709145427,\n",
      "val_loss = 0.6233009136924448,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  9\n",
      "validation acc increased (0.554264 ---> 0.562016)\n",
      "validation acc increased (0.562016 ---> 0.565891)\n",
      "epoch 9: train loss = 0.5705378363574523,   l1loss = 0.12834613378914161,   train acc = 0.6906546726636682,\n",
      "val_loss = 0.6323157320188921,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5613774951251371,   l1loss = 0.125647922248944,   train acc = 0.6946526736631684,\n",
      "val_loss = 0.6595740812693456,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5492115492584824,   l1loss = 0.12263813326651904,   train acc = 0.7066466766616691,\n",
      "val_loss = 0.6838388831116432,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.5365634637287651,   l1loss = 0.11940663491470227,   train acc = 0.712143928035982,\n",
      "val_loss = 0.7116500216630078,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5217770957636988,   l1loss = 0.11611300713937918,   train acc = 0.719640179910045,\n",
      "val_loss = 0.8096898549286894,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.49729873053077933,   l1loss = 0.1128313705094274,   train acc = 0.7466266866566716,\n",
      "val_loss = 0.7668287199597026,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  15\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 15: train loss = 0.47340078341371117,   l1loss = 0.1097236432168616,   train acc = 0.7606196901549226,\n",
      "val_loss = 0.7797975073489107,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  16\n",
      "validation acc increased (0.565891 ---> 0.577519)\n",
      "epoch 16: train loss = 0.44012472370098615,   l1loss = 0.10696254843014708,   train acc = 0.7786106946526736,\n",
      "val_loss = 0.738024488900059,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  17\n",
      "validation acc increased (0.577519 ---> 0.643411)\n",
      "epoch 17: train loss = 0.41693725505809315,   l1loss = 0.10464524865329176,   train acc = 0.783608195902049,\n",
      "val_loss = 0.7353155968725219,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.4004883180702406,   l1loss = 0.10272827999166463,   train acc = 0.8055972013993004,\n",
      "val_loss = 1.211284273354582,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.36191707039284504,   l1loss = 0.10097880754945637,   train acc = 0.8250874562718641,\n",
      "val_loss = 2.0748814187308615,     val_acc = 0.6511627906976745\n",
      "\n",
      "epoch:  20\n",
      "validation acc increased (0.643411 ---> 0.651163)\n",
      "epoch 20: train loss = 0.34419407603563157,   l1loss = 0.09956587383012543,   train acc = 0.8425787106446777,\n",
      "val_loss = 1.1840626740640448,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3332295252912227,   l1loss = 0.09810652255803451,   train acc = 0.8430784607696152,\n",
      "val_loss = 3.1099504592806793,     val_acc = 0.4806201550387597\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.2923158032485451,   l1loss = 0.09699563946070401,   train acc = 0.8785607196401799,\n",
      "val_loss = 1.7239803001862164,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.3031199698088349,   l1loss = 0.0963727720063189,   train acc = 0.8640679660169915,\n",
      "val_loss = 4.811507051220423,     val_acc = 0.4728682170542636\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.2679102631255068,   l1loss = 0.09541694263274643,   train acc = 0.8835582208895553,\n",
      "val_loss = 4.7253117746160935,     val_acc = 0.47674418604651164\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.26991559321555064,   l1loss = 0.0947239384509396,   train acc = 0.88055972013993,\n",
      "val_loss = 1.9052141544430754,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.2462208094089285,   l1loss = 0.09422039279322336,   train acc = 0.8970514742628686,\n",
      "val_loss = 2.0839634255845416,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.27374583970511457,   l1loss = 0.09358507321439107,   train acc = 0.8685657171414293,\n",
      "val_loss = 4.70158687118412,     val_acc = 0.4883720930232558\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.20702115298509002,   l1loss = 0.09327136474600559,   train acc = 0.9190404797601199,\n",
      "val_loss = 1.790774514393289,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.23099662097676404,   l1loss = 0.09279883265316576,   train acc = 0.8985507246376812,\n",
      "val_loss = 5.580494865908952,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.18276370340946135,   l1loss = 0.09199970285559463,   train acc = 0.9270364817591205,\n",
      "val_loss = 2.8631812488974266,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.15527670064578944,   l1loss = 0.0914434359698877,   train acc = 0.9470264867566217,\n",
      "val_loss = 4.503482984384912,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.16465340346202204,   l1loss = 0.0909644259528063,   train acc = 0.9370314842578711,\n",
      "val_loss = 1.5456568664589594,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.1859049220969235,   l1loss = 0.09095004587054908,   train acc = 0.9315342328835582,\n",
      "val_loss = 14.714020802993183,     val_acc = 0.46511627906976744\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.14878231415088508,   l1loss = 0.09091311531222385,   train acc = 0.9445277361319341,\n",
      "val_loss = 4.930351785911146,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.15755604538156176,   l1loss = 0.09075812100693918,   train acc = 0.9325337331334332,\n",
      "val_loss = 1.786901553471883,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.13036989256076847,   l1loss = 0.09029308312955586,   train acc = 0.9450274862568716,\n",
      "val_loss = 4.942993589149889,     val_acc = 0.6395348837209303\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.11117354914851334,   l1loss = 0.08992485350546153,   train acc = 0.9615192403798101,\n",
      "val_loss = 4.3989028816929,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.08873243333919831,   l1loss = 0.08950450801211915,   train acc = 0.9720139930034982,\n",
      "val_loss = 5.96283143804979,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.07572829289951068,   l1loss = 0.08899277692538747,   train acc = 0.9775112443778111,\n",
      "val_loss = 1.866163623887439,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.07354736827622944,   l1loss = 0.08869233787402339,   train acc = 0.9790104947526237,\n",
      "val_loss = 2.5748086192811184,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.07472476078525953,   l1loss = 0.08826551713730799,   train acc = 0.9735132433783108,\n",
      "val_loss = 3.9040491326800963,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.06888431988690032,   l1loss = 0.0877874091989931,   train acc = 0.9765117441279361,\n",
      "val_loss = 2.331109551496284,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.05748968711425399,   l1loss = 0.08741870758192113,   train acc = 0.9875062468765617,\n",
      "val_loss = 2.812307975088903,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.057824458753776696,   l1loss = 0.08714396681176728,   train acc = 0.9835082458770614,\n",
      "val_loss = 2.8747254637784736,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.0759028259409719,   l1loss = 0.08678379254615766,   train acc = 0.9740129935032483,\n",
      "val_loss = 4.5999353874561395,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.04053982199988384,   l1loss = 0.08636881607672621,   train acc = 0.993503248375812,\n",
      "val_loss = 3.032141833342323,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.03172164383823278,   l1loss = 0.08597096050160935,   train acc = 0.9945027486256871,\n",
      "val_loss = 5.050457126410433,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.030161406717326925,   l1loss = 0.08547273422705418,   train acc = 0.9940029985007496,\n",
      "val_loss = 4.39157882217289,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.026073823958962992,   l1loss = 0.08494537337489988,   train acc = 0.9975012493753124,\n",
      "val_loss = 6.5615896919947305,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.031470798644824126,   l1loss = 0.08458039399119152,   train acc = 0.993503248375812,\n",
      "val_loss = 3.8240739467532134,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.030669868893470842,   l1loss = 0.08433086723312624,   train acc = 0.993503248375812,\n",
      "val_loss = 4.175465990984162,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.02038246937382823,   l1loss = 0.08382471429667195,   train acc = 0.9970014992503748,\n",
      "val_loss = 3.628271795987953,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.018496526570907834,   l1loss = 0.08326089246266488,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.4422422157701598,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  54\n",
      "epoch 54: train loss = 0.02122760796430765,   l1loss = 0.08280747008883674,   train acc = 0.9970014992503748,\n",
      "val_loss = 3.4909615479698477,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  55\n",
      "epoch 55: train loss = 0.025122589815681662,   l1loss = 0.08247958504963017,   train acc = 0.992503748125937,\n",
      "val_loss = 3.1251121426737587,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  56\n",
      "epoch 56: train loss = 0.018375528820868077,   l1loss = 0.08209296630538147,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.3797425502954526,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  57\n",
      "epoch 57: train loss = 0.01760085413966892,   l1loss = 0.08163161436165647,   train acc = 0.9960019990004998,\n",
      "val_loss = 3.5595659359480982,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  58\n",
      "epoch 58: train loss = 0.01351920877006659,   l1loss = 0.08108949671680483,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.2694953966510387,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  59\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 59: train loss = 0.019704249462690848,   l1loss = 0.08070969545486746,   train acc = 0.9960019990004998,\n",
      "val_loss = 3.709986501207301,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  60\n",
