Thought-to-text-EEG/CNN-RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "902d927c",
   "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",
    "from sklearn.manifold import TSNE\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0bbf34",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''all:\n",
    "with open(\"data/all/X_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/all/X_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/all/y_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/all/y_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)\n",
    "    \n",
    "    vowel:\n",
    "with open(\"data/vowel/X_vowel_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/vowel/X_vowel_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/vowel/y_vowel_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/vowel/y_vowel_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)\n",
    "    \n",
    "    nasal:\n",
    "with open(\"data/nasal/X_nasal_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/nasal/X_nasal_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/nasal/y_nasal_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/nasal/y_nasal_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)\n",
    "    \n",
    "    bilabial:\n",
    "with open(\"data/bilabial/X_bilabial_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/bilabial/X_bilabial_test.pkl\", \"rb\") as f:\n",
    "    X__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)\n",
    "    \n",
    "    iy:\n",
    "with open(\"data/iy/X_iy_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/iy/X_iy_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/iy/y_iy_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/iy/y_iy_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)\n",
    "    \n",
    "    uw:\n",
    "with open(\"data/uw/X_uw_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/uw/X_uw_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/uw/y_uw_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/uw/y_uw_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5eed7b79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1721, 62, 1250) (192, 62, 1250) 1721 192\n"
     ]
    }
   ],
   "source": [
    "#load train and test data and labels\n",
    "with open(\"data/uw/X_uw_train.pkl\", \"rb\") as f:\n",
    "    X__train = pickle.load(f)\n",
    "with open(\"data/uw/X_uw_test.pkl\", \"rb\") as f:\n",
    "    X__test = pickle.load(f)\n",
    "with open(\"data/uw/y_uw_train.pkl\", \"rb\") as f:\n",
    "    y__train = pickle.load(f)\n",
    "with open(\"data/uw/y_uw_test.pkl\", \"rb\") as f:\n",
    "    y__test = pickle.load(f)\n",
    "print(X__train.shape, X__test.shape, len(y__train), len(y__test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4901d867",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1721/1721 [05:15<00:00,  5.45it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 192/192 [00:46<00:00,  4.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1721, 62, 20, 11) (192, 62, 20, 11)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#set parameters for MFCC extraction\n",
    "n_mfcc = 20\n",
    "framesize = 1 * 250\n",
    "hop_size = int(framesize/2)\n",
    "\n",
    "#calculate MFCCs and put them in a matrix with shape (#trial, #channels) for each sub. put all new subs in MFCC_data \n",
    "trials = []\n",
    "for i, trial in enumerate(tqdm(X__train)):\n",
    "    channels = []\n",
    "    for j, channel in enumerate(trial):\n",
    "        mfccs = librosa.feature.mfcc(y=channel, n_mfcc=n_mfcc, n_fft=framesize, hop_length=hop_size, sr=250)\n",
    "        channels.append(np.array(mfccs))\n",
    "    trials.append(np.array(channels)) \n",
    "mfc_train = np.array(trials)\n",
    "trials = []\n",
    "for i, trial in enumerate(tqdm(X__test)):\n",
    "    channels = []\n",
    "    for j, channel in enumerate(trial):\n",
    "        mfccs = librosa.feature.mfcc(y=channel, n_mfcc=n_mfcc, n_fft=framesize, hop_length=hop_size, sr=250)\n",
    "        channels.append(np.array(mfccs))\n",
    "    trials.append(np.array(channels)) \n",
    "mfc_test = np.array(trials)\n",
    "print(mfc_train.shape, mfc_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dec5d37e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1721, 62, 10, 11) (192, 62, 10, 11)\n"
     ]
    }
   ],
   "source": [
    "mfc_train1 = mfc_train[:,:,0:10,:]\n",
    "mfc_test1 = mfc_test[:,:,0:10,:]\n",
    "print(mfc_train1.shape, mfc_test1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93b33cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('uw_20mfc_train.pkl', 'wb') as f:\n",
    "    pickle.dump(mfc_train1, f)\n",
    "with open('uw_20mfc_test.pkl', 'wb') as f:\n",
    "    pickle.dump(mfc_test1, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3fb31100",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('bilab_20mfc_train.pkl', 'rb') as f:\n",
    "    mfc_train = pickle.load(f)\n",
    "with open('bilab_20mfc_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": 2,
   "id": "7cbb9002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1913, 62, 20, 11)\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",
    "    mfc_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(mfc_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc4650d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36382645060115004"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(nasal_label)/len(nasal_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "56e00da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1721, 62, 10, 11)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfc_train = mfc_train[:,:,2:12,:]\n",
    "mfc_test = mfc_test[:,:,2:12,:]\n",
    "mfc_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "198c529c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "data = mfc_train[:,:,:10,:]\n",
    "data = rearrange(data, 'n c m t -> n (c m t)')\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "X = tsne.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f33647be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11 = [], [], [], [], [], [], [], [], [], [], []\n",
    "for i, label in enumerate(y__train):\n",
    "    if label==0:\n",
    "        l1.append(X[i])\n",
    "    elif label==1:\n",
    "        l2.append(X[i])\n",
    "    elif label==2:\n",
    "        l3.append(X[i])\n",
    "    elif label==3:\n",
    "        l4.append(X[i])\n",
    "    elif label==4:\n",
    "        l5.append(X[i])\n",
    "    elif label==5:\n",
    "        l6.append(X[i])\n",
    "    elif label==6:\n",
    "        l7.append(X[i])\n",
    "    elif label==7:\n",
    "        l8.append(X[i])\n",
    "    elif label==8:\n",
    "        l9.append(X[i])\n",
    "    elif label==9:\n",
    "        l10.append(X[i])\n",
    "    elif label==10:\n",
    "        l11.append(X[i])\n",
    "plt.figure(figsize=(8, 7))\n",
    "for x in l1:\n",
    "    plt.scatter(x[0], x[1], c='r')\n",
    "for x in l2:\n",
    "    plt.scatter(x[0], x[1], c='g')\n",
    "for x in l3:\n",
    "    plt.scatter(x[0], x[1], c='b')\n",
    "for x in l4:\n",
    "    plt.scatter(x[0], x[1], c='c')\n",
    "for x in l5:\n",
    "    plt.scatter(x[0], x[1], c='y')\n",
    "for x in l6:\n",
    "    plt.scatter(x[0], x[1], c='m')\n",
    "for x in l7:\n",
    "    plt.scatter(x[0], x[1], c='k')\n",
    "for x in l8:\n",
    "    plt.scatter(x[0], x[1], c='w')\n",
    "for x in l9:\n",
    "    plt.scatter(x[0], x[1], c='orange')\n",
    "for x in l10:\n",
    "    plt.scatter(x[0], x[1], c='purple')\n",
    "for x in l11:\n",
    "    plt.scatter(x[0], x[1], c='bisque')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8a54e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4, 3, 0, 1, 0, 3, 0, 0, 0, 1], dtype=int64),\n",
       " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,1,1,1],[2,2,2,4],[5,5,5,9]])\n",
    "np.histogram(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "f440d5eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uMSeFFYkmJVwi0q+VVjWSk57MP26/APOmhpD9DvydNCTQw7xF+pLujxaRfm3bvkZGFmYp2RIRXynhEpF+656F6/nrxgpmjBzgdygx7YdXnRopO+djICL9mBIuEemXqhpa+d+3twAwb8pBD7WQTq6esX+KxPaQZpwXiQYlXCLSL029exEQfpTPlGH5/gYTB0YXZwHwYVk9b3yw1+doRPofJVwi0q8NG6ApDnpiwS1n8nHvOZP/8tulGjwv0suUcIlIv9Pctn9O5Xxv2gM5spz0FE7t1BK4eMs+/4IR6YeUcIlIv/OPTfuThbagxiT1VHF2WqS8cPUeHyMR6X+UcIlIv7KpvJ7P/G5pZPnSUwb7GE18Kc7Zn3D933tlPkYi0v9o4lMR6Tfm3PcmH5bVA3DltCHce/UUfwOKM3kZ+/8k1DS10dwWJD0l4GNEIv2HWrhEpF9wzkWSLYBvzJ3gYzTxaXRRNjecNYrPnTMa0AOtRXqTEi4R6ReqvWcmQjjZKslN9zGa+JSUZNx52USmjwhPFKuES6T3KOESkX5he2VjpFyYlepjJPGv0Bs8v7eumabWYDdbi0hPKOESkX6htKopUk4O6LmJx6MjYf38oys46dsv4/S8H5HjpoRLRPqFysZw99c5JxYzd9Ign6OJb8U5aSQZtLaHp9Qor2vxOSKR+KeES0Ti3rKtldz53FoAHrh+BpmpugH7eGSlJXPltKGR5c7dtSJybJRwiUhc21rRwN0vrI8spybrstYbzhpbFCkr4RI5fvoaKCJx7bwfv+F3CP3SSYNzI+VvPbeWU4bkMa4kx8eIROKbvgqKSNzSYO7oGVOcxZljCwFobA12mb1fRI6eEi4RiVu1Te1+h9BvJQeSeOzG2ZHllnY9k1LkeCjhEpG4VV7f9e65T8wY5lMk/V9eRorfIYjENSVcIhKXnHNsKKuLLC//1kX84KpTfYyof7rnikkAbNxbz+LN+3yORiR+KeESkbj067c2c/NjKwB49SvnRGZHl9517awRnDIkD4CvPb3a52hE4pcSLhGJSy+v3RMpFynZiqpThoYTrrZgiEff2eZzNCLxSQmXiMSlpE5P78nX+KKo+vZlExk7MJvdNc1867m11HR6ULiI9IwSLhGJS0m2P+NKStKzE6MpPSXA4Lz0yHJFgx71I3K0uk24zCzdzJaY2btmts7M/tOrH2Vmi81so5k9aWapXn2at7zRWz+y02vd7tV/YGaXRO1TiUi/1znhkui77NTBkXKFnq0octR60sLVAlzgnJsMTAHmmtls4AfAfc65sUAVcIO3/Q1AlVd/n7cdZjYRuAY4GZgL/NLMAr34WUQkQbzxwV6WbK30O4yE8onThvPCF88CYF9Dq8/RiMSfbhMuF1bvLaZ4/xxwAfC0V/8wcLlXnuct462/0MzMq3/COdfinNsCbARm9saHEJHE8ug72yPlmaMKfIwksZTkhrsV735hvWb5FzlKPRrDZWYBM1sF7AUWAZuAaudcxzTPpcAQrzwE2AHgra8BCjvXH2Kfzu91k5ktM7Nl5eXlR/2BRKT/S/bGbP3smik8esMsn6NJHAVZqQDsrmnm3dIan6MRiS89Sricc0Hn3BRgKOFWqQnRCsg59xvn3Azn3Izi4uJovY2IxKn739jEy+v2cNFJJcybMoTUZN3701cCScZN54wG4MU1u32ORiS+HNWVyjlXDbwOnA7km1myt2oosNMr7wSGAXjr84B9nesPsY+ISLdCIccPXn4fgAmDcnyOJjHdcelJnDe+mIWrd6tbUeQo9OQuxWIzy/fKGcDFwHuEE6+rvM3mAwu88vPeMt76v7jwWfk8cI13F+MoYBywpJc+h4gkgMrG/YO1zxxb5GMkie3iiSXsrG5iS0WD36GIxI3k7jdhMPCwd0dhEvCUc+4FM1sPPGFm3wVWAg962z8IPGJmG4FKwncm4pxbZ2ZPAeuBduAW51ywdz+OiPRne2qaASjKTmXGyAE+R5O4pg0P/+5Xl9Ywujjb52hE4kO3CZdzbjUw9RD1mznEXYbOuWbg44d5rXuAe44+TBEReODtzeGf808jJaCxW34ZNzCbzNQA72zex+VTD7r3SUQOQVcsEYkLFfUtPLdqFwCDctO72VqiKTmQxNxJg3hi6Q6eWV7qdzgicUEJl4jEvFDIccUv/waEZzwvydXDqv12xpjwGLrb/vguq0ur/Q1GJA4o4RKRmPf3TfvYUdnEvCkn8N+fnIrpsT6+K8pOjZTf313nYyQi8UEJl4jEvLc3lJMaSOIHHztVyVaMKMre38q4ZqcmQRXpjhIuEYl57+2pY1xJNukpevxqrCjO2Z9wlVY1+hiJSHxQwiUiMc05x/u7axmviU5jSsdjfgB2VTf7GIlIfOjJPFwiIr54f08t9776IXvrWpilh1THlM7TcuyqafIxEpH4oBYuEYlZNz+6glfXlwFw6SmDfY5GDvT218/nlvPHUNfczt83VvgdjkhMU8IlIjGr8/D4nPQU3+KQQxtWkMklJw8iJz2Zmx9bQX1Lu98hicQsJVwiEnM27q3jyaXbKfSmHvjKRSf6HJEczqlD8/nJxydT09TGvz+5ivZgyO+QRGKSxnCJSMy58pd/p7a5naEDMrj0lEHceuFYv0OSIxhemAnAq+vLeGtDORdMKPE5IpHYoxYuEYk5tc3hrqnSqiYG5qRr7q0YNyQ/I1J+T5OgihySEi4RiWm6OzH2dR5ft2JblY+RiMQuJVwiElMaDhh4/RHdnRgX/vOjJ1OSm8aybVW8v6fW73BEYo4SLhGJKd9esC5S/uocDZaPF/PPGMltF4+npqmNuT99mxXb1dIl0pkSLhGJKe+WVgPwtUvGc8v5GiwfT6aPHBApr9XzFUW6UMIlIjElyWDysHxuOme0BsvHmdFFWZHytxes44G3N/sYjUhsUcIlIjEjGHJsr2xkxogBXR4dI/HBzPjtv5zG4Lx0AL678D2fIxKJHbqiiUhMaGoNMuaOF2luCzGmONvvcOQYnT9hIE997nQAUpU0i0TobBCRmFBR3xIpzx6tqSDi2bCCTL4650RagyGa24J+hyMSE5RwiUhMqG1ui5RHdRoLJPFp6IDw7POlVU0+RyISG5RwiYivnHM8tngb2/Y1AvCHf52twfL9wLCC8OzzO6oafY5EJDboWYoi4qsPy+r5f8+ujSznZuiy1B+ohUukK7VwiYivyutauizndnpMjMSv4uw0UgNJlKqFSwRQwiUiPjvwD3JephKu/iApyRgyIINfv7mZBat2+h2OiO+UcImIr3ZWd+1yyk5Vl2J/MWFQDgBfemKVv4GIxAAlXCLim/W7alm4ZneXuqQkDZjvLyYNyYuU99W3HGFLkf6v24TLzIaZ2etmtt7M1pnZl7z6AjNbZGYbvJ8DvHozs5+b2UYzW21m0zq91nxv+w1mNj96H0tEYt2u6iYu/fnbbC5v4PzxxSz/1kUsvPUsv8OSXnTDWaP46OQTAFi5vdrfYER81pMWrnbgNufcRGA2cIuZTQS+CbzmnBsHvOYtA3wEGOf9uwm4H8IJGnAXMAuYCdzVkaSJSGLZXF7PM8tLI8ufOG0YhdlpnHxC3hH2kniTnhLgh1edCsCNv1/G3tpmnyMS8U+3CZdzbrdzboVXrgPeA4YA84CHvc0eBi73yvOA37uwd4B8MxsMXAIscs5VOueqgEXA3N78MCISHy74yZv8ZNGHALx227nMnTTY54gkWtJTApHyuT96Q12LkrCOagyXmY0EpgKLgRLnXMfgiz1AiVceAuzotFupV3e4+gPf4yYzW2Zmy8rLy48mPBGJQyMKMv0OQaLsxVvPBqCpLcj/vL7J52hE/NHjhMvMsoFngC8752o7r3POOcD1RkDOud8452Y452YUFxf3xkuKSAwJXy72S9YDjvu9kwbnRMpldepWlMTUoyudmaUQTrYec879yasu87oK8X7u9ep3AsM67T7UqztcvYgkkNqm9kj58+eO8TES6SudH9X00prdbCqv9zEaEX/05C5FAx4E3nPO3dtp1fNAx52G84EFneqv9+5WnA3UeF2PrwBzzGyAN1h+jlcnIglk674GAL4z72S+MXe8z9FIXws5+PvGCr/DEOlzPZlh8EzgOmCNma3y6u4Avg88ZWY3ANuAq711LwKXAhuBRuAzAM65SjO7G1jqbfcd51xlb3wIEYkff/X+2J45tkgPqU4gf/vmBYRCjn/6+dt8UFbndzgifa7bhMs591fgcFfFCw+xvQNuOcxrPQQ8dDQBikj/sXFvPT965QNAg+UTzZD8DADGD8ph/a7abrYW6X80WlVE+syKbVUAFGWnabB8gjptZAGrS2uoa27zOxSRPqUrnoj0ifvf2MTXn1lNRkqAd26/wO9wxCfnnlhMe8jx0po9foci0qeUcIlI1AVDjh+8/D4QHrul1q3EddrIAk4Zksev39J8XJJYdNUTkah7eW24NeO88cXc+4nJPkcjfkpKMj4+YyibyhvYuFeD5yVxKOESkaj7/T+2MqY4i/+9fga56Sl+hyM+u+TkQQBcdO9b/OX9Mp+jEekbSrhEJOo2VzQwbfgAUtSVKEBJbjqfnDkcgK8/vZpgqFceVCIS03T1E5Go+uUbGymva2FkUZbfoUgM+a8rJvGLT02lor6VpVs1JaP0f0q4RCSqfvhyeN6toQMyfI5EYomZcf74gaSnJPH8u7v8Dkck6pRwiUhUbCqvZ8KdLwGQmpwUGbcj0iErLZkLTyrh8cXbWb5NrVzSvynhEpFeV9vcxoU/eZPmthAA3/noyaSnBHyOSmLRty+bCMB/PL+extb2brYWiV9KuESk1y1YuTNSDiQZU4bn+xeMxLSS3HQA1uys4e4X1vscjUj09OTh1SIiR6XzPWfv3z1XdydKj6zcXu13CCJRo6ugiPS68roWAB6/cZaSLenWFy8YC8D2ykZCmiJC+ildCUWk15XVNjMwJ40zxhb5HYrEgdvmjOf7V55CY2uQHVWNfocjEhVKuESk1zS3BfnG06t5alkpFfUtfocjceSkwbkAnPujN/iX3y7xORqR3qeES0R6zbMrd/Lksh0AfHr2CJ+jkXgy8YRcSnLTAHjjg3Ka24I+RyTSu5RwiUiv+evGCkpy09jyvUv5zrxJfocjcSQlkMSz/3ZmZHl7pboWpX9RwiUivaK+pZ3X39/L+eMHYmZ+hyNx6IT8DH73mdMA+NZza9XKJf2KEi4ROS7twRC/fnMTk+56hcbWIFefNszvkCSOTR8xgMzUAEu2VHLtA4v9Dkek1yjhEpHj8ur6Mr730vuR5anD8v0LRuJeTnoKq749h1svHMfybVVUN7b6HZJIr9DEpyJyXLZUNADwq09PY0RhlroT5bilJidFEvcNe+s5bWSBvwGJ9AK1cInIcdlZ3cSAzBTmThocubVf5HiNK8kG4MU1uzWWS/oFJVwicsyWba3k8cXb1aolvW5IfgYAv/3bVj52/9+paWzzOSKR46OES0SOyYayOq761T8AaGhp9zka6W/MjAsnDARg3a5a/uvF93yOSOT4KOESkaMWCjl++MoHkeUnP3e6j9FIf3XfNVO4eGIJAC+v26MB9BLXlHCJyFF75J1tLFpfBsDPrpnCFN2ZKFGQm57Crz49nSH5GdQ0tXHL4yv8DknkmCnhEpGjsnxbJXc9vw6A9JQk5k0Z4nNE0p8FkozG1nCX9erSGp+jETl23SZcZvaQme01s7Wd6grMbJGZbfB+DvDqzcx+bmYbzWy1mU3rtM98b/sNZjY/Oh9HRKLtG8+siZSfuEldiRJ9k4bkAVDX3M7Njy6nLRjyOSKRo9eTFq7fAXMPqPsm8JpzbhzwmrcM8BFgnPfvJuB+CCdowF3ALGAmcFdHkiYi8eOF1bvYuLeeYQUZ/PHzp6srUfrELz45jdNGhv9kvLR2D/e/scnniESOXrcJl3PuLaDygOp5wMNe+WHg8k71v3dh7wD5ZjYYuARY5JyrdM5VAYs4OIkTkRgVCjmu/vU/+MLjK5k8LJ/XbztPk1FKn8nLTOHeq6dw0UnhuxbvXfQhy7dV+RyVyNE51jFcJc653V55D1DilYcAOzptV+rVHa7+IGZ2k5ktM7Nl5eXlxxieiPSmVaXVLNkS/t71y2unkRzQ8E/pW8MKMnlg/mn89yenAvCx+/9OeV2Lz1GJ9NxxXzWdcw5wvRBLx+v9xjk3wzk3o7i4uLdeVkSOUX1LO/ct+pBAkrH6P+ZEJqQU8cOgvPRIef3uWh8jETk6x5pwlXldhXg/93r1O4FhnbYb6tUdrl5EYtwdf1rD2xsqyEwJkJue4nc4kuAG5e5PuOY/tEQD6CVuHGvC9TzQcafhfGBBp/rrvbsVZwM1XtfjK8AcMxvgDZaf49WJSAx7d0c1z7+7C4AZI3Wfi/hvYG5al+XtlY0+RSJydJK728DM/gCcBxSZWSnhuw2/DzxlZjcA24Crvc1fBC4FNgKNwGcAnHOVZnY3sNTb7jvOuQMH4otIjHDOceeCtTz6znYAPnfOaG4+b4zPUYlAWnKAT80aHp4U9c1N7KhsZExxtt9hiXTLwkOwYtOMGTPcsmXL/A5DJOG8vaGc6x5cAsAXzh/LVy8Z73NEIl3trmni9O/9hetmj+Duyyf5HY4IAGa23Dk341Drum3hEpHEsre2mVse2/8Ilc+dO9rHaEQOrSQnPJbrkXe2UV7XwtWnDeWCCSXd7CXiH93bLSJd/Oy1DdQ2t3PjWaN45uYzyNFAeYlBSUnGvCknAOEHW9/53DpiucdGRAmXiADhcVv3LFzPY4u3M7wgk9svPYnpIzRQXmLX9688NVLeWd3Eiu3V/gUj0g0lXCICwI9e+YD/fXsLAGeOLSSQZD5HJHJkGakBPnfOaH788clkpgb43ovvsbe22e+wRA5JY7hEElww5Lj1iZUsXL2blIDx82umMnt0od9hifTI7ZeeFCl/9Y/vMut7r/HVOeO5eGIJJ5bk+BiZSFdq4RJJYKGQ48aHl7JwdfhJXQtuOYuPnDKYAVmpPkcmcnSumj6US04uwblwa+2c+95ib51auyR2KOESSWBrd9Xw+gf7n1k6YZBaBCR+5WV0vcHjIz9926dIRA6mLkWRBLRtXwMX3fsmbcHwXV13Xz6JORNLSNK4LYljt80ZT3ZaCinJxq/f3My+hla27WtgRGGW36GJqIVLJBH9/LWNkWQrJz2Z62aPoKTTM+pE4lFJbjrf/ueJfPnCE7ns1MEAfP3p1T5HJRKmhEskwXxYVseCVeFnx48pzmL5ty72OSKR3pWRGuAXn5rGJ2YMY/GWSt7dUe13SCLqUhRJFN98ZjV/37SPQJKRl5HCK185h6LstO53FIlTV80YypPLdjDvf/6mRwCJ79TCJZIAVpdW88TSHWyvbGRLRQO/uX66ki3p904bWcD9104Dwo8AGvnNhby0Zjdj7niRtTtrfI5OEo0SLpF+zDnHy2v38NFf/A2Aouw0nv786UwfUeBzZCJ94yOnDGb5ty6KLN/82AqCIcfjS7bT2h7yMTJJNOpSFOmHVu2o5nd/28Jzq3YBMHFwLvdcMYkpw/Ix052IklgKs9MYkJlCVWNbpO7xxdt5e0M5b3/9Ah8jk0SihEukn6lpbOOa3/yD5rbwt/eRhZk8csNMCtWFKAnsja+dD8Dz7+7izufWArCjsomW9iBpyQE/Q5MEoS5FkX7COceWigbO/MFfaG4LkRIwzhpbxOtfPU/JliS8vIwU8jJSuG72CP7v38+J1P/53d0+RiWJRC1cIv3AwtW7ueXxFZHlz545ijsunYCZqQtR5ABjB+aw9P9dxEd+9jZf/eO7NLUFCYUcG/fW605GiRolXCJxrj0Y6pJsLbz1LE4+Ic/HiERiX3FOGm9+7Ty++IeVkS5GgG//80RSAur8kd6n/1Uiccg5R3VjK4++s43L/vuvkforpg5RsiXSQ1lpyfz6uul87tzRkbr5Dy3hx698QH1Lu4+RSX9kzjm/YzisGTNmuGXLlvkdhkjMaG4L8vLaPXz5yVVd6s8YU8gjN8wioGchihyT9btqufTn+x92Pawgg8+cMYrLJg9mYI4eeyU9Y2bLnXMzDrlOCZdIbKtvaef/PbuG88YX88OXP2B3TXNk3emjC7n3E5Mpyk5TN4jIcXDO8eg72xhakMkXHltBQ2sQgBGFmbz6lXN0J6P0iBIukTjknKOstoX5Dy3hg7K6Lutmjizgqc+f7lNkIv1bWW0zdz63llfXlwHhpOsTpw3j5nPH6CYUOSIlXCJxYm9dM9v2NfLXDRX87LUNkfqUgNEWdHzvylOYOaqAwqxU8jNTfYxUpP9bu7OmyxjJCYNymDp8AP969ihGF2f7GJnEKiVcIjGstKqRrRWNfOOZ1eysbuqyLic9mbv++WSumDqEmqY2CrKUZIn0pfZgiNKqJu5csJalWysjEwpfO2s4makBJgzK5cppQ9TyJYASLpGYsG1fAxX1rRRnp1Fa1ch3F75HTVMbe2qbCYa6noe3XjCW688YSUFmKkkaCC8SEyrqW/jJqx/yhyXbu9R/dPIJzBpdwMyRBYwryfEpOokFSrhE+phzjg/L6gkkGWW1zQwdkMG5P3rjkNt2fFMuzknjopNK1FUhEuPqW9p5Z9M+MlIDXPvA4oPWF2WnUpCVyv2fns7ooiy1fiUQJVwiUbKlooGstADZack8t3IXwwsyaW4L8sLqXZEHRx/Ody+fxEmDc5g+oqCPohWR3tbcFuQbz6wmNZBEdVMbi9aXkZ+ZQrX3oOyi7DScc3z5onG8t6eO00cXkpES4LzxxSTrzuJ+J6YSLjObC/wMCAAPOOe+f7htlXCJX1rbQzgcqYEkqhrb2FffwurSGqoaW2loCfK3TRUkGSzfVkVqIImC7FR2VDYd9DoTBuVw6tA88jNTGVmYxcemDyE1kKRvvCL9kHOOyoZWCrPTWLh6N3/dWMHiLfvYXN5w0LYTBuWws6qJupZ2xg7M5t8vPpFzTywmJZBEWW0zA3PTSEsO4JyjNRjStBRxImYSLjMLAB8CFwOlwFLgk8659YfaXglX/9Lxf60j2QiGHO2hQ19I2oMh2kOO9JRAZN/a5nbyMlJoC4ZoaQ/R2NLOhr31DMxJIzs9mfK6FpKTkgiGHNnpydQ1t1Hb1E5Le5AkM3bXNJMcMIYXZFJW20ySGXtqmlm3q4Z3S2vYUtHArFEFlNe1sGVfA85BanISre2hw36mAZkpZKYmU5Kbxr+ePZry+hYG5abT2Brk0lMGk5qsb7Aiia65Lch//nkdLW0hKhtb2VBWf9ANMh067kjOSAkwMDeNyvpWGtuCnD++mLSUACfkpRNyUJCVSl5GCgADc9LITA0/qa8tFOKkQbnsrG6krLaFM8YUUtvUTlsoREluOtlpeqJfNB0p4err3/xMYKNzbjOAmT0BzAMOmXBFW31LO5fc9xbJASM5af9Dfg9MQt1hFjrXd96na33X93Sd1nZed7i893Cve9D+h3vdw8ZywGc83s91uDg7LbSFQjgHZpCXkUJDS5DG1nASlRwIJzat7SFagyGCIUcgyUhLTqItGN6vPeTISUumLgqP3CjKTgPCXYSpyUl8/twxrC6tZmBOOmMHZlNR38K4gTlcMGEg6SlJ1Da1k5+VQm56Sq/HIiL9S3pKgO9deWpk2TnHiu3VOOeYNCSP5duqWLylkkff2cYZYwo5c2wRK7ZV0dgapKU9xHu7a1mypZLa5uO/9oVb2CHJjCTvJwcsm1fev52RlATJSeF9O67VSWa0BkO0B0PkZsT+tXD26EJ+/PHJvr1/XydcQ4AdnZZLgVmdNzCzm4CbAIYPHx7VYAyYNbog3NISdAevPMxi5+6grvXdb3/QS3fZp9PrHva1DozrMPsc5k0O3v/Q+/QslsN3ix3qtZIsnH/VN7fTGgyRmRogIyXgzegc7r5LTfb+BQLUNIXHQKSnhJOx9JQA1U2tZKel4HDkZ6TS2h6iJDeNtpBjQGYKyUkGGBX1LV4S5SjOSaOxNciooizqW9opq23xtk0iIzXA8IJMkgya20JkpIab8Lvr8tMcWCJyrMyM6SMGRJbPHFvEmWOL+MpF4yLXnk/OPPjvX1ltM2nJSTS1BWluC7F0ayXThudTXtdKTVMryUlJpKcE+KCsjhPy0iMt9SW56QSSYGtFI63BECHncA5CIUfIQcj7whxyzvsXTgpDofCX+ZDr6JEIr09LTiI1EO5NSA6Er7kt7cE++d0dj/E+30Ha112KVwFznXM3esvXAbOcc1841PbqUhQREZF4caQuxb4eYLITGNZpeahXJyIiItJv9XXCtRQYZ2ajzCwVuAZ4vo9jEBEREelTfTqGyznXbmZfAF4hPC3EQ865dX0Zg4iIiEhf6/P7Q51zLwIv9vX7ioiIiPhFkwSJiIiIRJkSLhEREZEoU8IlIiIiEmVKuERERESirM8fXn00zKwc2NYHb1UEVPTB+0jP6ZjEJh2X2KNjEpt0XGJPXxyTEc654kOtiOmEq6+Y2bLDzQwr/tAxiU06LrFHxyQ26bjEHr+PiboURURERKJMCZeIiIhIlCnhCvuN3wHIQXRMYpOOS+zRMYlNOi6xx9djojFcIiIiIlGmFi4RERGRKFPCJSIiIhJlCZ1wmdlcM/vAzDaa2Tf9jieRmNkwM3vdzNab2Toz+5JXX2Bmi8xsg/dzgFdvZvZz71itNrNp/n6C/svMAma20sxe8JZHmdli73f/pJmlevVp3vJGb/1IXwPvx8ws38yeNrP3zew9Mztd54q/zOwr3rVrrZn9wczSda70PTN7yMz2mtnaTnVHfW6Y2Xxv+w1mNj8asSZswmVmAeB/gI8AE4FPmtlEf6NKKO3Abc65icBs4Bbv9/9N4DXn3DjgNW8ZwsdpnPfvJuD+vg85YXwJeK/T8g+A+5xzY4Eq4Aav/gagyqu/z9tOouNnwMvOuQnAZMLHR+eKT8xsCHArMMM5NwkIANegc8UPvwPmHlB3VOeGmRUAdwGzgJnAXR1JWm9K2ISL8C91o3Nus3OuFXgCmOdzTAnDObfbObfCK9cR/gMyhPAxeNjb7GHgcq88D/i9C3sHyDezwX0bdf9nZkOBfwIe8JYNuAB42tvkwGPScayeBi70tpdeZGZ5wDnAgwDOuVbnXDU6V/yWDGSYWTKQCexG50qfc869BVQeUH2058YlwCLnXKVzrgpYxMFJ3HFL5IRrCLCj03KpVyd9zGtenwosBkqcc7u9VXuAEq+s49U3fgp8HQh5y4VAtXOu3Vvu/HuPHBNvfY23vfSuUUA58Fuvq/cBM8tC54pvnHM7gR8D2wknWjXAcnSuxIqjPTf65JxJ5IRLYoCZZQPPAF92ztV2XufCc5Zo3pI+YmaXAXudc8v9jkW6SAamAfc756YCDezvIgF0rvQ1r7tpHuFk+AQgiyi0iMjxi6VzI5ETrp3AsE7LQ7066SNmlkI42XrMOfcnr7qso/vD+7nXq9fxir4zgY+a2VbCXewXEB47lO91m0DX33vkmHjr84B9fRlwgigFSp1zi73lpwknYDpX/HMRsMU5V+6cawP+RPj80bkSG4723OiTcyaRE66lwDjvrpJUwgMen/c5poThjV94EHjPOXdvp1XPAx13iMwHFnSqv967y2Q2UNOpyVh6gXPudufcUOfcSMLnw1+cc9cCrwNXeZsdeEw6jtVV3vYx8U2yP3HO7QF2mNl4r+pCYD06V/y0HZhtZpnetazjmOhciQ1He268AswxswFe6+Ucr65XJfRM82Z2KeExKwHgIefcPf5GlDjM7CzgbWAN+8cL3UF4HNdTwHBgG3C1c67Su6j9gnCzfSPwGefcsj4PPEGY2XnAV51zl5nZaMItXgXASuDTzrkWM0sHHiE8/q4SuMY5t9mnkPs1M5tC+EaGVGAz8BnCX5h1rvjEzP4T+AThO65XAjcSHvejc6UPmdkfgPOAIqCM8N2Gz3GU54aZfZbw3yCAe5xzv+31WBM54RIRERHpC4ncpSgiIiLSJ5RwiYiIiESZEi4RERGRKFPCJSIiIhJlSrhEREREokwJl4iIiEiUKeESERERibL/Dwq6OD29WuFYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = rearrange(mfc_train, 'n c m t -> m (n c t)')\n",
    "for m in data:\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    hist = np.histogram(m, bins=1000)\n",
    "    plt.plot(hist[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a4fc02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 62, 20, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57c5fc40",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'trials = []\\nfor 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)  \\npicture_data_test = np.array(trials)'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "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)\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": 4,
   "id": "650f9466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 62, 20, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49d1f044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 7, 9, 20, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "picture_data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60b82988",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'picture_data_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(picture_data_train\u001b[38;5;241m.\u001b[39mshape, \u001b[43mpicture_data_test\u001b[49m\u001b[38;5;241m.\u001b[39mshape)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'picture_data_test' is not defined"
     ]
    }
   ],
   "source": [
    "print(picture_data_train.shape, picture_data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cad2f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = picture_data_train\n",
    "labels = nasal_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "142b725f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1721, 62, 10, 11) 1913 (7, 9, 10, 11) 1913\n"
     ]
    }
   ],
   "source": [
    "#concat train and test data. concat train and test labels\n",
    "\n",
    "dataset = []\n",
    "for data in picture_data_train:\n",
    "    dataset.append(data)\n",
    "for data in picture_data_test:\n",
    "    dataset.append(data)\n",
    "\n",
    "labels = y__train + y__test\n",
    "    \n",
    "print(mfc_train.shape, len(dataset), dataset[0].shape, len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f287c055",
   "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": 10,
   "id": "959308f6",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c240889",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------Fold 1---------------\n",
      "preparing dataloaders...\n",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.703975),   val_acc = 0.39534884691238403\n",
      "validation acc increased (0.000000 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 1: train loss = 0.6921196075978445,   l1loss = 1.4813192892626534,   train acc = 0.5274282693862915,\n",
      "val_loss = 0.7102676463681598,     val_acc = 0.39534884691238403\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 = 0.6915809742517668,   l1loss = 1.4611903595816387,   train acc = 0.5274282693862915,\n",
      "val_loss = 0.7109477325927379,     val_acc = 0.39534884691238403\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 3: train loss = 0.6911844878743867,   l1loss = 1.4379649762238578,   train acc = 0.5274282693862915,\n",
      "val_loss = 0.7080174167026845,     val_acc = 0.39534884691238403\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 4: train loss = 0.6908970355807565,   l1loss = 1.4091885588429474,   train acc = 0.5274282693862915,\n",
      "val_loss = 0.7074278080186178,     val_acc = 0.39534884691238403\n",
      "\n",
      "epoch:  5\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 5: train loss = 0.6903622140522032,   l1loss = 1.3728010045504173,   train acc = 0.5274282693862915,\n",
      "val_loss = 0.7060032583946405,     val_acc = 0.39534884691238403\n",
      "\n",
      "epoch:  6\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "validation acc increased (0.395349 ---> 0.395349)\n",
      "epoch 6: train loss = 0.6897485629961487,   l1loss = 1.3268929230968858,   train acc = 0.5279315710067749,\n",
      "val_loss = 0.7049394624177799,     val_acc = 0.40310078859329224\n",
      "\n",
      "epoch:  7\n",
      "validation acc increased (0.395349 ---> 0.403101)\n",
      "validation acc increased (0.403101 ---> 0.406977)\n",
      "epoch 7: train loss = 0.6890018312068338,   l1loss = 1.2698341833027271,   train acc = 0.5294413566589355,\n",
      "val_loss = 0.7049807642781457,     val_acc = 0.40310078859329224\n",
      "\n",
      "epoch:  8\n",
      "validation loss decreased (0.703975 ---> 0.700499),   val_acc = 0.44573643803596497\n",
      "validation acc increased (0.406977 ---> 0.445736)\n",
      "epoch 8: train loss = 0.6881676978001837,   l1loss = 1.2007527305783492,   train acc = 0.5430296659469604,\n",
      "val_loss = 0.700941942458929,     val_acc = 0.44186046719551086\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6866489719312636,   l1loss = 1.119547625252753,   train acc = 0.534977376461029,\n",
      "val_loss = 0.7109170130981032,     val_acc = 0.41860464215278625\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.445736 ---> 0.453488)\n",
      "epoch 10: train loss = 0.6828876420308071,   l1loss = 1.026746424707145,   train acc = 0.5812783241271973,\n",
      "val_loss = 0.7096735031105751,     val_acc = 0.44961240887641907\n",
      "\n",
      "epoch:  11\n",
      "validation acc increased (0.453488 ---> 0.457364)\n",
      "validation loss decreased (0.700499 ---> 0.691370),   val_acc = 0.5155038833618164\n",
