SpaceTitanicV1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import adabound"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = pd.read_csv(\"data/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seperated_columns = training_data['Cabin'].str.split('/', expand=True)\n",
    "training_data[['C1', 'C2', 'C3']] = seperated_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "seperated_columns = training_data['PassengerId'].str.split('_', expand=True)\n",
    "training_data[['PI1', 'PI2']] = seperated_columns.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "seperated_columns = training_data['Name'].str.split(' ', expand=True)\n",
    "training_data['Last_name'] = seperated_columns[1]\n",
    "full_training_data = training_data\n",
    "training_data = full_training_data.drop(columns=['PassengerId', 'Cabin', 'Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['HomePlanet', 'CryoSleep', 'Destination', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Transported', 'C1', 'C2', 'C3', 'PI1', 'PI2', 'Last_name']\n"
     ]
    },
    {
     "data": {
      "text/plain": "     HomePlanet CryoSleep    Destination   Age    VIP  RoomService  FoodCourt  \\\n0        Europa     False    TRAPPIST-1e  39.0  False          0.0        0.0   \n1         Earth     False    TRAPPIST-1e  24.0  False        109.0        9.0   \n2        Europa     False    TRAPPIST-1e  58.0   True         43.0     3576.0   \n3        Europa     False    TRAPPIST-1e  33.0  False          0.0     1283.0   \n4         Earth     False    TRAPPIST-1e  16.0  False        303.0       70.0   \n...         ...       ...            ...   ...    ...          ...        ...   \n8688     Europa     False    55 Cancri e  41.0   True          0.0     6819.0   \n8689      Earth      True  PSO J318.5-22  18.0  False          0.0        0.0   \n8690      Earth     False    TRAPPIST-1e  26.0  False          0.0        0.0   \n8691     Europa     False    55 Cancri e  32.0  False          0.0     1049.0   \n8692     Europa     False    TRAPPIST-1e  44.0  False        126.0     4688.0   \n\n      ShoppingMall     Spa  VRDeck C1    C2 C3   PI1  PI2    Last_name  \\\n0              0.0     0.0     0.0  B     0  P     1    1    Ofracculy   \n1             25.0   549.0    44.0  F     0  S     2    1        Vines   \n2              0.0  6715.0    49.0  A     0  S     3    1       Susent   \n3            371.0  3329.0   193.0  A     0  S     3    2       Susent   \n4            151.0   565.0     2.0  F     1  S     4    1  Santantines   \n...            ...     ...     ... ..   ... ..   ...  ...          ...   \n8688           0.0  1643.0    74.0  A    98  P  9276    1    Noxnuther   \n8689           0.0     0.0     0.0  G  1499  S  9278    1    Mondalley   \n8690        1872.0     1.0     0.0  G  1500  S  9279    1       Connon   \n8691           0.0   353.0  3235.0  E   608  S  9280    1    Hontichre   \n8692           0.0     0.0    12.0  E   608  S  9280    2    Hontichre   \n\n      Transported  \n0           False  \n1            True  \n2           False  \n3           False  \n4            True  \n...           ...  \n8688        False  \n8689        False  \n8690         True  \n8691        False  \n8692         True  \n\n[8693 rows x 17 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>HomePlanet</th>\n      <th>CryoSleep</th>\n      <th>Destination</th>\n      <th>Age</th>\n      <th>VIP</th>\n      <th>RoomService</th>\n      <th>FoodCourt</th>\n      <th>ShoppingMall</th>\n      <th>Spa</th>\n      <th>VRDeck</th>\n      <th>C1</th>\n      <th>C2</th>\n      <th>C3</th>\n      <th>PI1</th>\n      <th>PI2</th>\n      <th>Last_name</th>\n      <th>Transported</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>39.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>B</td>\n      <td>0</td>\n      <td>P</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Ofracculy</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Earth</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>24.0</td>\n      <td>False</td>\n      <td>109.0</td>\n      <td>9.0</td>\n      <td>25.0</td>\n      <td>549.0</td>\n      <td>44.0</td>\n      <td>F</td>\n      <td>0</td>\n      <td>S</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Vines</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>58.0</td>\n      <td>True</td>\n      <td>43.0</td>\n      <td>3576.0</td>\n      <td>0.0</td>\n      <td>6715.0</td>\n      <td>49.0</td>\n      <td>A</td>\n      <td>0</td>\n      <td>S</td>\n      <td>3</td>\n      <td>1</td>\n      <td>Susent</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>33.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>1283.0</td>\n      <td>371.0</td>\n      <td>3329.0</td>\n      <td>193.0</td>\n      <td>A</td>\n      <td>0</td>\n      <td>S</td>\n      <td>3</td>\n      <td>2</td>\n      <td>Susent</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Earth</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>16.0</td>\n      <td>False</td>\n      <td>303.0</td>\n      <td>70.0</td>\n      <td>151.0</td>\n      <td>565.0</td>\n      <td>2.0</td>\n      <td>F</td>\n      <td>1</td>\n      <td>S</td>\n      <td>4</td>\n      <td>1</td>\n      <td>Santantines</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8688</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>55 Cancri e</td>\n      <td>41.0</td>\n      <td>True</td>\n      <td>0.0</td>\n      <td>6819.0</td>\n      <td>0.0</td>\n      <td>1643.0</td>\n      <td>74.0</td>\n      <td>A</td>\n      <td>98</td>\n      <td>P</td>\n      <td>9276</td>\n      <td>1</td>\n      <td>Noxnuther</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8689</th>\n      <td>Earth</td>\n      <td>True</td>\n      <td>PSO J318.5-22</td>\n      <td>18.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>G</td>\n      <td>1499</td>\n      <td>S</td>\n      <td>9278</td>\n      <td>1</td>\n      <td>Mondalley</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8690</th>\n      <td>Earth</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>26.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1872.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>G</td>\n      <td>1500</td>\n      <td>S</td>\n      <td>9279</td>\n      <td>1</td>\n      <td>Connon</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8691</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>55 Cancri e</td>\n      <td>32.0</td>\n      <td>False</td>\n      <td>0.0</td>\n      <td>1049.0</td>\n      <td>0.0</td>\n      <td>353.0</td>\n      <td>3235.0</td>\n      <td>E</td>\n      <td>608</td>\n      <td>S</td>\n      <td>9280</td>\n      <td>1</td>\n      <td>Hontichre</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8692</th>\n      <td>Europa</td>\n      <td>False</td>\n      <td>TRAPPIST-1e</td>\n      <td>44.0</td>\n      <td>False</td>\n      <td>126.0</td>\n      <td>4688.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>E</td>\n      <td>608</td>\n      <td>S</td>\n      <td>9280</td>\n      <td>2</td>\n      <td>Hontichre</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>8693 rows × 17 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = training_data.columns.tolist()\n",
    "print(cols)\n",
    "cols = cols[:10] + cols[11:] + [cols[10]]\n",
    "training_data = training_data[cols]\n",
    "training_data.fillna(0, inplace=True)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([['Europa', False, 'TRAPPIST-1e', ..., 1, 'Ofracculy', False],\n       ['Earth', False, 'TRAPPIST-1e', ..., 1, 'Vines', True],\n       ['Europa', False, 'TRAPPIST-1e', ..., 1, 'Susent', False],\n       ...,\n       ['Earth', False, 'TRAPPIST-1e', ..., 1, 'Connon', True],\n       ['Europa', False, '55 Cancri e', ..., 1, 'Hontichre', False],\n       ['Europa', False, 'TRAPPIST-1e', ..., 2, 'Hontichre', True]],\n      dtype=object)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = training_data.to_numpy()\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_mapping(column_number):\n",
    "    classes = {}\n",
    "    index = 0\n",
    "    for id in training_data[:, column_number]:\n",
    "        if id not in classes:\n",
    "            classes[id] = index\n",
    "            index += 1\n",
    "    return classes\n",
    "\n",
    "\n",
    "def class_mapping_for_name(column_number):\n",
    "    classes = {0: 0}\n",
    "    index = 1\n",
    "    for id in training_data[:, column_number]:\n",
    "        if id not in classes:\n",
    "            classes[id] = index\n",
    "            index += 1\n",
    "    return classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet_map = class_mapping(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{False: 0, True: 1}"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TF_map = class_mapping(1)\n",
    "TF_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'TRAPPIST-1e': 0, 'PSO J318.5-22': 1, '55 Cancri e': 2, 0: 3}"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination_map = class_mapping(2)\n",
    "destination_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'B': 0, 'F': 1, 'A': 2, 'G': 3, 0: 4, 'E': 5, 'D': 6, 'C': 7, 'T': 8}"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin0 = class_mapping(10)\n",
    "cabin0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'P': 0, 'S': 1, 0: 2}"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin2 = class_mapping(12)\n",
    "cabin2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2218\n"
     ]
    },
    {
     "data": {
      "text/plain": "{0: 0,\n 'Ofracculy': 1,\n 'Vines': 2,\n 'Susent': 3,\n 'Santantines': 4,\n 'Hinetthews': 5,\n 'Jacostaffey': 6,\n 'Beston': 7,\n 'Flatic': 8,\n 'Barne': 9,\n 'Baketton': 10,\n 'Bertsontry': 11,\n 'Pooles': 12,\n 'Eccle': 13,\n 'Hughriend': 14,\n 'Upead': 15,\n 'Brighttt': 16,\n 'Brantuarez': 17,\n 'Mcfaddennon': 18,\n 'Jacostanley': 19,\n 'Fullided': 20,\n 'Brookenson': 21,\n 'Unconary': 22,\n 'Mare': 23,\n 'Morsentley': 24,\n 'Datie': 25,\n 'Oingwhedly': 26,\n 'Butte': 27,\n 'Leodger': 28,\n 'Wheelez': 29,\n 'Batthewitt': 30,\n 'Moodsey': 31,\n 'Cylistrand': 32,\n 'Coopelandez': 33,\n 'Chmad': 34,\n 'Lancis': 35,\n 'Johnshines': 36,\n 'Hubbarton': 37,\n 'Hickerson': 38,\n 'Tractive': 39,\n 'Ayalazquez': 40,\n 'Salez': 41,\n 'Greeves': 42,\n 'Keen': 43,\n 'Pecketton': 44,\n 'Leeves': 45,\n 'Binie': 46,\n 'Dillines': 47,\n 'Make': 48,\n 'Mostedry': 49,\n 'Dal': 50,\n 'Blité': 51,\n 'Pokerheed': 52,\n 'Vinozarks': 53,\n 'Datte': 54,\n 'Coopezmaney': 55,\n 'Ellcefulve': 56,\n 'Kinson': 57,\n 'Sacre': 58,\n 'Santry': 59,\n 'Yorkland': 60,\n 'Dunnisey': 61,\n 'Hewson': 62,\n 'Fictful': 63,\n 'Carezquez': 64,\n 'Leetersoney': 65,\n 'Connelson': 66,\n 'Trad': 67,\n 'Holson': 68,\n 'Mclainez': 69,\n 'Cleachrand': 70,\n 'Hart': 71,\n 'Handertiz': 72,\n 'Aloubtled': 73,\n 'Bootious': 74,\n 'Supred': 75,\n 'Cowtale': 76,\n 'Carvis': 77,\n 'Beckerson': 78,\n 'Pead': 79,\n 'Suptive': 80,\n 'Coning': 81,\n 'Hayder': 82,\n 'Fielson': 83,\n 'Mish': 84,\n 'Mesty': 85,\n 'Fles': 86,\n 'Fowles': 87,\n 'Blie': 88,\n 'Harmontry': 89,\n 'Gambs': 90,\n 'Myling': 91,\n 'Mejiaddox': 92,\n 'Whitakers': 93,\n 'Harte': 94,\n 'Dayers': 95,\n 'Wolferguson': 96,\n 'Hubbarrison': 97,\n 'Pashe': 98,\n 'Simson': 99,\n 'Estron': 100,\n 'Atkinney': 101,\n 'Hetforhaft': 102,\n 'Hutchinton': 103,\n 'Anche': 104,\n 'Floaf': 105,\n 'Fisherry': 106,\n 'Mcmahoney': 107,\n 'Gordanieves': 108,\n 'Grifford': 109,\n 'Lambles': 110,\n 'Iniouser': 111,\n 'Knik': 112,\n 'Webstephrey': 113,\n 'Disight': 114,\n 'Weaves': 115,\n 'Shephendry': 116,\n 'Romez': 117,\n 'Booters': 118,\n 'Adavisons': 119,\n 'Raf': 120,\n 'Emead': 121,\n 'Dissper': 122,\n 'Cookson': 123,\n 'Morrows': 124,\n 'Colomonson': 125,\n 'Bacistion': 126,\n 'Embleng': 127,\n 'Heateele': 128,\n 'Oneiles': 129,\n 'Brugashed': 130,\n 'Inicont': 131,\n 'Appie': 132,\n 'Date': 133,\n 'Workmans': 134,\n 'Navages': 135,\n 'Garnes': 136,\n 'Shieldson': 137,\n 'River': 138,\n 'Wilsoney': 139,\n 'Blan': 140,\n 'Reke': 141,\n 'Perte': 142,\n 'Deva': 143,\n 'Sykess': 144,\n 'Queen': 145,\n 'Hortis': 146,\n 'Sellahaney': 147,\n 'Nate': 148,\n 'Bakerrison': 149,\n 'Wist': 150,\n 'Josey': 151,\n 'Aginge': 152,\n 'Resty': 153,\n 'Crie': 154,\n 'Dischod': 155,\n 'Sté': 156,\n 'Cartyernan': 157,\n 'Sad': 158,\n 'Repumparte': 159,\n 'Sesa': 160,\n 'Spanxibus': 161,\n 'Kramosley': 162,\n 'Amsive': 163,\n 'Byerry': 164,\n 'Cardner': 165,\n 'Stenson': 166,\n 'Mingious': 167,\n 'Berte': 168,\n 'Cooperkins': 169,\n 'Inderly': 170,\n 'Bookerson': 171,\n 'Merkins': 172,\n 'Herpumble': 173,\n 'Sancockett': 174,\n 'Polliamposs': 175,\n 'Fryersonis': 176,\n 'Obnoble': 177,\n 'Mcdanield': 178,\n 'Beachez': 179,\n 'Pierry': 180,\n 'Santin': 181,\n 'Invebodene': 182,\n 'Jimes': 183,\n 'Bootty': 184,\n 'Delez': 185,\n 'Objeciane': 186,\n 'Excialing': 187,\n 'Brakeril': 188,\n 'Roforhauge': 189,\n 'Gardonadox': 190,\n 'Sorbitter': 191,\n 'Wriggins': 192,\n 'Kirklander': 193,\n 'Ausivetpul': 194,\n 'Geousker': 195,\n 'Estevesters': 196,\n 'Hickett': 197,\n 'Bowerson': 198,\n 'Coudered': 199,\n 'Mckinsond': 200,\n 'Kochroeders': 201,\n 'Valezaley': 202,\n 'Bellarkerd': 203,\n 'Bache': 204,\n 'Blange': 205,\n 'Hooperez': 206,\n 'Edwartizman': 207,\n 'Ocherman': 208,\n 'Siveduced': 209,\n 'Burchard': 210,\n 'Melie': 211,\n 'Mushe': 212,\n 'Perle': 213,\n 'Guerson': 214,\n 'Poille': 215,\n 'Racke': 216,\n 'Chanan': 217,\n 'Kraie': 218,\n 'Mane': 219,\n 'Burchanez': 220,\n 'Strad': 221,\n 'Warrison': 222,\n 'Gloyanthy': 223,\n 'Mcleaney': 224,\n 'Connon': 225,\n 'Popez': 226,\n 'Staffersby': 227,\n 'Headardyer': 228,\n 'Jacostaney': 229,\n 'Pottonalden': 230,\n 'Coffmaney': 231,\n 'Dutte': 232,\n 'Mcneiderson': 233,\n 'Vincenton': 234,\n 'Spentley': 235,\n 'Pirejus': 236,\n 'Moodson': 237,\n 'Fuelddid': 238,\n 'Mosteraked': 239,\n 'Floydendley': 240,\n 'Wagnerray': 241,\n 'Simpsonks': 242,\n 'Klinson': 243,\n 'Merce': 244,\n 'Sullones': 245,\n 'Savaraldez': 246,\n 'Cowsearney': 247,\n 'Carreralend': 248,\n 'Hervel': 249,\n 'Robins': 250,\n 'Mepie': 251,\n 'Tité': 252,\n 'Pimne': 253,\n 'Sageng': 254,\n 'Wate': 255,\n 'Flowensley': 256,\n 'Mirez': 257,\n 'Ble': 258,\n 'Iccle': 259,\n 'Roachoanand': 260,\n 'Rushorney': 261,\n 'Quelfly': 262,\n 'Steinardson': 263,\n 'Poicaptic': 264,\n 'Unhearfus': 265,\n 'Pumbody': 266,\n 'Fiha': 267,\n 'Efulows': 268,\n 'Chocaters': 269,\n 'Ineysive': 270,\n 'Jorden': 271,\n 'Melto': 272,\n 'Knike': 273,\n 'Mormonized': 274,\n 'Pread': 275,\n 'Willynnedy': 276,\n 'Hodes': 277,\n 'Diateous': 278,\n 'Unciate': 279,\n 'Hahnstonsen': 280,\n 'Armstromez': 281,\n 'Curte': 282,\n 'Marshopper': 283,\n 'Singmage': 284,\n 'Mcclayton': 285,\n 'Hoppernardy': 286,\n 'Moongton': 287,\n 'Ecart': 288,\n 'Taptiritty': 289,\n 'Geoconne': 290,\n 'Fushausive': 291,\n 'Poperez': 292,\n 'Bedectical': 293,\n 'Nichoan': 294,\n 'Jenningley': 295,\n 'Sosanturney': 296,\n 'Staf': 297,\n 'Chaellerty': 298,\n 'Wynneyerson': 299,\n 'Fuelisent': 300,\n 'Solinez': 301,\n 'Chardst': 302,\n 'Gilesonston': 303,\n 'Chiff': 304,\n 'Peckerson': 305,\n 'Mallerez': 306,\n 'Unloweedic': 307,\n 'Obeaded': 308,\n 'Vantonson': 309,\n 'Preent': 310,\n 'Meake': 311,\n 'Cakie': 312,\n 'Spedlybood': 313,\n 'Swell': 314,\n 'Fone': 315,\n 'Unpasine': 316,\n 'Backe': 317,\n 'Mccarry': 318,\n 'Sinecthex': 319,\n 'Nateansive': 320,\n 'Blandez': 321,\n 'Swinvul': 322,\n 'Eneurordry': 323,\n 'Kimons': 324,\n 'Parta': 325,\n 'Farleyatton': 326,\n 'Mcguirez': 327,\n 'Hornettoney': 328,\n 'Parklaney': 329,\n 'Joyneidez': 330,\n 'Mirezavis': 331,\n 'Stité': 332,\n 'Stiven': 333,\n 'Alentonway': 334,\n 'Stike': 335,\n 'Parbage': 336,\n 'Swanardy': 337,\n 'Nalanet': 338,\n 'Conie': 339,\n 'Ments': 340,\n 'Sextones': 341,\n 'Weavesend': 342,\n 'Webstes': 343,\n 'Watte': 344,\n 'Cleminglas': 345,\n 'Bradlerson': 346,\n 'Brathful': 347,\n 'Valindle': 348,\n 'Graciansus': 349,\n 'Rames': 350,\n 'Harmond': 351,\n 'Hortez': 352,\n 'Flemaney': 353,\n 'Petie': 354,\n 'Dinsprody': 355,\n 'Munozanson': 356,\n 'Baciffhaut': 357,\n 'Tramsor': 358,\n 'Ousious': 359,\n 'Hednigic': 360,\n 'Lamberguson': 361,\n 'Dickley': 362,\n 'Herry': 363,\n 'Trankliney': 364,\n 'Woodgezalez': 365,\n 'Chort': 366,\n 'Erle': 367,\n 'Oillicaly': 368,\n 'Tecre': 369,\n 'Delazarson': 370,\n 'Hardley': 371,\n 'Kra': 372,\n 'Flynner': 373,\n 'Gene': 374,\n 'Buckeelez': 375,\n 'Crité': 376,\n 'Ormler': 377,\n 'Brine': 378,\n 'Scheron': 379,\n 'Hontichre': 380,\n 'Rilley': 381,\n 'Sacle': 382,\n 'Potters': 383,\n 'Ranaly': 384,\n 'Carterson': 385,\n 'Pollarkeeks': 386,\n 'Holatirint': 387,\n 'Gaington': 388,\n 'Gainebergan': 389,\n 'Portananney': 390,\n 'Anpie': 391,\n 'She': 392,\n 'Gread': 393,\n 'Wolfaddox': 394,\n 'Powery': 395,\n 'Pamca': 396,\n 'Wooterston': 397,\n 'Yanton': 398,\n 'Kiling': 399,\n 'Watie': 400,\n 'Prucerod': 401,\n 'Grahangory': 402,\n 'Trattle': 403,\n 'Deeke': 404,\n 'Hammonton': 405,\n 'Paska': 406,\n 'Cure': 407,\n 'Gordond': 408,\n 'Jarvey': 409,\n 'Andackson': 410,\n 'Saley': 411,\n 'Ticagent': 412,\n 'Buckentry': 413,\n 'Winie': 414,\n 'Torrez': 415,\n 'Hardson': 416,\n 'Dillips': 417,\n 'Moodman': 418,\n 'Camerrison': 419,\n 'Cla': 420,\n 'Parté': 421,\n 'Lerez': 422,\n 'Bert': 423,\n 'Concy': 424,\n 'Erpie': 425,\n 'Motive': 426,\n 'Start': 427,\n 'Potte': 428,\n 'Dedometeel': 429,\n 'Haydenzier': 430,\n 'Buchansen': 431,\n 'Sha': 432,\n 'Rhuba': 433,\n 'Voliat': 434,\n 'Estein': 435,\n 'Wartyson': 436,\n 'Mccarveymon': 437,\n 'Missefle': 438,\n 'Coflé': 439,\n 'Apeau': 440,\n 'Stantiagord': 441,\n 'Mondsey': 442,\n 'Reate': 443,\n 'Bensley': 444,\n 'Fitzgerson': 445,\n 'Potthews': 446,\n 'Currisones': 447,\n 'Lewinton': 448,\n 'Bairdford': 449,\n 'Hamberterry': 450,\n 'Ewiseston': 451,\n 'Mcbritts': 452,\n 'Inghtal': 453,\n 'Waltonnedy': 454,\n 'Ennalve': 455,\n 'Fusimodent': 456,\n 'Ainserfle': 457,\n 'Geraing': 458,\n 'Traie': 459,\n 'Fliblerolt': 460,\n 'Toddleton': 461,\n 'Burle': 462,\n 'Meltonway': 463,\n 'Oilloody': 464,\n 'Reeddommy': 465,\n 'Mclardson': 466,\n 'Steeletters': 467,\n 'Plegred': 468,\n 'Roycentes': 469,\n 'Plattering': 470,\n 'Rodger': 471,\n 'Valexandez': 472,\n 'Walé': 473,\n 'Aiming': 474,\n 'Hartz': 475,\n 'Quinnerry': 476,\n 'Imprank': 477,\n 'Frolestty': 478,\n 'Pottmanley': 479,\n 'Mcfarmerson': 480,\n 'Powenoble': 481,\n 'Klindsey': 482,\n 'Unfile': 483,\n 'Noelan': 484,\n 'Gelle': 485,\n 'Presstic': 486,\n 'Chuble': 487,\n 'Nieldson': 