      "epoch 60: train loss = 0.023627269952588233,   l1loss = 0.08051739055951913,   train acc = 0.9940029985007496,\n",
      "val_loss = 4.3342692463897,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  61\n",
      "epoch 61: train loss = 0.03571600694002687,   l1loss = 0.08089180287347919,   train acc = 0.9900049975012494,\n",
      "val_loss = 3.6718489259712457,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  62\n",
      "epoch 62: train loss = 0.04068389410960085,   l1loss = 0.0812820299514111,   train acc = 0.9910044977511244,\n",
      "val_loss = 22.92412723866544,     val_acc = 0.4806201550387597\n",
      "\n",
      "epoch:  63\n",
      "epoch 63: train loss = 0.03855133038023482,   l1loss = 0.08120857460656088,   train acc = 0.9930034982508745,\n",
      "val_loss = 2.7737932450087497,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  64\n",
      "epoch 64: train loss = 0.04269264168341061,   l1loss = 0.0811207454906649,   train acc = 0.9875062468765617,\n",
      "val_loss = 5.258781780568204,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  65\n",
      "epoch 65: train loss = 0.028871186090626756,   l1loss = 0.08079952534394286,   train acc = 0.9950024987506247,\n",
      "val_loss = 4.771393295406371,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  66\n",
      "epoch 66: train loss = 0.016369871543846032,   l1loss = 0.08030458839088007,   train acc = 0.999000499750125,\n",
      "val_loss = 4.057015269301658,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  67\n",
      "epoch 67: train loss = 0.013506758245762841,   l1loss = 0.07978588878467999,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.792802465054416,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  68\n",
      "epoch 68: train loss = 0.011354195068533095,   l1loss = 0.07937957041118218,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.2458120704621307,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  69\n",
      "epoch 69: train loss = 0.014591847184760639,   l1loss = 0.07902957535516852,   train acc = 0.999000499750125,\n",
      "val_loss = 3.9379879207343094,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  70\n",
      "epoch 70: train loss = 0.01913830605863795,   l1loss = 0.07874485357352223,   train acc = 0.9960019990004998,\n",
      "val_loss = 3.228848675251469,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  71\n",
      "epoch 71: train loss = 0.013023115446449428,   l1loss = 0.0784264379541675,   train acc = 0.999000499750125,\n",
      "val_loss = 3.240803785102312,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  72\n",
      "epoch 72: train loss = 0.011582904643770577,   l1loss = 0.0781244968903297,   train acc = 0.9995002498750625,\n",
      "val_loss = 3.635261055110961,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  73\n",
      "epoch 73: train loss = 0.008814260559524226,   l1loss = 0.07784299137650699,   train acc = 1.0,\n",
      "val_loss = 3.3489851297796234,     val_acc = 0.5930232558139535\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1]\n",
      "[0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471, 0.6073298429319371, 0.5602094240837696]\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
      "        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
      "        0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
      "        1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
      "        1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
      "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
      "        1, 0, 1, 0, 0, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
      "        1, 0, 0, 0, 0, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 0, 1, 0, 1, 0, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
      "        1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
      "        1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 0, 0, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
      "        1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
      "        0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n",
      "        0, 0, 1, 0, 1, 0, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
      "        0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
      "        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
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      "        0, 0, 1, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
      "        1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
      "        0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
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      "        1, 0, 1, 0, 0, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
      "        1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
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      "        0, 1, 0, 1, 1, 1, 0, 0])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
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      "        0, 1, 1, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
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      "        0, 0, 1, 0, 1, 1, 1, 1])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "label =  tensor([1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
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      "        1, 0, 0, 1, 0, 1, 0, 1, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708, 0.7, 0.6841579210394803]\n",
      "[0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1]\n",
      "[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822, 0.6492146596858639, 0.6335078534031413]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331, 0.638, 0.999000499750125]\n",
      "[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]\n",
      "[1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964, 0.6387434554973822, 0.643979057591623]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082, 0.519, 0.47226386806596704]\n",
      "[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1]\n",
      "-----------------------------Fold 8---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([81, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.690483),   val_acc = 0.6162790697674418\n",
      "validation acc increased (0.000000 ---> 0.616279)\n",
      "validation acc increased (0.616279 ---> 0.616279)\n",
      "epoch 1: train loss = 0.681558296144248,   l1loss = 0.13785075955840959,   train acc = 0.47540160642570284,\n",
      "val_loss = 0.6908809380937916,     val_acc = 0.6201550387596899\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.616279 ---> 0.616279)\n",
      "epoch 2: train loss = 0.6601414388442135,   l1loss = 0.13744411087897887,   train acc = 0.5727911646586346,\n",
      "val_loss = 0.6930313258208045,     val_acc = 0.5038759689922481\n",
      "\n",
      "epoch:  3\n",
      "validation loss decreased (0.690483 ---> 0.688592),   val_acc = 0.5465116279069767\n",
      "epoch 3: train loss = 0.6413024204323091,   l1loss = 0.1369472207673582,   train acc = 0.6360441767068273,\n",
      "val_loss = 0.682868339294611,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  4\n",
      "validation loss decreased (0.688592 ---> 0.681418),   val_acc = 0.5387596899224806\n",
      "validation loss decreased (0.681418 ---> 0.657421),   val_acc = 0.5542635658914729\n",
      "epoch 4: train loss = 0.6253758257172672,   l1loss = 0.13630323943842845,   train acc = 0.6521084337349398,\n",
      "val_loss = 0.6426847808120787,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.657421 ---> 0.639681),   val_acc = 0.562015503875969\n",
      "validation loss decreased (0.639681 ---> 0.615633),   val_acc = 0.5697674418604651\n",
      "epoch 5: train loss = 0.6111369527966143,   l1loss = 0.13545335679647913,   train acc = 0.6556224899598394,\n",
      "val_loss = 0.6082370059434757,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.615633 ---> 0.607422),   val_acc = 0.5775193798449613\n",
      "validation loss decreased (0.607422 ---> 0.601718),   val_acc = 0.5775193798449613\n",
      "epoch 6: train loss = 0.599387328548125,   l1loss = 0.13432514200727624,   train acc = 0.6606425702811245,\n",
      "val_loss = 0.6018130197081455,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.5901381636719149,   l1loss = 0.13286473970097232,   train acc = 0.6686746987951807,\n",
      "val_loss = 0.6033216104951016,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.5820648083725128,   l1loss = 0.13100589835739518,   train acc = 0.6746987951807228,\n",
      "val_loss = 0.6082159008166587,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.5762511060898563,   l1loss = 0.12873437647599292,   train acc = 0.6757028112449799,\n",
      "val_loss = 0.6154433927794759,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5673032103772144,   l1loss = 0.12611201866324168,   train acc = 0.6827309236947792,\n",
      "val_loss = 0.6142219150251196,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5576656654656652,   l1loss = 0.12317741024925048,   train acc = 0.6907630522088354,\n",
      "val_loss = 0.6357968532761862,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.544440203881168,   l1loss = 0.12002360970380316,   train acc = 0.7043172690763052,\n",
      "val_loss = 0.6717778880928837,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5326818835783196,   l1loss = 0.11672397023702721,   train acc = 0.7178714859437751,\n",
      "val_loss = 0.6902429789535759,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5085236329630197,   l1loss = 0.1134706104555762,   train acc = 0.7339357429718876,\n",
      "val_loss = 0.658633234195931,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.48935657404512767,   l1loss = 0.11044141605316875,   train acc = 0.7459839357429718,\n",
      "val_loss = 0.6858358637307042,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.45904517054079047,   l1loss = 0.1077926407556936,   train acc = 0.7740963855421686,\n",
      "val_loss = 0.8338234337550307,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.426940765964937,   l1loss = 0.1055830192254729,   train acc = 0.7911646586345381,\n",