      "validation acc increased (0.457364 ---> 0.515504)\n",
      "epoch 11: train loss = 0.6756821570530811,   l1loss = 0.9239571331491648,   train acc = 0.606441855430603,\n",
      "val_loss = 0.7130429273427918,     val_acc = 0.4651162922382355\n",
      "\n",
      "epoch:  12\n",
      "validation loss decreased (0.691370 ---> 0.687802),   val_acc = 0.5426356792449951\n",
      "validation acc increased (0.515504 ---> 0.542636)\n",
      "epoch 12: train loss = 0.6597185275275083,   l1loss = 0.8148023310653156,   train acc = 0.6170105934143066,\n",
      "val_loss = 0.7542387963265411,     val_acc = 0.45736435055732727\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.6367104136589848,   l1loss = 0.7034740930054736,   train acc = 0.6371414065361023,\n",
      "val_loss = 0.7009645912998407,     val_acc = 0.5348837375640869\n",
      "\n",
      "epoch:  14\n",
      "validation acc increased (0.542636 ---> 0.542636)\n",
      "validation acc increased (0.542636 ---> 0.589147)\n",
      "epoch 14: train loss = 0.5822035336860413,   l1loss = 0.594905492913861,   train acc = 0.7121288180351257,\n",
      "val_loss = 0.7594922891882963,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5122891185148073,   l1loss = 0.4949571643124049,   train acc = 0.7488676309585571,\n",
      "val_loss = 0.8891759768936985,     val_acc = 0.4883720874786377\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.39873673111035346,   l1loss = 0.40662467002748665,   train acc = 0.8349270224571228,\n",
      "val_loss = 1.1463301422059997,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.2904471275670839,   l1loss = 0.3356658784540442,   train acc = 0.8917967081069946,\n",
      "val_loss = 1.2913649451825047,     val_acc = 0.5\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.22612523326906175,   l1loss = 0.28161148739689007,   train acc = 0.9068948030471802,\n",
      "val_loss = 1.6142784735953162,     val_acc = 0.5813953280448914\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.1450976847132427,   l1loss = 0.247697628168347,   train acc = 0.9531957507133484,\n",
      "val_loss = 1.5399292397868725,     val_acc = 0.5620155334472656\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.11934498384422433,   l1loss = 0.23167835120496044,   train acc = 0.9557121396064758,\n",
      "val_loss = 1.7687409589456957,     val_acc = 0.4844961166381836\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.06861750959270134,   l1loss = 0.22078036307298662,   train acc = 0.9773527979850769,\n",
      "val_loss = 2.424526694393175,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.04279914844610551,   l1loss = 0.21008030950993103,   train acc = 0.9899345636367798,\n",
      "val_loss = 1.830416483472484,     val_acc = 0.5310077667236328\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.032723448187240295,   l1loss = 0.20147271192607774,   train acc = 0.9924509525299072,\n",
      "val_loss = 2.2050810784332513,     val_acc = 0.4922480583190918\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.02353123065494807,   l1loss = 0.19340241685787157,   train acc = 0.9964771270751953,\n",
      "val_loss = 2.1999229627062182,     val_acc = 0.5736433863639832\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.032386840027243756,   l1loss = 0.18697129591931994,   train acc = 0.9904378652572632,\n",
      "val_loss = 2.24283122277075,     val_acc = 0.5465116500854492\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.05402209995587267,   l1loss = 0.18239764653982443,   train acc = 0.9798691272735596,\n",
      "val_loss = 2.1508019247720407,     val_acc = 0.5891472697257996\n",
      "\n",
      "epoch:  27\n",
      "validation acc increased (0.589147 ---> 0.596899)\n",
      "epoch 27: train loss = 0.08302161952074773,   l1loss = 0.17984621981219784,   train acc = 0.9667841196060181,\n",
      "val_loss = 2.134554007256678,     val_acc = 0.5155038833618164\n",
      "\n",
      "epoch:  28\n",
      "validation acc increased (0.596899 ---> 0.608527)\n",
      "epoch 28: train loss = 0.08317013363863393,   l1loss = 0.17741221547966696,   train acc = 0.9672873616218567,\n",
      "val_loss = 1.8020918643058732,     val_acc = 0.5813953280448914\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.09245095192021201,   l1loss = 0.1736254430201165,   train acc = 0.9642677307128906,\n",
      "val_loss = 2.263783588426101,     val_acc = 0.6356589198112488\n",
      "\n",
      "epoch:  30\n",
      "validation acc increased (0.608527 ---> 0.612403)\n",
      "epoch 30: train loss = 0.03266293139847968,   l1loss = 0.167058823288057,   train acc = 0.9939607381820679,\n",
      "val_loss = 1.8842465008876121,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.009013392774094783,   l1loss = 0.16012636431510208,   train acc = 1.0,\n",
      "val_loss = 1.957195477892262,     val_acc = 0.565891444683075\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(74)\n",
      "tensor(38)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833)]\n",
      "\t [tensor(0.5737)]\n",
      "tensor(67)\n",
      "tensor(35)\n",
      "full train results:\n",
      "\t [tensor(0.5312)]\n",
      "\t [tensor(0.9995)]\n",
      "tensor(77)\n",
      "tensor(39)\n",
      "best accs results:\n",
      "\t [tensor(0.6042)]\n",
      "\t [tensor(0.6064)]\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.685728),   val_acc = 0.643410861492157\n",
      "validation acc increased (0.000000 ---> 0.643411)\n",
      "validation acc increased (0.643411 ---> 0.643411)\n",
      "epoch 1: train loss = 0.6923627504481037,   l1loss = 1.486846898458096,   train acc = 0.5132699012756348,\n",
      "val_loss = 0.6893179130184558,     val_acc = 0.643410861492157\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.643411 ---> 0.643411)\n",
      "epoch 2: train loss = 0.690640912243409,   l1loss = 1.4669305257458178,   train acc = 0.5353029370307922,\n",
      "val_loss = 0.6956856370896332,     val_acc = 0.3759689927101135\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.6898900556098716,   l1loss = 1.4436278268582952,   train acc = 0.5348021984100342,\n",
      "val_loss = 0.7077792864437251,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6896268491692464,   l1loss = 1.41467766739096,   train acc = 0.5348021984100342,\n",
      "val_loss = 0.716421481712844,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  5\n",
      "epoch 5: train loss = 0.6888132073250065,   l1loss = 1.3779625838914393,   train acc = 0.5348021984100342,\n",
      "val_loss = 0.7130461672479792,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  6\n",
      "epoch 6: train loss = 0.6880481983042981,   l1loss = 1.3316323196643223,   train acc = 0.5353029370307922,\n",
      "val_loss = 0.7145648002624512,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.6870320929129481,   l1loss = 1.2742788348725633,   train acc = 0.5373059511184692,\n",
      "val_loss = 0.7159286654272745,     val_acc = 0.3527131676673889\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.6852890617201551,   l1loss = 1.205061872063963,   train acc = 0.5448172092437744,\n",
      "val_loss = 0.7084857664367025,     val_acc = 0.39147287607192993\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6824781329678367,   l1loss = 1.1237744811659285,   train acc = 0.5553330183029175,\n",
      "val_loss = 0.7270350784294365,     val_acc = 0.3488371968269348\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.6778379736629557,   l1loss = 1.0311813556557008,   train acc = 0.5823735594749451,\n",
      "val_loss = 0.721730862477029,     val_acc = 0.38759690523147583\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6686287961068247,   l1loss = 0.9295802932712037,   train acc = 0.6119178533554077,\n",
      "val_loss = 0.7199302791625031,     val_acc = 0.4844961166381836\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6473726140121608,   l1loss = 0.8220474930124756,   train acc = 0.6479719877243042,\n",
      "val_loss = 0.7618678161340167,     val_acc = 0.44186046719551086\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.6235634726050142,   l1loss = 0.7134110764616659,   train acc = 0.6604907512664795,\n",
      "val_loss = 0.7779512396154478,     val_acc = 0.44961240887641907\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5916976472348885,   l1loss = 0.6070883066939543,   train acc = 0.6935403347015381,\n",
      "val_loss = 0.7408287622207819,     val_acc = 0.5348837375640869\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5074248785487878,   l1loss = 0.5080034436287735,   train acc = 0.7611417174339294,\n",
      "val_loss = 0.9050711670587229,     val_acc = 0.44186046719551086\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.43145891812125864,   l1loss = 0.41937342439822456,   train acc = 0.8137205839157104,\n",
      "val_loss = 0.8636050733715989,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.33662977910185077,   l1loss = 0.3446814462908161,   train acc = 0.8642964363098145,\n",
      "val_loss = 0.9003204245899998,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.24278796418314882,   l1loss = 0.28942148506074294,   train acc = 0.9108663201332092,\n",
      "val_loss = 1.8521458714507346,     val_acc = 0.6356589198112488\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.30993230177512093,   l1loss = 0.25730218698397,   train acc = 0.8688032031059265,\n",
      "val_loss = 1.1883431146311205,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.1617210699160456,   l1loss = 0.2381301120736925,   train acc = 0.947421133518219,\n",
      "val_loss = 1.535721439261769,     val_acc = 0.45348837971687317\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.12891582561900272,   l1loss = 0.2264571423433874,   train acc = 0.9594391584396362,\n",
      "val_loss = 2.21431942595992,     val_acc = 0.42248061299324036\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.10533707820226028,   l1loss = 0.22040308403801667,   train acc = 0.9609414339065552,\n",
      "val_loss = 1.6402112753816354,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.05758423222024619,   l1loss = 0.21273435418702508,   train acc = 0.9834752082824707,\n",
      "val_loss = 1.7986319601073746,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.027758270035729626,   l1loss = 0.20449841585109158,   train acc = 0.993990957736969,\n",
      "val_loss = 1.8702633103658988,     val_acc = 0.5620155334472656\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.017600334982089717,   l1loss = 0.19607467192766603,   train acc = 0.9969955086708069,\n",
      "val_loss = 1.982220847015233,     val_acc = 0.5155038833618164\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.012600567348536457,   l1loss = 0.1878898390006828,   train acc = 0.998497724533081,\n",
      "val_loss = 2.2955358172153204,     val_acc = 0.4961240291595459\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.009947275661042126,   l1loss = 0.18061071683540306,   train acc = 1.0,\n",
      "val_loss = 1.9011228786882504,     val_acc = 0.5193798542022705\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(81)\n",
      "tensor(41)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354)]\n",
      "\t [tensor(0.5737), tensor(0.4652)]\n",
      "tensor(75)\n",
      "tensor(28)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365)]\n",
      "\t [tensor(0.9995), tensor(1.)]\n",
      "tensor(83)\n",
      "tensor(39)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354)]\n",
      "\t [tensor(0.6064), tensor(0.4652)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.695385),   val_acc = 0.3217054307460785\n",
      "validation acc increased (0.000000 ---> 0.321705)\n",
      "validation loss decreased (0.695385 ---> 0.694880),   val_acc = 0.3217054307460785\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 1: train loss = 0.6919325688781434,   l1loss = 1.4844660898029864,   train acc = 0.5353938341140747,\n",
      "val_loss = 0.696869459263114,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 2: train loss = 0.6903208250538302,   l1loss = 1.4645608618394923,   train acc = 0.5408773422241211,\n",
      "val_loss = 0.7094094993532166,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 3: train loss = 0.6888462312794398,   l1loss = 1.4412288264049253,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7203627150188121,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 4: train loss = 0.6883660529926791,   l1loss = 1.4121827829632898,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7268370288287023,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  5\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 5: train loss = 0.6879507299078069,   l1loss = 1.375367067509135,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7243463373923487,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  6\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 6: train loss = 0.6870695925424486,   l1loss = 1.3291421180232572,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7238615367763727,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  7\n",