488,\n 'Ramoran': 489,\n 'Nutte': 490,\n 'Katte': 491,\n 'Meyersones': 492,\n 'Asoppor': 493,\n 'Baxters': 494,\n 'Couseced': 495,\n 'Gainney': 496,\n 'Strongers': 497,\n 'Sweeton': 498,\n 'Carrishley': 499,\n 'Chpie': 500,\n 'Wardendez': 501,\n 'Hoopez': 502,\n 'Carverson': 503,\n 'Hinesh': 504,\n 'Pacerty': 505,\n 'Frazie': 506,\n 'Braymon': 507,\n 'Fueling': 508,\n 'Simstravery': 509,\n 'Raca': 510,\n 'Oidederval': 511,\n 'Nan': 512,\n 'Sarios': 513,\n 'Ageurante': 514,\n 'Walkerez': 515,\n 'Sandul': 516,\n 'Stephendsey': 517,\n 'Peckers': 518,\n 'Sweene': 519,\n 'Maynardner': 520,\n 'Geneltic': 521,\n 'Franton': 522,\n 'Briggston': 523,\n 'Covetive': 524,\n 'Bancy': 525,\n 'Lanks': 526,\n 'Reedall': 527,\n 'Malliamsey': 528,\n 'Pittson': 529,\n 'Polksy': 530,\n 'Barris': 531,\n 'Alldson': 532,\n 'Warrishales': 533,\n 'Porki': 534,\n 'Alcemblery': 535,\n 'Kin': 536,\n 'Cassiding': 537,\n 'Burryerson': 538,\n 'Blate': 539,\n 'Parymider': 540,\n 'Fuenterson': 541,\n 'Vinston': 542,\n 'Obedaming': 543,\n 'Stiviorad': 544,\n 'Asticit': 545,\n 'Avisnydes': 546,\n 'Ryanglasey': 547,\n 'Sweett': 548,\n 'Flynnis': 549,\n 'Pette': 550,\n 'Fawnsive': 551,\n 'Rocknight': 552,\n 'Peter': 553,\n 'Byerton': 554,\n 'Vloaf': 555,\n 'Weaverays': 556,\n 'Emenez': 557,\n 'Frandriquez': 558,\n 'Johnsby': 559,\n 'Lunapperts': 560,\n 'Colle': 561,\n 'Smalloney': 562,\n 'Bartez': 563,\n 'Hapie': 564,\n 'Youngrayes': 565,\n 'Lewinez': 566,\n 'Platch': 567,\n 'Depie': 568,\n 'Fulloydez': 569,\n 'Figuez': 570,\n 'Emone': 571,\n 'Blace': 572,\n 'Humphreyes': 573,\n 'Coxterez': 574,\n 'Wiggs': 575,\n 'Woodwin': 576,\n 'Sweekstarks': 577,\n 'Reyerson': 578,\n 'Norrison': 579,\n 'Moranthons': 580,\n 'Cochrisons': 581,\n 'Acobsond': 582,\n 'Miltongson': 583,\n 'Barmant': 584,\n 'Shpie': 585,\n 'Suptibler': 586,\n 'Llonovancis': 587,\n 'Leste': 588,\n 'Wassird': 589,\n 'Juartis': 590,\n 'Summington': 591,\n 'Spistory': 592,\n 'Prinson': 593,\n 'Frasp': 594,\n 'Antoshipson': 595,\n 'Blanglison': 596,\n 'Wilderssen': 597,\n 'Wala': 598,\n 'Chavezalez': 599,\n 'Nichaner': 600,\n 'Harverez': 601,\n 'Leonzaley': 602,\n 'Garzaley': 603,\n 'Gonzaley': 604,\n 'Cleman': 605,\n 'Bra': 606,\n 'Georgasey': 607,\n 'Knoxonway': 608,\n 'Heedry': 609,\n 'Talls': 610,\n 'Unteckery': 611,\n 'Mcdon': 612,\n 'Prie': 613,\n 'Gallard': 614,\n 'Colleruces': 615,\n 'Braned': 616,\n 'Ingston': 617,\n 'Clayson': 618,\n 'Wolffy': 619,\n 'Ingwhed': 620,\n 'Jaf': 621,\n 'Pughanders': 622,\n 'Dingauge': 623,\n 'Bardinard': 624,\n 'Ewins': 625,\n 'Traverdy': 626,\n 'Parte': 627,\n 'Sfin': 628,\n 'Holcompson': 629,\n 'Carpennels': 630,\n 'Willangsey': 631,\n 'Pottney': 632,\n 'Pronfraked': 633,\n 'Miste': 634,\n 'Webstenson': 635,\n 'Drelcate': 636,\n 'Weeke': 637,\n 'Mcneiley': 638,\n 'Cuche': 639,\n 'Frité': 640,\n 'Dan': 641,\n 'Seen': 642,\n 'Opshaft': 643,\n 'Fielsenders': 644,\n 'Kidne': 645,\n 'Oneidson': 646,\n 'Barnolaney': 647,\n 'Burnerez': 648,\n 'Praded': 649,\n 'Dishocatal': 650,\n 'Bellyonsley': 651,\n 'Cako': 652,\n 'Benney': 653,\n 'Lake': 654,\n 'Sobvisted': 655,\n 'Bowenasey': 656,\n 'Sofraten': 657,\n 'Pugherman': 658,\n 'Barnolderg': 659,\n 'Roses': 660,\n 'Semageary': 661,\n 'Clugete': 662,\n 'Logannon': 663,\n 'Quinnerettt': 664,\n 'Vaught': 665,\n 'Brookes': 666,\n 'Corman': 667,\n 'Ingascomet': 668,\n 'Hessey': 669,\n 'Motter': 670,\n 'Anderking': 671,\n 'Radisiouss': 672,\n 'Heermy': 673,\n 'Waring': 674,\n 'Shoeweely': 675,\n 'Terry': 676,\n 'Sin': 677,\n 'Gallandez': 678,\n 'Lessonerry': 679,\n 'Brin': 680,\n 'Wasterivel': 681,\n 'Irwines': 682,\n 'Pota': 683,\n 'Nutty': 684,\n 'Browlerson': 685,\n 'Matts': 686,\n 'Sorticbar': 687,\n 'Folhal': 688,\n 'Tie': 689,\n 'Hubbarnett': 690,\n 'Unrent': 691,\n 'Blainson': 692,\n 'Brooker': 693,\n 'Alvasquez': 694,\n 'Vales': 695,\n 'Huntt': 696,\n 'Pentss': 697,\n 'Gilleranks': 698,\n 'Fanchy': 699,\n 'Alfordonard': 700,\n 'Hubert': 701,\n 'Coweboded': 702,\n 'Morrodgers': 703,\n 'Purle': 704,\n 'Scottuez': 705,\n 'Lancock': 706,\n 'Bookson': 707,\n 'Clugeadry': 708,\n 'Drivery': 709,\n 'Oderient': 710,\n 'Schmondez': 711,\n 'Breke': 712,\n 'Scotterkins': 713,\n 'Healted': 714,\n 'Roberrenn': 715,\n 'Pane': 716,\n 'Cruzaley': 717,\n 'Howayery': 718,\n 'Dynigic': 719,\n 'Leton': 720,\n 'Tranciams': 721,\n 'Knité': 722,\n 'Fache': 723,\n 'Fultz': 724,\n 'Ponie': 725,\n 'Conale': 726,\n 'Gulcepusy': 727,\n 'Erry': 728,\n 'Quinnedy': 729,\n 'Irintious': 730,\n 'Coillery': 731,\n 'Masquez': 732,\n 'Gamberson': 733,\n 'Gilleyons': 734,\n 'Barbes': 735,\n 'Gelte': 736,\n 'Rigginsen': 737,\n 'Datty': 738,\n 'Alshipson': 739,\n 'Hurchez': 740,\n 'Fryes': 741,\n 'Shermann': 742,\n 'Harrenoldez': 743,\n 'Mayder': 744,\n 'Horners': 745,\n 'Balightal': 746,\n 'Cleang': 747,\n 'Gerry': 748,\n 'Tepie': 749,\n 'Paynard': 750,\n 'Rothewson': 751,\n 'Stre': 752,\n 'Exnutch': 753,\n 'Hestes': 754,\n 'Waterson': 755,\n 'Kuche': 756,\n 'Coniple': 757,\n 'Caineson': 758,\n 'Mcgowaymond': 759,\n 'Fuleng': 760,\n 'Daughttley': 761,\n 'Resendent': 762,\n 'Mayods': 763,\n 'Foxter': 764,\n 'Hacle': 765,\n 'Lowelliott': 766,\n 'Meltoney': 767,\n 'Apie': 768,\n 'Kake': 769,\n 'Cookentaney': 770,\n 'Popelase': 771,\n 'Burtiz': 772,\n 'Blacks': 773,\n 'Mornaned': 774,\n 'Gres': 775,\n 'Winsley': 776,\n 'Higast': 777,\n 'Macdonadows': 778,\n 'Spedlinale': 779,\n 'Shbre': 780,\n 'Cres': 781,\n 'Rodwative': 782,\n 'Hanghanson': 783,\n 'Noeley': 784,\n 'Cartez': 785,\n 'Sancasey': 786,\n 'Lette': 787,\n 'Coarswing': 788,\n 'Wellenez': 789,\n 'Ailled': 790,\n 'Proorbeng': 791,\n 'Distured': 792,\n 'Thorney': 793,\n 'Righturter': 794,\n 'Conaly': 795,\n 'Combson': 796,\n 'Williotters': 797,\n 'Melte': 798,\n 'Prist': 799,\n 'Man': 800,\n 'Betie': 801,\n 'Gallencis': 802,\n 'Crioncery': 803,\n 'Sosalinson': 804,\n 'Keithunts': 805,\n 'Fryan': 806,\n 'Haste': 807,\n 'Jeffey': 808,\n 'Swin': 809,\n 'Spandisket': 810,\n 'Whealeed': 811,\n 'Oconley': 812,\n 'Flité': 813,\n 'Shawforden': 814,\n 'Gin': 815,\n 'Sotond': 816,\n 'Boderbint': 817,\n 'Madan': 818,\n 'Slable': 819,\n 'Puckley': 820,\n 'Bealvewd': 821,\n 'Nuthe': 822,\n 'Clumshangs': 823,\n 'Woody': 824,\n 'Seche': 825,\n 'Clidly': 826,\n 'Hoppers': 827,\n 'Unicting': 828,\n 'Stivelte': 829,\n 'Supiensive': 830,\n 'Batestonley': 831,\n 'Mcdowns': 832,\n 'Guezaley': 833,\n 'Briggins': 834,\n 'Simmonders': 835,\n 'Merie': 836,\n 'Replic': 837,\n 'Burchrison': 838,\n 'Hac': 839,\n 'Pager': 840,\n 'Garrincent': 841,\n 'Che': 842,\n 'Thony': 843,\n 'Crosarios': 844,\n 'Fullous': 845,\n 'Crakenecal': 846,\n 'Idivent': 847,\n 'Reming': 848,\n 'Replipent': 849,\n 'Mulley': 850,\n 'Chardsons': 851,\n 'Rostarks': 852,\n 'Dicksons': 853,\n 'Entenedy': 854,\n 'Lopelases': 855,\n 'Harperez': 856,\n 'Hinglendez': 857,\n 'Dukertyler': 858,\n 'Sau': 859,\n 'Patimonne': 860,\n 'Foraud': 861,\n 'Cofferson': 862,\n 'Kennetty': 863,\n 'Fatted': 864,\n 'Conleydenan': 865,\n 'Tuckers': 866,\n 'Ricews': 867,\n 'Ballocks': 868,\n 'Thonyderson': 869,\n 'Monroellynn': 870,\n 'Fisheparks': 871,\n 'Wonglasquez': 872,\n 'Lynnerett': 873,\n 'Alest': 874,\n 'Part': 875,\n 'Fringh': 876,\n 'Earmeter': 877,\n 'Sterson': 878,\n 'Villenson': 879,\n 'Retefule': 880,\n 'Watsongley': 881,\n 'Spane': 882,\n 'Solivers': 883,\n 'Perrerojass': 884,\n 'Englence': 885,\n 'Dillestron': 886,\n 'Batte': 887,\n 'Paulllegory': 888,\n 'Giba': 889,\n 'Moraleney': 890,\n 'Iderinelay': 891,\n 'Barkeretton': 892,\n 'Fincy': 893,\n 'Chake': 894,\n 'Hopez': 895,\n 'Minen': 896,\n 'Dyers': 897,\n 'Parsoney': 898,\n 'Mirandry': 899,\n 'Franankson': 900,\n 'Reyervaney': 901,\n 'Barte': 902,\n 'Bakerson': 903,\n 'Curton': 904,\n 'Patrichane': 905,\n 'Blancoy': 906,\n 'Sawyerson': 907,\n 'Coley': 908,\n 'Bradamss': 909,\n 'Cardsay': 910,\n 'Casez': 911,\n 'Stering': 912,\n 'Laai': 913,\n 'Gleroling': 914,\n 'Stewder': 915,\n 'Beckeey': 916,\n 'Sinda': 917,\n 'Wincer': 918,\n 'Butty': 919,\n 'Canie': 920,\n 'Adkinson': 921,\n 'Hermanapp': 922,\n 'Brailidy': 923,\n 'Garnettiz': 924,\n 'Kniva': 925,\n 'Brazo': 926,\n 'Hariourcal': 927,\n 'Blanspe': 928,\n 'Mcfarley': 929,\n 'Leonaldez': 930,\n 'Goffey': 931,\n 'Curle': 932,\n 'Hotte': 933,\n 'Ingetrody': 934,\n 'Ruité': 935,\n 'Hickson': 936,\n 'Cainghannon': 937,\n 'Dicomeng': 938,\n 'Tertchty': 939,\n 'Cohendright': 940,\n 'Byerson': 941,\n 'Lewintos': 942,\n 'Hultz': 943,\n 'Gloother': 944,\n 'Persed': 945,\n 'Scoudelle': 946,\n 'Meleon': 947,\n 'Flynney': 948,\n 'Unaasor': 949,\n 'Stannondez': 950,\n 'Mejiamsey': 951,\n 'Sellentry': 952,\n 'Schwardson': 953,\n 'Unprive': 954,\n 'Jenson': 955,\n 'Headfair': 956,\n 'Clake': 957,\n 'Erflé': 958,\n 'Owery': 959,\n 'Barrison': 960,\n 'Ellahan': 961,\n 'Chenson': 962,\n 'Clean': 963,\n 'Valey': 964,\n 'Antcal': 965,\n 'Pooley': 966,\n 'Barrelliver': 967,\n 'Stpingepid': 968,\n 'Gocent': 969,\n 'Fishopper': 970,\n 'Loway': 971,\n 'Booneid': 972,\n 'Neceriont': 973,\n 'Chcak': 974,\n 'Castery': 975,\n 'Assibler': 976,\n 'Horte': 977,\n 'Dister': 978,\n 'Conney': 979,\n 'Anthon': 980,\n 'Dressink': 981,\n 'Hammons': 982,\n 'Gallenry': 983,\n 'Knoxtones': 984,\n 'Charpelaney': 985,\n 'Burta': 986,\n 'Bate': 987,\n 'Hendrewsons': 988,\n 'Gaineyerson': 989,\n 'Castannison': 990,\n 'Vandan': 991,\n 'Welles': 992,\n 'Exibuctich': 993,\n 'Giller': 994,\n 'Buckeenez': 995,\n 'Ryanga': 996,\n 'Fowlaney': 997,\n 'Irwinson': 998,\n 'Clindy': 999,\n ...}"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_name_map = class_mapping_for_name(15)\n",
    "print(len(last_name_map))\n",
    "last_name_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_with_mapped_value(column_number, map_name, data_set):\n",
    "    data_set[:, column_number] = np.vectorize(map_name.get)(data_set[:, column_number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0, False, 'TRAPPIST-1e', ..., 1, 'Ofracculy', False],\n       [1, False, 'TRAPPIST-1e', ..., 1, 'Vines', True],\n       [0, False, 'TRAPPIST-1e', ..., 1, 'Susent', False],\n       ...,\n       [1, False, 'TRAPPIST-1e', ..., 1, 'Connon', True],\n       [0, False, '55 Cancri e', ..., 1, 'Hontichre', False],\n       [0, False, 'TRAPPIST-1e', ..., 2, 'Hontichre', True]], dtype=object)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace_with_mapped_value(0, planet_map, training_data)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_mapped_value(1, TF_map, training_data)\n",
    "replace_with_mapped_value(2, destination_map, training_data)\n",
    "replace_with_mapped_value(4, TF_map, training_data)\n",
    "replace_with_mapped_value(10, cabin0, training_data)\n",
    "replace_with_mapped_value(12, cabin2, training_data)\n",
    "replace_with_mapped_value(15, last_name_map, training_data)\n",
    "replace_with_mapped_value(16, TF_map, training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0, 0, 0, ..., 1, 1, 0],\n       [1, 0, 0, ..., 1, 2, 1],\n       [0, 0, 0, ..., 1, 3, 0],\n       ...,\n       [1, 0, 0, ..., 1, 225, 1],\n       [0, 0, 2, ..., 1, 380, 0],\n       [0, 0, 0, ..., 2, 380, 1]], dtype=object)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(training_data)\n",
    "training_data, test_data = training_data[:int(len(training_data) * 0.8)], training_data[int(len(training_data) * 0.8):]\n",
    "# np.random.shuffle(training_data)\n",
    "# training_data, test_data = training_data, training_data\n",
    "\n",
    "\n",
    "# x=np.delete(training_data,[0,1,2],1).astype(float)\n",
    "# y=training_data[:,[1]].astype(float)\n",
    "# tensor_x=torch.Tensor(x)\n",
    "# tensor_y=torch.Tensor(y)\n",
    "# training_data=TensorDataset(tensor_x,tensor_y)\n",
    "def to_tensor(np_array, target_column):\n",
    "    x = np.delete(np_array, target_column, axis=1).astype(float)\n",
    "    y = np_array[:, target_column].astype(int)\n",
    "    y_with_zeros = []\n",
    "    for class_element in y:\n",
    "        y_with_zeros.append(np.zeros(len(TF_map)))\n",
    "        y_with_zeros[-1][class_element] = 1\n",
    "    y = y_with_zeros\n",
    "    tensor_x = torch.Tensor(x)\n",
    "    tensor_y = torch.Tensor(y)\n",
    "    tensor = TensorDataset(tensor_x, tensor_y)\n",
    "    return tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 16])\n",
      "torch.Size([128, 2]) torch.float32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaow\\AppData\\Local\\Temp\\ipykernel_22812\\1188960576.py:21: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\utils\\tensor_new.cpp:210.)\n",
      "  tensor_y = torch.Tensor(y)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "\n",
    "# Create data loaders.\n",
    "train_dataloader = DataLoader(to_tensor(training_data, 16), batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(to_tensor(test_data, 16), batch_size=batch_size)\n",
    "\n",
    "for X, y in train_dataloader:\n",
    "    print(X.shape)\n",
    "    print(y.shape, y.dtype)\n",
    "    break\n",
    "\n",
    "# # Display sample data\n",
    "# figure = plt.figure(figsize=(10, 8))\n",
    "# cols, rows = 5, 5\n",
    "# for i in range(1, cols * rows + 1):\n",
    "#     idx = torch.randint(len(test_data), size=(1,)).item()\n",
    "#     img, label = test_data[idx]\n",
    "#     figure.add_subplot(rows, cols, i)\n",
    "#     plt.title(label)\n",
    "#     plt.axis(\"off\")\n",
    "#     plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda device\n",
      "NeuralNetwork(\n",
      "  (linear_relu_stack): Sequential(\n",
      "    (0): Linear(in_features=16, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (5): ReLU()\n",
      "    (6): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (7): ReLU()\n",
      "    (8): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (9): ReLU()\n",
      "    (10): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (11): ReLU()\n",
      "    (12): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (13): ReLU()\n",
      "    (14): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (15): ReLU()\n",
      "    (16): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (17): ReLU()\n",
      "    (18): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (19): ReLU()\n",
      "    (20): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (21): ReLU()\n",
      "    (22): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (23): ReLU()\n",
      "    (24): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (25): ReLU()\n",
      "    (26): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (27): ReLU()\n",
      "    (28): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (29): ReLU()\n",
      "    (30): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (31): ReLU()\n",
      "    (32): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (33): ReLU()\n",
      "    (34): Linear(in_features=512, out_features=2, bias=True)\n",
      "    (35): Sigmoid()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Get cpu or gpu device for training.\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))\n",
    "layer_size = 512\n",
    "input_size = 16\n",
    "\n",
    "\n",
    "# Define model\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(input_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, len(TF_map)),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        return logits\n",
    "\n",
    "\n",
    "model = NeuralNetwork().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss_fn = nn.L1Loss()\n",
    "loss_fn = nn.MSELoss()\n",
    "learning_rate = 1e-3\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "optimizer = adabound.AdaBound(model.parameters(), lr=learning_rate,final_lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        # y=y.type(torch.float)\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 8 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataloader, model):\n",
    "    size = len(dataloader.dataset)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
    "    test_loss /= size\n",
    "    correct /= size\n",
    "    try:\n",
    "        if correct > 0.82:\n",
    "            torch.save(model, 'model/m3')\n",
    "        elif correct > 0.815:\n",
    "            torch.save(model, 'model/m2')\n",
    "        elif correct > 0.81:\n",
    "            torch.save(model, 'model/m1')\n",
    "        elif correct > 0.805:\n",
    "            torch.save(model, 'model/m0')\n",
    "        elif correct > 0.8:\n",
    "            torch.save(model, 'model/ml')\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "    print(f\"Test Error: \\n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaow\\anaconda3\\envs\\study\\lib\\site-packages\\adabound\\adabound.py:94: UserWarning: This overload of add_ is deprecated:\n",
      "\tadd_(Number alpha, Tensor other)\n",
      "Consider using one of the following signatures instead:\n",
      "\tadd_(Tensor other, *, Number alpha) (Triggered internally at  C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\utils\\python_arg_parser.cpp:1055.)\n",
      "  exp_avg.mul_(beta1).add_(1 - beta1, grad)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.250408  [    0/ 6954]\n",
      "loss: 0.250017  [ 1024/ 6954]\n",
      "loss: 0.249839  [ 2048/ 6954]\n",
      "loss: 0.250481  [ 3072/ 6954]\n",
      "loss: 0.250053  [ 4096/ 6954]\n",
      "loss: 0.250172  [ 5120/ 6954]\n",
      "loss: 0.249850  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 48.2%, Avg loss: 0.002013 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.250150  [    0/ 6954]\n",
      "loss: 0.249652  [ 1024/ 6954]\n",
      "loss: 0.249956  [ 2048/ 6954]\n",
      "loss: 0.249995  [ 3072/ 6954]\n",
      "loss: 0.249445  [ 4096/ 6954]\n",
      "loss: 0.249863  [ 5120/ 6954]\n",
      "loss: 0.250075  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 48.2%, Avg loss: 0.002013 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.249438  [    0/ 6954]\n",
      "loss: 0.250900  [ 1024/ 6954]\n",
      "loss: 0.249774  [ 2048/ 6954]\n",
      "loss: 0.249893  [ 3072/ 6954]\n",
      "loss: 0.249653  [ 4096/ 6954]\n",
      "loss: 0.250026  [ 5120/ 6954]\n",
      "loss: 0.249593  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 73.5%, Avg loss: 0.002007 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss: 0.249376  [    0/ 6954]\n",
      "loss: 0.249074  [ 1024/ 6954]\n",
      "loss: 0.248978  [ 2048/ 6954]\n",
      "loss: 0.248549  [ 3072/ 6954]\n",
      "loss: 0.248548  [ 4096/ 6954]\n",
      "loss: 0.248117  [ 5120/ 6954]\n",