      "val_loss = 1.16572989049808,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.4012284585271016,   l1loss = 0.10380807138949513,   train acc = 0.8092369477911646,\n",
      "val_loss = 1.6921033988627352,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.3755404464451663,   l1loss = 0.10228538163096072,   train acc = 0.8273092369477911,\n",
      "val_loss = 1.4203764996671862,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  20\n",
      "validation acc increased (0.616279 ---> 0.616279)\n",
      "epoch 20: train loss = 0.3591645778901127,   l1loss = 0.10097318358090987,   train acc = 0.8288152610441767,\n",
      "val_loss = 1.1321186076763063,     val_acc = 0.6162790697674418\n",
      "\n",
      "epoch:  21\n",
      "validation acc increased (0.616279 ---> 0.620155)\n",
      "epoch 21: train loss = 0.32908376967571823,   l1loss = 0.09975837880229375,   train acc = 0.8473895582329317,\n",
      "val_loss = 0.9505737890568815,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.29188525078287086,   l1loss = 0.09870235529170937,   train acc = 0.8699799196787149,\n",
      "val_loss = 0.92200702197792,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  23\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 23: train loss = 0.26653667099504585,   l1loss = 0.09784682783257051,   train acc = 0.8910642570281124,\n",
      "val_loss = 1.039930680001429,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  24\n",
      "validation acc increased (0.620155 ---> 0.627907)\n",
      "epoch 24: train loss = 0.29164418386647023,   l1loss = 0.09723315346073434,   train acc = 0.857429718875502,\n",
      "val_loss = 1.7529087842896927,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  25\n",
      "validation acc increased (0.627907 ---> 0.631783)\n",
      "validation acc increased (0.631783 ---> 0.631783)\n",
      "epoch 25: train loss = 0.2822917654332387,   l1loss = 0.09672807504134963,   train acc = 0.8815261044176707,\n",
      "val_loss = 3.657799425051194,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.26554612988927756,   l1loss = 0.09617703916677031,   train acc = 0.8790160642570282,\n",
      "val_loss = 2.485792124918265,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.254531506015115,   l1loss = 0.09513618028068159,   train acc = 0.8895582329317269,\n",
      "val_loss = 2.655083281125209,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.24327343211595312,   l1loss = 0.09413622313713932,   train acc = 0.8845381526104418,\n",
      "val_loss = 1.9870535994684972,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.19294745029215832,   l1loss = 0.09322106197895295,   train acc = 0.9312248995983936,\n",
      "val_loss = 1.6232661962971207,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.17906709673653645,   l1loss = 0.09269103892596371,   train acc = 0.9201807228915663,\n",
      "val_loss = 1.2391475744025653,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.16923785616595102,   l1loss = 0.09213589386169212,   train acc = 0.9412650602409639,\n",
      "val_loss = 2.774588699488677,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.18963111925077247,   l1loss = 0.09187775111222363,   train acc = 0.9181726907630522,\n",
      "val_loss = 3.4193982689879663,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.21511334994710593,   l1loss = 0.0914924276700939,   train acc = 0.8945783132530121,\n",
      "val_loss = 1.4032248768580051,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  34\n",
      "validation acc increased (0.631783 ---> 0.635659)\n",
      "epoch 34: train loss = 0.1589769302123043,   l1loss = 0.09112457344570313,   train acc = 0.9392570281124498,\n",
      "val_loss = 1.755115381969038,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.14009647250893603,   l1loss = 0.0910465270280838,   train acc = 0.9513052208835341,\n",
      "val_loss = 1.4941087914991749,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.12341947081577347,   l1loss = 0.0905273190763102,   train acc = 0.9573293172690763,\n",
      "val_loss = 2.225553594818411,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  37\n",
      "validation acc increased (0.635659 ---> 0.639535)\n",
      "epoch 37: train loss = 0.09714583913425365,   l1loss = 0.08966618833173232,   train acc = 0.9698795180722891,\n",
      "val_loss = 2.304292928340823,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.11655990438288953,   l1loss = 0.0893175832657929,   train acc = 0.9568273092369478,\n",
      "val_loss = 2.1228176657716897,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.09807442242361934,   l1loss = 0.08927847062009406,   train acc = 0.964859437751004,\n",
      "val_loss = 4.155446446450879,     val_acc = 0.6317829457364341\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.0872385489294328,   l1loss = 0.08882969411381755,   train acc = 0.9774096385542169,\n",
      "val_loss = 1.9643646534099541,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.07711015104589214,   l1loss = 0.08862146217540565,   train acc = 0.9754016064257028,\n",
      "val_loss = 3.4088204403718314,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.07405169415904815,   l1loss = 0.08791267791067262,   train acc = 0.9759036144578314,\n",
      "val_loss = 2.432727573453918,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.1035525700054973,   l1loss = 0.08760496441379609,   train acc = 0.9583333333333334,\n",
      "val_loss = 4.112110699794089,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.07531021014753594,   l1loss = 0.08808258729407108,   train acc = 0.9799196787148594,\n",
      "val_loss = 4.904226697272487,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.08077501409862893,   l1loss = 0.0878749838076442,   train acc = 0.9693775100401606,\n",
      "val_loss = 2.515559282413749,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.07068757228104465,   l1loss = 0.08739253621264155,   train acc = 0.9804216867469879,\n",
      "val_loss = 4.238404756368593,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.056927473190797856,   l1loss = 0.0867786297956145,   train acc = 0.9844377510040161,\n",
      "val_loss = 2.2621954614801925,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.07424461281293607,   l1loss = 0.08710216084517629,   train acc = 0.9844377510040161,\n",
      "val_loss = 8.002929717071297,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.08703712670199842,   l1loss = 0.08769192863779375,   train acc = 0.9688755020080321,\n",
      "val_loss = 3.551590413086174,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.07011409313324465,   l1loss = 0.08753819362705491,   train acc = 0.9844377510040161,\n",
      "val_loss = 3.0541449490241535,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.04092977885680505,   l1loss = 0.08705330301958873,   train acc = 0.9944779116465864,\n",
      "val_loss = 3.317158439362696,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.03851072603170891,   l1loss = 0.08622222872503311,   train acc = 0.9914658634538153,\n",
      "val_loss = 2.734001413796299,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.03234308925139377,   l1loss = 0.08536305176804822,   train acc = 0.9954819277108434,\n",
      "val_loss = 3.4601848698401634,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  54\n",
      "epoch 54: train loss = 0.026902885224864666,   l1loss = 0.08468511724567797,   train acc = 0.9964859437751004,\n",
      "val_loss = 3.1516257589177568,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  55\n",
      "epoch 55: train loss = 0.02750487214171264,   l1loss = 0.08413356647314317,   train acc = 0.9959839357429718,\n",
      "val_loss = 3.372754837696751,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  56\n",
      "epoch 56: train loss = 0.02194071931382978,   l1loss = 0.08356957251288326,   train acc = 0.9969879518072289,\n",
      "val_loss = 3.443614445915518,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  57\n",
      "epoch 57: train loss = 0.02077264787561922,   l1loss = 0.0830162249834662,   train acc = 0.9979919678714859,\n",
      "val_loss = 3.4553179718958313,     val_acc = 0.6007751937984496\n",
      "\n",
      "epoch:  58\n",
      "epoch 58: train loss = 0.02582511185372929,   l1loss = 0.08288956104392507,   train acc = 0.9959839357429718,\n",
      "val_loss = 3.5070084046947865,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  59\n",
      "epoch 59: train loss = 0.017465517126831663,   l1loss = 0.08231255358242127,   train acc = 0.9974899598393574,\n",
      "val_loss = 3.79313347598379,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  60\n",
      "epoch 60: train loss = 0.014348378337650415,   l1loss = 0.08186340673142169,   train acc = 0.9994979919678715,\n",
      "val_loss = 4.290767019109208,     val_acc = 0.6356589147286822\n",
      "\n",
      "epoch:  61\n",
      "epoch 61: train loss = 0.014340946001999349,   l1loss = 0.08129345541857333,   train acc = 1.0,\n",
      "val_loss = 3.978316806668286,     val_acc = 0.5930232558139535\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1]\n",
      "[0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471, 0.6073298429319371, 0.5602094240837696, 0.5340314136125655]\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
      "        0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
      "        1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
      "        0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
      "        0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 1, 0, 1, 0, 1, 0])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
      "        0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
      "        0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
      "        1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        0, 0, 1, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
      "        0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
      "        1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
      "        0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