      "validation acc increased (0.321705 ---> 0.321705)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 7: train loss = 0.6860224290835417,   l1loss = 1.271995098854704,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7287569512692533,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 8: train loss = 0.6840933574993138,   l1loss = 1.2031428917336202,   train acc = 0.5413758754730225,\n",
      "val_loss = 0.7273619147234185,     val_acc = 0.3217054307460785\n",
      "\n",
      "epoch:  9\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "validation acc increased (0.321705 ---> 0.321705)\n",
      "epoch 9: train loss = 0.6811107120746868,   l1loss = 1.1223840780058507,   train acc = 0.5488534569740295,\n",
      "val_loss = 0.7214035119197165,     val_acc = 0.3682170510292053\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.321705 ---> 0.368217)\n",
      "validation acc increased (0.368217 ---> 0.379845)\n",
      "epoch 10: train loss = 0.6758952574620575,   l1loss = 1.0303770931268619,   train acc = 0.5947158336639404,\n",
      "val_loss = 0.7813697923985563,     val_acc = 0.3178294599056244\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6630049520213963,   l1loss = 0.9292449082952673,   train acc = 0.6096709966659546,\n",
      "val_loss = 0.7966540109279544,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6346044056080393,   l1loss = 0.8222741206526637,   train acc = 0.6495513319969177,\n",
      "val_loss = 1.1256857214047926,     val_acc = 0.3178294599056244\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.5827583085266448,   l1loss = 0.7148247218559889,   train acc = 0.7008973360061646,\n",
      "val_loss = 0.9412916704665782,     val_acc = 0.38759690523147583\n",
      "\n",
      "epoch:  14\n",
      "validation acc increased (0.379845 ---> 0.395349)\n",
      "epoch 14: train loss = 0.521890408674242,   l1loss = 0.6116738263536189,   train acc = 0.7442672252655029,\n",
      "val_loss = 1.0655778175176576,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  15\n",
      "validation acc increased (0.395349 ---> 0.492248)\n",
      "epoch 15: train loss = 0.43515452614809913,   l1loss = 0.5178841115648701,   train acc = 0.8125622868537903,\n",
      "val_loss = 1.1385327799375666,     val_acc = 0.46124032139778137\n",
      "\n",
      "epoch:  16\n",
      "validation acc increased (0.492248 ---> 0.534884)\n",
      "epoch 16: train loss = 0.36243219033314483,   l1loss = 0.4364416953454821,   train acc = 0.8459621071815491,\n",
      "val_loss = 1.0747009594311085,     val_acc = 0.5426356792449951\n",
      "\n",
      "epoch:  17\n",
      "validation acc increased (0.534884 ---> 0.534884)\n",
      "validation acc increased (0.534884 ---> 0.577519)\n",
      "epoch 17: train loss = 0.26884116884599535,   l1loss = 0.37061460038362926,   train acc = 0.8918245434761047,\n",
      "val_loss = 1.05600640709086,     val_acc = 0.5503876209259033\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.22932734512022937,   l1loss = 0.320850499219457,   train acc = 0.9082751870155334,\n",
      "val_loss = 1.3345337708791096,     val_acc = 0.6085271239280701\n",
      "\n",
      "epoch:  19\n",
      "validation acc increased (0.577519 ---> 0.600775)\n",
      "validation acc increased (0.600775 ---> 0.643411)\n",
      "epoch 19: train loss = 0.19543913260830245,   l1loss = 0.28918363494150423,   train acc = 0.9252243041992188,\n",
      "val_loss = 1.4304973793887468,     val_acc = 0.6589147448539734\n",
      "\n",
      "epoch:  20\n",
      "validation acc increased (0.643411 ---> 0.662791)\n",
      "epoch 20: train loss = 0.12316853676366425,   l1loss = 0.27048087601409715,   train acc = 0.9526420831680298,\n",
      "val_loss = 1.3577455007290655,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.04757456736990129,   l1loss = 0.25799264823451473,   train acc = 0.989531397819519,\n",
      "val_loss = 1.6723881137463474,     val_acc = 0.565891444683075\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.02772095015389494,   l1loss = 0.24743169342592017,   train acc = 0.9950149655342102,\n",
      "val_loss = 1.8008276674564379,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.03146858667120976,   l1loss = 0.2371698537561021,   train acc = 0.9940179586410522,\n",
      "val_loss = 1.9554185228234575,     val_acc = 0.6124030947685242\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.10344059998587384,   l1loss = 0.23161495476218305,   train acc = 0.9561315774917603,\n",
      "val_loss = 1.9649058589495199,     val_acc = 0.538759708404541\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.1441766292168754,   l1loss = 0.22718537122694588,   train acc = 0.9411764740943909,\n",
      "val_loss = 1.2929859771284946,     val_acc = 0.5891472697257996\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.048959363251924515,   l1loss = 0.21805687045171515,   train acc = 0.9880359172821045,\n",
      "val_loss = 1.5774617324503817,     val_acc = 0.6124030947685242\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.012611901554244673,   l1loss = 0.20935222295559533,   train acc = 0.9995014667510986,\n",
      "val_loss = 1.885564662227335,     val_acc = 0.6201550364494324\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.006157807443116384,   l1loss = 0.19971955642624128,   train acc = 1.0,\n",
      "val_loss = 1.8642971059993314,     val_acc = 0.5852712988853455\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(51)\n",
      "tensor(19)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414)]\n",
      "tensor(80)\n",
      "tensor(34)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.)]\n",
      "tensor(76)\n",
      "tensor(36)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.699053),   val_acc = 0.3255814015865326\n",
      "validation acc increased (0.000000 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 1: train loss = 0.6915451809986757,   l1loss = 1.482684857348502,   train acc = 0.5393818616867065,\n",
      "val_loss = 0.7069418707559275,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 2: train loss = 0.6905436350602809,   l1loss = 1.4623768342694161,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7145971189173617,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 3: train loss = 0.6898230171631483,   l1loss = 1.438654139533951,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7176525223162747,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 4: train loss = 0.6894210968036595,   l1loss = 1.4091600701435731,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7221627840700076,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  5\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 5: train loss = 0.688976728904283,   l1loss = 1.3717759866657429,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7216660745384157,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  6\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 6: train loss = 0.6885826580545839,   l1loss = 1.324531928254505,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7219718865645949,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  7\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 7: train loss = 0.6881202621569306,   l1loss = 1.26606709008678,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7203695473744888,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 8: train loss = 0.6871786932883448,   l1loss = 1.1954490566776614,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7235223895819612,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 9: train loss = 0.6859321622168673,   l1loss = 1.1123363999046334,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.7276398294655851,     val_acc = 0.3255814015865326\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "validation acc increased (0.325581 ---> 0.325581)\n",
      "epoch 10: train loss = 0.6838702996849182,   l1loss = 1.0172734350887158,   train acc = 0.5403788685798645,\n",
      "val_loss = 0.728483495324157,     val_acc = 0.3294573724269867\n",
      "\n",
      "epoch:  11\n",
      "validation acc increased (0.325581 ---> 0.329457)\n",
      "validation loss decreased (0.699053 ---> 0.696120),   val_acc = 0.4883720874786377\n",
      "validation acc increased (0.329457 ---> 0.488372)\n",
      "epoch 11: train loss = 0.6797237210235709,   l1loss = 0.9119024760464015,   train acc = 0.5677965879440308,\n",
      "val_loss = 0.6912261903748032,     val_acc = 0.5077519416809082\n",
      "\n",
      "epoch:  12\n",
      "validation loss decreased (0.696120 ---> 0.692533),   val_acc = 0.5155038833618164\n",
      "validation acc increased (0.488372 ---> 0.515504)\n",
      "epoch 12: train loss = 0.6665826211660238,   l1loss = 0.7991669664710969,   train acc = 0.6091724634170532,\n",
      "val_loss = 0.7750974052636198,     val_acc = 0.3527131676673889\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.6396277128521015,   l1loss = 0.6842512581664567,   train acc = 0.6535393595695496,\n",
      "val_loss = 0.637429671694142,     val_acc = 0.6472868323326111\n",
      "\n",
      "epoch:  14\n",
      "validation loss decreased (0.692533 ---> 0.639330),   val_acc = 0.6666666865348816\n",
      "validation acc increased (0.515504 ---> 0.666667)\n",
      "epoch 14: train loss = 0.5922036307640114,   l1loss = 0.5737616670571438,   train acc = 0.6919242143630981,\n",
      "val_loss = 0.7422484321187633,     val_acc = 0.5465116500854492\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.4942521263927905,   l1loss = 0.4735739782586293,   train acc = 0.776669979095459,\n",
      "val_loss = 0.8247309513101282,     val_acc = 0.6472868323326111\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.4356083417402307,   l1loss = 0.38790225777645054,   train acc = 0.7911266088485718,\n",
      "val_loss = 0.8231048981348673,     val_acc = 0.6317829489707947\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.3488304465563442,   l1loss = 0.3178104176181382,   train acc = 0.8534396886825562,\n",
      "val_loss = 0.9343741550001987,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.2602038436670484,   l1loss = 0.2649042825934181,   train acc = 0.8983050584793091,\n",
      "val_loss = 1.105872887511586,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.16879668974448533,   l1loss = 0.2346440425898475,   train acc = 0.9421734809875488,\n",
      "val_loss = 1.0993869045908138,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.12419395824729029,   l1loss = 0.22091067336492262,   train acc = 0.9596211314201355,\n",
      "val_loss = 1.4720232468242793,     val_acc = 0.6317829489707947\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.1183394953786792,   l1loss = 0.20997104164610356,   train acc = 0.9586241245269775,\n",
      "val_loss = 1.61035810514938,     val_acc = 0.6124030947685242\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.048374136068588,   l1loss = 0.20068507425390475,   train acc = 0.9885343909263611,\n",
      "val_loss = 1.7387195844749213,     val_acc = 0.6395348906517029\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.02681651372362229,   l1loss = 0.19265636617497933,   train acc = 0.9950149655342102,\n",
      "val_loss = 1.9591693790384042,     val_acc = 0.6356589198112488\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.12268258048851018,   l1loss = 0.19024286480808067,   train acc = 0.9506480693817139,\n",
      "val_loss = 1.5421607235605403,     val_acc = 0.6007751822471619\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.12608384469511028,   l1loss = 0.1905509570422225,   train acc = 0.9526420831680298,\n",
      "val_loss = 1.6125494640089513,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.04950928593338546,   l1loss = 0.1808391643366809,   train acc = 0.9845463633537292,\n",
      "val_loss = 1.5583655085674553,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.023618471229324492,   l1loss = 0.1748058880611526,   train acc = 0.9955134391784668,\n",
      "val_loss = 1.6373267991598262,     val_acc = 0.6317829489707947\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.009286384627497861,   l1loss = 0.16702499122526923,   train acc = 0.9995014667510986,\n",
      "val_loss = 1.7560179769530777,     val_acc = 0.5736433863639832\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.004661366911762852,   l1loss = 0.1595693781808509,   train acc = 1.0,\n",
      "val_loss = 1.8593065498411194,     val_acc = 0.5736433863639832\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(77)\n",
      "tensor(40)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115)]\n",