      "loss: 0.247536  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 71.4%, Avg loss: 0.001966 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss: 0.244799  [    0/ 6954]\n",
      "loss: 0.242522  [ 1024/ 6954]\n",
      "loss: 0.240917  [ 2048/ 6954]\n",
      "loss: 0.259614  [ 3072/ 6954]\n",
      "loss: 0.262786  [ 4096/ 6954]\n",
      "loss: 0.251260  [ 5120/ 6954]\n",
      "loss: 0.248377  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 49.0%, Avg loss: 0.002013 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "loss: 0.249333  [    0/ 6954]\n",
      "loss: 0.247561  [ 1024/ 6954]\n",
      "loss: 0.246199  [ 2048/ 6954]\n",
      "loss: 0.245847  [ 3072/ 6954]\n",
      "loss: 0.236000  [ 4096/ 6954]\n",
      "loss: 0.219683  [ 5120/ 6954]\n",
      "loss: 0.189495  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 58.6%, Avg loss: 0.001908 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "loss: 0.243750  [    0/ 6954]\n",
      "loss: 0.267777  [ 1024/ 6954]\n",
      "loss: 0.246334  [ 2048/ 6954]\n",
      "loss: 0.254670  [ 3072/ 6954]\n",
      "loss: 0.245965  [ 4096/ 6954]\n",
      "loss: 0.237042  [ 5120/ 6954]\n",
      "loss: 0.243802  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 72.9%, Avg loss: 0.001793 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "loss: 0.227920  [    0/ 6954]\n",
      "loss: 0.228679  [ 1024/ 6954]\n",
      "loss: 0.318970  [ 2048/ 6954]\n",
      "loss: 0.253467  [ 3072/ 6954]\n",
      "loss: 0.242168  [ 4096/ 6954]\n",
      "loss: 0.231209  [ 5120/ 6954]\n",
      "loss: 0.230475  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 67.5%, Avg loss: 0.001740 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "loss: 0.228712  [    0/ 6954]\n",
      "loss: 0.221413  [ 1024/ 6954]\n",
      "loss: 0.167157  [ 2048/ 6954]\n",
      "loss: 0.211908  [ 3072/ 6954]\n",
      "loss: 0.217740  [ 4096/ 6954]\n",
      "loss: 0.182467  [ 5120/ 6954]\n",
      "loss: 0.191982  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 74.6%, Avg loss: 0.001511 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "loss: 0.206644  [    0/ 6954]\n",
      "loss: 0.204456  [ 1024/ 6954]\n",
      "loss: 0.200001  [ 2048/ 6954]\n",
      "loss: 0.172910  [ 3072/ 6954]\n",
      "loss: 0.169560  [ 4096/ 6954]\n",
      "loss: 0.225922  [ 5120/ 6954]\n",
      "loss: 0.162352  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 62.6%, Avg loss: 0.001873 \n",
      "\n",
      "Epoch 11\n",
      "-------------------------------\n",
      "loss: 0.232657  [    0/ 6954]\n",
      "loss: 0.253023  [ 1024/ 6954]\n",
      "loss: 0.233909  [ 2048/ 6954]\n",
      "loss: 0.244259  [ 3072/ 6954]\n",
      "loss: 0.213757  [ 4096/ 6954]\n",
      "loss: 0.199977  [ 5120/ 6954]\n",
      "loss: 0.213255  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001464 \n",
      "\n",
      "Epoch 12\n",
      "-------------------------------\n",
      "loss: 0.160180  [    0/ 6954]\n",
      "loss: 0.188418  [ 1024/ 6954]\n",
      "loss: 0.194192  [ 2048/ 6954]\n",
      "loss: 0.181298  [ 3072/ 6954]\n",
      "loss: 0.226779  [ 4096/ 6954]\n",
      "loss: 0.189135  [ 5120/ 6954]\n",
      "loss: 0.179288  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001336 \n",
      "\n",
      "Epoch 13\n",
      "-------------------------------\n",
      "loss: 0.164879  [    0/ 6954]\n",
      "loss: 0.224901  [ 1024/ 6954]\n",
      "loss: 0.201833  [ 2048/ 6954]\n",
      "loss: 0.197385  [ 3072/ 6954]\n",
      "loss: 0.194163  [ 4096/ 6954]\n",
      "loss: 0.163214  [ 5120/ 6954]\n",
      "loss: 0.173052  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001288 \n",
      "\n",
      "Epoch 14\n",
      "-------------------------------\n",
      "loss: 0.178428  [    0/ 6954]\n",
      "loss: 0.182265  [ 1024/ 6954]\n",
      "loss: 0.167675  [ 2048/ 6954]\n",
      "loss: 0.163439  [ 3072/ 6954]\n",
      "loss: 0.148255  [ 4096/ 6954]\n",
      "loss: 0.164151  [ 5120/ 6954]\n",
      "loss: 0.147201  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001245 \n",
      "\n",
      "Epoch 15\n",
      "-------------------------------\n",
      "loss: 0.157616  [    0/ 6954]\n",
      "loss: 0.164830  [ 1024/ 6954]\n",
      "loss: 0.157279  [ 2048/ 6954]\n",
      "loss: 0.136896  [ 3072/ 6954]\n",
      "loss: 0.154354  [ 4096/ 6954]\n",
      "loss: 0.190608  [ 5120/ 6954]\n",
      "loss: 0.153351  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.5%, Avg loss: 0.001231 \n",
      "\n",
      "Epoch 16\n",
      "-------------------------------\n",
      "loss: 0.156561  [    0/ 6954]\n",
      "loss: 0.169167  [ 1024/ 6954]\n",
      "loss: 0.154056  [ 2048/ 6954]\n",
      "loss: 0.189171  [ 3072/ 6954]\n",
      "loss: 0.184888  [ 4096/ 6954]\n",
      "loss: 0.185494  [ 5120/ 6954]\n",
      "loss: 0.177934  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001253 \n",
      "\n",
      "Epoch 17\n",
      "-------------------------------\n",
      "loss: 0.178422  [    0/ 6954]\n",
      "loss: 0.166323  [ 1024/ 6954]\n",
      "loss: 0.144925  [ 2048/ 6954]\n",
      "loss: 0.167508  [ 3072/ 6954]\n",
      "loss: 0.148090  [ 4096/ 6954]\n",
      "loss: 0.173172  [ 5120/ 6954]\n",
      "loss: 0.165286  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001253 \n",
      "\n",
      "Epoch 18\n",
      "-------------------------------\n",
      "loss: 0.151048  [    0/ 6954]\n",
      "loss: 0.157309  [ 1024/ 6954]\n",
      "loss: 0.138625  [ 2048/ 6954]\n",
      "loss: 0.131680  [ 3072/ 6954]\n",
      "loss: 0.161321  [ 4096/ 6954]\n",
      "loss: 0.176594  [ 5120/ 6954]\n",
      "loss: 0.156168  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001275 \n",
      "\n",
      "Epoch 19\n",
      "-------------------------------\n",
      "loss: 0.153739  [    0/ 6954]\n",
      "loss: 0.155673  [ 1024/ 6954]\n",
      "loss: 0.162865  [ 2048/ 6954]\n",
      "loss: 0.161177  [ 3072/ 6954]\n",
      "loss: 0.141834  [ 4096/ 6954]\n",
      "loss: 0.163054  [ 5120/ 6954]\n",
      "loss: 0.143637  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001227 \n",
      "\n",
      "Epoch 20\n",
      "-------------------------------\n",
      "loss: 0.169957  [    0/ 6954]\n",
      "loss: 0.156882  [ 1024/ 6954]\n",
      "loss: 0.167729  [ 2048/ 6954]\n",
      "loss: 0.193151  [ 3072/ 6954]\n",
      "loss: 0.163913  [ 4096/ 6954]\n",
      "loss: 0.167168  [ 5120/ 6954]\n",
      "loss: 0.153288  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.4%, Avg loss: 0.001210 \n",
      "\n",
      "Epoch 21\n",
      "-------------------------------\n",
      "loss: 0.178310  [    0/ 6954]\n",
      "loss: 0.165035  [ 1024/ 6954]\n",
      "loss: 0.154230  [ 2048/ 6954]\n",
      "loss: 0.144437  [ 3072/ 6954]\n",
      "loss: 0.181215  [ 4096/ 6954]\n",
      "loss: 0.155925  [ 5120/ 6954]\n",
      "loss: 0.165721  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001251 \n",
      "\n",
      "Epoch 22\n",
      "-------------------------------\n",
      "loss: 0.161609  [    0/ 6954]\n",
      "loss: 0.173790  [ 1024/ 6954]\n",
      "loss: 0.136697  [ 2048/ 6954]\n",
      "loss: 0.189808  [ 3072/ 6954]\n",
      "loss: 0.160704  [ 4096/ 6954]\n",
      "loss: 0.142694  [ 5120/ 6954]\n",
      "loss: 0.174924  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001288 \n",
      "\n",
      "Epoch 23\n",
      "-------------------------------\n",
      "loss: 0.171046  [    0/ 6954]\n",
      "loss: 0.178413  [ 1024/ 6954]\n",
      "loss: 0.202387  [ 2048/ 6954]\n",
      "loss: 0.179524  [ 3072/ 6954]\n",
      "loss: 0.165774  [ 4096/ 6954]\n",
      "loss: 0.160496  [ 5120/ 6954]\n",
      "loss: 0.140753  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.4%, Avg loss: 0.001193 \n",
      "\n",
      "Epoch 24\n",
      "-------------------------------\n",
      "loss: 0.124154  [    0/ 6954]\n",
      "loss: 0.162729  [ 1024/ 6954]\n",
      "loss: 0.143575  [ 2048/ 6954]\n",
      "loss: 0.143778  [ 3072/ 6954]\n",
      "loss: 0.105171  [ 4096/ 6954]\n",
      "loss: 0.146406  [ 5120/ 6954]\n",
      "loss: 0.164570  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001214 \n",
      "\n",
      "Epoch 25\n",
      "-------------------------------\n",
      "loss: 0.158728  [    0/ 6954]\n",
      "loss: 0.159179  [ 1024/ 6954]\n",
      "loss: 0.158347  [ 2048/ 6954]\n",
      "loss: 0.166624  [ 3072/ 6954]\n",
      "loss: 0.138010  [ 4096/ 6954]\n",
      "loss: 0.141092  [ 5120/ 6954]\n",
      "loss: 0.166348  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001189 \n",
      "\n",
      "Epoch 26\n",
      "-------------------------------\n",
      "loss: 0.126225  [    0/ 6954]\n",
      "loss: 0.168706  [ 1024/ 6954]\n",
      "loss: 0.159595  [ 2048/ 6954]\n",
      "loss: 0.182989  [ 3072/ 6954]\n",
      "loss: 0.193701  [ 4096/ 6954]\n",
      "loss: 0.177067  [ 5120/ 6954]\n",
      "loss: 0.163295  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001194 \n",
      "\n",
      "Epoch 27\n",
      "-------------------------------\n",
      "loss: 0.140481  [    0/ 6954]\n",
      "loss: 0.161389  [ 1024/ 6954]\n",
      "loss: 0.171436  [ 2048/ 6954]\n",
      "loss: 0.136038  [ 3072/ 6954]\n",
      "loss: 0.106256  [ 4096/ 6954]\n",
      "loss: 0.152396  [ 5120/ 6954]\n",
      "loss: 0.159684  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001189 \n",
      "\n",
      "Epoch 28\n",
      "-------------------------------\n",
      "loss: 0.161341  [    0/ 6954]\n",
      "loss: 0.162075  [ 1024/ 6954]\n",
      "loss: 0.123660  [ 2048/ 6954]\n",
      "loss: 0.145743  [ 3072/ 6954]\n",
      "loss: 0.129400  [ 4096/ 6954]\n",
      "loss: 0.170952  [ 5120/ 6954]\n",
      "loss: 0.188282  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001199 \n",
      "\n",
      "Epoch 29\n",
      "-------------------------------\n",
      "loss: 0.185449  [    0/ 6954]\n",
      "loss: 0.164967  [ 1024/ 6954]\n",
      "loss: 0.151949  [ 2048/ 6954]\n",
      "loss: 0.180817  [ 3072/ 6954]\n",
      "loss: 0.155048  [ 4096/ 6954]\n",
      "loss: 0.166912  [ 5120/ 6954]\n",
      "loss: 0.168396  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001196 \n",
      "\n",
      "Epoch 30\n",
      "-------------------------------\n",
      "loss: 0.161318  [    0/ 6954]\n",
      "loss: 0.157428  [ 1024/ 6954]\n",
      "loss: 0.175440  [ 2048/ 6954]\n",
      "loss: 0.139495  [ 3072/ 6954]\n",
      "loss: 0.184813  [ 4096/ 6954]\n",
      "loss: 0.119867  [ 5120/ 6954]\n",
      "loss: 0.159279  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001182 \n",
      "\n",
      "Epoch 31\n",
      "-------------------------------\n",
      "loss: 0.182067  [    0/ 6954]\n",
      "loss: 0.142287  [ 1024/ 6954]\n",
      "loss: 0.142889  [ 2048/ 6954]\n",
      "loss: 0.159775  [ 3072/ 6954]\n",
      "loss: 0.136875  [ 4096/ 6954]\n",
      "loss: 0.173670  [ 5120/ 6954]\n",
      "loss: 0.167916  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001215 \n",
      "\n",
      "Epoch 32\n",
      "-------------------------------\n",
      "loss: 0.149725  [    0/ 6954]\n",
      "loss: 0.187497  [ 1024/ 6954]\n",
      "loss: 0.145029  [ 2048/ 6954]\n",
      "loss: 0.168377  [ 3072/ 6954]\n",
      "loss: 0.188045  [ 4096/ 6954]\n",
      "loss: 0.147588  [ 5120/ 6954]\n",
      "loss: 0.153472  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001193 \n",
      "\n",
      "Epoch 33\n",
      "-------------------------------\n",
      "loss: 0.126826  [    0/ 6954]\n",
      "loss: 0.157793  [ 1024/ 6954]\n",
      "loss: 0.165353  [ 2048/ 6954]\n",
      "loss: 0.153336  [ 3072/ 6954]\n",
      "loss: 0.171285  [ 4096/ 6954]\n",
      "loss: 0.157988  [ 5120/ 6954]\n",
      "loss: 0.140200  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001175 \n",
      "\n",
      "Epoch 34\n",
      "-------------------------------\n",
      "loss: 0.140686  [    0/ 6954]\n",
      "loss: 0.168367  [ 1024/ 6954]\n",
      "loss: 0.146038  [ 2048/ 6954]\n",
      "loss: 0.146722  [ 3072/ 6954]\n",
      "loss: 0.164601  [ 4096/ 6954]\n",
      "loss: 0.167019  [ 5120/ 6954]\n",
      "loss: 0.113502  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.5%, Avg loss: 0.001182 \n",
      "\n",
      "Epoch 35\n",
      "-------------------------------\n",
      "loss: 0.173653  [    0/ 6954]\n",
      "loss: 0.167758  [ 1024/ 6954]\n",
      "loss: 0.145255  [ 2048/ 6954]\n",
      "loss: 0.152901  [ 3072/ 6954]\n",
      "loss: 0.151565  [ 4096/ 6954]\n",
      "loss: 0.132191  [ 5120/ 6954]\n",
      "loss: 0.127274  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 36\n",
      "-------------------------------\n",
      "loss: 0.169935  [    0/ 6954]\n",
      "loss: 0.168680  [ 1024/ 6954]\n",
      "loss: 0.186714  [ 2048/ 6954]\n",
      "loss: 0.206300  [ 3072/ 6954]\n",
      "loss: 0.139117  [ 4096/ 6954]\n",
      "loss: 0.141147  [ 5120/ 6954]\n",
      "loss: 0.123853  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001166 \n",
      "\n",
      "Epoch 37\n",
      "-------------------------------\n",
      "loss: 0.162763  [    0/ 6954]\n",
      "loss: 0.124180  [ 1024/ 6954]\n",
      "loss: 0.159997  [ 2048/ 6954]\n",
      "loss: 0.186661  [ 3072/ 6954]\n",
      "loss: 0.129605  [ 4096/ 6954]\n",
      "loss: 0.130237  [ 5120/ 6954]\n",
      "loss: 0.187633  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.9%, Avg loss: 0.001164 \n",
      "\n",
      "Epoch 38\n",
      "-------------------------------\n",
      "loss: 0.140922  [    0/ 6954]\n",
      "loss: 0.128501  [ 1024/ 6954]\n",
      "loss: 0.155260  [ 2048/ 6954]\n",
      "loss: 0.145064  [ 3072/ 6954]\n",
      "loss: 0.148539  [ 4096/ 6954]\n",
      "loss: 0.167154  [ 5120/ 6954]\n",
      "loss: 0.186911  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.5%, Avg loss: 0.001178 \n",
      "\n",
      "Epoch 39\n",
      "-------------------------------\n",
      "loss: 0.158937  [    0/ 6954]\n",
      "loss: 0.134685  [ 1024/ 6954]\n",
      "loss: 0.165430  [ 2048/ 6954]\n",
      "loss: 0.218924  [ 3072/ 6954]\n",
      "loss: 0.174831  [ 4096/ 6954]\n",
      "loss: 0.149835  [ 5120/ 6954]\n",
      "loss: 0.158858  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001225 \n",
      "\n",
      "Epoch 40\n",
      "-------------------------------\n",
      "loss: 0.175686  [    0/ 6954]\n",
      "loss: 0.172870  [ 1024/ 6954]\n",
      "loss: 0.134656  [ 2048/ 6954]\n",
      "loss: 0.128998  [ 3072/ 6954]\n",
      "loss: 0.126568  [ 4096/ 6954]\n",
      "loss: 0.165691  [ 5120/ 6954]\n",
      "loss: 0.150402  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001185 \n",
      "\n",
      "Epoch 41\n",
      "-------------------------------\n",
      "loss: 0.114575  [    0/ 6954]\n",
      "loss: 0.141651  [ 1024/ 6954]\n",
      "loss: 0.160513  [ 2048/ 6954]\n",
      "loss: 0.135483  [ 3072/ 6954]\n",
      "loss: 0.183903  [ 4096/ 6954]\n",
      "loss: 0.172102  [ 5120/ 6954]\n",
      "loss: 0.138550  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001169 \n",
      "\n",
      "Epoch 42\n",
      "-------------------------------\n",
      "loss: 0.129601  [    0/ 6954]\n",
      "loss: 0.162417  [ 1024/ 6954]\n",
      "loss: 0.132315  [ 2048/ 6954]\n",
      "loss: 0.175743  [ 3072/ 6954]\n",
      "loss: 0.149808  [ 4096/ 6954]\n",
      "loss: 0.154574  [ 5120/ 6954]\n",
      "loss: 0.153415  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 43\n",
      "-------------------------------\n",
      "loss: 0.170439  [    0/ 6954]\n",
      "loss: 0.162900  [ 1024/ 6954]\n",
      "loss: 0.177168  [ 2048/ 6954]\n",
      "loss: 0.190512  [ 3072/ 6954]\n",
      "loss: 0.159144  [ 4096/ 6954]\n",
      "loss: 0.139512  [ 5120/ 6954]\n",
      "loss: 0.161401  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001172 \n",
      "\n",
      "Epoch 44\n",
      "-------------------------------\n",
      "loss: 0.190472  [    0/ 6954]\n",
      "loss: 0.141562  [ 1024/ 6954]\n",
      "loss: 0.132419  [ 2048/ 6954]\n",
      "loss: 0.139130  [ 3072/ 6954]\n",
      "loss: 0.155354  [ 4096/ 6954]\n",
      "loss: 0.124545  [ 5120/ 6954]\n",
      "loss: 0.157592  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001169 \n",
      "\n",
      "Epoch 45\n",
      "-------------------------------\n",
      "loss: 0.117608  [    0/ 6954]\n",
      "loss: 0.156929  [ 1024/ 6954]\n",
      "loss: 0.159005  [ 2048/ 6954]\n",
      "loss: 0.169838  [ 3072/ 6954]\n",
      "loss: 0.160138  [ 4096/ 6954]\n",
      "loss: 0.158406  [ 5120/ 6954]\n",
      "loss: 0.169717  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001171 \n",
      "\n",
      "Epoch 46\n",
      "-------------------------------\n",
      "loss: 0.162933  [    0/ 6954]\n",
      "loss: 0.136620  [ 1024/ 6954]\n",
      "loss: 0.161016  [ 2048/ 6954]\n",
      "loss: 0.153069  [ 3072/ 6954]\n",
      "loss: 0.134847  [ 4096/ 6954]\n",
      "loss: 0.150820  [ 5120/ 6954]\n",
      "loss: 0.130725  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001175 \n",
      "\n",
      "Epoch 47\n",
      "-------------------------------\n",
      "loss: 0.161962  [    0/ 6954]\n",
      "loss: 0.124738  [ 1024/ 6954]\n",
      "loss: 0.134675  [ 2048/ 6954]\n",
      "loss: 0.168588  [ 3072/ 6954]\n",
      "loss: 0.133945  [ 4096/ 6954]\n",
      "loss: 0.169459  [ 5120/ 6954]\n",
      "loss: 0.144603  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001174 \n",
      "\n",
      "Epoch 48\n",
      "-------------------------------\n",
      "loss: 0.176018  [    0/ 6954]\n",
      "loss: 0.133298  [ 1024/ 6954]\n",
      "loss: 0.148976  [ 2048/ 6954]\n",
      "loss: 0.164627  [ 3072/ 6954]\n",
      "loss: 0.157647  [ 4096/ 6954]\n",
      "loss: 0.168265  [ 5120/ 6954]\n",
      "loss: 0.158268  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.3%, Avg loss: 0.001204 \n",
      "\n",
      "Epoch 49\n",
      "-------------------------------\n",
      "loss: 0.171090  [    0/ 6954]\n",
      "loss: 0.150716  [ 1024/ 6954]\n",
      "loss: 0.184648  [ 2048/ 6954]\n",
      "loss: 0.173519  [ 3072/ 6954]\n",
      "loss: 0.133006  [ 4096/ 6954]\n",
      "loss: 0.151946  [ 5120/ 6954]\n",
      "loss: 0.146021  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 50\n",