      "        1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "        1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
      "        0, 0, 1, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
      "        1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
      "        0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
      "        0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
      "        1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
      "        1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
      "        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
      "        1, 0, 1, 0, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
      "        1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
      "        1, 1, 0, 0, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
      "        0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
      "        0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
      "        1, 1, 0, 1, 0, 0, 0, 0])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
      "        0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
      "        0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
      "        0, 1, 1, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
      "        0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 0, 1, 1, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
      "        0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
      "        0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
      "        0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
      "        1, 0, 1, 1, 1, 0, 0, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
      "        0, 1, 1, 0, 0, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708, 0.7, 0.6841579210394803, 0.6651606425702812]\n",
      "[0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]\n",
      "[1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822, 0.6492146596858639, 0.6335078534031413, 0.6335078534031413]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331, 0.638, 0.999000499750125, 0.9949799196787149]\n",
      "[1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1]\n",
      "[0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964, 0.6387434554973822, 0.643979057591623, 0.6387434554973822]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082, 0.519, 0.47226386806596704, 0.7761044176706827]\n",
      "[1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0]\n",
      "-----------------------------Fold 9---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([72, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.682980),   val_acc = 0.627906976744186\n",
      "validation acc increased (0.000000 ---> 0.627907)\n",
      "validation acc increased (0.627907 ---> 0.627907)\n",
      "epoch 1: train loss = 0.6777922638376853,   l1loss = 0.13775174053838676,   train acc = 0.48370927318295737,\n",
      "val_loss = 0.687888598257257,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.627907 ---> 0.627907)\n",
      "epoch 2: train loss = 0.6605406909060657,   l1loss = 0.13724787228537683,   train acc = 0.6210526315789474,\n",
      "val_loss = 0.7003788606140965,     val_acc = 0.42248062015503873\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.6454371491171662,   l1loss = 0.13666182353831174,   train acc = 0.6416040100250626,\n",
      "val_loss = 0.7020500177560851,     val_acc = 0.4844961240310077\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6296094764444164,   l1loss = 0.13591576377700146,   train acc = 0.6571428571428571,\n",
      "val_loss = 0.6741203370020371,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.682980 ---> 0.672345),   val_acc = 0.5271317829457365\n",
      "validation loss decreased (0.672345 ---> 0.651531),   val_acc = 0.5271317829457365\n",
      "epoch 5: train loss = 0.6174353536507838,   l1loss = 0.13493877780019192,   train acc = 0.6571428571428571,\n",
      "val_loss = 0.64598062149314,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.651531 ---> 0.644699),   val_acc = 0.5310077519379846\n",
      "validation loss decreased (0.644699 ---> 0.633400),   val_acc = 0.5310077519379846\n",
      "epoch 6: train loss = 0.6050603376295334,   l1loss = 0.13365735947189475,   train acc = 0.6621553884711779,\n",
      "val_loss = 0.6314658402472504,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.633400 ---> 0.632519),   val_acc = 0.5348837209302325\n",
      "validation loss decreased (0.632519 ---> 0.631722),   val_acc = 0.5348837209302325\n",
      "epoch 7: train loss = 0.5956596527183265,   l1loss = 0.13198910928459692,   train acc = 0.6656641604010025,\n",
      "val_loss = 0.6331861458083455,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.5859128608141926,   l1loss = 0.1298997061070344,   train acc = 0.6716791979949874,\n",
      "val_loss = 0.6339161365993263,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.5775983289967205,   l1loss = 0.12739512194368177,   train acc = 0.6796992481203008,\n",
      "val_loss = 0.6418065218500388,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.5680718240283784,   l1loss = 0.12452510148286819,   train acc = 0.6907268170426065,\n",
      "val_loss = 0.6507704207139422,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5571945123803944,   l1loss = 0.1213724056954372,   train acc = 0.6962406015037594,\n",
      "val_loss = 0.6841371244238329,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 12: train loss = 0.5443704637369715,   l1loss = 0.11801029294729233,   train acc = 0.7072681704260652,\n",
      "val_loss = 0.7370998619138732,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5282434655012642,   l1loss = 0.11461027522657748,   train acc = 0.7263157894736842,\n",
      "val_loss = 0.7001163677651753,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5089050915008201,   l1loss = 0.11128778580884288,   train acc = 0.7413533834586467,\n",
      "val_loss = 0.7310682345730389,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.4865566873311399,   l1loss = 0.10820232349305524,   train acc = 0.7533834586466165,\n",
      "val_loss = 0.9019363850586174,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.45884659435217245,   l1loss = 0.10554263851322924,   train acc = 0.7789473684210526,\n",
      "val_loss = 0.9704440212989038,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.4202355332541884,   l1loss = 0.10339735007674473,   train acc = 0.8070175438596491,\n",
      "val_loss = 0.9909111167802367,     val_acc = 0.6124031007751938\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.39302192711292355,   l1loss = 0.1019542952826746,   train acc = 0.8120300751879699,\n",
      "val_loss = 1.3908450917680135,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.3705816494641746,   l1loss = 0.10042775794303507,   train acc = 0.8165413533834587,\n",
      "val_loss = 0.8365242425785508,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.35236290118150543,   l1loss = 0.0989852717533745,   train acc = 0.8370927318295739,\n",
      "val_loss = 1.175207398658575,     val_acc = 0.5271317829457365\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3512345191828888,   l1loss = 0.09775472859242805,   train acc = 0.8335839598997494,\n",
      "val_loss = 1.720713703958101,     val_acc = 0.5116279069767442\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.32802224415436126,   l1loss = 0.09652294553162759,   train acc = 0.8526315789473684,\n",
      "val_loss = 1.0351900666259055,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.2810892618987196,   l1loss = 0.09586043051236255,   train acc = 0.8847117794486216,\n",
      "val_loss = 1.7488028725912406,     val_acc = 0.5193798449612403\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.29594317181666097,   l1loss = 0.09554791460957443,   train acc = 0.8736842105263158,\n",
      "val_loss = 1.383325032485548,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.2908367050545556,   l1loss = 0.09524397529605635,   train acc = 0.8741854636591478,\n",
      "val_loss = 1.0075195944586466,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.23876858004053733,   l1loss = 0.09456468856050855,   train acc = 0.9042606516290727,\n",
      "val_loss = 3.7633727769731307,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  27\n",
      "validation acc increased (0.627907 ---> 0.631783)\n",
      "epoch 27: train loss = 0.24321200991035405,   l1loss = 0.09383204626409632,   train acc = 0.8897243107769424,\n",
      "val_loss = 2.632583130237668,     val_acc = 0.5155038759689923\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.19356993710188042,   l1loss = 0.0934341524291158,   train acc = 0.9238095238095239,\n",
      "val_loss = 1.41927497885948,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.19538922365147965,   l1loss = 0.09319527589512946,   train acc = 0.9167919799498747,\n",
      "val_loss = 3.835814361424409,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.17916062683389897,   l1loss = 0.09250437699837194,   train acc = 0.9303258145363409,\n",
      "val_loss = 1.621931373026193,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.21077537299098825,   l1loss = 0.09211045807614959,   train acc = 0.9087719298245615,\n",
      "val_loss = 1.3185462252807247,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.23887639734380525,   l1loss = 0.09190204342579782,   train acc = 0.8952380952380953,\n",
      "val_loss = 1.9735888972762943,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.14902917222122203,   l1loss = 0.09144701687315651,   train acc = 0.9478696741854636,\n",
      "val_loss = 1.734970021617505,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.1352653970246327,   l1loss = 0.09123586411389492,   train acc = 0.9609022556390977,\n",
      "val_loss = 1.6061633298563402,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.12018011457713923,   l1loss = 0.0913496767742592,   train acc = 0.9654135338345865,\n",
      "val_loss = 1.4085034085798633,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.12157767949845259,   l1loss = 0.09087181371405609,   train acc = 0.9568922305764411,\n",