      "tensor(67)\n",
      "tensor(39)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.)]\n",
      "tensor(72)\n",
      "tensor(45)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.680743),   val_acc = 0.5930232405662537\n",
      "validation acc increased (0.000000 ---> 0.593023)\n",
      "validation acc increased (0.593023 ---> 0.593023)\n",
      "epoch 1: train loss = 0.6970352851473715,   l1loss = 1.4848045129920133,   train acc = 0.4750629663467407,\n",
      "val_loss = 0.6833919952082079,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.593023 ---> 0.593023)\n",
      "validation acc increased (0.593023 ---> 0.593023)\n",
      "epoch 2: train loss = 0.6939090573517441,   l1loss = 1.4651734015202942,   train acc = 0.49068009853363037,\n",
      "val_loss = 0.6874888668688692,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.593023 ---> 0.593023)\n",
      "epoch 3: train loss = 0.6920081238902787,   l1loss = 1.4423043520084255,   train acc = 0.5219143629074097,\n",
      "val_loss = 0.6943495569303054,     val_acc = 0.46124032139778137\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6908973218511875,   l1loss = 1.4135601722323323,   train acc = 0.5249370336532593,\n",
      "val_loss = 0.7015382374903952,     val_acc = 0.40697672963142395\n",
      "\n",
      "epoch:  5\n",
      "epoch 5: train loss = 0.6905066842095978,   l1loss = 1.37683726215843,   train acc = 0.5249370336532593,\n",
      "val_loss = 0.7027538501939108,     val_acc = 0.40697672963142395\n",
      "\n",
      "epoch:  6\n",
      "epoch 6: train loss = 0.689899053081157,   l1loss = 1.33054918318011,   train acc = 0.5254408121109009,\n",
      "val_loss = 0.7039248813954435,     val_acc = 0.40697672963142395\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.6889980239291635,   l1loss = 1.2732717847343655,   train acc = 0.5254408121109009,\n",
      "val_loss = 0.7034929233003956,     val_acc = 0.40697672963142395\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.6880159931158839,   l1loss = 1.2041282832172115,   train acc = 0.5471032857894897,\n",
      "val_loss = 0.7000225662261017,     val_acc = 0.43410852551460266\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6862827549953605,   l1loss = 1.1230295440712263,   train acc = 0.5496221780776978,\n",
      "val_loss = 0.7079056478285974,     val_acc = 0.41860464215278625\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.6821412534197272,   l1loss = 1.0302875974916992,   train acc = 0.575818657875061,\n",
      "val_loss = 0.7016733090082804,     val_acc = 0.4728682041168213\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6741724207058962,   l1loss = 0.9277493146564858,   train acc = 0.6120907068252563,\n",
      "val_loss = 0.7347481019737184,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6619942795419573,   l1loss = 0.8190199558020239,   train acc = 0.6171284914016724,\n",
      "val_loss = 0.7454949183057445,     val_acc = 0.4728682041168213\n",
      "\n",
      "epoch:  13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 13: train loss = 0.637912023097502,   l1loss = 0.7075121821023955,   train acc = 0.6392946839332581,\n",
      "val_loss = 0.7158694054729254,     val_acc = 0.4922480583190918\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.606015481966869,   l1loss = 0.5979608490425034,   train acc = 0.6806045174598694,\n",
      "val_loss = 0.7537827140601107,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5577827427189056,   l1loss = 0.49493083063541193,   train acc = 0.7193954586982727,\n",
      "val_loss = 0.7263471793758777,     val_acc = 0.565891444683075\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.4718546643965791,   l1loss = 0.40398407203124215,   train acc = 0.7874055504798889,\n",
      "val_loss = 1.424316991669263,     val_acc = 0.41860464215278625\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.3839408636843828,   l1loss = 0.3292035478038211,   train acc = 0.8372796177864075,\n",
      "val_loss = 1.0002119476481002,     val_acc = 0.5193798542022705\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.3173008106217276,   l1loss = 0.27490125777439145,   train acc = 0.8755667209625244,\n",
      "val_loss = 1.1687451869018317,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.16879188284765864,   l1loss = 0.2434915311315498,   train acc = 0.9465994834899902,\n",
      "val_loss = 1.2804822773896447,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.13567791892689482,   l1loss = 0.2295223989249477,   train acc = 0.9491183757781982,\n",
      "val_loss = 1.385039623393569,     val_acc = 0.5620155334472656\n",
      "\n",
      "epoch:  21\n",
      "validation acc increased (0.593023 ---> 0.624031)\n",
      "epoch 21: train loss = 0.13815371947789973,   l1loss = 0.22059971798277023,   train acc = 0.9465994834899902,\n",
      "val_loss = 2.150006371875142,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.10025845674858885,   l1loss = 0.21158002366497175,   train acc = 0.9622166156768799,\n",
      "val_loss = 1.5413802104402883,     val_acc = 0.5426356792449951\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.030761046739534406,   l1loss = 0.2031533922371396,   train acc = 0.9939546585083008,\n",
      "val_loss = 1.933851578438929,     val_acc = 0.5891472697257996\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.012612140763748052,   l1loss = 0.19433939178434367,   train acc = 0.9994962215423584,\n",
      "val_loss = 1.86677174789961,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.007508058874616845,   l1loss = 0.1846848609315358,   train acc = 1.0,\n",
      "val_loss = 1.7752613688624181,     val_acc = 0.5930232405662537\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(80)\n",
      "tensor(41)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751)]\n",
      "tensor(64)\n",
      "tensor(36)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.)]\n",
      "tensor(75)\n",
      "tensor(32)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.720092),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.000000 ---> 0.372093)\n",
      "validation loss decreased (0.720092 ---> 0.713030),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 1: train loss = 0.6914953364651564,   l1loss = 1.4853989268497096,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7110218992528989,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  2\n",
      "validation loss decreased (0.713030 ---> 0.710726),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation loss decreased (0.710726 ---> 0.709240),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 2: train loss = 0.6908083540745221,   l1loss = 1.4647457792144363,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7090809151183727,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  3\n",
      "validation loss decreased (0.709240 ---> 0.709072),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation loss decreased (0.709072 ---> 0.709056),   val_acc = 0.3720930218696594\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 3: train loss = 0.6904735120990451,   l1loss = 1.4407883356423412,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7095573918763981,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 4: train loss = 0.6901337791947927,   l1loss = 1.4112883624485288,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7101693338201952,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  5\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 5: train loss = 0.6895435992310733,   l1loss = 1.3740417406097458,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7115494068278823,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  6\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 6: train loss = 0.6889353504510916,   l1loss = 1.3271373138504259,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7145411603210509,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  7\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 7: train loss = 0.6881078454771395,   l1loss = 1.2690683422739073,   train acc = 0.5315948128700256,\n",
      "val_loss = 0.7155752542407013,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "validation loss decreased (0.709056 ---> 0.707930),   val_acc = 0.3682170510292053\n",
      "epoch 8: train loss = 0.6867945939152984,   l1loss = 1.198984569627997,   train acc = 0.5461384057998657,\n",
      "val_loss = 0.7080177447592565,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  9\n",
      "validation acc increased (0.372093 ---> 0.372093)\n",
      "epoch 9: train loss = 0.6847166879963851,   l1loss = 1.1167998019766545,   train acc = 0.5511534810066223,\n",
      "val_loss = 0.715700043726337,     val_acc = 0.41472867131233215\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.372093 ---> 0.414729)\n",
      "epoch 10: train loss = 0.6799348440906827,   l1loss = 1.0231370177407682,   train acc = 0.5797392129898071,\n",
      "val_loss = 0.7157923727072486,     val_acc = 0.41860464215278625\n",
      "\n",
      "epoch:  11\n",
      "validation acc increased (0.414729 ---> 0.449612)\n",
      "validation loss decreased (0.707930 ---> 0.696232),   val_acc = 0.5116279125213623\n",
      "validation acc increased (0.449612 ---> 0.511628)\n",
      "epoch 11: train loss = 0.672254242533547,   l1loss = 0.9196460943523357,   train acc = 0.6078234910964966,\n",
      "val_loss = 0.7157423847405485,     val_acc = 0.45348837971687317\n",
      "\n",
      "epoch:  12\n",
      "validation acc increased (0.511628 ---> 0.515504)\n",
      "epoch 12: train loss = 0.6543741083312536,   l1loss = 0.8098590250240046,   train acc = 0.6424272656440735,\n",
      "val_loss = 0.8028264553971993,     val_acc = 0.43410852551460266\n",
      "\n",
      "epoch:  13\n",
      "validation acc increased (0.515504 ---> 0.554264)\n",
      "epoch 13: train loss = 0.6407015342769795,   l1loss = 0.6978255600006196,   train acc = 0.6399197578430176,\n",
      "val_loss = 0.7011320503183114,     val_acc = 0.6085271239280701\n",
      "\n",
      "epoch:  14\n",
      "validation acc increased (0.554264 ---> 0.558140)\n",
      "validation acc increased (0.558140 ---> 0.600775)\n",
      "epoch 14: train loss = 0.5929560238045221,   l1loss = 0.5874572954660912,   train acc = 0.6965897679328918,\n",
      "val_loss = 0.7719989308091098,     val_acc = 0.5\n",
      "\n",
      "epoch:  15\n",
      "validation acc increased (0.600775 ---> 0.616279)\n",
      "epoch 15: train loss = 0.5356296086263513,   l1loss = 0.4843397445700233,   train acc = 0.7311936020851135,\n",
      "val_loss = 1.2483898142511531,     val_acc = 0.3798449635505676\n",
      "\n",
      "epoch:  16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 16: train loss = 0.4664823236831332,   l1loss = 0.39368396988484183,   train acc = 0.7918756008148193,\n",
      "val_loss = 0.9508252638254979,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.3485482805889612,   l1loss = 0.32071299114102947,   train acc = 0.8515546917915344,\n",
      "val_loss = 1.4121340199034342,     val_acc = 0.43023255467414856\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.2641293948428442,   l1loss = 0.26571550107408787,   train acc = 0.8966900706291199,\n",
      "val_loss = 2.8454202820164287,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.2165779782872023,   l1loss = 0.23378948680496026,   train acc = 0.9132397174835205,\n",
      "val_loss = 1.407352695169375,     val_acc = 0.5193798542022705\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.14256087192890038,   l1loss = 0.21813757950766993,   train acc = 0.9538615942001343,\n",
      "val_loss = 1.7007370909979178,     val_acc = 0.4883720874786377\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.0572467178868554,   l1loss = 0.20835738851050795,   train acc = 0.9869608879089355,\n",
      "val_loss = 1.6843582021086947,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.03993170134117874,   l1loss = 0.20134628987838415,   train acc = 0.9904714226722717,\n",
      "val_loss = 1.7536737527770474,     val_acc = 0.565891444683075\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.037888755216641555,   l1loss = 0.19423958542593264,   train acc = 0.9899699091911316,\n",
      "val_loss = 1.9527307499286741,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.05171581631976599,   l1loss = 0.18909807017478444,   train acc = 0.9849548935890198,\n",
      "val_loss = 2.5161986521972244,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.07914294568875967,   l1loss = 0.18467072341422977,   train acc = 0.9719157218933105,\n",
      "val_loss = 3.2461071727190944,     val_acc = 0.45348837971687317\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.057620100116538425,   l1loss = 0.18046283077751263,   train acc = 0.9789367914199829,\n",
      "val_loss = 2.2363041315891947,     val_acc = 0.6279069781303406\n",