      "-------------------------------\n",
      "loss: 0.128602  [    0/ 6954]\n",
      "loss: 0.165044  [ 1024/ 6954]\n",
      "loss: 0.170474  [ 2048/ 6954]\n",
      "loss: 0.137881  [ 3072/ 6954]\n",
      "loss: 0.156692  [ 4096/ 6954]\n",
      "loss: 0.169795  [ 5120/ 6954]\n",
      "loss: 0.142341  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001162 \n",
      "\n",
      "Epoch 51\n",
      "-------------------------------\n",
      "loss: 0.150857  [    0/ 6954]\n",
      "loss: 0.138971  [ 1024/ 6954]\n",
      "loss: 0.155385  [ 2048/ 6954]\n",
      "loss: 0.187040  [ 3072/ 6954]\n",
      "loss: 0.120833  [ 4096/ 6954]\n",
      "loss: 0.159422  [ 5120/ 6954]\n",
      "loss: 0.104680  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001251 \n",
      "\n",
      "Epoch 52\n",
      "-------------------------------\n",
      "loss: 0.138139  [    0/ 6954]\n",
      "loss: 0.151992  [ 1024/ 6954]\n",
      "loss: 0.153059  [ 2048/ 6954]\n",
      "loss: 0.142573  [ 3072/ 6954]\n",
      "loss: 0.162653  [ 4096/ 6954]\n",
      "loss: 0.161767  [ 5120/ 6954]\n",
      "loss: 0.156928  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001208 \n",
      "\n",
      "Epoch 53\n",
      "-------------------------------\n",
      "loss: 0.156660  [    0/ 6954]\n",
      "loss: 0.165178  [ 1024/ 6954]\n",
      "loss: 0.139842  [ 2048/ 6954]\n",
      "loss: 0.162605  [ 3072/ 6954]\n",
      "loss: 0.158227  [ 4096/ 6954]\n",
      "loss: 0.144860  [ 5120/ 6954]\n",
      "loss: 0.154243  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001171 \n",
      "\n",
      "Epoch 54\n",
      "-------------------------------\n",
      "loss: 0.154366  [    0/ 6954]\n",
      "loss: 0.149437  [ 1024/ 6954]\n",
      "loss: 0.161587  [ 2048/ 6954]\n",
      "loss: 0.172592  [ 3072/ 6954]\n",
      "loss: 0.139386  [ 4096/ 6954]\n",
      "loss: 0.124139  [ 5120/ 6954]\n",
      "loss: 0.130503  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001167 \n",
      "\n",
      "Epoch 55\n",
      "-------------------------------\n",
      "loss: 0.170311  [    0/ 6954]\n",
      "loss: 0.136311  [ 1024/ 6954]\n",
      "loss: 0.145806  [ 2048/ 6954]\n",
      "loss: 0.177820  [ 3072/ 6954]\n",
      "loss: 0.170278  [ 4096/ 6954]\n",
      "loss: 0.133428  [ 5120/ 6954]\n",
      "loss: 0.155442  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001176 \n",
      "\n",
      "Epoch 56\n",
      "-------------------------------\n",
      "loss: 0.143172  [    0/ 6954]\n",
      "loss: 0.159081  [ 1024/ 6954]\n",
      "loss: 0.149862  [ 2048/ 6954]\n",
      "loss: 0.135651  [ 3072/ 6954]\n",
      "loss: 0.171874  [ 4096/ 6954]\n",
      "loss: 0.167617  [ 5120/ 6954]\n",
      "loss: 0.165596  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001166 \n",
      "\n",
      "Epoch 57\n",
      "-------------------------------\n",
      "loss: 0.150988  [    0/ 6954]\n",
      "loss: 0.138277  [ 1024/ 6954]\n",
      "loss: 0.140508  [ 2048/ 6954]\n",
      "loss: 0.159908  [ 3072/ 6954]\n",
      "loss: 0.127968  [ 4096/ 6954]\n",
      "loss: 0.145764  [ 5120/ 6954]\n",
      "loss: 0.131882  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001198 \n",
      "\n",
      "Epoch 58\n",
      "-------------------------------\n",
      "loss: 0.144070  [    0/ 6954]\n",
      "loss: 0.144528  [ 1024/ 6954]\n",
      "loss: 0.160280  [ 2048/ 6954]\n",
      "loss: 0.171432  [ 3072/ 6954]\n",
      "loss: 0.159307  [ 4096/ 6954]\n",
      "loss: 0.151212  [ 5120/ 6954]\n",
      "loss: 0.136212  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001164 \n",
      "\n",
      "Epoch 59\n",
      "-------------------------------\n",
      "loss: 0.145738  [    0/ 6954]\n",
      "loss: 0.131156  [ 1024/ 6954]\n",
      "loss: 0.135002  [ 2048/ 6954]\n",
      "loss: 0.114065  [ 3072/ 6954]\n",
      "loss: 0.160338  [ 4096/ 6954]\n",
      "loss: 0.138555  [ 5120/ 6954]\n",
      "loss: 0.140473  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001152 \n",
      "\n",
      "Epoch 60\n",
      "-------------------------------\n",
      "loss: 0.115179  [    0/ 6954]\n",
      "loss: 0.160173  [ 1024/ 6954]\n",
      "loss: 0.131424  [ 2048/ 6954]\n",
      "loss: 0.156291  [ 3072/ 6954]\n",
      "loss: 0.131302  [ 4096/ 6954]\n",
      "loss: 0.170807  [ 5120/ 6954]\n",
      "loss: 0.134489  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001159 \n",
      "\n",
      "Epoch 61\n",
      "-------------------------------\n",
      "loss: 0.157457  [    0/ 6954]\n",
      "loss: 0.143656  [ 1024/ 6954]\n",
      "loss: 0.135403  [ 2048/ 6954]\n",
      "loss: 0.160596  [ 3072/ 6954]\n",
      "loss: 0.158308  [ 4096/ 6954]\n",
      "loss: 0.128142  [ 5120/ 6954]\n",
      "loss: 0.171619  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001152 \n",
      "\n",
      "Epoch 62\n",
      "-------------------------------\n",
      "loss: 0.134794  [    0/ 6954]\n",
      "loss: 0.150748  [ 1024/ 6954]\n",
      "loss: 0.149595  [ 2048/ 6954]\n",
      "loss: 0.152706  [ 3072/ 6954]\n",
      "loss: 0.144816  [ 4096/ 6954]\n",
      "loss: 0.129524  [ 5120/ 6954]\n",
      "loss: 0.132378  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001144 \n",
      "\n",
      "Epoch 63\n",
      "-------------------------------\n",
      "loss: 0.160214  [    0/ 6954]\n",
      "loss: 0.131468  [ 1024/ 6954]\n",
      "loss: 0.131202  [ 2048/ 6954]\n",
      "loss: 0.146092  [ 3072/ 6954]\n",
      "loss: 0.134161  [ 4096/ 6954]\n",
      "loss: 0.111793  [ 5120/ 6954]\n",
      "loss: 0.136680  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001192 \n",
      "\n",
      "Epoch 64\n",
      "-------------------------------\n",
      "loss: 0.146016  [    0/ 6954]\n",
      "loss: 0.164133  [ 1024/ 6954]\n",
      "loss: 0.146563  [ 2048/ 6954]\n",
      "loss: 0.149822  [ 3072/ 6954]\n",
      "loss: 0.141124  [ 4096/ 6954]\n",
      "loss: 0.160967  [ 5120/ 6954]\n",
      "loss: 0.144002  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001185 \n",
      "\n",
      "Epoch 65\n",
      "-------------------------------\n",
      "loss: 0.143947  [    0/ 6954]\n",
      "loss: 0.150540  [ 1024/ 6954]\n",
      "loss: 0.133289  [ 2048/ 6954]\n",
      "loss: 0.120773  [ 3072/ 6954]\n",
      "loss: 0.156259  [ 4096/ 6954]\n",
      "loss: 0.145654  [ 5120/ 6954]\n",
      "loss: 0.172705  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001175 \n",
      "\n",
      "Epoch 66\n",
      "-------------------------------\n",
      "loss: 0.154551  [    0/ 6954]\n",
      "loss: 0.146385  [ 1024/ 6954]\n",
      "loss: 0.116276  [ 2048/ 6954]\n",
      "loss: 0.156288  [ 3072/ 6954]\n",
      "loss: 0.184682  [ 4096/ 6954]\n",
      "loss: 0.147809  [ 5120/ 6954]\n",
      "loss: 0.192944  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001164 \n",
      "\n",
      "Epoch 67\n",
      "-------------------------------\n",
      "loss: 0.141600  [    0/ 6954]\n",
      "loss: 0.164816  [ 1024/ 6954]\n",
      "loss: 0.136212  [ 2048/ 6954]\n",
      "loss: 0.116359  [ 3072/ 6954]\n",
      "loss: 0.166627  [ 4096/ 6954]\n",
      "loss: 0.188546  [ 5120/ 6954]\n",
      "loss: 0.152037  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001163 \n",
      "\n",
      "Epoch 68\n",
      "-------------------------------\n",
      "loss: 0.120907  [    0/ 6954]\n",
      "loss: 0.160123  [ 1024/ 6954]\n",
      "loss: 0.123380  [ 2048/ 6954]\n",
      "loss: 0.144082  [ 3072/ 6954]\n",
      "loss: 0.151134  [ 4096/ 6954]\n",
      "loss: 0.102132  [ 5120/ 6954]\n",
      "loss: 0.115676  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001160 \n",
      "\n",
      "Epoch 69\n",
      "-------------------------------\n",
      "loss: 0.148163  [    0/ 6954]\n",
      "loss: 0.157452  [ 1024/ 6954]\n",
      "loss: 0.141775  [ 2048/ 6954]\n",
      "loss: 0.153617  [ 3072/ 6954]\n",
      "loss: 0.165104  [ 4096/ 6954]\n",
      "loss: 0.120061  [ 5120/ 6954]\n",
      "loss: 0.155443  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.5%, Avg loss: 0.001138 \n",
      "\n",
      "Epoch 70\n",
      "-------------------------------\n",
      "loss: 0.126965  [    0/ 6954]\n",
      "loss: 0.164827  [ 1024/ 6954]\n",
      "loss: 0.203026  [ 2048/ 6954]\n",
      "loss: 0.114195  [ 3072/ 6954]\n",
      "loss: 0.142810  [ 4096/ 6954]\n",
      "loss: 0.134863  [ 5120/ 6954]\n",
      "loss: 0.120400  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001184 \n",
      "\n",
      "Epoch 71\n",
      "-------------------------------\n",
      "loss: 0.138141  [    0/ 6954]\n",
      "loss: 0.130327  [ 1024/ 6954]\n",
      "loss: 0.171862  [ 2048/ 6954]\n",
      "loss: 0.172490  [ 3072/ 6954]\n",
      "loss: 0.125786  [ 4096/ 6954]\n",
      "loss: 0.152428  [ 5120/ 6954]\n",
      "loss: 0.136986  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.7%, Avg loss: 0.001185 \n",
      "\n",
      "Epoch 72\n",
      "-------------------------------\n",
      "loss: 0.142265  [    0/ 6954]\n",
      "loss: 0.160803  [ 1024/ 6954]\n",
      "loss: 0.136931  [ 2048/ 6954]\n",
      "loss: 0.145198  [ 3072/ 6954]\n",
      "loss: 0.128225  [ 4096/ 6954]\n",
      "loss: 0.127950  [ 5120/ 6954]\n",
      "loss: 0.178110  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001150 \n",
      "\n",
      "Epoch 73\n",
      "-------------------------------\n",
      "loss: 0.134016  [    0/ 6954]\n",
      "loss: 0.150659  [ 1024/ 6954]\n",
      "loss: 0.122688  [ 2048/ 6954]\n",
      "loss: 0.177428  [ 3072/ 6954]\n",
      "loss: 0.153921  [ 4096/ 6954]\n",
      "loss: 0.157594  [ 5120/ 6954]\n",
      "loss: 0.158561  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001144 \n",
      "\n",
      "Epoch 74\n",
      "-------------------------------\n",
      "loss: 0.155923  [    0/ 6954]\n",
      "loss: 0.125010  [ 1024/ 6954]\n",
      "loss: 0.166540  [ 2048/ 6954]\n",
      "loss: 0.131133  [ 3072/ 6954]\n",
      "loss: 0.123962  [ 4096/ 6954]\n",
      "loss: 0.176568  [ 5120/ 6954]\n",
      "loss: 0.182418  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001154 \n",
      "\n",
      "Epoch 75\n",
      "-------------------------------\n",
      "loss: 0.141952  [    0/ 6954]\n",
      "loss: 0.128134  [ 1024/ 6954]\n",
      "loss: 0.145412  [ 2048/ 6954]\n",
      "loss: 0.155953  [ 3072/ 6954]\n",
      "loss: 0.133563  [ 4096/ 6954]\n",
      "loss: 0.206453  [ 5120/ 6954]\n",
      "loss: 0.176710  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001186 \n",
      "\n",
      "Epoch 76\n",
      "-------------------------------\n",
      "loss: 0.140725  [    0/ 6954]\n",
      "loss: 0.146118  [ 1024/ 6954]\n",
      "loss: 0.140103  [ 2048/ 6954]\n",
      "loss: 0.121878  [ 3072/ 6954]\n",
      "loss: 0.152754  [ 4096/ 6954]\n",
      "loss: 0.177195  [ 5120/ 6954]\n",
      "loss: 0.133522  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001145 \n",
      "\n",
      "Epoch 77\n",
      "-------------------------------\n",
      "loss: 0.132685  [    0/ 6954]\n",
      "loss: 0.154973  [ 1024/ 6954]\n",
      "loss: 0.155900  [ 2048/ 6954]\n",
      "loss: 0.146409  [ 3072/ 6954]\n",
      "loss: 0.162528  [ 4096/ 6954]\n",
      "loss: 0.156188  [ 5120/ 6954]\n",
      "loss: 0.171839  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001141 \n",
      "\n",
      "Epoch 78\n",
      "-------------------------------\n",
      "loss: 0.141089  [    0/ 6954]\n",
      "loss: 0.165429  [ 1024/ 6954]\n",
      "loss: 0.155284  [ 2048/ 6954]\n",
      "loss: 0.128272  [ 3072/ 6954]\n",
      "loss: 0.149560  [ 4096/ 6954]\n",
      "loss: 0.135086  [ 5120/ 6954]\n",
      "loss: 0.134177  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001145 \n",
      "\n",
      "Epoch 79\n",
      "-------------------------------\n",
      "loss: 0.143455  [    0/ 6954]\n",
      "loss: 0.131482  [ 1024/ 6954]\n",
      "loss: 0.160215  [ 2048/ 6954]\n",
      "loss: 0.169940  [ 3072/ 6954]\n",
      "loss: 0.114701  [ 4096/ 6954]\n",
      "loss: 0.152843  [ 5120/ 6954]\n",
      "loss: 0.166850  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.4%, Avg loss: 0.001137 \n",
      "\n",
      "Epoch 80\n",
      "-------------------------------\n",
      "loss: 0.145275  [    0/ 6954]\n",
      "loss: 0.145376  [ 1024/ 6954]\n",
      "loss: 0.146181  [ 2048/ 6954]\n",
      "loss: 0.119558  [ 3072/ 6954]\n",
      "loss: 0.158473  [ 4096/ 6954]\n",
      "loss: 0.128353  [ 5120/ 6954]\n",
      "loss: 0.155233  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001157 \n",
      "\n",
      "Epoch 81\n",
      "-------------------------------\n",
      "loss: 0.150000  [    0/ 6954]\n",
      "loss: 0.156444  [ 1024/ 6954]\n",
      "loss: 0.144369  [ 2048/ 6954]\n",
      "loss: 0.129637  [ 3072/ 6954]\n",
      "loss: 0.150429  [ 4096/ 6954]\n",
      "loss: 0.159023  [ 5120/ 6954]\n",
      "loss: 0.131586  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001161 \n",
      "\n",
      "Epoch 82\n",
      "-------------------------------\n",
      "loss: 0.143136  [    0/ 6954]\n",
      "loss: 0.117579  [ 1024/ 6954]\n",
      "loss: 0.119694  [ 2048/ 6954]\n",
      "loss: 0.131009  [ 3072/ 6954]\n",
      "loss: 0.133817  [ 4096/ 6954]\n",
      "loss: 0.129934  [ 5120/ 6954]\n",
      "loss: 0.159411  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001145 \n",
      "\n",
      "Epoch 83\n",
      "-------------------------------\n",
      "loss: 0.151458  [    0/ 6954]\n",
      "loss: 0.131141  [ 1024/ 6954]\n",
      "loss: 0.145866  [ 2048/ 6954]\n",
      "loss: 0.149550  [ 3072/ 6954]\n",
      "loss: 0.167124  [ 4096/ 6954]\n",
      "loss: 0.125528  [ 5120/ 6954]\n",
      "loss: 0.182734  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001155 \n",
      "\n",
      "Epoch 84\n",
      "-------------------------------\n",
      "loss: 0.132433  [    0/ 6954]\n",
      "loss: 0.127587  [ 1024/ 6954]\n",
      "loss: 0.124759  [ 2048/ 6954]\n",
      "loss: 0.142872  [ 3072/ 6954]\n",
      "loss: 0.164821  [ 4096/ 6954]\n",
      "loss: 0.145041  [ 5120/ 6954]\n",
      "loss: 0.161237  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001149 \n",
      "\n",
      "Epoch 85\n",
      "-------------------------------\n",
      "loss: 0.130663  [    0/ 6954]\n",
      "loss: 0.135405  [ 1024/ 6954]\n",
      "loss: 0.114364  [ 2048/ 6954]\n",
      "loss: 0.136876  [ 3072/ 6954]\n",
      "loss: 0.154985  [ 4096/ 6954]\n",
      "loss: 0.160991  [ 5120/ 6954]\n",
      "loss: 0.110524  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001130 \n",
      "\n",
      "Epoch 86\n",
      "-------------------------------\n",
      "loss: 0.166574  [    0/ 6954]\n",
      "loss: 0.160869  [ 1024/ 6954]\n",
      "loss: 0.119298  [ 2048/ 6954]\n",
      "loss: 0.147648  [ 3072/ 6954]\n",
      "loss: 0.153707  [ 4096/ 6954]\n",
      "loss: 0.132603  [ 5120/ 6954]\n",
      "loss: 0.156255  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001170 \n",
      "\n",
      "Epoch 87\n",
      "-------------------------------\n",
      "loss: 0.139124  [    0/ 6954]\n",
      "loss: 0.152342  [ 1024/ 6954]\n",
      "loss: 0.137223  [ 2048/ 6954]\n",
      "loss: 0.145492  [ 3072/ 6954]\n",
      "loss: 0.163232  [ 4096/ 6954]\n",
      "loss: 0.186666  [ 5120/ 6954]\n",
      "loss: 0.113873  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001354 \n",
      "\n",
      "Epoch 88\n",
      "-------------------------------\n",
      "loss: 0.157411  [    0/ 6954]\n",
      "loss: 0.139881  [ 1024/ 6954]\n",
      "loss: 0.135700  [ 2048/ 6954]\n",
      "loss: 0.143105  [ 3072/ 6954]\n",
      "loss: 0.174276  [ 4096/ 6954]\n",
      "loss: 0.151344  [ 5120/ 6954]\n",
      "loss: 0.119789  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001139 \n",
      "\n",
      "Epoch 89\n",
      "-------------------------------\n",
      "loss: 0.139852  [    0/ 6954]\n",
      "loss: 0.131341  [ 1024/ 6954]\n",
      "loss: 0.143213  [ 2048/ 6954]\n",
      "loss: 0.122404  [ 3072/ 6954]\n",
      "loss: 0.111917  [ 4096/ 6954]\n",
      "loss: 0.129735  [ 5120/ 6954]\n",
      "loss: 0.168084  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001169 \n",
      "\n",
      "Epoch 90\n",
      "-------------------------------\n",
      "loss: 0.117580  [    0/ 6954]\n",
      "loss: 0.124013  [ 1024/ 6954]\n",
      "loss: 0.144260  [ 2048/ 6954]\n",
      "loss: 0.114902  [ 3072/ 6954]\n",
      "loss: 0.148160  [ 4096/ 6954]\n",
      "loss: 0.128815  [ 5120/ 6954]\n",
      "loss: 0.128770  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001180 \n",
      "\n",
      "Epoch 91\n",
      "-------------------------------\n",
      "loss: 0.140376  [    0/ 6954]\n",
      "loss: 0.133563  [ 1024/ 6954]\n",
      "loss: 0.146003  [ 2048/ 6954]\n",
      "loss: 0.184427  [ 3072/ 6954]\n",
      "loss: 0.119000  [ 4096/ 6954]\n",
      "loss: 0.145669  [ 5120/ 6954]\n",
      "loss: 0.127425  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001145 \n",
      "\n",
      "Epoch 92\n",
      "-------------------------------\n",
      "loss: 0.126222  [    0/ 6954]\n",
      "loss: 0.167613  [ 1024/ 6954]\n",
      "loss: 0.127285  [ 2048/ 6954]\n",
      "loss: 0.124449  [ 3072/ 6954]\n",
      "loss: 0.160408  [ 4096/ 6954]\n",
      "loss: 0.164572  [ 5120/ 6954]\n",
      "loss: 0.120559  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001151 \n",
      "\n",
      "Epoch 93\n",
      "-------------------------------\n",
      "loss: 0.148988  [    0/ 6954]\n",
      "loss: 0.122797  [ 1024/ 6954]\n",
      "loss: 0.138679  [ 2048/ 6954]\n",
      "loss: 0.145132  [ 3072/ 6954]\n",
      "loss: 0.110319  [ 4096/ 6954]\n",
      "loss: 0.128161  [ 5120/ 6954]\n",
      "loss: 0.123996  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001179 \n",
      "\n",
      "Epoch 94\n",
      "-------------------------------\n",
      "loss: 0.162006  [    0/ 6954]\n",
      "loss: 0.160735  [ 1024/ 6954]\n",
      "loss: 0.160181  [ 2048/ 6954]\n",
      "loss: 0.170453  [ 3072/ 6954]\n",
      "loss: 0.129913  [ 4096/ 6954]\n",
      "loss: 0.171835  [ 5120/ 6954]\n",
      "loss: 0.134726  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001141 \n",