      "val_loss = 12.18474845738374,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.11420494081980005,   l1loss = 0.09056822333419533,   train acc = 0.9588972431077695,\n",
      "val_loss = 7.133128994195036,     val_acc = 0.627906976744186\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.08377009170844142,   l1loss = 0.08998052694444968,   train acc = 0.9769423558897243,\n",
      "val_loss = 1.5790407207123405,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.07196493960711592,   l1loss = 0.08941901628310818,   train acc = 0.9824561403508771,\n",
      "val_loss = 1.7764077741046285,     val_acc = 0.5426356589147286\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.06884303719813663,   l1loss = 0.08907348288778673,   train acc = 0.981453634085213,\n",
      "val_loss = 2.0529281263665635,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.08752110526152422,   l1loss = 0.08877328430351458,   train acc = 0.9669172932330827,\n",
      "val_loss = 3.8814146389332853,     val_acc = 0.624031007751938\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.0534191582846761,   l1loss = 0.08847359084619914,   train acc = 0.9899749373433584,\n",
      "val_loss = 1.7713334769472595,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.053076960512420585,   l1loss = 0.08797531594011121,   train acc = 0.9854636591478697,\n",
      "val_loss = 3.003138168837673,     val_acc = 0.5310077519379846\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.03749572625173662,   l1loss = 0.08741736609610101,   train acc = 0.9954887218045113,\n",
      "val_loss = 2.302085431047188,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.03470267659067211,   l1loss = 0.086931109906438,   train acc = 0.9944862155388471,\n",
      "val_loss = 3.2026045303936153,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.03238022409398155,   l1loss = 0.08665003874397517,   train acc = 0.9949874686716792,\n",
      "val_loss = 2.3425228115200074,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.026900880723108624,   l1loss = 0.08609663713396641,   train acc = 0.9979949874686717,\n",
      "val_loss = 2.393414729440859,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.023233257417093242,   l1loss = 0.08549760701064776,   train acc = 0.9984962406015038,\n",
      "val_loss = 2.7446321505446765,     val_acc = 0.5348837209302325\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.024097669448582153,   l1loss = 0.08505817094168866,   train acc = 0.9974937343358395,\n",
      "val_loss = 2.4446980241657226,     val_acc = 0.5387596899224806\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.01827189443740331,   l1loss = 0.08443690010330133,   train acc = 0.9989974937343359,\n",
      "val_loss = 2.4035656581553377,     val_acc = 0.5232558139534884\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.016350781231334335,   l1loss = 0.08388166891452961,   train acc = 1.0,\n",
      "val_loss = 2.443206698395485,     val_acc = 0.5155038759689923\n",
      "\n",
      "!!! overfitted !!!\n",
      "[1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1]\n",
      "[1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471, 0.6073298429319371, 0.5602094240837696, 0.5340314136125655, 0.5654450261780105]\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
      "        1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 0, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
      "        1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
      "        1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
      "        1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
      "        1, 0, 0, 0, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
      "        0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
      "        0, 1, 1, 1, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
      "        0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
      "        0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
      "        1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 0, 0, 1, 0])\n",
      "output =  [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
      "        1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
      "        1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
      "        1, 0, 1, 1, 0, 0, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
      "        0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "        1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 1, 1, 1, 1, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
      "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
      "        0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 1, 1, 0, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
      "        1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 0, 1, 1, 0, 0, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
      "        1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
      "        1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
      "        0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
      "        1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
      "        1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
      "        0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1,\n",
      "        1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
      "        1, 0, 1, 1, 0, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,\n",
      "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 1, 0, 1])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output =  [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
      "        1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
      "        1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
      "        1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
      "        1, 1, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708, 0.7, 0.6841579210394803, 0.6651606425702812, 0.668671679197995]\n",
      "[1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1]\n",
      "[1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822, 0.6492146596858639, 0.6335078534031413, 0.6335078534031413, 0.612565445026178]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331, 0.638, 0.999000499750125, 0.9949799196787149, 0.9508771929824561]\n",
      "[1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0]\n",
      "[1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964, 0.6387434554973822, 0.643979057591623, 0.6387434554973822, 0.643979057591623]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082, 0.519, 0.47226386806596704, 0.7761044176706827, 0.49473684210526314]\n",
      "[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]\n",
      "-----------------------------Fold 10---------------\n",
      "preparing dataloaders...\n",
      "torch.Size([75, 7, 9, 20])\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.695332),   val_acc = 0.39147286821705424\n",
      "validation acc increased (0.000000 ---> 0.391473)\n",
      "validation acc increased (0.391473 ---> 0.391473)\n",
      "epoch 1: train loss = 0.6719437708207711,   l1loss = 0.13840212986696904,   train acc = 0.6165829145728643,\n",
      "val_loss = 0.7145830023196317,     val_acc = 0.39147286821705424\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.391473 ---> 0.391473)\n",
      "validation acc increased (0.391473 ---> 0.391473)\n",
      "epoch 2: train loss = 0.6526712950150572,   l1loss = 0.13800431019696757,   train acc = 0.6326633165829145,\n",
      "val_loss = 0.7263189609660659,     val_acc = 0.39147286821705424\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.391473 ---> 0.391473)\n",
      "validation acc increased (0.391473 ---> 0.406977)\n",
      "epoch 3: train loss = 0.6384344094362691,   l1loss = 0.13752041265892623,   train acc = 0.6432160804020101,\n",
      "val_loss = 0.719265153241712,     val_acc = 0.45348837209302323\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.406977 ---> 0.457364)\n",
      "validation loss decreased (0.695332 ---> 0.691640),   val_acc = 0.5310077519379846\n",
      "validation acc increased (0.457364 ---> 0.531008)\n",
      "epoch 4: train loss = 0.6233027500123834,   l1loss = 0.13689041630407076,   train acc = 0.6472361809045226,\n",
      "val_loss = 0.674999581981999,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  5\n",
      "validation loss decreased (0.691640 ---> 0.671344),   val_acc = 0.5465116279069767\n",
      "validation acc increased (0.531008 ---> 0.546512)\n",
      "validation loss decreased (0.671344 ---> 0.640276),   val_acc = 0.5542635658914729\n",
      "validation acc increased (0.546512 ---> 0.554264)\n",
      "epoch 5: train loss = 0.6108603898604311,   l1loss = 0.1360374202231067,   train acc = 0.650251256281407,\n",
      "val_loss = 0.6320977502090986,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  6\n",
      "validation loss decreased (0.640276 ---> 0.630428),   val_acc = 0.5581395348837209\n",
      "validation acc increased (0.554264 ---> 0.558140)\n",
      "validation loss decreased (0.630428 ---> 0.619005),   val_acc = 0.5658914728682171\n",
      "validation acc increased (0.558140 ---> 0.565891)\n",
      "epoch 6: train loss = 0.6001691482773978,   l1loss = 0.13489290275166382,   train acc = 0.657788944723618,\n",
      "val_loss = 0.6185085505478142,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  7\n",
      "validation loss decreased (0.619005 ---> 0.617354),   val_acc = 0.5658914728682171\n",
      "validation acc increased (0.565891 ---> 0.565891)\n",
      "validation loss decreased (0.617354 ---> 0.615356),   val_acc = 0.5658914728682171\n",
      "validation acc increased (0.565891 ---> 0.565891)\n",
      "epoch 7: train loss = 0.5907169395355722,   l1loss = 0.13343299792040533,   train acc = 0.6693467336683417,\n",
      "val_loss = 0.6119359452133031,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.615356 ---> 0.612685),   val_acc = 0.5736434108527132\n",
      "validation acc increased (0.565891 ---> 0.573643)\n",
      "validation loss decreased (0.612685 ---> 0.608328),   val_acc = 0.5736434108527132\n",
      "validation acc increased (0.573643 ---> 0.573643)\n",