      "\n",
      "epoch:  27\n",
      "validation acc increased (0.616279 ---> 0.624031)\n",
      "epoch 27: train loss = 0.035453362097083746,   l1loss = 0.17314500579145273,   train acc = 0.9899699091911316,\n",
      "val_loss = 2.3932818331459695,     val_acc = 0.5116279125213623\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.017172363360665566,   l1loss = 0.1665714439621182,   train acc = 0.9964894652366638,\n",
      "val_loss = 2.209194153778313,     val_acc = 0.5736433863639832\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.010634527754677514,   l1loss = 0.16049564522929272,   train acc = 0.9989969730377197,\n",
      "val_loss = 2.0558686441229295,     val_acc = 0.5116279125213623\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.005607646728614523,   l1loss = 0.15422261991316719,   train acc = 1.0,\n",
      "val_loss = 2.0884801685348036,     val_acc = 0.5581395626068115\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(65)\n",
      "tensor(33)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335), tensor(0.5131)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751), tensor(0.5817)]\n",
      "tensor(73)\n",
      "tensor(35)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236), tensor(0.5654)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(1.)]\n",
      "tensor(77)\n",
      "tensor(43)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602), tensor(0.6283)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521), tensor(0.7914)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.680365),   val_acc = 0.643410861492157\n",
      "validation acc increased (0.000000 ---> 0.643411)\n",
      "validation acc increased (0.643411 ---> 0.643411)\n",
      "epoch 1: train loss = 0.6937421182204987,   l1loss = 1.4836181387297804,   train acc = 0.48824411630630493,\n",
      "val_loss = 0.6849656885908556,     val_acc = 0.643410861492157\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.643411 ---> 0.643411)\n",
      "epoch 2: train loss = 0.6914882965538727,   l1loss = 1.46388185632533,   train acc = 0.5357679128646851,\n",
      "val_loss = 0.6942343249801517,     val_acc = 0.44573643803596497\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.6897589440581917,   l1loss = 1.4407587112218754,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7051206457522489,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.689329623609498,   l1loss = 1.4119728003340164,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7143772563268972,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  5\n",
      "epoch 5: train loss = 0.6887832490905277,   l1loss = 1.375555465255993,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7188024733417718,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  6\n",
      "epoch 6: train loss = 0.6878896750409106,   l1loss = 1.3297643308463007,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7172646388527035,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.6867665288864583,   l1loss = 1.273135135625827,   train acc = 0.5357679128646851,\n",
      "val_loss = 0.7201978106831395,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.685014221297317,   l1loss = 1.2046409373047233,   train acc = 0.5402701497077942,\n",
      "val_loss = 0.7213430737340173,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.681378725679473,   l1loss = 1.1243868157409203,   train acc = 0.5477738976478577,\n",
      "val_loss = 0.73843981591306,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.6753599447808067,   l1loss = 1.033160796071244,   train acc = 0.5892946720123291,\n",
      "val_loss = 0.7419206292130226,     val_acc = 0.3682170510292053\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6624713613129426,   l1loss = 0.9328325188654909,   train acc = 0.614307165145874,\n",
      "val_loss = 0.688416297583617,     val_acc = 0.5348837375640869\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6369260627070089,   l1loss = 0.8273587237005534,   train acc = 0.6518259048461914,\n",
      "val_loss = 0.7440652865772099,     val_acc = 0.4689922332763672\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.593051583365001,   l1loss = 0.7211097616622184,   train acc = 0.6943472027778625,\n",
      "val_loss = 0.7471253086430157,     val_acc = 0.6356589198112488\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5171202323387837,   l1loss = 0.619771498659362,   train acc = 0.751375675201416,\n",
      "val_loss = 0.8451099765393161,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.4243705461715805,   l1loss = 0.5292422390538731,   train acc = 0.8104051947593689,\n",
      "val_loss = 0.9671652009782865,     val_acc = 0.5348837375640869\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.3493039304164125,   l1loss = 0.45268860447400805,   train acc = 0.8559279441833496,\n",
      "val_loss = 1.1312974515811418,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.2992922716763331,   l1loss = 0.3898296642148417,   train acc = 0.8874437212944031,\n",
      "val_loss = 1.2580841289934261,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.17743351521821188,   l1loss = 0.3432618670310898,   train acc = 0.9384692311286926,\n",
      "val_loss = 1.4614967828573182,     val_acc = 0.45348837971687317\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.12819814724123674,   l1loss = 0.3091015477309291,   train acc = 0.9579789638519287,\n",
      "val_loss = 2.0725105788356575,     val_acc = 0.6395348906517029\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.14354813504794528,   l1loss = 0.2900429670067654,   train acc = 0.9454727172851562,\n",
      "val_loss = 2.0894346117049225,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.09740429143400386,   l1loss = 0.276254555369807,   train acc = 0.9649825096130371,\n",
      "val_loss = 1.6611481997393822,     val_acc = 0.5155038833618164\n",
      "\n",
      "epoch:  22\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 22: train loss = 0.03162910269686077,   l1loss = 0.2640630051963266,   train acc = 0.9929965138435364,\n",
      "val_loss = 2.8269113385400106,     val_acc = 0.41472867131233215\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.02073831153961794,   l1loss = 0.25253280331606626,   train acc = 0.9969984889030457,\n",
      "val_loss = 1.9658752079157866,     val_acc = 0.604651153087616\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.012647862177856524,   l1loss = 0.24088280176508123,   train acc = 0.9984992742538452,\n",
      "val_loss = 2.1244828877320816,     val_acc = 0.5348837375640869\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.006505623935028098,   l1loss = 0.2303655490748819,   train acc = 1.0,\n",
      "val_loss = 2.1552340097205582,     val_acc = 0.4961240291595459\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(80)\n",
      "tensor(42)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335), tensor(0.5131), tensor(0.6387)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751), tensor(0.5817), tensor(0.4647)]\n",
      "tensor(79)\n",
      "tensor(36)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236), tensor(0.5654), tensor(0.6021)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(0.9990)]\n",
      "tensor(81)\n",
      "tensor(41)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602), tensor(0.6283), tensor(0.6387)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521), tensor(0.7914), tensor(0.4647)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.704280),   val_acc = 0.356589138507843\n",
      "validation acc increased (0.000000 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 1: train loss = 0.6914031615908471,   l1loss = 1.4855487954324338,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7122278712516608,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 2: train loss = 0.6906723660370777,   l1loss = 1.4651722050476457,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7149748654328576,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 3: train loss = 0.6902823968193661,   l1loss = 1.4415272097995486,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7170244894286458,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  4\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 4: train loss = 0.6898218136897619,   l1loss = 1.412299975327935,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7177939595178117,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  5\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 5: train loss = 0.6893795061672015,   l1loss = 1.3753384496045744,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.715468819751296,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  6\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 6: train loss = 0.6886429281876408,   l1loss = 1.3287717812415538,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7151580522226733,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  7\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "epoch 7: train loss = 0.6878388487976631,   l1loss = 1.27108275150883,   train acc = 0.5352676510810852,\n",
      "val_loss = 0.7185324430465698,     val_acc = 0.356589138507843\n",
      "\n",
      "epoch:  8\n",
      "validation acc increased (0.356589 ---> 0.356589)\n",
      "validation acc increased (0.356589 ---> 0.375969)\n",
      "epoch 8: train loss = 0.687089113010771,   l1loss = 1.2014408532591567,   train acc = 0.5377689003944397,\n",
      "val_loss = 0.7098801186842512,     val_acc = 0.3759689927101135\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6857657482052756,   l1loss = 1.1198362330903764,   train acc = 0.5357679128646851,\n",
      "val_loss = 0.7146235921586207,     val_acc = 0.3720930218696594\n",
      "\n",
      "epoch:  10\n",
      "validation acc increased (0.375969 ---> 0.391473)\n",
      "validation loss decreased (0.704280 ---> 0.700316),   val_acc = 0.43410852551460266\n",
      "validation acc increased (0.391473 ---> 0.434109)\n",
      "epoch 10: train loss = 0.6807256672846311,   l1loss = 1.0265806999547653,   train acc = 0.5732866525650024,\n",
      "val_loss = 0.6975175391796024,     val_acc = 0.44573643803596497\n",
      "\n",
      "epoch:  11\n",
      "validation loss decreased (0.700316 ---> 0.692875),   val_acc = 0.4651162922382355\n",
      "validation acc increased (0.434109 ---> 0.465116)\n",
      "validation loss decreased (0.692875 ---> 0.687179),   val_acc = 0.5348837375640869\n",
      "validation acc increased (0.465116 ---> 0.534884)\n",
      "epoch 11: train loss = 0.673976903053568,   l1loss = 0.9234890513505979,   train acc = 0.5992996692657471,\n",
      "val_loss = 0.7138355277305426,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  12\n",
      "validation loss decreased (0.687179 ---> 0.655739),   val_acc = 0.6472868323326111\n",
      "validation acc increased (0.534884 ---> 0.647287)\n",
      "epoch 12: train loss = 0.6544435339727779,   l1loss = 0.8135775458878312,   train acc = 0.6203101277351379,\n",
      "val_loss = 0.8063760263513226,     val_acc = 0.38759690523147583\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.6309017556318347,   l1loss = 0.7015269837300738,   train acc = 0.639319658279419,\n",
      "val_loss = 0.8180261628572331,     val_acc = 0.42635658383369446\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.6293001123939292,   l1loss = 0.5926253258913621,   train acc = 0.6403201818466187,\n",
      "val_loss = 0.6947672367095947,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5548516084934366,   l1loss = 0.4896452505329718,   train acc = 0.7323662042617798,\n",
      "val_loss = 0.8717553079590317,     val_acc = 0.46124032139778137\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.4710256685430614,   l1loss = 0.3975880754745382,   train acc = 0.7793896794319153,\n",
      "val_loss = 0.8850018007810726,     val_acc = 0.6317829489707947\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.40750853099365003,   l1loss = 0.3214303405956366,   train acc = 0.8174086809158325,\n",
      "val_loss = 0.8342532101412152,     val_acc = 0.6007751822471619\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.33526081428222504,   l1loss = 0.2646906571411502,   train acc = 0.8569284677505493,\n",
      "val_loss = 1.3167366464008656,     val_acc = 0.4844961166381836\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.24304662869237792,   l1loss = 0.23158110821318661,   train acc = 0.9064532518386841,\n",
      "val_loss = 1.1697414286376895,     val_acc = 0.6472868323326111\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.20508646148034726,   l1loss = 0.21585142629602183,   train acc = 0.9144572019577026,\n",
      "val_loss = 1.3775318278822788,     val_acc = 0.6007751822471619\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.12821232205468336,   l1loss = 0.2060455378366745,   train acc = 0.9564782381057739,\n",