      "\n",
      "Epoch 95\n",
      "-------------------------------\n",
      "loss: 0.108223  [    0/ 6954]\n",
      "loss: 0.127736  [ 1024/ 6954]\n",
      "loss: 0.168600  [ 2048/ 6954]\n",
      "loss: 0.104914  [ 3072/ 6954]\n",
      "loss: 0.131515  [ 4096/ 6954]\n",
      "loss: 0.140765  [ 5120/ 6954]\n",
      "loss: 0.139120  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001147 \n",
      "\n",
      "Epoch 96\n",
      "-------------------------------\n",
      "loss: 0.171984  [    0/ 6954]\n",
      "loss: 0.122161  [ 1024/ 6954]\n",
      "loss: 0.151418  [ 2048/ 6954]\n",
      "loss: 0.176530  [ 3072/ 6954]\n",
      "loss: 0.132190  [ 4096/ 6954]\n",
      "loss: 0.114254  [ 5120/ 6954]\n",
      "loss: 0.152976  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001131 \n",
      "\n",
      "Epoch 97\n",
      "-------------------------------\n",
      "loss: 0.133709  [    0/ 6954]\n",
      "loss: 0.119814  [ 1024/ 6954]\n",
      "loss: 0.143132  [ 2048/ 6954]\n",
      "loss: 0.123447  [ 3072/ 6954]\n",
      "loss: 0.141146  [ 4096/ 6954]\n",
      "loss: 0.111400  [ 5120/ 6954]\n",
      "loss: 0.128450  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001157 \n",
      "\n",
      "Epoch 98\n",
      "-------------------------------\n",
      "loss: 0.137692  [    0/ 6954]\n",
      "loss: 0.136570  [ 1024/ 6954]\n",
      "loss: 0.132526  [ 2048/ 6954]\n",
      "loss: 0.151294  [ 3072/ 6954]\n",
      "loss: 0.144725  [ 4096/ 6954]\n",
      "loss: 0.142341  [ 5120/ 6954]\n",
      "loss: 0.128014  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001201 \n",
      "\n",
      "Epoch 99\n",
      "-------------------------------\n",
      "loss: 0.160353  [    0/ 6954]\n",
      "loss: 0.166499  [ 1024/ 6954]\n",
      "loss: 0.154382  [ 2048/ 6954]\n",
      "loss: 0.151214  [ 3072/ 6954]\n",
      "loss: 0.149847  [ 4096/ 6954]\n",
      "loss: 0.132436  [ 5120/ 6954]\n",
      "loss: 0.139872  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001127 \n",
      "\n",
      "Epoch 100\n",
      "-------------------------------\n",
      "loss: 0.143126  [    0/ 6954]\n",
      "loss: 0.167695  [ 1024/ 6954]\n",
      "loss: 0.139326  [ 2048/ 6954]\n",
      "loss: 0.116036  [ 3072/ 6954]\n",
      "loss: 0.128441  [ 4096/ 6954]\n",
      "loss: 0.151005  [ 5120/ 6954]\n",
      "loss: 0.136177  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001191 \n",
      "\n",
      "Epoch 101\n",
      "-------------------------------\n",
      "loss: 0.154973  [    0/ 6954]\n",
      "loss: 0.177343  [ 1024/ 6954]\n",
      "loss: 0.138238  [ 2048/ 6954]\n",
      "loss: 0.112767  [ 3072/ 6954]\n",
      "loss: 0.138662  [ 4096/ 6954]\n",
      "loss: 0.148316  [ 5120/ 6954]\n",
      "loss: 0.130068  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001185 \n",
      "\n",
      "Epoch 102\n",
      "-------------------------------\n",
      "loss: 0.166179  [    0/ 6954]\n",
      "loss: 0.119344  [ 1024/ 6954]\n",
      "loss: 0.143435  [ 2048/ 6954]\n",
      "loss: 0.133295  [ 3072/ 6954]\n",
      "loss: 0.117262  [ 4096/ 6954]\n",
      "loss: 0.179045  [ 5120/ 6954]\n",
      "loss: 0.145574  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001141 \n",
      "\n",
      "Epoch 103\n",
      "-------------------------------\n",
      "loss: 0.128897  [    0/ 6954]\n",
      "loss: 0.124536  [ 1024/ 6954]\n",
      "loss: 0.123183  [ 2048/ 6954]\n",
      "loss: 0.141002  [ 3072/ 6954]\n",
      "loss: 0.134994  [ 4096/ 6954]\n",
      "loss: 0.151596  [ 5120/ 6954]\n",
      "loss: 0.126839  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001158 \n",
      "\n",
      "Epoch 104\n",
      "-------------------------------\n",
      "loss: 0.172203  [    0/ 6954]\n",
      "loss: 0.143913  [ 1024/ 6954]\n",
      "loss: 0.139057  [ 2048/ 6954]\n",
      "loss: 0.101365  [ 3072/ 6954]\n",
      "loss: 0.158370  [ 4096/ 6954]\n",
      "loss: 0.133440  [ 5120/ 6954]\n",
      "loss: 0.155264  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.3%, Avg loss: 0.001142 \n",
      "\n",
      "Epoch 105\n",
      "-------------------------------\n",
      "loss: 0.106394  [    0/ 6954]\n",
      "loss: 0.100515  [ 1024/ 6954]\n",
      "loss: 0.147425  [ 2048/ 6954]\n",
      "loss: 0.127913  [ 3072/ 6954]\n",
      "loss: 0.149054  [ 4096/ 6954]\n",
      "loss: 0.171362  [ 5120/ 6954]\n",
      "loss: 0.137128  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 106\n",
      "-------------------------------\n",
      "loss: 0.140354  [    0/ 6954]\n",
      "loss: 0.149322  [ 1024/ 6954]\n",
      "loss: 0.161643  [ 2048/ 6954]\n",
      "loss: 0.125525  [ 3072/ 6954]\n",
      "loss: 0.141271  [ 4096/ 6954]\n",
      "loss: 0.129535  [ 5120/ 6954]\n",
      "loss: 0.125014  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.4%, Avg loss: 0.001143 \n",
      "\n",
      "Epoch 107\n",
      "-------------------------------\n",
      "loss: 0.136588  [    0/ 6954]\n",
      "loss: 0.119425  [ 1024/ 6954]\n",
      "loss: 0.129536  [ 2048/ 6954]\n",
      "loss: 0.136351  [ 3072/ 6954]\n",
      "loss: 0.115831  [ 4096/ 6954]\n",
      "loss: 0.129338  [ 5120/ 6954]\n",
      "loss: 0.150743  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001157 \n",
      "\n",
      "Epoch 108\n",
      "-------------------------------\n",
      "loss: 0.121800  [    0/ 6954]\n",
      "loss: 0.167798  [ 1024/ 6954]\n",
      "loss: 0.187552  [ 2048/ 6954]\n",
      "loss: 0.116103  [ 3072/ 6954]\n",
      "loss: 0.154170  [ 4096/ 6954]\n",
      "loss: 0.150224  [ 5120/ 6954]\n",
      "loss: 0.155831  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001163 \n",
      "\n",
      "Epoch 109\n",
      "-------------------------------\n",
      "loss: 0.117949  [    0/ 6954]\n",
      "loss: 0.115942  [ 1024/ 6954]\n",
      "loss: 0.152381  [ 2048/ 6954]\n",
      "loss: 0.122028  [ 3072/ 6954]\n",
      "loss: 0.175580  [ 4096/ 6954]\n",
      "loss: 0.149738  [ 5120/ 6954]\n",
      "loss: 0.119674  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001147 \n",
      "\n",
      "Epoch 110\n",
      "-------------------------------\n",
      "loss: 0.134443  [    0/ 6954]\n",
      "loss: 0.115577  [ 1024/ 6954]\n",
      "loss: 0.161959  [ 2048/ 6954]\n",
      "loss: 0.111953  [ 3072/ 6954]\n",
      "loss: 0.144417  [ 4096/ 6954]\n",
      "loss: 0.146540  [ 5120/ 6954]\n",
      "loss: 0.126139  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 111\n",
      "-------------------------------\n",
      "loss: 0.132749  [    0/ 6954]\n",
      "loss: 0.151841  [ 1024/ 6954]\n",
      "loss: 0.126664  [ 2048/ 6954]\n",
      "loss: 0.160553  [ 3072/ 6954]\n",
      "loss: 0.155528  [ 4096/ 6954]\n",
      "loss: 0.114713  [ 5120/ 6954]\n",
      "loss: 0.121627  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.4%, Avg loss: 0.001136 \n",
      "\n",
      "Epoch 112\n",
      "-------------------------------\n",
      "loss: 0.138101  [    0/ 6954]\n",
      "loss: 0.140597  [ 1024/ 6954]\n",
      "loss: 0.146424  [ 2048/ 6954]\n",
      "loss: 0.114054  [ 3072/ 6954]\n",
      "loss: 0.142732  [ 4096/ 6954]\n",
      "loss: 0.113400  [ 5120/ 6954]\n",
      "loss: 0.141676  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001158 \n",
      "\n",
      "Epoch 113\n",
      "-------------------------------\n",
      "loss: 0.149752  [    0/ 6954]\n",
      "loss: 0.141619  [ 1024/ 6954]\n",
      "loss: 0.168530  [ 2048/ 6954]\n",
      "loss: 0.187872  [ 3072/ 6954]\n",
      "loss: 0.131525  [ 4096/ 6954]\n",
      "loss: 0.131993  [ 5120/ 6954]\n",
      "loss: 0.134228  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001153 \n",
      "\n",
      "Epoch 114\n",
      "-------------------------------\n",
      "loss: 0.141875  [    0/ 6954]\n",
      "loss: 0.152449  [ 1024/ 6954]\n",
      "loss: 0.124927  [ 2048/ 6954]\n",
      "loss: 0.120357  [ 3072/ 6954]\n",
      "loss: 0.088098  [ 4096/ 6954]\n",
      "loss: 0.149751  [ 5120/ 6954]\n",
      "loss: 0.141476  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001163 \n",
      "\n",
      "Epoch 115\n",
      "-------------------------------\n",
      "loss: 0.131129  [    0/ 6954]\n",
      "loss: 0.136935  [ 1024/ 6954]\n",
      "loss: 0.137074  [ 2048/ 6954]\n",
      "loss: 0.188222  [ 3072/ 6954]\n",
      "loss: 0.146248  [ 4096/ 6954]\n",
      "loss: 0.129012  [ 5120/ 6954]\n",
      "loss: 0.163502  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001188 \n",
      "\n",
      "Epoch 116\n",
      "-------------------------------\n",
      "loss: 0.148096  [    0/ 6954]\n",
      "loss: 0.144814  [ 1024/ 6954]\n",
      "loss: 0.136319  [ 2048/ 6954]\n",
      "loss: 0.136251  [ 3072/ 6954]\n",
      "loss: 0.121333  [ 4096/ 6954]\n",
      "loss: 0.128830  [ 5120/ 6954]\n",
      "loss: 0.172874  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001138 \n",
      "\n",
      "Epoch 117\n",
      "-------------------------------\n",
      "loss: 0.147438  [    0/ 6954]\n",
      "loss: 0.116260  [ 1024/ 6954]\n",
      "loss: 0.123612  [ 2048/ 6954]\n",
      "loss: 0.128002  [ 3072/ 6954]\n",
      "loss: 0.121411  [ 4096/ 6954]\n",
      "loss: 0.161001  [ 5120/ 6954]\n",
      "loss: 0.120478  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001174 \n",
      "\n",
      "Epoch 118\n",
      "-------------------------------\n",
      "loss: 0.119020  [    0/ 6954]\n",
      "loss: 0.144154  [ 1024/ 6954]\n",
      "loss: 0.158777  [ 2048/ 6954]\n",
      "loss: 0.135562  [ 3072/ 6954]\n",
      "loss: 0.103045  [ 4096/ 6954]\n",
      "loss: 0.160931  [ 5120/ 6954]\n",
      "loss: 0.147667  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.1%, Avg loss: 0.001141 \n",
      "\n",
      "Epoch 119\n",
      "-------------------------------\n",
      "loss: 0.142370  [    0/ 6954]\n",
      "loss: 0.103845  [ 1024/ 6954]\n",
      "loss: 0.127667  [ 2048/ 6954]\n",
      "loss: 0.165096  [ 3072/ 6954]\n",
      "loss: 0.145394  [ 4096/ 6954]\n",
      "loss: 0.128827  [ 5120/ 6954]\n",
      "loss: 0.175490  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001156 \n",
      "\n",
      "Epoch 120\n",
      "-------------------------------\n",
      "loss: 0.151540  [    0/ 6954]\n",
      "loss: 0.148887  [ 1024/ 6954]\n",
      "loss: 0.148878  [ 2048/ 6954]\n",
      "loss: 0.120657  [ 3072/ 6954]\n",
      "loss: 0.127557  [ 4096/ 6954]\n",
      "loss: 0.152765  [ 5120/ 6954]\n",
      "loss: 0.119796  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.9%, Avg loss: 0.001153 \n",
      "\n",
      "Epoch 121\n",
      "-------------------------------\n",
      "loss: 0.111332  [    0/ 6954]\n",
      "loss: 0.153332  [ 1024/ 6954]\n",
      "loss: 0.157001  [ 2048/ 6954]\n",
      "loss: 0.171301  [ 3072/ 6954]\n",
      "loss: 0.142808  [ 4096/ 6954]\n",
      "loss: 0.135186  [ 5120/ 6954]\n",
      "loss: 0.155727  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001147 \n",
      "\n",
      "Epoch 122\n",
      "-------------------------------\n",
      "loss: 0.147551  [    0/ 6954]\n",
      "loss: 0.136166  [ 1024/ 6954]\n",
      "loss: 0.127017  [ 2048/ 6954]\n",
      "loss: 0.132900  [ 3072/ 6954]\n",
      "loss: 0.167823  [ 4096/ 6954]\n",
      "loss: 0.133430  [ 5120/ 6954]\n",
      "loss: 0.170523  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001155 \n",
      "\n",
      "Epoch 123\n",
      "-------------------------------\n",
      "loss: 0.135466  [    0/ 6954]\n",
      "loss: 0.168476  [ 1024/ 6954]\n",
      "loss: 0.112240  [ 2048/ 6954]\n",
      "loss: 0.130720  [ 3072/ 6954]\n",
      "loss: 0.150722  [ 4096/ 6954]\n",
      "loss: 0.136126  [ 5120/ 6954]\n",
      "loss: 0.133351  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001151 \n",
      "\n",
      "Epoch 124\n",
      "-------------------------------\n",
      "loss: 0.104304  [    0/ 6954]\n",
      "loss: 0.120145  [ 1024/ 6954]\n",
      "loss: 0.110245  [ 2048/ 6954]\n",
      "loss: 0.112009  [ 3072/ 6954]\n",
      "loss: 0.137486  [ 4096/ 6954]\n",
      "loss: 0.157465  [ 5120/ 6954]\n",
      "loss: 0.119269  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001158 \n",
      "\n",
      "Epoch 125\n",
      "-------------------------------\n",
      "loss: 0.153348  [    0/ 6954]\n",
      "loss: 0.152673  [ 1024/ 6954]\n",
      "loss: 0.122550  [ 2048/ 6954]\n",
      "loss: 0.084171  [ 3072/ 6954]\n",
      "loss: 0.146062  [ 4096/ 6954]\n",
      "loss: 0.130003  [ 5120/ 6954]\n",
      "loss: 0.128598  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001153 \n",
      "\n",
      "Epoch 126\n",
      "-------------------------------\n",
      "loss: 0.125140  [    0/ 6954]\n",
      "loss: 0.118525  [ 1024/ 6954]\n",
      "loss: 0.138421  [ 2048/ 6954]\n",
      "loss: 0.115901  [ 3072/ 6954]\n",
      "loss: 0.141084  [ 4096/ 6954]\n",
      "loss: 0.155384  [ 5120/ 6954]\n",
      "loss: 0.142238  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001161 \n",
      "\n",
      "Epoch 127\n",
      "-------------------------------\n",
      "loss: 0.135955  [    0/ 6954]\n",
      "loss: 0.126194  [ 1024/ 6954]\n",
      "loss: 0.110667  [ 2048/ 6954]\n",
      "loss: 0.137478  [ 3072/ 6954]\n",
      "loss: 0.121551  [ 4096/ 6954]\n",
      "loss: 0.140221  [ 5120/ 6954]\n",
      "loss: 0.139027  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001156 \n",
      "\n",
      "Epoch 128\n",
      "-------------------------------\n",
      "loss: 0.121802  [    0/ 6954]\n",
      "loss: 0.127529  [ 1024/ 6954]\n",
      "loss: 0.165372  [ 2048/ 6954]\n",
      "loss: 0.126128  [ 3072/ 6954]\n",
      "loss: 0.138603  [ 4096/ 6954]\n",
      "loss: 0.145153  [ 5120/ 6954]\n",
      "loss: 0.144336  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001182 \n",
      "\n",
      "Epoch 129\n",
      "-------------------------------\n",
      "loss: 0.129863  [    0/ 6954]\n",
      "loss: 0.126555  [ 1024/ 6954]\n",
      "loss: 0.119300  [ 2048/ 6954]\n",
      "loss: 0.122132  [ 3072/ 6954]\n",
      "loss: 0.105240  [ 4096/ 6954]\n",
      "loss: 0.110419  [ 5120/ 6954]\n",
      "loss: 0.110246  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001150 \n",
      "\n",
      "Epoch 130\n",
      "-------------------------------\n",
      "loss: 0.094115  [    0/ 6954]\n",
      "loss: 0.137387  [ 1024/ 6954]\n",
      "loss: 0.127628  [ 2048/ 6954]\n",
      "loss: 0.141781  [ 3072/ 6954]\n",
      "loss: 0.141647  [ 4096/ 6954]\n",
      "loss: 0.101569  [ 5120/ 6954]\n",
      "loss: 0.128200  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.3%, Avg loss: 0.001149 \n",
      "\n",
      "Epoch 131\n",
      "-------------------------------\n",
      "loss: 0.118322  [    0/ 6954]\n",
      "loss: 0.128495  [ 1024/ 6954]\n",
      "loss: 0.149877  [ 2048/ 6954]\n",
      "loss: 0.136679  [ 3072/ 6954]\n",
      "loss: 0.141460  [ 4096/ 6954]\n",
      "loss: 0.118426  [ 5120/ 6954]\n",
      "loss: 0.133175  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001151 \n",
      "\n",
      "Epoch 132\n",
      "-------------------------------\n",
      "loss: 0.106998  [    0/ 6954]\n",
      "loss: 0.135356  [ 1024/ 6954]\n",
      "loss: 0.147409  [ 2048/ 6954]\n",
      "loss: 0.116962  [ 3072/ 6954]\n",
      "loss: 0.143920  [ 4096/ 6954]\n",
      "loss: 0.163871  [ 5120/ 6954]\n",
      "loss: 0.131815  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001164 \n",
      "\n",
      "Epoch 133\n",
      "-------------------------------\n",
      "loss: 0.114040  [    0/ 6954]\n",
      "loss: 0.131057  [ 1024/ 6954]\n",
      "loss: 0.134799  [ 2048/ 6954]\n",
      "loss: 0.159231  [ 3072/ 6954]\n",
      "loss: 0.119487  [ 4096/ 6954]\n",
      "loss: 0.107976  [ 5120/ 6954]\n",
      "loss: 0.152419  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001158 \n",
      "\n",
      "Epoch 134\n",
      "-------------------------------\n",
      "loss: 0.104071  [    0/ 6954]\n",
      "loss: 0.170597  [ 1024/ 6954]\n",
      "loss: 0.157905  [ 2048/ 6954]\n",
      "loss: 0.138556  [ 3072/ 6954]\n",
      "loss: 0.170575  [ 4096/ 6954]\n",
      "loss: 0.155087  [ 5120/ 6954]\n",
      "loss: 0.142405  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001184 \n",
      "\n",
      "Epoch 135\n",
      "-------------------------------\n",
      "loss: 0.110398  [    0/ 6954]\n",
      "loss: 0.140298  [ 1024/ 6954]\n",
      "loss: 0.111362  [ 2048/ 6954]\n",
      "loss: 0.131453  [ 3072/ 6954]\n",
      "loss: 0.105564  [ 4096/ 6954]\n",
      "loss: 0.128305  [ 5120/ 6954]\n",
      "loss: 0.146343  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001143 \n",
      "\n",
      "Epoch 136\n",
      "-------------------------------\n",
      "loss: 0.110757  [    0/ 6954]\n",
      "loss: 0.119812  [ 1024/ 6954]\n",
      "loss: 0.111084  [ 2048/ 6954]\n",
      "loss: 0.145321  [ 3072/ 6954]\n",
      "loss: 0.113657  [ 4096/ 6954]\n",
      "loss: 0.133063  [ 5120/ 6954]\n",
      "loss: 0.161419  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.7%, Avg loss: 0.001187 \n",
      "\n",
      "Epoch 137\n",
      "-------------------------------\n",
      "loss: 0.169419  [    0/ 6954]\n",
      "loss: 0.121810  [ 1024/ 6954]\n",
      "loss: 0.142921  [ 2048/ 6954]\n",
      "loss: 0.115678  [ 3072/ 6954]\n",
      "loss: 0.139364  [ 4096/ 6954]\n",
      "loss: 0.153345  [ 5120/ 6954]\n",
      "loss: 0.101238  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001147 \n",
      "\n",
      "Epoch 138\n",
      "-------------------------------\n",
      "loss: 0.135554  [    0/ 6954]\n",
      "loss: 0.143060  [ 1024/ 6954]\n",
      "loss: 0.163646  [ 2048/ 6954]\n",
      "loss: 0.150926  [ 3072/ 6954]\n",
      "loss: 0.146219  [ 4096/ 6954]\n",
      "loss: 0.121816  [ 5120/ 6954]\n",
      "loss: 0.147197  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001184 \n",
      "\n",
      "Epoch 139\n",
      "-------------------------------\n",
      "loss: 0.133040  [    0/ 6954]\n",
      "loss: 0.142619  [ 1024/ 6954]\n",
      "loss: 0.136343  [ 2048/ 6954]\n",
      "loss: 0.111135  [ 3072/ 6954]\n",
      "loss: 0.154065  [ 4096/ 6954]\n",
      "loss: 0.127035  [ 5120/ 6954]\n",