      "epoch 8: train loss = 0.5808460336234701,   l1loss = 0.13162351006539025,   train acc = 0.6733668341708543,\n",
      "val_loss = 0.6133937794108724,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  9\n",
      "validation acc increased (0.573643 ---> 0.573643)\n",
      "validation acc increased (0.573643 ---> 0.577519)\n",
      "epoch 9: train loss = 0.5721211679017724,   l1loss = 0.12944562336308274,   train acc = 0.6798994974874372,\n",
      "val_loss = 0.6197530188301738,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.577519 ---> 0.577519)\n",
      "validation acc increased (0.577519 ---> 0.581395)\n",
      "epoch 10: train loss = 0.5629525616540382,   l1loss = 0.12692890809708504,   train acc = 0.6889447236180904,\n",
      "val_loss = 0.6338218380083409,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.5500448384476666,   l1loss = 0.12408753369920816,   train acc = 0.699497487437186,\n",
      "val_loss = 0.6388333800227143,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 12: train loss = 0.5367419248250261,   l1loss = 0.12101081039887576,   train acc = 0.7075376884422111,\n",
      "val_loss = 0.6473393957744273,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5212755465627316,   l1loss = 0.11783038612136888,   train acc = 0.7266331658291457,\n",
      "val_loss = 0.6885562600322472,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5001930103230117,   l1loss = 0.11466055483973805,   train acc = 0.735678391959799,\n",
      "val_loss = 0.7727257928182912,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.4774602065134288,   l1loss = 0.11163033707507292,   train acc = 0.7597989949748744,\n",
      "val_loss = 0.9347423278084097,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  16\n",
      "validation acc increased (0.581395 ---> 0.596899)\n",
      "epoch 16: train loss = 0.45671038232257016,   l1loss = 0.10889189650664977,   train acc = 0.7688442211055276,\n",
      "val_loss = 0.6646126000456107,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.4233617796969773,   l1loss = 0.10659007389641287,   train acc = 0.7919597989949749,\n",
      "val_loss = 0.7088340003361073,     val_acc = 0.5542635658914729\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.39575232902363916,   l1loss = 0.10463322919966588,   train acc = 0.807035175879397,\n",
      "val_loss = 0.7800197698349176,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.3720384899695315,   l1loss = 0.10311959861361202,   train acc = 0.8321608040201005,\n",
      "val_loss = 1.0530221295911213,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.35293013776966076,   l1loss = 0.10156447164077854,   train acc = 0.8321608040201005,\n",
      "val_loss = 0.8265702262405277,     val_acc = 0.5503875968992248\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.3344811164853561,   l1loss = 0.10016776281385566,   train acc = 0.8361809045226131,\n",
      "val_loss = 1.0450448842011681,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.2859235891145677,   l1loss = 0.09900103884575955,   train acc = 0.8763819095477386,\n",
      "val_loss = 1.4950751149377157,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.27330368340913974,   l1loss = 0.09814592776586063,   train acc = 0.8748743718592965,\n",
      "val_loss = 1.6195507289827331,     val_acc = 0.6085271317829457\n",
      "\n",
      "epoch:  24\n",
      "validation acc increased (0.596899 ---> 0.608527)\n",
      "epoch 24: train loss = 0.2554657350233452,   l1loss = 0.09719441908238521,   train acc = 0.8904522613065327,\n",
      "val_loss = 1.7599091363507648,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  25\n",
      "validation acc increased (0.608527 ---> 0.612403)\n",
      "epoch 25: train loss = 0.26244540217533785,   l1loss = 0.09658051813068103,   train acc = 0.8854271356783919,\n",
      "val_loss = 1.328415922416273,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.2488035890025709,   l1loss = 0.09598230193907292,   train acc = 0.9025125628140703,\n",
      "val_loss = 3.3820917366086976,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.24395602191213386,   l1loss = 0.09520171582698822,   train acc = 0.8814070351758794,\n",
      "val_loss = 3.163267457207968,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.2519955996891961,   l1loss = 0.09456676208793219,   train acc = 0.8798994974874372,\n",
      "val_loss = 2.3792159464932228,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.20381304789428137,   l1loss = 0.09389582407384661,   train acc = 0.9130653266331659,\n",
      "val_loss = 1.6001185594603073,     val_acc = 0.5581395348837209\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.17334565265693858,   l1loss = 0.09341333552670839,   train acc = 0.9341708542713568,\n",
      "val_loss = 1.851118143214736,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  31\n",
      "validation acc increased (0.612403 ---> 0.620155)\n",
      "epoch 31: train loss = 0.14162640643479238,   l1loss = 0.09275954460828148,   train acc = 0.9552763819095478,\n",
      "val_loss = 2.0247100628623667,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.15501669086703104,   l1loss = 0.09234665035452674,   train acc = 0.9371859296482412,\n",
      "val_loss = 2.2792202677837636,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  33\n",
      "epoch 33: train loss = 0.12440619978248774,   l1loss = 0.09191003219416394,   train acc = 0.9582914572864322,\n",
      "val_loss = 2.0286328884982323,     val_acc = 0.5968992248062015\n",
      "\n",
      "epoch:  34\n",
      "epoch 34: train loss = 0.12274269695258021,   l1loss = 0.09157498074536348,   train acc = 0.9547738693467337,\n",
      "val_loss = 2.5109643936157227,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  35\n",
      "epoch 35: train loss = 0.09087972700895376,   l1loss = 0.09097352933943571,   train acc = 0.9753768844221106,\n",
      "val_loss = 2.107085255227348,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  36\n",
      "epoch 36: train loss = 0.09877167490109727,   l1loss = 0.09090764223780465,   train acc = 0.9693467336683417,\n",
      "val_loss = 2.899521066236866,     val_acc = 0.5775193798449613\n",
      "\n",
      "epoch:  37\n",
      "epoch 37: train loss = 0.12718479042526465,   l1loss = 0.09116000884292114,   train acc = 0.9567839195979899,\n",
      "val_loss = 2.9109171711850563,     val_acc = 0.5697674418604651\n",
      "\n",
      "epoch:  38\n",
      "epoch 38: train loss = 0.15343157828752718,   l1loss = 0.09161678471008138,   train acc = 0.9492462311557789,\n",
      "val_loss = 3.6789348217868065,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  39\n",
      "epoch 39: train loss = 0.17688065675934356,   l1loss = 0.09161391867015829,   train acc = 0.9256281407035176,\n",
      "val_loss = 2.8331502167753473,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  40\n",
      "epoch 40: train loss = 0.12294016767985856,   l1loss = 0.09109236833887484,   train acc = 0.957286432160804,\n",
      "val_loss = 3.345622025718985,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  41\n",
      "epoch 41: train loss = 0.09555761652750586,   l1loss = 0.0903443555211901,   train acc = 0.9708542713567839,\n",
      "val_loss = 2.0581300794616224,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  42\n",
      "epoch 42: train loss = 0.07512064163559046,   l1loss = 0.08976309295724984,   train acc = 0.9748743718592965,\n",
      "val_loss = 3.453665764756905,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  43\n",
      "epoch 43: train loss = 0.04777320917677041,   l1loss = 0.0891012175897857,   train acc = 0.9909547738693467,\n",
      "val_loss = 2.352656833885252,     val_acc = 0.5930232558139535\n",
      "\n",
      "epoch:  44\n",
      "epoch 44: train loss = 0.049835165397231304,   l1loss = 0.08854852927539816,   train acc = 0.9889447236180905,\n",
      "val_loss = 2.9295913517013075,     val_acc = 0.5658914728682171\n",
      "\n",
      "epoch:  45\n",
      "epoch 45: train loss = 0.04242832651689424,   l1loss = 0.08794202994761155,   train acc = 0.9919597989949749,\n",
      "val_loss = 2.413541268932727,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  46\n",
      "epoch 46: train loss = 0.03263931775549848,   l1loss = 0.08736478100769483,   train acc = 0.9939698492462311,\n",
      "val_loss = 2.5269698264048426,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  47\n",
      "epoch 47: train loss = 0.026620606537065914,   l1loss = 0.08684112468556543,   train acc = 0.9969849246231156,\n",
      "val_loss = 3.387900699940763,     val_acc = 0.5813953488372093\n",
      "\n",
      "epoch:  48\n",
      "epoch 48: train loss = 0.02269124810457529,   l1loss = 0.08632632712622983,   train acc = 0.9979899497487437,\n",
      "val_loss = 2.787818894451737,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  49\n",
      "epoch 49: train loss = 0.022012453552466543,   l1loss = 0.08572817495719871,   train acc = 0.9964824120603015,\n",
      "val_loss = 2.598147943962452,     val_acc = 0.5891472868217055\n",
      "\n",
      "epoch:  50\n",
      "epoch 50: train loss = 0.02431201655885682,   l1loss = 0.0853168428168824,   train acc = 0.9979899497487437,\n",
      "val_loss = 2.618208586574034,     val_acc = 0.5852713178294574\n",
      "\n",
      "epoch:  51\n",
      "epoch 51: train loss = 0.042026931474256755,   l1loss = 0.08488889236995323,   train acc = 0.9869346733668342,\n",
      "val_loss = 5.366614888804827,     val_acc = 0.5465116279069767\n",
      "\n",
      "epoch:  52\n",
      "epoch 52: train loss = 0.05808617710767679,   l1loss = 0.08540894663214084,   train acc = 0.9819095477386934,\n",
      "val_loss = 2.219164011090301,     val_acc = 0.6046511627906976\n",
      "\n",
      "epoch:  53\n",
      "epoch 53: train loss = 0.04891583280796981,   l1loss = 0.0857906321185318,   train acc = 0.9894472361809046,\n",
      "val_loss = 2.5789132524830425,     val_acc = 0.5736434108527132\n",
      "\n",
      "epoch:  54\n",
      "epoch 54: train loss = 0.04095752583301846,   l1loss = 0.08530921218682773,   train acc = 0.9899497487437185,\n",
      "val_loss = 2.9466089703315914,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  55\n",