      "val_loss = 1.4499094604059708,     val_acc = 0.6162790656089783\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.05814009320071007,   l1loss = 0.19943478927873504,   train acc = 0.9834917187690735,\n",
      "val_loss = 1.5952766163404597,     val_acc = 0.5581395626068115\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.043333539148497247,   l1loss = 0.19244094265288506,   train acc = 0.9879940152168274,\n",
      "val_loss = 2.111579608518717,     val_acc = 0.5155038833618164\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.05830567527288434,   l1loss = 0.18697950598029509,   train acc = 0.980990469455719,\n",
      "val_loss = 1.6956204277600428,     val_acc = 0.5620155334472656\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.05772003486730147,   l1loss = 0.18347380734819838,   train acc = 0.9799900054931641,\n",
      "val_loss = 2.1974048300306928,     val_acc = 0.4961240291595459\n",
      "\n",
      "epoch:  26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 26: train loss = 0.10959840359062836,   l1loss = 0.18189016606969916,   train acc = 0.9599800109863281,\n",
      "val_loss = 2.2403581733851468,     val_acc = 0.5775193572044373\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.05633044383279409,   l1loss = 0.17777645592393726,   train acc = 0.9814907312393188,\n",
      "val_loss = 3.4118495135344276,     val_acc = 0.41085270047187805\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.04293046985380631,   l1loss = 0.17149413792624243,   train acc = 0.9854927659034729,\n",
      "val_loss = 2.1026175188463787,     val_acc = 0.5852712988853455\n",
      "\n",
      "epoch:  29\n",
      "epoch 29: train loss = 0.018007467739869203,   l1loss = 0.16545488913098594,   train acc = 0.994997501373291,\n",
      "val_loss = 1.9395856857299805,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.012381990377992959,   l1loss = 0.15917553348264557,   train acc = 0.9984992742538452,\n",
      "val_loss = 2.3071911690337945,     val_acc = 0.5116279125213623\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.00495839840150147,   l1loss = 0.15346068114772804,   train acc = 0.9994997382164001,\n",
      "val_loss = 2.027354578639186,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  32\n",
      "epoch 32: train loss = 0.006408099178271391,   l1loss = 0.14823033495537336,   train acc = 1.0,\n",
      "val_loss = 1.955235477565795,     val_acc = 0.569767415523529\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(78)\n",
      "tensor(43)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335), tensor(0.5131), tensor(0.6387), tensor(0.6335)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751), tensor(0.5817), tensor(0.4647), tensor(0.5123)]\n",
      "tensor(64)\n",
      "tensor(37)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236), tensor(0.5654), tensor(0.6021), tensor(0.5288)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(0.9990), tensor(1.)]\n",
      "tensor(74)\n",
      "tensor(47)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602), tensor(0.6283), tensor(0.6387), tensor(0.6335)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521), tensor(0.7914), tensor(0.4647), tensor(0.5123)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.688638),   val_acc = 0.6627907156944275\n",
      "validation acc increased (0.000000 ---> 0.662791)\n",
      "epoch 1: train loss = 0.6933125124244157,   l1loss = 1.483249993381386,   train acc = 0.5079840421676636,\n",
      "val_loss = 0.7031067840812742,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  2\n",
      "epoch 2: train loss = 0.6910889809003133,   l1loss = 1.4634534901011729,   train acc = 0.5394211411476135,\n",
      "val_loss = 0.7151631089143975,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  3\n",
      "epoch 3: train loss = 0.690039867650487,   l1loss = 1.4402860893222862,   train acc = 0.538922131061554,\n",
      "val_loss = 0.7211975704791934,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6893733679177518,   l1loss = 1.411496847451566,   train acc = 0.538922131061554,\n",
      "val_loss = 0.721796124942543,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  5\n",
      "epoch 5: train loss = 0.6888950928718506,   l1loss = 1.3750012099385975,   train acc = 0.538922131061554,\n",
      "val_loss = 0.7237857270610425,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  6\n",
      "epoch 6: train loss = 0.688418993335998,   l1loss = 1.328940457926539,   train acc = 0.538922131061554,\n",
      "val_loss = 0.7278173852336499,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.6875811150212012,   l1loss = 1.271831663068897,   train acc = 0.538922131061554,\n",
      "val_loss = 0.7228789542072503,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.6866033931454261,   l1loss = 1.2027254354454087,   train acc = 0.538922131061554,\n",
      "val_loss = 0.7285417683364809,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6847465848494433,   l1loss = 1.1213995715577207,   train acc = 0.5434131622314453,\n",
      "val_loss = 0.7351138185161029,     val_acc = 0.3372093141078949\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.6824056646543111,   l1loss = 1.0283273393046595,   train acc = 0.5508981943130493,\n",
      "val_loss = 0.7407340176345766,     val_acc = 0.3333333432674408\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6756234464055287,   l1loss = 0.9253893102000573,   train acc = 0.5743513107299805,\n",
      "val_loss = 0.7472086497979571,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6602505656059631,   l1loss = 0.8152883985561287,   train acc = 0.6162674427032471,\n",
      "val_loss = 0.7113825424697048,     val_acc = 0.4922480583190918\n",
      "\n",
      "epoch:  13\n",
      "validation loss decreased (0.688638 ---> 0.667874),   val_acc = 0.6550387740135193\n",
      "epoch 13: train loss = 0.6188409713927857,   l1loss = 0.704593446916211,   train acc = 0.6756486892700195,\n",
      "val_loss = 0.7028621758601462,     val_acc = 0.5930232405662537\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5674543769773609,   l1loss = 0.5986516261529066,   train acc = 0.7080838084220886,\n",
      "val_loss = 0.7258701370668041,     val_acc = 0.538759708404541\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.5195131490449467,   l1loss = 0.5008506833198304,   train acc = 0.7554890513420105,\n",
      "val_loss = 0.7875658929810043,     val_acc = 0.5310077667236328\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.4449116040370659,   l1loss = 0.4148543224244299,   train acc = 0.8003991842269897,\n",
      "val_loss = 0.9337220635525015,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.3717906239503872,   l1loss = 0.34227201402068375,   train acc = 0.8303393125534058,\n",
      "val_loss = 0.9147336353627287,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.2456365298487231,   l1loss = 0.2895531968442266,   train acc = 0.9016966223716736,\n",
      "val_loss = 1.224231436271076,     val_acc = 0.5193798542022705\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.2006593743365206,   l1loss = 0.25701168244946265,   train acc = 0.9211576581001282,\n",
      "val_loss = 1.278371164040972,     val_acc = 0.5\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.2456775011416681,   l1loss = 0.2422227506747027,   train acc = 0.8952096104621887,\n",
      "val_loss = 1.319511427435764,     val_acc = 0.6356589198112488\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.10676692857475814,   l1loss = 0.2299664880701168,   train acc = 0.9675648808479309,\n",
      "val_loss = 1.6988744661789532,     val_acc = 0.5038759708404541\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.06530988484085677,   l1loss = 0.2210731065558816,   train acc = 0.9795409440994263,\n",
      "val_loss = 1.772416866102884,     val_acc = 0.4806201457977295\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.16125833081033178,   l1loss = 0.21659656228895435,   train acc = 0.9346307516098022,\n",
      "val_loss = 2.660450824471407,     val_acc = 0.4767441749572754\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.052061744673761304,   l1loss = 0.2090652662063549,   train acc = 0.9890219569206238,\n",
      "val_loss = 1.6618810494740803,     val_acc = 0.5736433863639832\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.025476095487852533,   l1loss = 0.1998468009000291,   train acc = 0.9960079789161682,\n",
      "val_loss = 1.7238089602455036,     val_acc = 0.604651153087616\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.02256299738220112,   l1loss = 0.19226344292630215,   train acc = 0.9915169477462769,\n",
      "val_loss = 2.2160921032114547,     val_acc = 0.5232558250427246\n",
      "\n",
      "epoch:  27\n",
      "epoch 27: train loss = 0.0615687958464889,   l1loss = 0.18798830180230017,   train acc = 0.9765468835830688,\n",
      "val_loss = 2.2735826359238733,     val_acc = 0.6124030947685242\n",
      "\n",
      "epoch:  28\n",
      "epoch 28: train loss = 0.22541590647664136,   l1loss = 0.19491120203288492,   train acc = 0.9036926031112671,\n",
      "val_loss = 1.4376536790252656,     val_acc = 0.4728682041168213\n",
      "\n",
      "epoch:  29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 29: train loss = 0.0631909598698635,   l1loss = 0.1892870161050332,   train acc = 0.9780439138412476,\n",
      "val_loss = 2.1181586296983466,     val_acc = 0.4728682041168213\n",
      "\n",
      "epoch:  30\n",
      "epoch 30: train loss = 0.01965019029905339,   l1loss = 0.17764762243229948,   train acc = 0.9970059990882874,\n",
      "val_loss = 1.861120948495791,     val_acc = 0.5465116500854492\n",
      "\n",
      "epoch:  31\n",
      "epoch 31: train loss = 0.008150098203103282,   l1loss = 0.16982665143446057,   train acc = 1.0,\n",
      "val_loss = 1.9675745483516722,     val_acc = 0.538759708404541\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(80)\n",
      "tensor(42)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335), tensor(0.5131), tensor(0.6387), tensor(0.6335), tensor(0.6387)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751), tensor(0.5817), tensor(0.4647), tensor(0.5123), tensor(0.4775)]\n",
      "tensor(80)\n",
      "tensor(33)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236), tensor(0.5654), tensor(0.6021), tensor(0.5288), tensor(0.5916)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(0.9990), tensor(1.), tensor(0.9990)]\n",
      "tensor(87)\n",
      "tensor(35)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602), tensor(0.6283), tensor(0.6387), tensor(0.6335), tensor(0.6387)]\n",
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521), tensor(0.7914), tensor(0.4647), tensor(0.5123), tensor(0.4611)]\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",
      "coef when 0 > 1 1\n",
      "creating model...\n",
      "calculating total steps...\n",
      "epoch:  1\n",
      "validation loss decreased (inf ---> 0.672692),   val_acc = 0.6550387740135193\n",
      "validation acc increased (0.000000 ---> 0.655039)\n",
      "validation acc increased (0.655039 ---> 0.655039)\n",
      "epoch 1: train loss = 0.6991829303475646,   l1loss = 1.4839057058959335,   train acc = 0.4625374674797058,\n",
      "val_loss = 0.6754856922829798,     val_acc = 0.6550387740135193\n",
      "\n",
      "epoch:  2\n",
      "validation acc increased (0.655039 ---> 0.655039)\n",
      "validation acc increased (0.655039 ---> 0.655039)\n",
      "epoch 2: train loss = 0.6939829035357876,   l1loss = 1.4645228752723107,   train acc = 0.49450549483299255,\n",
      "val_loss = 0.6890089049819828,     val_acc = 0.6550387740135193\n",
      "\n",
      "epoch:  3\n",
      "validation acc increased (0.655039 ---> 0.658915)\n",
      "epoch 3: train loss = 0.6903928536754269,   l1loss = 1.4417118324742808,   train acc = 0.5394605398178101,\n",
      "val_loss = 0.7046850910482481,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  4\n",
      "epoch 4: train loss = 0.6891815625466071,   l1loss = 1.4131007423172226,   train acc = 0.5374625325202942,\n",
      "val_loss = 0.719056154406348,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  5\n",
      "epoch 5: train loss = 0.6889630373898562,   l1loss = 1.376648348885459,   train acc = 0.5374625325202942,\n",
      "val_loss = 0.7238496870033501,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  6\n",
      "epoch 6: train loss = 0.6881177404543737,   l1loss = 1.3303997759576087,   train acc = 0.5374625325202942,\n",
      "val_loss = 0.7200313155041185,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  7\n",
      "epoch 7: train loss = 0.6869667621401997,   l1loss = 1.2731195258332062,   train acc = 0.5374625325202942,\n",
      "val_loss = 0.7144513324249623,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  8\n",