      "loss: 0.117377  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001143 \n",
      "\n",
      "Epoch 140\n",
      "-------------------------------\n",
      "loss: 0.137790  [    0/ 6954]\n",
      "loss: 0.121416  [ 1024/ 6954]\n",
      "loss: 0.141639  [ 2048/ 6954]\n",
      "loss: 0.136814  [ 3072/ 6954]\n",
      "loss: 0.145864  [ 4096/ 6954]\n",
      "loss: 0.156232  [ 5120/ 6954]\n",
      "loss: 0.141904  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001166 \n",
      "\n",
      "Epoch 141\n",
      "-------------------------------\n",
      "loss: 0.158116  [    0/ 6954]\n",
      "loss: 0.125095  [ 1024/ 6954]\n",
      "loss: 0.144841  [ 2048/ 6954]\n",
      "loss: 0.141224  [ 3072/ 6954]\n",
      "loss: 0.132952  [ 4096/ 6954]\n",
      "loss: 0.094209  [ 5120/ 6954]\n",
      "loss: 0.124203  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001144 \n",
      "\n",
      "Epoch 142\n",
      "-------------------------------\n",
      "loss: 0.122387  [    0/ 6954]\n",
      "loss: 0.160227  [ 1024/ 6954]\n",
      "loss: 0.124673  [ 2048/ 6954]\n",
      "loss: 0.154979  [ 3072/ 6954]\n",
      "loss: 0.121336  [ 4096/ 6954]\n",
      "loss: 0.130808  [ 5120/ 6954]\n",
      "loss: 0.158466  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001161 \n",
      "\n",
      "Epoch 143\n",
      "-------------------------------\n",
      "loss: 0.147042  [    0/ 6954]\n",
      "loss: 0.151206  [ 1024/ 6954]\n",
      "loss: 0.108563  [ 2048/ 6954]\n",
      "loss: 0.109907  [ 3072/ 6954]\n",
      "loss: 0.130362  [ 4096/ 6954]\n",
      "loss: 0.148451  [ 5120/ 6954]\n",
      "loss: 0.119433  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001172 \n",
      "\n",
      "Epoch 144\n",
      "-------------------------------\n",
      "loss: 0.165059  [    0/ 6954]\n",
      "loss: 0.118437  [ 1024/ 6954]\n",
      "loss: 0.099557  [ 2048/ 6954]\n",
      "loss: 0.128373  [ 3072/ 6954]\n",
      "loss: 0.126117  [ 4096/ 6954]\n",
      "loss: 0.169783  [ 5120/ 6954]\n",
      "loss: 0.151488  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001270 \n",
      "\n",
      "Epoch 145\n",
      "-------------------------------\n",
      "loss: 0.170647  [    0/ 6954]\n",
      "loss: 0.140905  [ 1024/ 6954]\n",
      "loss: 0.145221  [ 2048/ 6954]\n",
      "loss: 0.127536  [ 3072/ 6954]\n",
      "loss: 0.121176  [ 4096/ 6954]\n",
      "loss: 0.141186  [ 5120/ 6954]\n",
      "loss: 0.127349  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001168 \n",
      "\n",
      "Epoch 146\n",
      "-------------------------------\n",
      "loss: 0.127987  [    0/ 6954]\n",
      "loss: 0.138530  [ 1024/ 6954]\n",
      "loss: 0.135421  [ 2048/ 6954]\n",
      "loss: 0.131416  [ 3072/ 6954]\n",
      "loss: 0.125148  [ 4096/ 6954]\n",
      "loss: 0.121946  [ 5120/ 6954]\n",
      "loss: 0.123444  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001160 \n",
      "\n",
      "Epoch 147\n",
      "-------------------------------\n",
      "loss: 0.129163  [    0/ 6954]\n",
      "loss: 0.141442  [ 1024/ 6954]\n",
      "loss: 0.103615  [ 2048/ 6954]\n",
      "loss: 0.113313  [ 3072/ 6954]\n",
      "loss: 0.131120  [ 4096/ 6954]\n",
      "loss: 0.126759  [ 5120/ 6954]\n",
      "loss: 0.121198  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001151 \n",
      "\n",
      "Epoch 148\n",
      "-------------------------------\n",
      "loss: 0.122814  [    0/ 6954]\n",
      "loss: 0.128463  [ 1024/ 6954]\n",
      "loss: 0.128651  [ 2048/ 6954]\n",
      "loss: 0.113855  [ 3072/ 6954]\n",
      "loss: 0.152299  [ 4096/ 6954]\n",
      "loss: 0.152100  [ 5120/ 6954]\n",
      "loss: 0.163143  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001168 \n",
      "\n",
      "Epoch 149\n",
      "-------------------------------\n",
      "loss: 0.136455  [    0/ 6954]\n",
      "loss: 0.100485  [ 1024/ 6954]\n",
      "loss: 0.138591  [ 2048/ 6954]\n",
      "loss: 0.112949  [ 3072/ 6954]\n",
      "loss: 0.126514  [ 4096/ 6954]\n",
      "loss: 0.116044  [ 5120/ 6954]\n",
      "loss: 0.138476  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001160 \n",
      "\n",
      "Epoch 150\n",
      "-------------------------------\n",
      "loss: 0.128246  [    0/ 6954]\n",
      "loss: 0.113467  [ 1024/ 6954]\n",
      "loss: 0.155366  [ 2048/ 6954]\n",
      "loss: 0.137395  [ 3072/ 6954]\n",
      "loss: 0.119784  [ 4096/ 6954]\n",
      "loss: 0.146188  [ 5120/ 6954]\n",
      "loss: 0.113888  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.3%, Avg loss: 0.001143 \n",
      "\n",
      "Epoch 151\n",
      "-------------------------------\n",
      "loss: 0.145280  [    0/ 6954]\n",
      "loss: 0.113057  [ 1024/ 6954]\n",
      "loss: 0.115585  [ 2048/ 6954]\n",
      "loss: 0.124425  [ 3072/ 6954]\n",
      "loss: 0.135790  [ 4096/ 6954]\n",
      "loss: 0.122101  [ 5120/ 6954]\n",
      "loss: 0.132651  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001127 \n",
      "\n",
      "Epoch 152\n",
      "-------------------------------\n",
      "loss: 0.100667  [    0/ 6954]\n",
      "loss: 0.121834  [ 1024/ 6954]\n",
      "loss: 0.122759  [ 2048/ 6954]\n",
      "loss: 0.134141  [ 3072/ 6954]\n",
      "loss: 0.124951  [ 4096/ 6954]\n",
      "loss: 0.129700  [ 5120/ 6954]\n",
      "loss: 0.146034  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001172 \n",
      "\n",
      "Epoch 153\n",
      "-------------------------------\n",
      "loss: 0.100558  [    0/ 6954]\n",
      "loss: 0.134327  [ 1024/ 6954]\n",
      "loss: 0.116678  [ 2048/ 6954]\n",
      "loss: 0.114651  [ 3072/ 6954]\n",
      "loss: 0.151289  [ 4096/ 6954]\n",
      "loss: 0.121715  [ 5120/ 6954]\n",
      "loss: 0.139489  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001145 \n",
      "\n",
      "Epoch 154\n",
      "-------------------------------\n",
      "loss: 0.127256  [    0/ 6954]\n",
      "loss: 0.094907  [ 1024/ 6954]\n",
      "loss: 0.135194  [ 2048/ 6954]\n",
      "loss: 0.151726  [ 3072/ 6954]\n",
      "loss: 0.122231  [ 4096/ 6954]\n",
      "loss: 0.138674  [ 5120/ 6954]\n",
      "loss: 0.150898  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001160 \n",
      "\n",
      "Epoch 155\n",
      "-------------------------------\n",
      "loss: 0.129388  [    0/ 6954]\n",
      "loss: 0.125069  [ 1024/ 6954]\n",
      "loss: 0.093112  [ 2048/ 6954]\n",
      "loss: 0.124725  [ 3072/ 6954]\n",
      "loss: 0.120211  [ 4096/ 6954]\n",
      "loss: 0.134785  [ 5120/ 6954]\n",
      "loss: 0.150567  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001164 \n",
      "\n",
      "Epoch 156\n",
      "-------------------------------\n",
      "loss: 0.133501  [    0/ 6954]\n",
      "loss: 0.152638  [ 1024/ 6954]\n",
      "loss: 0.119399  [ 2048/ 6954]\n",
      "loss: 0.137542  [ 3072/ 6954]\n",
      "loss: 0.113635  [ 4096/ 6954]\n",
      "loss: 0.120271  [ 5120/ 6954]\n",
      "loss: 0.149457  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001180 \n",
      "\n",
      "Epoch 157\n",
      "-------------------------------\n",
      "loss: 0.120341  [    0/ 6954]\n",
      "loss: 0.121598  [ 1024/ 6954]\n",
      "loss: 0.154769  [ 2048/ 6954]\n",
      "loss: 0.127180  [ 3072/ 6954]\n",
      "loss: 0.155728  [ 4096/ 6954]\n",
      "loss: 0.143706  [ 5120/ 6954]\n",
      "loss: 0.159866  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001156 \n",
      "\n",
      "Epoch 158\n",
      "-------------------------------\n",
      "loss: 0.090187  [    0/ 6954]\n",
      "loss: 0.107579  [ 1024/ 6954]\n",
      "loss: 0.149843  [ 2048/ 6954]\n",
      "loss: 0.111170  [ 3072/ 6954]\n",
      "loss: 0.152240  [ 4096/ 6954]\n",
      "loss: 0.129604  [ 5120/ 6954]\n",
      "loss: 0.179100  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001171 \n",
      "\n",
      "Epoch 159\n",
      "-------------------------------\n",
      "loss: 0.106571  [    0/ 6954]\n",
      "loss: 0.143386  [ 1024/ 6954]\n",
      "loss: 0.156804  [ 2048/ 6954]\n",
      "loss: 0.154408  [ 3072/ 6954]\n",
      "loss: 0.169214  [ 4096/ 6954]\n",
      "loss: 0.131660  [ 5120/ 6954]\n",
      "loss: 0.098943  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001139 \n",
      "\n",
      "Epoch 160\n",
      "-------------------------------\n",
      "loss: 0.129121  [    0/ 6954]\n",
      "loss: 0.107452  [ 1024/ 6954]\n",
      "loss: 0.143160  [ 2048/ 6954]\n",
      "loss: 0.120668  [ 3072/ 6954]\n",
      "loss: 0.133473  [ 4096/ 6954]\n",
      "loss: 0.095291  [ 5120/ 6954]\n",
      "loss: 0.121158  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001148 \n",
      "\n",
      "Epoch 161\n",
      "-------------------------------\n",
      "loss: 0.127996  [    0/ 6954]\n",
      "loss: 0.141343  [ 1024/ 6954]\n",
      "loss: 0.133848  [ 2048/ 6954]\n",
      "loss: 0.120498  [ 3072/ 6954]\n",
      "loss: 0.115712  [ 4096/ 6954]\n",
      "loss: 0.132084  [ 5120/ 6954]\n",
      "loss: 0.134401  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001173 \n",
      "\n",
      "Epoch 162\n",
      "-------------------------------\n",
      "loss: 0.112944  [    0/ 6954]\n",
      "loss: 0.120068  [ 1024/ 6954]\n",
      "loss: 0.141577  [ 2048/ 6954]\n",
      "loss: 0.109217  [ 3072/ 6954]\n",
      "loss: 0.144822  [ 4096/ 6954]\n",
      "loss: 0.137997  [ 5120/ 6954]\n",
      "loss: 0.149211  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001172 \n",
      "\n",
      "Epoch 163\n",
      "-------------------------------\n",
      "loss: 0.117852  [    0/ 6954]\n",
      "loss: 0.117250  [ 1024/ 6954]\n",
      "loss: 0.104962  [ 2048/ 6954]\n",
      "loss: 0.107577  [ 3072/ 6954]\n",
      "loss: 0.142006  [ 4096/ 6954]\n",
      "loss: 0.126265  [ 5120/ 6954]\n",
      "loss: 0.094396  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001191 \n",
      "\n",
      "Epoch 164\n",
      "-------------------------------\n",
      "loss: 0.109178  [    0/ 6954]\n",
      "loss: 0.141841  [ 1024/ 6954]\n",
      "loss: 0.135928  [ 2048/ 6954]\n",
      "loss: 0.095912  [ 3072/ 6954]\n",
      "loss: 0.129347  [ 4096/ 6954]\n",
      "loss: 0.084669  [ 5120/ 6954]\n",
      "loss: 0.146477  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001212 \n",
      "\n",
      "Epoch 165\n",
      "-------------------------------\n",
      "loss: 0.084400  [    0/ 6954]\n",
      "loss: 0.147566  [ 1024/ 6954]\n",
      "loss: 0.088460  [ 2048/ 6954]\n",
      "loss: 0.111422  [ 3072/ 6954]\n",
      "loss: 0.094710  [ 4096/ 6954]\n",
      "loss: 0.111630  [ 5120/ 6954]\n",
      "loss: 0.123509  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001195 \n",
      "\n",
      "Epoch 166\n",
      "-------------------------------\n",
      "loss: 0.134015  [    0/ 6954]\n",
      "loss: 0.130959  [ 1024/ 6954]\n",
      "loss: 0.137673  [ 2048/ 6954]\n",
      "loss: 0.131721  [ 3072/ 6954]\n",
      "loss: 0.119311  [ 4096/ 6954]\n",
      "loss: 0.157248  [ 5120/ 6954]\n",
      "loss: 0.123750  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001211 \n",
      "\n",
      "Epoch 167\n",
      "-------------------------------\n",
      "loss: 0.130446  [    0/ 6954]\n",
      "loss: 0.119885  [ 1024/ 6954]\n",
      "loss: 0.143181  [ 2048/ 6954]\n",
      "loss: 0.153651  [ 3072/ 6954]\n",
      "loss: 0.122566  [ 4096/ 6954]\n",
      "loss: 0.143604  [ 5120/ 6954]\n",
      "loss: 0.113364  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001192 \n",
      "\n",
      "Epoch 168\n",
      "-------------------------------\n",
      "loss: 0.105552  [    0/ 6954]\n",
      "loss: 0.152620  [ 1024/ 6954]\n",
      "loss: 0.114013  [ 2048/ 6954]\n",
      "loss: 0.130453  [ 3072/ 6954]\n",
      "loss: 0.139840  [ 4096/ 6954]\n",
      "loss: 0.114592  [ 5120/ 6954]\n",
      "loss: 0.116458  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.9%, Avg loss: 0.001183 \n",
      "\n",
      "Epoch 169\n",
      "-------------------------------\n",
      "loss: 0.132840  [    0/ 6954]\n",
      "loss: 0.119804  [ 1024/ 6954]\n",
      "loss: 0.116438  [ 2048/ 6954]\n",
      "loss: 0.098526  [ 3072/ 6954]\n",
      "loss: 0.100756  [ 4096/ 6954]\n",
      "loss: 0.128469  [ 5120/ 6954]\n",
      "loss: 0.105278  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001198 \n",
      "\n",
      "Epoch 170\n",
      "-------------------------------\n",
      "loss: 0.129258  [    0/ 6954]\n",
      "loss: 0.088820  [ 1024/ 6954]\n",
      "loss: 0.113558  [ 2048/ 6954]\n",
      "loss: 0.089543  [ 3072/ 6954]\n",
      "loss: 0.126684  [ 4096/ 6954]\n",
      "loss: 0.103590  [ 5120/ 6954]\n",
      "loss: 0.120120  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001180 \n",
      "\n",
      "Epoch 171\n",
      "-------------------------------\n",
      "loss: 0.105858  [    0/ 6954]\n",
      "loss: 0.111005  [ 1024/ 6954]\n",
      "loss: 0.132857  [ 2048/ 6954]\n",
      "loss: 0.143921  [ 3072/ 6954]\n",
      "loss: 0.129990  [ 4096/ 6954]\n",
      "loss: 0.099145  [ 5120/ 6954]\n",
      "loss: 0.127619  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.9%, Avg loss: 0.001171 \n",
      "\n",
      "Epoch 172\n",
      "-------------------------------\n",
      "loss: 0.126048  [    0/ 6954]\n",
      "loss: 0.117022  [ 1024/ 6954]\n",
      "loss: 0.121280  [ 2048/ 6954]\n",
      "loss: 0.094437  [ 3072/ 6954]\n",
      "loss: 0.171148  [ 4096/ 6954]\n",
      "loss: 0.159745  [ 5120/ 6954]\n",
      "loss: 0.112006  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001237 \n",
      "\n",
      "Epoch 173\n",
      "-------------------------------\n",
      "loss: 0.168898  [    0/ 6954]\n",
      "loss: 0.125394  [ 1024/ 6954]\n",
      "loss: 0.156467  [ 2048/ 6954]\n",
      "loss: 0.117986  [ 3072/ 6954]\n",
      "loss: 0.137243  [ 4096/ 6954]\n",
      "loss: 0.118422  [ 5120/ 6954]\n",
      "loss: 0.110665  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001139 \n",
      "\n",
      "Epoch 174\n",
      "-------------------------------\n",
      "loss: 0.142495  [    0/ 6954]\n",
      "loss: 0.140136  [ 1024/ 6954]\n",
      "loss: 0.111761  [ 2048/ 6954]\n",
      "loss: 0.129349  [ 3072/ 6954]\n",
      "loss: 0.105560  [ 4096/ 6954]\n",
      "loss: 0.152818  [ 5120/ 6954]\n",
      "loss: 0.141081  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001152 \n",
      "\n",
      "Epoch 175\n",
      "-------------------------------\n",
      "loss: 0.121595  [    0/ 6954]\n",
      "loss: 0.150465  [ 1024/ 6954]\n",
      "loss: 0.141320  [ 2048/ 6954]\n",
      "loss: 0.153894  [ 3072/ 6954]\n",
      "loss: 0.129333  [ 4096/ 6954]\n",
      "loss: 0.132640  [ 5120/ 6954]\n",
      "loss: 0.162842  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001169 \n",
      "\n",
      "Epoch 176\n",
      "-------------------------------\n",
      "loss: 0.111635  [    0/ 6954]\n",
      "loss: 0.110881  [ 1024/ 6954]\n",
      "loss: 0.120907  [ 2048/ 6954]\n",
      "loss: 0.123245  [ 3072/ 6954]\n",
      "loss: 0.117447  [ 4096/ 6954]\n",
      "loss: 0.145666  [ 5120/ 6954]\n",
      "loss: 0.111063  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001189 \n",
      "\n",
      "Epoch 177\n",
      "-------------------------------\n",
      "loss: 0.121485  [    0/ 6954]\n",
      "loss: 0.097474  [ 1024/ 6954]\n",
      "loss: 0.111596  [ 2048/ 6954]\n",
      "loss: 0.168015  [ 3072/ 6954]\n",
      "loss: 0.085589  [ 4096/ 6954]\n",
      "loss: 0.147637  [ 5120/ 6954]\n",
      "loss: 0.131494  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001204 \n",
      "\n",
      "Epoch 178\n",
      "-------------------------------\n",
      "loss: 0.109512  [    0/ 6954]\n",
      "loss: 0.109405  [ 1024/ 6954]\n",
      "loss: 0.127458  [ 2048/ 6954]\n",
      "loss: 0.119746  [ 3072/ 6954]\n",
      "loss: 0.109756  [ 4096/ 6954]\n",
      "loss: 0.150224  [ 5120/ 6954]\n",
      "loss: 0.104210  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001187 \n",
      "\n",
      "Epoch 179\n",
      "-------------------------------\n",
      "loss: 0.126098  [    0/ 6954]\n",
      "loss: 0.133125  [ 1024/ 6954]\n",
      "loss: 0.129845  [ 2048/ 6954]\n",
      "loss: 0.140587  [ 3072/ 6954]\n",
      "loss: 0.125639  [ 4096/ 6954]\n",
      "loss: 0.128054  [ 5120/ 6954]\n",
      "loss: 0.127588  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001165 \n",
      "\n",
      "Epoch 180\n",
      "-------------------------------\n",
      "loss: 0.112910  [    0/ 6954]\n",
      "loss: 0.128568  [ 1024/ 6954]\n",
      "loss: 0.166700  [ 2048/ 6954]\n",
      "loss: 0.141091  [ 3072/ 6954]\n",
      "loss: 0.131315  [ 4096/ 6954]\n",
      "loss: 0.115317  [ 5120/ 6954]\n",
      "loss: 0.168188  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001168 \n",
      "\n",
      "Epoch 181\n",
      "-------------------------------\n",
      "loss: 0.105331  [    0/ 6954]\n",
      "loss: 0.122652  [ 1024/ 6954]\n",
      "loss: 0.122955  [ 2048/ 6954]\n",
      "loss: 0.131541  [ 3072/ 6954]\n",
      "loss: 0.112668  [ 4096/ 6954]\n",
      "loss: 0.139605  [ 5120/ 6954]\n",
      "loss: 0.111324  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001183 \n",
      "\n",
      "Epoch 182\n",
      "-------------------------------\n",
      "loss: 0.113428  [    0/ 6954]\n",
      "loss: 0.120284  [ 1024/ 6954]\n",
      "loss: 0.159642  [ 2048/ 6954]\n",
      "loss: 0.110627  [ 3072/ 6954]\n",
      "loss: 0.121443  [ 4096/ 6954]\n",
      "loss: 0.108737  [ 5120/ 6954]\n",
      "loss: 0.121397  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001186 \n",
      "\n",
      "Epoch 183\n",
      "-------------------------------\n",
      "loss: 0.140303  [    0/ 6954]\n",
      "loss: 0.094162  [ 1024/ 6954]\n",
      "loss: 0.128890  [ 2048/ 6954]\n",
      "loss: 0.113352  [ 3072/ 6954]\n",
      "loss: 0.120633  [ 4096/ 6954]\n",
      "loss: 0.140633  [ 5120/ 6954]\n",
      "loss: 0.124479  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001193 \n",
      "\n",
      "Epoch 184\n",
      "-------------------------------\n",
      "loss: 0.129004  [    0/ 6954]\n",
      "loss: 0.138007  [ 1024/ 6954]\n",
      "loss: 0.125840  [ 2048/ 6954]\n",
      "loss: 0.118674  [ 3072/ 6954]\n",
      "loss: 0.131020  [ 4096/ 6954]\n",
      "loss: 0.078366  [ 5120/ 6954]\n",