      "epoch 55: train loss = 0.01827716198960441,   l1loss = 0.08464146351544702,   train acc = 0.9994974874371859,\n",
      "val_loss = 2.840330068455186,     val_acc = 0.562015503875969\n",
      "\n",
      "epoch:  56\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 56: train loss = 0.01295484382724717,   l1loss = 0.08385968863514799,   train acc = 1.0,\n",
      "val_loss = 3.1219512177992237,     val_acc = 0.5658914728682171\n",
      "\n",
      "!!! overfitted !!!\n",
      "[0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1]\n",
      "[1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1]\n",
      "early stoping results:\n",
      "\t [0.6041666666666666, 0.5572916666666666, 0.5208333333333334, 0.5078534031413613, 0.5445026178010471, 0.6073298429319371, 0.5602094240837696, 0.5340314136125655, 0.5654450261780105, 0.5287958115183246]\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n",
      "        1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
      "        0, 1, 1, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,\n",
      "        1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
      "        0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 1, 0, 1])\n",
      "output =  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
      "        1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "        0, 0, 1, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
      "        0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
      "        0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
      "        0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
      "        1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n",
      "        0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,\n",
      "        0, 0, 0, 0, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "label =  tensor([0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
      "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
      "        1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
      "        0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 0, 0, 1, 1, 1, 0])\n",
      "output =  [1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
      "        0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
      "        0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
      "        1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n",
      "        0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
      "        1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
      "        0, 0, 1, 0, 1, 1, 0, 0])\n",
      "output =  [0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
      "        0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
      "        0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
      "        0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
      "        0, 1, 0, 1, 0, 0, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
      "        1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
      "        0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
      "        0, 0, 1, 1, 0, 1, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
      "        0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
      "        1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
      "        0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
      "        1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
      "        1, 0, 1, 0, 0, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
      "        1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1,\n",
      "        1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "label =  tensor([1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
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      "        1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n",
      "        1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 1, 1, 1, 1, 0])\n",
      "output =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "label =  tensor([0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
      "        1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
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      "        0, 1, 0, 1, 0, 1, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "label =  tensor([1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
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      "        1, 0, 0, 1, 1, 0, 0, 1])\n",
      "output =  [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0]\n",
      "label =  tensor([0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,\n",
      "        0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1])\n",
      "\t [0.6673376950176145, 0.6722054380664653, 0.6802005012531328, 0.6826347305389222, 0.7250876314471708, 0.7, 0.6841579210394803, 0.6651606425702812, 0.668671679197995, 0.6798994974874372]\n",
      "[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1]\n",
      "[0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1]\n",
      "full train results:\n",
      "\t [0.59375, 0.5885416666666666, 0.6354166666666666, 0.612565445026178, 0.6387434554973822, 0.6492146596858639, 0.6335078534031413, 0.6335078534031413, 0.612565445026178, 0.6335078534031413]\n",
      "\t [0.9949672873678913, 0.9974823766364552, 0.8666666666666667, 0.8393213572854291, 0.99949924887331, 0.638, 0.999000499750125, 0.9949799196787149, 0.9508771929824561, 0.9798994974874372]\n",
      "[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1]\n",
      "[0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1]\n",
      "best accs results:\n",
      "\t [0.59375, 0.5625, 0.6354166666666666, 0.6282722513089005, 0.6020942408376964, 0.6387434554973822, 0.643979057591623, 0.6387434554973822, 0.643979057591623, 0.6230366492146597]\n",
      "\t [0.8037242073477604, 0.8670694864048338, 0.4967418546365915, 0.5489021956087824, 0.9744616925388082, 0.519, 0.47226386806596704, 0.7761044176706827, 0.49473684210526314, 0.91356783919598]\n",
      "[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "train_accs, test_accs = [], []\n",
    "train_accs_over, test_accs_over = [], []\n",
    "train_accs_acc, test_accs_acc = [], []\n",
    "\n",
    "for fold, (train_val_idx, test_idx) in enumerate(skf.split(data1, labels)):\n",
    "    \n",
    "    print('-----------------------------Fold {}---------------'.format(fold + 1))\n",
    "\n",
    "    \n",
    "    print('preparing dataloaders...')\n",
    "    print(data.shape)\n",
    "    train_val_data = np.stack([data1[index] for index in train_val_idx])\n",
    "    train_val_label = [labels[index] for index in train_val_idx]\n",
    "    test_data = np.stack([data1[index] for index in test_idx])\n",
    "    test_label = [labels[index] for index in test_idx]\n",
    "    \n",
    "    \n",
    "    Max = np.max(train_val_data, axis=(0,1,2), keepdims=True)\n",
    "    Min = np.min(train_val_data, axis=(0,1,2), keepdims=True)\n",
    "    train_val_data = (train_val_data-Min)/(Max-Min)\n",
    "    \n",
    "    Max_test = np.max(test_data, axis=(0,1,2), keepdims=True)\n",
    "    Min_test = np.min(test_data, axis=(0,1,2), keepdims=True)\n",
    "    test_data = (test_data-Min)/(Max-Min)\n",
    "    \n",
    "    \n",
    "    train_val = [[train_val_data[i], train_val_label[i]] for i in range(len(train_val_data))]\n",
    "    test = [[test_data[i], test_label[i]] for i in range(len(test_data))]\n",
    "    \n",
    "    num_train_val = len(train_val)\n",
    "    indices = list(range(num_train_val))\n",
    "    np.random.shuffle(indices)\n",
    "    split = int(np.floor(val_size*num_train_val))\n",
    "    train, val = [train_val[i] for i in indices[split:]] ,[train_val[i] for i in indices[:split]]\n",
    "    \n",
    "    train_labels = [data[1] for data in train]\n",
    "    \n",
    "    oversample = 1\n",
    "    _, counts = np.unique(train_labels, return_counts=True)\n",
    "    if oversample==1:\n",
    "        if counts[1]>counts[0]:\n",
    "            label0 = [data for data in train if data[1]==0]\n",
    "            coef = int(counts[1]/counts[0])\n",
    "            print('coef when 1 > 0', coef)\n",
    "            for i in range(coef):\n",
    "                train = train + label0\n",
    "        elif counts[1]<counts[0]:\n",
    "            label1 = [data for data in train if data[1]==1]\n",
    "            coef = int(counts[0]/counts[1])\n",
    "            print('coef when 0 > 1', coef)\n",
    "            for i in range(coef):\n",
    "                train = train + label1\n",
    "    \n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=True)\n",
    "    test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=True)\n",
    "    \n",
    "    print('creating model...')\n",
    "    model = cnn().float()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "    criterion = nn.BCELoss()\n",
    "    \n",
    "    print('calculating total steps...')\n",
    "    steps = 0\n",
    "    for epoch in range(n_epochs):\n",
    "        for data, label in train_loader:\n",
    "            steps += 1\n",
    "\n",
    "    scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, total_steps=steps, max_lr=0.001)\n",
    "    scheduler1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)\n",
    "    l1_lambda = 0.0001\n",
    "    \n",
    "    min_val_loss = np.inf\n",
    "    max_val_acc = 0\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        print('epoch: ', epoch+1)\n",
    "        train_loss = 0\n",
    "        l1_loss = 0\n",
    "        train_correct = 0\n",
    "        model.train()\n",
    "        '''for name, param in model.named_parameters():\n",
    "                print(name, param.data)\n",
    "                break'''\n",
    "        for iteration, (data,label) in enumerate(train_loader):\n",
    "            #print('\\ndata = ', torch.amax(data, axis=(0,1,2,4)), torch.amin(data, axis=(0,1,2,4)))\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data.float())\n",
    "            label = torch.reshape(label, (-1,1))\n",