      "epoch 8: train loss = 0.6860746289347555,   l1loss = 1.2041123083659582,   train acc = 0.546953022480011,\n",
      "val_loss = 0.7131708472274071,     val_acc = 0.3488371968269348\n",
      "\n",
      "epoch:  9\n",
      "epoch 9: train loss = 0.6840823413608791,   l1loss = 1.1235749534555486,   train acc = 0.5389610528945923,\n",
      "val_loss = 0.7343198874200038,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  10\n",
      "epoch 10: train loss = 0.6786974231441776,   l1loss = 1.03154473943072,   train acc = 0.580919086933136,\n",
      "val_loss = 0.7059320497882459,     val_acc = 0.44961240887641907\n",
      "\n",
      "epoch:  11\n",
      "epoch 11: train loss = 0.6691220754629129,   l1loss = 0.9298823592546103,   train acc = 0.6143856048583984,\n",
      "val_loss = 0.8592184712720472,     val_acc = 0.3449612259864807\n",
      "\n",
      "epoch:  12\n",
      "epoch 12: train loss = 0.6548706972515667,   l1loss = 0.8217827181716065,   train acc = 0.6238760948181152,\n",
      "val_loss = 0.7483981838522031,     val_acc = 0.43798449635505676\n",
      "\n",
      "epoch:  13\n",
      "epoch 13: train loss = 0.6237266789544951,   l1loss = 0.7114838892882401,   train acc = 0.6543456315994263,\n",
      "val_loss = 0.9002822369568108,     val_acc = 0.3759689927101135\n",
      "\n",
      "epoch:  14\n",
      "epoch 14: train loss = 0.5636290751851641,   l1loss = 0.6049504718342266,   train acc = 0.726773202419281,\n",
      "val_loss = 0.7929284221442171,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  15\n",
      "epoch 15: train loss = 0.500030604811696,   l1loss = 0.5065306650472807,   train acc = 0.7647352814674377,\n",
      "val_loss = 0.8221829214761424,     val_acc = 0.5426356792449951\n",
      "\n",
      "epoch:  16\n",
      "epoch 16: train loss = 0.432999442179839,   l1loss = 0.4196841673893886,   train acc = 0.8066933155059814,\n",
      "val_loss = 0.9935263626335203,     val_acc = 0.6589147448539734\n",
      "\n",
      "epoch:  17\n",
      "epoch 17: train loss = 0.3120971677246151,   l1loss = 0.34845078170120897,   train acc = 0.8761239051818848,\n",
      "val_loss = 1.0305215822633846,     val_acc = 0.643410861492157\n",
      "\n",
      "epoch:  18\n",
      "epoch 18: train loss = 0.21123728623518814,   l1loss = 0.2952757461683138,   train acc = 0.9240759015083313,\n",
      "val_loss = 1.2388130657432614,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  19\n",
      "epoch 19: train loss = 0.1134848499021211,   l1loss = 0.2615779391475967,   train acc = 0.9630369544029236,\n",
      "val_loss = 1.480191317177558,     val_acc = 0.5891472697257996\n",
      "\n",
      "epoch:  20\n",
      "epoch 20: train loss = 0.127544645990406,   l1loss = 0.24558655981655483,   train acc = 0.9500499367713928,\n",
      "val_loss = 1.6672501929299257,     val_acc = 0.5813953280448914\n",
      "\n",
      "epoch:  21\n",
      "epoch 21: train loss = 0.07767527621168714,   l1loss = 0.23245742854538498,   train acc = 0.9760239720344543,\n",
      "val_loss = 1.5230142534241196,     val_acc = 0.6007751822471619\n",
      "\n",
      "epoch:  22\n",
      "epoch 22: train loss = 0.07988126125636992,   l1loss = 0.22322249095458965,   train acc = 0.9710289835929871,\n",
      "val_loss = 1.4321563678194387,     val_acc = 0.5968992114067078\n",
      "\n",
      "epoch:  23\n",
      "epoch 23: train loss = 0.04452426001228593,   l1loss = 0.213434277267961,   train acc = 0.9900099635124207,\n",
      "val_loss = 1.7915307799527465,     val_acc = 0.643410861492157\n",
      "\n",
      "epoch:  24\n",
      "epoch 24: train loss = 0.028254755680541414,   l1loss = 0.20372207654820573,   train acc = 0.9940059781074524,\n",
      "val_loss = 1.940356590951136,     val_acc = 0.604651153087616\n",
      "\n",
      "epoch:  25\n",
      "epoch 25: train loss = 0.01316713175264659,   l1loss = 0.19553671531624847,   train acc = 0.9985014796257019,\n",
      "val_loss = 1.7937852486159451,     val_acc = 0.569767415523529\n",
      "\n",
      "epoch:  26\n",
      "epoch 26: train loss = 0.005751309507469703,   l1loss = 0.18567656404845842,   train acc = 1.0,\n",
      "val_loss = 1.7987251420353734,     val_acc = 0.604651153087616\n",
      "\n",
      "!!! overfitted !!!\n",
      "tensor(80)\n",
      "tensor(42)\n",
      "early stoping results:\n",
      "\t [tensor(0.5833), tensor(0.6354), tensor(0.3646), tensor(0.6126), tensor(0.6335), tensor(0.5131), tensor(0.6387), tensor(0.6335), tensor(0.6387), tensor(0.6387)]\n",
      "\t [tensor(0.5737), tensor(0.4652), tensor(0.5414), tensor(0.5115), tensor(0.4751), tensor(0.5817), tensor(0.4647), tensor(0.5123), tensor(0.4775), tensor(0.4625)]\n",
      "tensor(75)\n",
      "tensor(28)\n",
      "full train results:\n",
      "\t [tensor(0.5312), tensor(0.5365), tensor(0.5938), tensor(0.5550), tensor(0.5236), tensor(0.5654), tensor(0.6021), tensor(0.5288), tensor(0.5916), tensor(0.5393)]\n",
      "\t [tensor(0.9995), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(1.), tensor(0.9990), tensor(1.), tensor(0.9990), tensor(1.)]\n",
      "tensor(85)\n",
      "tensor(39)\n",
      "best accs results:\n",
      "\t [tensor(0.6042), tensor(0.6354), tensor(0.5833), tensor(0.6126), tensor(0.5602), tensor(0.6283), tensor(0.6387), tensor(0.6335), tensor(0.6387), tensor(0.6492)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t [tensor(0.6064), tensor(0.4652), tensor(0.6461), tensor(0.5115), tensor(0.9521), tensor(0.7914), tensor(0.4647), tensor(0.5123), tensor(0.4611), tensor(0.4670)]\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(dataset, labels)):\n",
    "    \n",
    "    print('-----------------------------Fold {}---------------'.format(fold + 1))\n",
    "\n",
    "    \n",
    "    print('preparing dataloaders...')\n",
    "    train_val_data = np.stack([dataset[index] for index in train_val_idx])\n",
    "    train_val_label = [labels[index] for index in train_val_idx]\n",
    "    test_data = np.stack([dataset[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=(0,1,2,4), keepdims=True)\n",
    "    Min = np.min(train_val_data, axis=(0,1,2,4), keepdims=True)\n",
    "    train_val_data = (train_val_data-Min)/(Max-Min)\n",
    "    \n",
    "    Max_test = np.max(test_data, axis=(0,1,2,4), keepdims=True)\n",
    "    Min_test = np.min(test_data, axis=(0,1,2,4), 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_RNN().float()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "    criterion = nn.CrossEntropyLoss()\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",
    "            targets = sum(torch.argmax(output,dim=1)==label)\n",
    "            train_correct += 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",
    "                    targets = sum(torch.argmax(output,dim=1)==label)\n",
    "                    val_correct += 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(), 'captum/nasal/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(), 'captum/nasal/model'+str(fold)+'_acc.pt')\n",
    "                    max_val_acc = val_acc\n",
    "                torch.save(model.state_dict(), 'captum/nasal/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",
    "            targets = sum(torch.argmax(output,dim=1)==label)\n",
    "            val_correct += 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_RNN().float()\n",
    "    model.load_state_dict(torch.load('captum/nasal/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",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        print(targets)\n",
    "        n_correct += 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",
    "        #targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        n_correct += targets\n",
    "        \n",
    "    train_accs.append(n_correct/len(train_loader.sampler))\n",
    "    print('\\t', train_accs)\n",
    "    \n",
    "    model = CNN_RNN().float()\n",
    "    model.load_state_dict(torch.load('captum/nasal/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",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        print(targets)\n",
    "        n_correct += 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",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        n_correct += targets\n",
    "    train_accs_over.append(n_correct/len(train_loader.sampler))\n",
    "    print('\\t', train_accs_over)\n",
    "    \n",
    "    model = CNN_RNN().float()\n",
    "    model.load_state_dict(torch.load('captum/nasal/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",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        print(targets)\n",
    "        n_correct += 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",
    "        targets = sum(torch.argmax(output,dim=1)==label)\n",
    "        n_correct += 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": 16,
   "id": "93a5fc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8107657068062828"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum(test_accs_acc))/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74a75907",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_accs, test_accs = [], []\n",
    "train_accs_over, test_accs_over = [], []\n",
    "train_accs_acc, test_accs_acc = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "078e125c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8020833333333334,\n",
       " 0.8177083333333334,\n",
       " 0.8177083333333334,\n",
       " 0.8272251308900523,\n",
       " 0.7958115183246073,\n",
       " 0.8219895287958116,\n",
       " 0.8167539267015707,\n",
       " 0.8167539267015707,\n",
       " 0.806282722513089,\n",
       " 0.7853403141361257]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_accs_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5abb4609",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5518dc3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62e3322",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "91c032f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6354166666666667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - sum(y__test)/len(y__test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d49fc26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "+ l1_lambda*(sum(torch.linalg.norm(p, 1) for p in model.parameters()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c7cbed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8a75e16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "c02888f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN_RNN(\n",
      "  (conv1): Conv2d(10, 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",
      "  (batchnorm): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (lstm): LSTM(256, 128, num_layers=2, batch_first=True)\n",
      "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saeed\\AppData\\Local\\Temp\\ipykernel_16720\\3694016727.py:7: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
      "  torch.nn.init.xavier_normal(self.conv1.weight)\n",
      "C:\\Users\\saeed\\AppData\\Local\\Temp\\ipykernel_16720\\3694016727.py:10: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
      "  torch.nn.init.xavier_normal(self.conv2.weight)\n",
      "C:\\Users\\saeed\\AppData\\Local\\Temp\\ipykernel_16720\\3694016727.py:14: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
      "  torch.nn.init.xavier_normal(self.fc.weight)\n"
     ]
    }
   ],
   "source": [
    "model = model = CNN_RNN().float()\n",
    "model.load_state_dict(torch.load('train/fc1_10_20_11_vowel/model.pt'))\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "fd9bd508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7864583333333334\n"
     ]
    }
   ],
   "source": [
    "test_loss = 0\n",
    "n_correct = 0\n",
    "\n",
    "model.eval()\n",
    "for data, label in test_loader:\n",
    "    output = model(data.float())\n",
    "    pred = torch.argmax(output, dim=1)\n",
    "    n_correct += torch.sum(pred==label).item()\n",
    "test_acc = n_correct/len(test_loader.sampler)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c7fd86ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3645833333333333\n"
     ]
    }
   ],
   "source": [
    "print(sum(test_label)/len(test_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20273a2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "037040c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "99ab9dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5189a944",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "354e419a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2, 6, 3, 5, 1]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55070b74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9174a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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