      "loss: 0.107635  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001194 \n",
      "\n",
      "Epoch 185\n",
      "-------------------------------\n",
      "loss: 0.161041  [    0/ 6954]\n",
      "loss: 0.105683  [ 1024/ 6954]\n",
      "loss: 0.111664  [ 2048/ 6954]\n",
      "loss: 0.151941  [ 3072/ 6954]\n",
      "loss: 0.124132  [ 4096/ 6954]\n",
      "loss: 0.112884  [ 5120/ 6954]\n",
      "loss: 0.092967  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001212 \n",
      "\n",
      "Epoch 186\n",
      "-------------------------------\n",
      "loss: 0.117292  [    0/ 6954]\n",
      "loss: 0.129573  [ 1024/ 6954]\n",
      "loss: 0.133306  [ 2048/ 6954]\n",
      "loss: 0.111609  [ 3072/ 6954]\n",
      "loss: 0.102901  [ 4096/ 6954]\n",
      "loss: 0.141190  [ 5120/ 6954]\n",
      "loss: 0.150885  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001195 \n",
      "\n",
      "Epoch 187\n",
      "-------------------------------\n",
      "loss: 0.125223  [    0/ 6954]\n",
      "loss: 0.117054  [ 1024/ 6954]\n",
      "loss: 0.123272  [ 2048/ 6954]\n",
      "loss: 0.111109  [ 3072/ 6954]\n",
      "loss: 0.153782  [ 4096/ 6954]\n",
      "loss: 0.098697  [ 5120/ 6954]\n",
      "loss: 0.140009  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001182 \n",
      "\n",
      "Epoch 188\n",
      "-------------------------------\n",
      "loss: 0.127229  [    0/ 6954]\n",
      "loss: 0.135984  [ 1024/ 6954]\n",
      "loss: 0.139771  [ 2048/ 6954]\n",
      "loss: 0.114336  [ 3072/ 6954]\n",
      "loss: 0.114744  [ 4096/ 6954]\n",
      "loss: 0.124471  [ 5120/ 6954]\n",
      "loss: 0.129711  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001191 \n",
      "\n",
      "Epoch 189\n",
      "-------------------------------\n",
      "loss: 0.134047  [    0/ 6954]\n",
      "loss: 0.100090  [ 1024/ 6954]\n",
      "loss: 0.118786  [ 2048/ 6954]\n",
      "loss: 0.096135  [ 3072/ 6954]\n",
      "loss: 0.133724  [ 4096/ 6954]\n",
      "loss: 0.142363  [ 5120/ 6954]\n",
      "loss: 0.122341  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001246 \n",
      "\n",
      "Epoch 190\n",
      "-------------------------------\n",
      "loss: 0.118067  [    0/ 6954]\n",
      "loss: 0.148746  [ 1024/ 6954]\n",
      "loss: 0.106857  [ 2048/ 6954]\n",
      "loss: 0.119221  [ 3072/ 6954]\n",
      "loss: 0.138470  [ 4096/ 6954]\n",
      "loss: 0.127008  [ 5120/ 6954]\n",
      "loss: 0.111716  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001218 \n",
      "\n",
      "Epoch 191\n",
      "-------------------------------\n",
      "loss: 0.110567  [    0/ 6954]\n",
      "loss: 0.114928  [ 1024/ 6954]\n",
      "loss: 0.107637  [ 2048/ 6954]\n",
      "loss: 0.106141  [ 3072/ 6954]\n",
      "loss: 0.140986  [ 4096/ 6954]\n",
      "loss: 0.117022  [ 5120/ 6954]\n",
      "loss: 0.139586  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001163 \n",
      "\n",
      "Epoch 192\n",
      "-------------------------------\n",
      "loss: 0.156057  [    0/ 6954]\n",
      "loss: 0.130739  [ 1024/ 6954]\n",
      "loss: 0.120622  [ 2048/ 6954]\n",
      "loss: 0.109647  [ 3072/ 6954]\n",
      "loss: 0.128241  [ 4096/ 6954]\n",
      "loss: 0.116355  [ 5120/ 6954]\n",
      "loss: 0.137623  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001166 \n",
      "\n",
      "Epoch 193\n",
      "-------------------------------\n",
      "loss: 0.144077  [    0/ 6954]\n",
      "loss: 0.107198  [ 1024/ 6954]\n",
      "loss: 0.135972  [ 2048/ 6954]\n",
      "loss: 0.145463  [ 3072/ 6954]\n",
      "loss: 0.132026  [ 4096/ 6954]\n",
      "loss: 0.159714  [ 5120/ 6954]\n",
      "loss: 0.111137  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001287 \n",
      "\n",
      "Epoch 194\n",
      "-------------------------------\n",
      "loss: 0.147934  [    0/ 6954]\n",
      "loss: 0.105797  [ 1024/ 6954]\n",
      "loss: 0.140822  [ 2048/ 6954]\n",
      "loss: 0.124293  [ 3072/ 6954]\n",
      "loss: 0.111734  [ 4096/ 6954]\n",
      "loss: 0.124881  [ 5120/ 6954]\n",
      "loss: 0.139119  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001233 \n",
      "\n",
      "Epoch 195\n",
      "-------------------------------\n",
      "loss: 0.104111  [    0/ 6954]\n",
      "loss: 0.140415  [ 1024/ 6954]\n",
      "loss: 0.111421  [ 2048/ 6954]\n",
      "loss: 0.152476  [ 3072/ 6954]\n",
      "loss: 0.213964  [ 4096/ 6954]\n",
      "loss: 0.122203  [ 5120/ 6954]\n",
      "loss: 0.121722  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.6%, Avg loss: 0.001196 \n",
      "\n",
      "Epoch 196\n",
      "-------------------------------\n",
      "loss: 0.154596  [    0/ 6954]\n",
      "loss: 0.114440  [ 1024/ 6954]\n",
      "loss: 0.123447  [ 2048/ 6954]\n",
      "loss: 0.123114  [ 3072/ 6954]\n",
      "loss: 0.103236  [ 4096/ 6954]\n",
      "loss: 0.116615  [ 5120/ 6954]\n",
      "loss: 0.086753  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.9%, Avg loss: 0.001189 \n",
      "\n",
      "Epoch 197\n",
      "-------------------------------\n",
      "loss: 0.095695  [    0/ 6954]\n",
      "loss: 0.125729  [ 1024/ 6954]\n",
      "loss: 0.100278  [ 2048/ 6954]\n",
      "loss: 0.105458  [ 3072/ 6954]\n",
      "loss: 0.089012  [ 4096/ 6954]\n",
      "loss: 0.106333  [ 5120/ 6954]\n",
      "loss: 0.122436  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001222 \n",
      "\n",
      "Epoch 198\n",
      "-------------------------------\n",
      "loss: 0.132602  [    0/ 6954]\n",
      "loss: 0.134581  [ 1024/ 6954]\n",
      "loss: 0.126870  [ 2048/ 6954]\n",
      "loss: 0.162687  [ 3072/ 6954]\n",
      "loss: 0.126033  [ 4096/ 6954]\n",
      "loss: 0.129015  [ 5120/ 6954]\n",
      "loss: 0.109582  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001216 \n",
      "\n",
      "Epoch 199\n",
      "-------------------------------\n",
      "loss: 0.139704  [    0/ 6954]\n",
      "loss: 0.103363  [ 1024/ 6954]\n",
      "loss: 0.117022  [ 2048/ 6954]\n",
      "loss: 0.102213  [ 3072/ 6954]\n",
      "loss: 0.143206  [ 4096/ 6954]\n",
      "loss: 0.132277  [ 5120/ 6954]\n",
      "loss: 0.133837  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001178 \n",
      "\n",
      "Epoch 200\n",
      "-------------------------------\n",
      "loss: 0.122916  [    0/ 6954]\n",
      "loss: 0.121271  [ 1024/ 6954]\n",
      "loss: 0.114410  [ 2048/ 6954]\n",
      "loss: 0.133078  [ 3072/ 6954]\n",
      "loss: 0.133883  [ 4096/ 6954]\n",
      "loss: 0.137069  [ 5120/ 6954]\n",
      "loss: 0.119232  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001211 \n",
      "\n",
      "Epoch 201\n",
      "-------------------------------\n",
      "loss: 0.110668  [    0/ 6954]\n",
      "loss: 0.100617  [ 1024/ 6954]\n",
      "loss: 0.096778  [ 2048/ 6954]\n",
      "loss: 0.115101  [ 3072/ 6954]\n",
      "loss: 0.123034  [ 4096/ 6954]\n",
      "loss: 0.096256  [ 5120/ 6954]\n",
      "loss: 0.124801  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001215 \n",
      "\n",
      "Epoch 202\n",
      "-------------------------------\n",
      "loss: 0.102199  [    0/ 6954]\n",
      "loss: 0.140325  [ 1024/ 6954]\n",
      "loss: 0.097321  [ 2048/ 6954]\n",
      "loss: 0.111105  [ 3072/ 6954]\n",
      "loss: 0.132425  [ 4096/ 6954]\n",
      "loss: 0.128485  [ 5120/ 6954]\n",
      "loss: 0.114018  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001201 \n",
      "\n",
      "Epoch 203\n",
      "-------------------------------\n",
      "loss: 0.137257  [    0/ 6954]\n",
      "loss: 0.113282  [ 1024/ 6954]\n",
      "loss: 0.098020  [ 2048/ 6954]\n",
      "loss: 0.141202  [ 3072/ 6954]\n",
      "loss: 0.143980  [ 4096/ 6954]\n",
      "loss: 0.137276  [ 5120/ 6954]\n",
      "loss: 0.109008  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001218 \n",
      "\n",
      "Epoch 204\n",
      "-------------------------------\n",
      "loss: 0.125420  [    0/ 6954]\n",
      "loss: 0.093420  [ 1024/ 6954]\n",
      "loss: 0.129438  [ 2048/ 6954]\n",
      "loss: 0.141689  [ 3072/ 6954]\n",
      "loss: 0.109882  [ 4096/ 6954]\n",
      "loss: 0.137808  [ 5120/ 6954]\n",
      "loss: 0.119418  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001208 \n",
      "\n",
      "Epoch 205\n",
      "-------------------------------\n",
      "loss: 0.095056  [    0/ 6954]\n",
      "loss: 0.100185  [ 1024/ 6954]\n",
      "loss: 0.142633  [ 2048/ 6954]\n",
      "loss: 0.095786  [ 3072/ 6954]\n",
      "loss: 0.116690  [ 4096/ 6954]\n",
      "loss: 0.104492  [ 5120/ 6954]\n",
      "loss: 0.153202  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001207 \n",
      "\n",
      "Epoch 206\n",
      "-------------------------------\n",
      "loss: 0.107897  [    0/ 6954]\n",
      "loss: 0.126447  [ 1024/ 6954]\n",
      "loss: 0.098793  [ 2048/ 6954]\n",
      "loss: 0.134510  [ 3072/ 6954]\n",
      "loss: 0.127980  [ 4096/ 6954]\n",
      "loss: 0.098170  [ 5120/ 6954]\n",
      "loss: 0.124434  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001188 \n",
      "\n",
      "Epoch 207\n",
      "-------------------------------\n",
      "loss: 0.124513  [    0/ 6954]\n",
      "loss: 0.127390  [ 1024/ 6954]\n",
      "loss: 0.122563  [ 2048/ 6954]\n",
      "loss: 0.109546  [ 3072/ 6954]\n",
      "loss: 0.120076  [ 4096/ 6954]\n",
      "loss: 0.149426  [ 5120/ 6954]\n",
      "loss: 0.130023  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001225 \n",
      "\n",
      "Epoch 208\n",
      "-------------------------------\n",
      "loss: 0.148304  [    0/ 6954]\n",
      "loss: 0.121449  [ 1024/ 6954]\n",
      "loss: 0.107609  [ 2048/ 6954]\n",
      "loss: 0.090793  [ 3072/ 6954]\n",
      "loss: 0.118744  [ 4096/ 6954]\n",
      "loss: 0.123406  [ 5120/ 6954]\n",
      "loss: 0.102900  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001225 \n",
      "\n",
      "Epoch 209\n",
      "-------------------------------\n",
      "loss: 0.123442  [    0/ 6954]\n",
      "loss: 0.089153  [ 1024/ 6954]\n",
      "loss: 0.131426  [ 2048/ 6954]\n",
      "loss: 0.170670  [ 3072/ 6954]\n",
      "loss: 0.129897  [ 4096/ 6954]\n",
      "loss: 0.095949  [ 5120/ 6954]\n",
      "loss: 0.126043  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.001152 \n",
      "\n",
      "Epoch 210\n",
      "-------------------------------\n",
      "loss: 0.113578  [    0/ 6954]\n",
      "loss: 0.145781  [ 1024/ 6954]\n",
      "loss: 0.104116  [ 2048/ 6954]\n",
      "loss: 0.113935  [ 3072/ 6954]\n",
      "loss: 0.089963  [ 4096/ 6954]\n",
      "loss: 0.124418  [ 5120/ 6954]\n",
      "loss: 0.133498  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001235 \n",
      "\n",
      "Epoch 211\n",
      "-------------------------------\n",
      "loss: 0.095204  [    0/ 6954]\n",
      "loss: 0.112230  [ 1024/ 6954]\n",
      "loss: 0.084643  [ 2048/ 6954]\n",
      "loss: 0.110228  [ 3072/ 6954]\n",
      "loss: 0.136282  [ 4096/ 6954]\n",
      "loss: 0.124204  [ 5120/ 6954]\n",
      "loss: 0.118980  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001218 \n",
      "\n",
      "Epoch 212\n",
      "-------------------------------\n",
      "loss: 0.111250  [    0/ 6954]\n",
      "loss: 0.125462  [ 1024/ 6954]\n",
      "loss: 0.116901  [ 2048/ 6954]\n",
      "loss: 0.125654  [ 3072/ 6954]\n",
      "loss: 0.159462  [ 4096/ 6954]\n",
      "loss: 0.106533  [ 5120/ 6954]\n",
      "loss: 0.129722  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001188 \n",
      "\n",
      "Epoch 213\n",
      "-------------------------------\n",
      "loss: 0.112370  [    0/ 6954]\n",
      "loss: 0.139981  [ 1024/ 6954]\n",
      "loss: 0.145688  [ 2048/ 6954]\n",
      "loss: 0.109111  [ 3072/ 6954]\n",
      "loss: 0.153808  [ 4096/ 6954]\n",
      "loss: 0.103207  [ 5120/ 6954]\n",
      "loss: 0.107824  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001239 \n",
      "\n",
      "Epoch 214\n",
      "-------------------------------\n",
      "loss: 0.129022  [    0/ 6954]\n",
      "loss: 0.161416  [ 1024/ 6954]\n",
      "loss: 0.118290  [ 2048/ 6954]\n",
      "loss: 0.101114  [ 3072/ 6954]\n",
      "loss: 0.111441  [ 4096/ 6954]\n",
      "loss: 0.135747  [ 5120/ 6954]\n",
      "loss: 0.097075  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001227 \n",
      "\n",
      "Epoch 215\n",
      "-------------------------------\n",
      "loss: 0.108936  [    0/ 6954]\n",
      "loss: 0.154603  [ 1024/ 6954]\n",
      "loss: 0.093731  [ 2048/ 6954]\n",
      "loss: 0.123517  [ 3072/ 6954]\n",
      "loss: 0.098146  [ 4096/ 6954]\n",
      "loss: 0.123514  [ 5120/ 6954]\n",
      "loss: 0.094246  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001271 \n",
      "\n",
      "Epoch 216\n",
      "-------------------------------\n",
      "loss: 0.124179  [    0/ 6954]\n",
      "loss: 0.128356  [ 1024/ 6954]\n",
      "loss: 0.134546  [ 2048/ 6954]\n",
      "loss: 0.109196  [ 3072/ 6954]\n",
      "loss: 0.111510  [ 4096/ 6954]\n",
      "loss: 0.122543  [ 5120/ 6954]\n",
      "loss: 0.116254  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001222 \n",
      "\n",
      "Epoch 217\n",
      "-------------------------------\n",
      "loss: 0.096730  [    0/ 6954]\n",
      "loss: 0.101391  [ 1024/ 6954]\n",
      "loss: 0.111732  [ 2048/ 6954]\n",
      "loss: 0.152508  [ 3072/ 6954]\n",
      "loss: 0.097171  [ 4096/ 6954]\n",
      "loss: 0.129205  [ 5120/ 6954]\n",
      "loss: 0.107716  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001188 \n",
      "\n",
      "Epoch 218\n",
      "-------------------------------\n",
      "loss: 0.117016  [    0/ 6954]\n",
      "loss: 0.160386  [ 1024/ 6954]\n",
      "loss: 0.114037  [ 2048/ 6954]\n",
      "loss: 0.100332  [ 3072/ 6954]\n",
      "loss: 0.102264  [ 4096/ 6954]\n",
      "loss: 0.100637  [ 5120/ 6954]\n",
      "loss: 0.100954  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001245 \n",
      "\n",
      "Epoch 219\n",
      "-------------------------------\n",
      "loss: 0.117871  [    0/ 6954]\n",
      "loss: 0.143180  [ 1024/ 6954]\n",
      "loss: 0.130364  [ 2048/ 6954]\n",
      "loss: 0.120999  [ 3072/ 6954]\n",
      "loss: 0.108759  [ 4096/ 6954]\n",
      "loss: 0.114525  [ 5120/ 6954]\n",
      "loss: 0.100093  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001218 \n",
      "\n",
      "Epoch 220\n",
      "-------------------------------\n",
      "loss: 0.090169  [    0/ 6954]\n",
      "loss: 0.109059  [ 1024/ 6954]\n",
      "loss: 0.100291  [ 2048/ 6954]\n",
      "loss: 0.146059  [ 3072/ 6954]\n",
      "loss: 0.108735  [ 4096/ 6954]\n",
      "loss: 0.105778  [ 5120/ 6954]\n",
      "loss: 0.111844  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001214 \n",
      "\n",
      "Epoch 221\n",
      "-------------------------------\n",
      "loss: 0.116692  [    0/ 6954]\n",
      "loss: 0.132063  [ 1024/ 6954]\n",
      "loss: 0.143197  [ 2048/ 6954]\n",
      "loss: 0.120856  [ 3072/ 6954]\n",
      "loss: 0.136670  [ 4096/ 6954]\n",
      "loss: 0.136898  [ 5120/ 6954]\n",
      "loss: 0.101191  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001192 \n",
      "\n",
      "Epoch 222\n",
      "-------------------------------\n",
      "loss: 0.092694  [    0/ 6954]\n",
      "loss: 0.113835  [ 1024/ 6954]\n",
      "loss: 0.127779  [ 2048/ 6954]\n",
      "loss: 0.104139  [ 3072/ 6954]\n",
      "loss: 0.106984  [ 4096/ 6954]\n",
      "loss: 0.112498  [ 5120/ 6954]\n",
      "loss: 0.105708  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.9%, Avg loss: 0.001189 \n",
      "\n",
      "Epoch 223\n",
      "-------------------------------\n",
      "loss: 0.120673  [    0/ 6954]\n",
      "loss: 0.115724  [ 1024/ 6954]\n",
      "loss: 0.126063  [ 2048/ 6954]\n",
      "loss: 0.101590  [ 3072/ 6954]\n",
      "loss: 0.127158  [ 4096/ 6954]\n",
      "loss: 0.125338  [ 5120/ 6954]\n",
      "loss: 0.113039  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001232 \n",
      "\n",
      "Epoch 224\n",
      "-------------------------------\n",
      "loss: 0.090920  [    0/ 6954]\n",
      "loss: 0.120416  [ 1024/ 6954]\n",
      "loss: 0.114512  [ 2048/ 6954]\n",
      "loss: 0.141092  [ 3072/ 6954]\n",
      "loss: 0.097799  [ 4096/ 6954]\n",
      "loss: 0.111414  [ 5120/ 6954]\n",
      "loss: 0.108617  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.3%, Avg loss: 0.001179 \n",
      "\n",
      "Epoch 225\n",
      "-------------------------------\n",
      "loss: 0.129596  [    0/ 6954]\n",
      "loss: 0.111906  [ 1024/ 6954]\n",
      "loss: 0.085253  [ 2048/ 6954]\n",
      "loss: 0.121392  [ 3072/ 6954]\n",
      "loss: 0.106259  [ 4096/ 6954]\n",
      "loss: 0.143066  [ 5120/ 6954]\n",
      "loss: 0.121857  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001205 \n",
      "\n",
      "Epoch 226\n",
      "-------------------------------\n",
      "loss: 0.128708  [    0/ 6954]\n",
      "loss: 0.121982  [ 1024/ 6954]\n",
      "loss: 0.169287  [ 2048/ 6954]\n",
      "loss: 0.115391  [ 3072/ 6954]\n",
      "loss: 0.138584  [ 4096/ 6954]\n",
      "loss: 0.139216  [ 5120/ 6954]\n",
      "loss: 0.159274  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001192 \n",
      "\n",
      "Epoch 227\n",
      "-------------------------------\n",
      "loss: 0.109497  [    0/ 6954]\n",
      "loss: 0.107525  [ 1024/ 6954]\n",
      "loss: 0.104381  [ 2048/ 6954]\n",
      "loss: 0.140594  [ 3072/ 6954]\n",
      "loss: 0.108539  [ 4096/ 6954]\n",
      "loss: 0.091893  [ 5120/ 6954]\n",
      "loss: 0.107880  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001209 \n",
      "\n",
      "Epoch 228\n",
      "-------------------------------\n",
      "loss: 0.123628  [    0/ 6954]\n",
      "loss: 0.126888  [ 1024/ 6954]\n",
      "loss: 0.143349  [ 2048/ 6954]\n",
      "loss: 0.115874  [ 3072/ 6954]\n",
      "loss: 0.131207  [ 4096/ 6954]\n",
      "loss: 0.111693  [ 5120/ 6954]\n",
      "loss: 0.129917  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001217 \n",
      "\n",
      "Epoch 229\n",
      "-------------------------------\n",
      "loss: 0.124667  [    0/ 6954]\n",
      "loss: 0.110124  [ 1024/ 6954]\n",
      "loss: 0.126348  [ 2048/ 6954]\n",
      "loss: 0.112351  [ 3072/ 6954]\n",