    "            label = label.float()\n",
    "            loss = criterion(output, label)\n",
    "            add_loss = loss\n",
    "            ex_loss = 0\n",
    "            for W in model.parameters():\n",
    "                ex_loss += l1_lambda*W.norm(1)\n",
    "                loss = loss + l1_lambda*W.norm(1)    \n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "            targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "            train_correct += sum(targets)\n",
    "            train_loss += add_loss.item()*data.shape[0]\n",
    "            l1_loss += ex_loss.item()*data.shape[0]\n",
    "            \n",
    "            if iteration % print_every == 0:\n",
    "                is_training = True\n",
    "                val_loss = 0\n",
    "                val_correct = 0\n",
    "                model.eval()\n",
    "                for data, label in val_loader:\n",
    "                    output = model(data.float())\n",
    "                    label = torch.reshape(label, (-1,1))\n",
    "                    label = label.float()\n",
    "                    loss = criterion(output, label) \n",
    "                    val_loss += loss.item()*data.shape[0]\n",
    "                    targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "                    val_correct += sum(targets)\n",
    "                val_loss = val_loss/len(val_loader.sampler)\n",
    "                val_acc = val_correct/len(val_loader.sampler)\n",
    "\n",
    "                if val_loss <= min_val_loss:\n",
    "                    print(\"validation loss decreased ({:.6f} ---> {:.6f}),   val_acc = {}\".format(min_val_loss, val_loss, val_acc))\n",
    "                    torch.save(model.state_dict(), 'sal/model'+str(fold)+'.pt')\n",
    "                    min_val_loss = val_loss\n",
    "                if val_acc >= max_val_acc:\n",
    "                    print(\"validation acc increased ({:.6f} ---> {:.6f})\".format(max_val_acc, val_acc))\n",
    "                    torch.save(model.state_dict(), 'sal/model'+str(fold)+'_acc.pt')\n",
    "                    max_val_acc = val_acc\n",
    "                torch.save(model.state_dict(), 'sal/last_model'+str(fold)+'.pt')\n",
    "                model.train(mode=is_training)\n",
    "                \n",
    "        train_acc = train_correct/len(train_loader.sampler)       \n",
    "        train_loss = train_loss/len(train_loader.sampler)\n",
    "        loss1 = l1_loss/len(train_loader.sampler)\n",
    "        \n",
    "        val_loss = 0\n",
    "        val_correct = 0\n",
    "        model.eval()\n",
    "        for data, label in val_loader:\n",
    "            output = model(data.float())\n",
    "            label = torch.reshape(label, (-1,1))\n",
    "            label = label.float()\n",
    "            loss = criterion(output, label) \n",
    "            val_loss += loss.item()*data.shape[0]\n",
    "            targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "            val_correct += sum(targets)\n",
    "            \n",
    "        val_loss = val_loss/len(val_loader.sampler)\n",
    "        val_acc = val_correct/len(val_loader.sampler)\n",
    "        \n",
    "        print('epoch {}: train loss = {},   l1loss = {},   train acc = {},\\nval_loss = {},     val_acc = {}\\n'\n",
    "              .format(epoch+1, train_loss, loss1, train_acc, val_loss, val_acc))\n",
    "        if int(train_acc)==1:\n",
    "            print('!!! overfitted !!!')\n",
    "            break\n",
    "        model.train()\n",
    "        #scheduler1.step(val_loss)\n",
    "            \n",
    "    model =cnn().float()\n",
    "    model.load_state_dict(torch.load('sal/model'+str(fold)+'.pt'))\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in test_loader:\n",
    "        output = model(data.float())\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        print(targets)\n",
    "        n_correct += sum(targets)\n",
    "        \n",
    "    test_accs.append(n_correct/len(test_loader.sampler))\n",
    "    print('early stoping results:\\n\\t', test_accs)\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in train_loader:\n",
    "        output = model(data.float())\n",
    "        print('output = ', [output[i].round().item() for i in range(len(label))])\n",
    "        print('label = ', label)\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        n_correct += sum(targets)\n",
    "        \n",
    "    train_accs.append(n_correct/len(train_loader.sampler))\n",
    "    print('\\t', train_accs)\n",
    "    \n",
    "    model = cnn().float()\n",
    "    model.load_state_dict(torch.load('sal/last_model'+str(fold)+'.pt'))\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in test_loader:\n",
    "        output = model(data.float())\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        print(targets)\n",
    "        n_correct += sum(targets)\n",
    "    test_accs_over.append(n_correct/len(test_loader.sampler))\n",
    "    print('full train results:\\n\\t', test_accs_over)\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in train_loader:\n",
    "        output = model(data.float())\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        n_correct += sum(targets)\n",
    "    train_accs_over.append(n_correct/len(train_loader.sampler))\n",
    "    print('\\t', train_accs_over)\n",
    "    \n",
    "    model = cnn().float()\n",
    "    model.load_state_dict(torch.load('sal/model'+str(fold)+'_acc.pt'))\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in test_loader:\n",
    "        output = model(data.float())\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        print(targets)\n",
    "        n_correct += sum(targets)\n",
    "    test_accs_acc.append(n_correct/len(test_loader.sampler))\n",
    "    print('best accs results:\\n\\t', test_accs_acc)\n",
    "    \n",
    "    n_correct = 0\n",
    "    model.eval()\n",
    "    for data, label in train_loader:\n",
    "        output = model(data.float())\n",
    "        targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        n_correct += sum(targets)\n",
    "    train_accs_acc.append(n_correct/len(train_loader.sampler))\n",
    "    print('\\t', train_accs_acc)\n",
    "    print(test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "27ca56ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6210514834205934"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum(test_accs_acc))/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4043da8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "468a56e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 20, 11)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = picture_data_train\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "d4dc1a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 7, 9, 20) 1913\n"
     ]
    }
   ],
   "source": [
    "mean_data = np.mean(dataset, axis=(4))\n",
    "labels = vowel_label\n",
    "print(mean_data.shape, len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "904a96d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 5) (1913, 5)\n"
     ]
    }
   ],
   "source": [
    "data1 = mean_data[:,[2,2,3,3,2],[2,4,5,5,3],[16,2,9,3,2]]\n",
    "X = data1.reshape((-1,5))\n",
    "print(data1.shape, X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "fd76f71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "c9ddc846",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = Perceptron(tol=1e-3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "d3b2a352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-28 {color: black;background-color: white;}#sk-container-id-28 pre{padding: 0;}#sk-container-id-28 div.sk-toggleable {background-color: white;}#sk-container-id-28 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-28 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-28 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-28 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-28 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-28 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-28 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-28 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-28 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-28 div.sk-item {position: relative;z-index: 1;}#sk-container-id-28 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-28 div.sk-item::before, #sk-container-id-28 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-28 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-28 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-28 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-28 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-28 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-28 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-28 div.sk-label-container {text-align: center;}#sk-container-id-28 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-28 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Perceptron()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Perceptron</label><div class=\"sk-toggleable__content\"><pre>Perceptron()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Perceptron()"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "6c07a732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8128994770482277"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "472916ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8697916666666666"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83916158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.0001,\n",
       " 'class_weight': None,\n",
       " 'early_stopping': False,\n",
       " 'eta0': 1.0,\n",
       " 'fit_intercept': True,\n",
       " 'l1_ratio': 0.15,\n",
       " 'max_iter': 1000,\n",
       " 'n_iter_no_change': 5,\n",
       " 'n_jobs': None,\n",
       " 'penalty': None,\n",
       " 'random_state': 0,\n",
       " 'shuffle': True,\n",
       " 'tol': 0.001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19bffc72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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