      "loss: 0.144515  [ 4096/ 6954]\n",
      "loss: 0.116398  [ 5120/ 6954]\n",
      "loss: 0.086089  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001249 \n",
      "\n",
      "Epoch 230\n",
      "-------------------------------\n",
      "loss: 0.113624  [    0/ 6954]\n",
      "loss: 0.105420  [ 1024/ 6954]\n",
      "loss: 0.111431  [ 2048/ 6954]\n",
      "loss: 0.093709  [ 3072/ 6954]\n",
      "loss: 0.112670  [ 4096/ 6954]\n",
      "loss: 0.165540  [ 5120/ 6954]\n",
      "loss: 0.110541  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001178 \n",
      "\n",
      "Epoch 231\n",
      "-------------------------------\n",
      "loss: 0.123839  [    0/ 6954]\n",
      "loss: 0.113985  [ 1024/ 6954]\n",
      "loss: 0.108635  [ 2048/ 6954]\n",
      "loss: 0.153712  [ 3072/ 6954]\n",
      "loss: 0.105938  [ 4096/ 6954]\n",
      "loss: 0.141306  [ 5120/ 6954]\n",
      "loss: 0.146897  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001216 \n",
      "\n",
      "Epoch 232\n",
      "-------------------------------\n",
      "loss: 0.072869  [    0/ 6954]\n",
      "loss: 0.124573  [ 1024/ 6954]\n",
      "loss: 0.087954  [ 2048/ 6954]\n",
      "loss: 0.147913  [ 3072/ 6954]\n",
      "loss: 0.135570  [ 4096/ 6954]\n",
      "loss: 0.114558  [ 5120/ 6954]\n",
      "loss: 0.113515  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001229 \n",
      "\n",
      "Epoch 233\n",
      "-------------------------------\n",
      "loss: 0.102918  [    0/ 6954]\n",
      "loss: 0.092646  [ 1024/ 6954]\n",
      "loss: 0.121334  [ 2048/ 6954]\n",
      "loss: 0.119656  [ 3072/ 6954]\n",
      "loss: 0.097910  [ 4096/ 6954]\n",
      "loss: 0.113435  [ 5120/ 6954]\n",
      "loss: 0.111115  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001243 \n",
      "\n",
      "Epoch 234\n",
      "-------------------------------\n",
      "loss: 0.137515  [    0/ 6954]\n",
      "loss: 0.098452  [ 1024/ 6954]\n",
      "loss: 0.127864  [ 2048/ 6954]\n",
      "loss: 0.105625  [ 3072/ 6954]\n",
      "loss: 0.139655  [ 4096/ 6954]\n",
      "loss: 0.091116  [ 5120/ 6954]\n",
      "loss: 0.118421  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001245 \n",
      "\n",
      "Epoch 235\n",
      "-------------------------------\n",
      "loss: 0.069407  [    0/ 6954]\n",
      "loss: 0.159671  [ 1024/ 6954]\n",
      "loss: 0.131458  [ 2048/ 6954]\n",
      "loss: 0.103107  [ 3072/ 6954]\n",
      "loss: 0.106131  [ 4096/ 6954]\n",
      "loss: 0.116576  [ 5120/ 6954]\n",
      "loss: 0.145919  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001231 \n",
      "\n",
      "Epoch 236\n",
      "-------------------------------\n",
      "loss: 0.129517  [    0/ 6954]\n",
      "loss: 0.092459  [ 1024/ 6954]\n",
      "loss: 0.143329  [ 2048/ 6954]\n",
      "loss: 0.135793  [ 3072/ 6954]\n",
      "loss: 0.121732  [ 4096/ 6954]\n",
      "loss: 0.109097  [ 5120/ 6954]\n",
      "loss: 0.133943  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001200 \n",
      "\n",
      "Epoch 237\n",
      "-------------------------------\n",
      "loss: 0.128907  [    0/ 6954]\n",
      "loss: 0.101079  [ 1024/ 6954]\n",
      "loss: 0.122516  [ 2048/ 6954]\n",
      "loss: 0.100705  [ 3072/ 6954]\n",
      "loss: 0.112263  [ 4096/ 6954]\n",
      "loss: 0.139076  [ 5120/ 6954]\n",
      "loss: 0.121524  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001270 \n",
      "\n",
      "Epoch 238\n",
      "-------------------------------\n",
      "loss: 0.103319  [    0/ 6954]\n",
      "loss: 0.134104  [ 1024/ 6954]\n",
      "loss: 0.082269  [ 2048/ 6954]\n",
      "loss: 0.119181  [ 3072/ 6954]\n",
      "loss: 0.177923  [ 4096/ 6954]\n",
      "loss: 0.094141  [ 5120/ 6954]\n",
      "loss: 0.120603  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001201 \n",
      "\n",
      "Epoch 239\n",
      "-------------------------------\n",
      "loss: 0.122571  [    0/ 6954]\n",
      "loss: 0.168650  [ 1024/ 6954]\n",
      "loss: 0.114804  [ 2048/ 6954]\n",
      "loss: 0.111024  [ 3072/ 6954]\n",
      "loss: 0.136638  [ 4096/ 6954]\n",
      "loss: 0.083029  [ 5120/ 6954]\n",
      "loss: 0.114337  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 70.8%, Avg loss: 0.001786 \n",
      "\n",
      "Epoch 240\n",
      "-------------------------------\n",
      "loss: 0.267265  [    0/ 6954]\n",
      "loss: 0.187942  [ 1024/ 6954]\n",
      "loss: 0.173062  [ 2048/ 6954]\n",
      "loss: 0.152526  [ 3072/ 6954]\n",
      "loss: 0.161173  [ 4096/ 6954]\n",
      "loss: 0.175327  [ 5120/ 6954]\n",
      "loss: 0.171883  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001215 \n",
      "\n",
      "Epoch 241\n",
      "-------------------------------\n",
      "loss: 0.176558  [    0/ 6954]\n",
      "loss: 0.116331  [ 1024/ 6954]\n",
      "loss: 0.146897  [ 2048/ 6954]\n",
      "loss: 0.160985  [ 3072/ 6954]\n",
      "loss: 0.149992  [ 4096/ 6954]\n",
      "loss: 0.140481  [ 5120/ 6954]\n",
      "loss: 0.155207  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001198 \n",
      "\n",
      "Epoch 242\n",
      "-------------------------------\n",
      "loss: 0.169554  [    0/ 6954]\n",
      "loss: 0.177162  [ 1024/ 6954]\n",
      "loss: 0.138870  [ 2048/ 6954]\n",
      "loss: 0.139255  [ 3072/ 6954]\n",
      "loss: 0.105394  [ 4096/ 6954]\n",
      "loss: 0.131224  [ 5120/ 6954]\n",
      "loss: 0.180728  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.2%, Avg loss: 0.001199 \n",
      "\n",
      "Epoch 243\n",
      "-------------------------------\n",
      "loss: 0.144067  [    0/ 6954]\n",
      "loss: 0.140898  [ 1024/ 6954]\n",
      "loss: 0.141348  [ 2048/ 6954]\n",
      "loss: 0.133782  [ 3072/ 6954]\n",
      "loss: 0.126882  [ 4096/ 6954]\n",
      "loss: 0.150387  [ 5120/ 6954]\n",
      "loss: 0.107101  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 0.001180 \n",
      "\n",
      "Epoch 244\n",
      "-------------------------------\n",
      "loss: 0.123349  [    0/ 6954]\n",
      "loss: 0.109680  [ 1024/ 6954]\n",
      "loss: 0.126857  [ 2048/ 6954]\n",
      "loss: 0.121912  [ 3072/ 6954]\n",
      "loss: 0.131887  [ 4096/ 6954]\n",
      "loss: 0.123379  [ 5120/ 6954]\n",
      "loss: 0.110470  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001221 \n",
      "\n",
      "Epoch 245\n",
      "-------------------------------\n",
      "loss: 0.098782  [    0/ 6954]\n",
      "loss: 0.088880  [ 1024/ 6954]\n",
      "loss: 0.111152  [ 2048/ 6954]\n",
      "loss: 0.107944  [ 3072/ 6954]\n",
      "loss: 0.127272  [ 4096/ 6954]\n",
      "loss: 0.151510  [ 5120/ 6954]\n",
      "loss: 0.125162  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001238 \n",
      "\n",
      "Epoch 246\n",
      "-------------------------------\n",
      "loss: 0.130402  [    0/ 6954]\n",
      "loss: 0.135763  [ 1024/ 6954]\n",
      "loss: 0.090567  [ 2048/ 6954]\n",
      "loss: 0.119389  [ 3072/ 6954]\n",
      "loss: 0.139221  [ 4096/ 6954]\n",
      "loss: 0.107718  [ 5120/ 6954]\n",
      "loss: 0.123320  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001242 \n",
      "\n",
      "Epoch 247\n",
      "-------------------------------\n",
      "loss: 0.096419  [    0/ 6954]\n",
      "loss: 0.105443  [ 1024/ 6954]\n",
      "loss: 0.115689  [ 2048/ 6954]\n",
      "loss: 0.145640  [ 3072/ 6954]\n",
      "loss: 0.115178  [ 4096/ 6954]\n",
      "loss: 0.101489  [ 5120/ 6954]\n",
      "loss: 0.090343  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.2%, Avg loss: 0.001214 \n",
      "\n",
      "Epoch 248\n",
      "-------------------------------\n",
      "loss: 0.106974  [    0/ 6954]\n",
      "loss: 0.133117  [ 1024/ 6954]\n",
      "loss: 0.104331  [ 2048/ 6954]\n",
      "loss: 0.090709  [ 3072/ 6954]\n",
      "loss: 0.130392  [ 4096/ 6954]\n",
      "loss: 0.114302  [ 5120/ 6954]\n",
      "loss: 0.125121  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001255 \n",
      "\n",
      "Epoch 249\n",
      "-------------------------------\n",
      "loss: 0.099699  [    0/ 6954]\n",
      "loss: 0.110463  [ 1024/ 6954]\n",
      "loss: 0.095437  [ 2048/ 6954]\n",
      "loss: 0.105735  [ 3072/ 6954]\n",
      "loss: 0.132667  [ 4096/ 6954]\n",
      "loss: 0.103208  [ 5120/ 6954]\n",
      "loss: 0.117075  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001254 \n",
      "\n",
      "Epoch 250\n",
      "-------------------------------\n",
      "loss: 0.108676  [    0/ 6954]\n",
      "loss: 0.129566  [ 1024/ 6954]\n",
      "loss: 0.138490  [ 2048/ 6954]\n",
      "loss: 0.119786  [ 3072/ 6954]\n",
      "loss: 0.101284  [ 4096/ 6954]\n",
      "loss: 0.109116  [ 5120/ 6954]\n",
      "loss: 0.124835  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001230 \n",
      "\n",
      "Epoch 251\n",
      "-------------------------------\n",
      "loss: 0.092654  [    0/ 6954]\n",
      "loss: 0.110603  [ 1024/ 6954]\n",
      "loss: 0.109760  [ 2048/ 6954]\n",
      "loss: 0.127864  [ 3072/ 6954]\n",
      "loss: 0.108007  [ 4096/ 6954]\n",
      "loss: 0.090713  [ 5120/ 6954]\n",
      "loss: 0.113181  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001199 \n",
      "\n",
      "Epoch 252\n",
      "-------------------------------\n",
      "loss: 0.106964  [    0/ 6954]\n",
      "loss: 0.094249  [ 1024/ 6954]\n",
      "loss: 0.095172  [ 2048/ 6954]\n",
      "loss: 0.119948  [ 3072/ 6954]\n",
      "loss: 0.133389  [ 4096/ 6954]\n",
      "loss: 0.123890  [ 5120/ 6954]\n",
      "loss: 0.112282  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.8%, Avg loss: 0.001211 \n",
      "\n",
      "Epoch 253\n",
      "-------------------------------\n",
      "loss: 0.105225  [    0/ 6954]\n",
      "loss: 0.142905  [ 1024/ 6954]\n",
      "loss: 0.128456  [ 2048/ 6954]\n",
      "loss: 0.112122  [ 3072/ 6954]\n",
      "loss: 0.111593  [ 4096/ 6954]\n",
      "loss: 0.087994  [ 5120/ 6954]\n",
      "loss: 0.066892  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001299 \n",
      "\n",
      "Epoch 254\n",
      "-------------------------------\n",
      "loss: 0.121568  [    0/ 6954]\n",
      "loss: 0.104705  [ 1024/ 6954]\n",
      "loss: 0.117687  [ 2048/ 6954]\n",
      "loss: 0.104191  [ 3072/ 6954]\n",
      "loss: 0.129746  [ 4096/ 6954]\n",
      "loss: 0.107871  [ 5120/ 6954]\n",
      "loss: 0.115854  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001203 \n",
      "\n",
      "Epoch 255\n",
      "-------------------------------\n",
      "loss: 0.114941  [    0/ 6954]\n",
      "loss: 0.121238  [ 1024/ 6954]\n",
      "loss: 0.134292  [ 2048/ 6954]\n",
      "loss: 0.085571  [ 3072/ 6954]\n",
      "loss: 0.134754  [ 4096/ 6954]\n",
      "loss: 0.097399  [ 5120/ 6954]\n",
      "loss: 0.106143  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001294 \n",
      "\n",
      "Epoch 256\n",
      "-------------------------------\n",
      "loss: 0.079538  [    0/ 6954]\n",
      "loss: 0.120451  [ 1024/ 6954]\n",
      "loss: 0.139943  [ 2048/ 6954]\n",
      "loss: 0.123270  [ 3072/ 6954]\n",
      "loss: 0.116536  [ 4096/ 6954]\n",
      "loss: 0.121558  [ 5120/ 6954]\n",
      "loss: 0.108083  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001302 \n",
      "\n",
      "Epoch 257\n",
      "-------------------------------\n",
      "loss: 0.116704  [    0/ 6954]\n",
      "loss: 0.118056  [ 1024/ 6954]\n",
      "loss: 0.135500  [ 2048/ 6954]\n",
      "loss: 0.099434  [ 3072/ 6954]\n",
      "loss: 0.141813  [ 4096/ 6954]\n",
      "loss: 0.101461  [ 5120/ 6954]\n",
      "loss: 0.119349  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.1%, Avg loss: 0.001205 \n",
      "\n",
      "Epoch 258\n",
      "-------------------------------\n",
      "loss: 0.071619  [    0/ 6954]\n",
      "loss: 0.091506  [ 1024/ 6954]\n",
      "loss: 0.097833  [ 2048/ 6954]\n",
      "loss: 0.123803  [ 3072/ 6954]\n",
      "loss: 0.105362  [ 4096/ 6954]\n",
      "loss: 0.144222  [ 5120/ 6954]\n",
      "loss: 0.121608  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001233 \n",
      "\n",
      "Epoch 259\n",
      "-------------------------------\n",
      "loss: 0.130333  [    0/ 6954]\n",
      "loss: 0.115804  [ 1024/ 6954]\n",
      "loss: 0.109558  [ 2048/ 6954]\n",
      "loss: 0.119670  [ 3072/ 6954]\n",
      "loss: 0.098783  [ 4096/ 6954]\n",
      "loss: 0.153978  [ 5120/ 6954]\n",
      "loss: 0.125257  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001316 \n",
      "\n",
      "Epoch 260\n",
      "-------------------------------\n",
      "loss: 0.113134  [    0/ 6954]\n",
      "loss: 0.127202  [ 1024/ 6954]\n",
      "loss: 0.132943  [ 2048/ 6954]\n",
      "loss: 0.095175  [ 3072/ 6954]\n",
      "loss: 0.078376  [ 4096/ 6954]\n",
      "loss: 0.098521  [ 5120/ 6954]\n",
      "loss: 0.109322  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001225 \n",
      "\n",
      "Epoch 261\n",
      "-------------------------------\n",
      "loss: 0.103702  [    0/ 6954]\n",
      "loss: 0.099185  [ 1024/ 6954]\n",
      "loss: 0.121917  [ 2048/ 6954]\n",
      "loss: 0.135595  [ 3072/ 6954]\n",
      "loss: 0.096400  [ 4096/ 6954]\n",
      "loss: 0.135073  [ 5120/ 6954]\n",
      "loss: 0.134261  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001269 \n",
      "\n",
      "Epoch 262\n",
      "-------------------------------\n",
      "loss: 0.119197  [    0/ 6954]\n",
      "loss: 0.098490  [ 1024/ 6954]\n",
      "loss: 0.127270  [ 2048/ 6954]\n",
      "loss: 0.109908  [ 3072/ 6954]\n",
      "loss: 0.111148  [ 4096/ 6954]\n",
      "loss: 0.104871  [ 5120/ 6954]\n",
      "loss: 0.100606  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001258 \n",
      "\n",
      "Epoch 263\n",
      "-------------------------------\n",
      "loss: 0.085774  [    0/ 6954]\n",
      "loss: 0.105799  [ 1024/ 6954]\n",
      "loss: 0.088050  [ 2048/ 6954]\n",
      "loss: 0.124690  [ 3072/ 6954]\n",
      "loss: 0.089860  [ 4096/ 6954]\n",
      "loss: 0.109385  [ 5120/ 6954]\n",
      "loss: 0.120725  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001263 \n",
      "\n",
      "Epoch 264\n",
      "-------------------------------\n",
      "loss: 0.122927  [    0/ 6954]\n",
      "loss: 0.126298  [ 1024/ 6954]\n",
      "loss: 0.120235  [ 2048/ 6954]\n",
      "loss: 0.102734  [ 3072/ 6954]\n",
      "loss: 0.167384  [ 4096/ 6954]\n",
      "loss: 0.129452  [ 5120/ 6954]\n",
      "loss: 0.089376  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001275 \n",
      "\n",
      "Epoch 265\n",
      "-------------------------------\n",
      "loss: 0.100533  [    0/ 6954]\n",
      "loss: 0.118733  [ 1024/ 6954]\n",
      "loss: 0.088564  [ 2048/ 6954]\n",
      "loss: 0.098275  [ 3072/ 6954]\n",
      "loss: 0.104408  [ 4096/ 6954]\n",
      "loss: 0.095932  [ 5120/ 6954]\n",
      "loss: 0.134778  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001313 \n",
      "\n",
      "Epoch 266\n",
      "-------------------------------\n",
      "loss: 0.098832  [    0/ 6954]\n",
      "loss: 0.085602  [ 1024/ 6954]\n",
      "loss: 0.103159  [ 2048/ 6954]\n",
      "loss: 0.102868  [ 3072/ 6954]\n",
      "loss: 0.129259  [ 4096/ 6954]\n",
      "loss: 0.104855  [ 5120/ 6954]\n",
      "loss: 0.119732  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001303 \n",
      "\n",
      "Epoch 267\n",
      "-------------------------------\n",
      "loss: 0.100272  [    0/ 6954]\n",
      "loss: 0.129599  [ 1024/ 6954]\n",
      "loss: 0.089370  [ 2048/ 6954]\n",
      "loss: 0.113887  [ 3072/ 6954]\n",
      "loss: 0.134380  [ 4096/ 6954]\n",
      "loss: 0.099660  [ 5120/ 6954]\n",
      "loss: 0.129460  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001273 \n",
      "\n",
      "Epoch 268\n",
      "-------------------------------\n",
      "loss: 0.117564  [    0/ 6954]\n",
      "loss: 0.087018  [ 1024/ 6954]\n",
      "loss: 0.084368  [ 2048/ 6954]\n",
      "loss: 0.113178  [ 3072/ 6954]\n",
      "loss: 0.115073  [ 4096/ 6954]\n",
      "loss: 0.113289  [ 5120/ 6954]\n",
      "loss: 0.104267  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001238 \n",
      "\n",
      "Epoch 269\n",
      "-------------------------------\n",
      "loss: 0.098112  [    0/ 6954]\n",
      "loss: 0.108285  [ 1024/ 6954]\n",
      "loss: 0.092018  [ 2048/ 6954]\n",
      "loss: 0.112694  [ 3072/ 6954]\n",
      "loss: 0.109754  [ 4096/ 6954]\n",
      "loss: 0.121130  [ 5120/ 6954]\n",
      "loss: 0.105156  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001254 \n",
      "\n",
      "Epoch 270\n",
      "-------------------------------\n",
      "loss: 0.106943  [    0/ 6954]\n",
      "loss: 0.068189  [ 1024/ 6954]\n",
      "loss: 0.078449  [ 2048/ 6954]\n",
      "loss: 0.111463  [ 3072/ 6954]\n",
      "loss: 0.077035  [ 4096/ 6954]\n",
      "loss: 0.132122  [ 5120/ 6954]\n",
      "loss: 0.092905  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001226 \n",
      "\n",
      "Epoch 271\n",
      "-------------------------------\n",
      "loss: 0.103780  [    0/ 6954]\n",
      "loss: 0.117703  [ 1024/ 6954]\n",
      "loss: 0.114626  [ 2048/ 6954]\n",
      "loss: 0.100720  [ 3072/ 6954]\n",
      "loss: 0.096112  [ 4096/ 6954]\n",
      "loss: 0.116232  [ 5120/ 6954]\n",
      "loss: 0.116555  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001227 \n",
      "\n",
      "Epoch 272\n",
      "-------------------------------\n",
      "loss: 0.112607  [    0/ 6954]\n",
      "loss: 0.100610  [ 1024/ 6954]\n",
      "loss: 0.111549  [ 2048/ 6954]\n",
      "loss: 0.091407  [ 3072/ 6954]\n",
      "loss: 0.102984  [ 4096/ 6954]\n",
      "loss: 0.136266  [ 5120/ 6954]\n",
      "loss: 0.102568  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001268 \n",
      "\n",
      "Epoch 273\n",
      "-------------------------------\n",
      "loss: 0.124539  [    0/ 6954]\n",
      "loss: 0.087412  [ 1024/ 6954]\n",
      "loss: 0.102192  [ 2048/ 6954]\n",
      "loss: 0.102014  [ 3072/ 6954]\n",
      "loss: 0.099453  [ 4096/ 6954]\n",
      "loss: 0.097758  [ 5120/ 6954]\n",
      "loss: 0.141167  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001308 \n",
      "\n",
      "Epoch 274\n",
      "-------------------------------\n",
      "loss: 0.099940  [    0/ 6954]\n",
      "loss: 0.075912  [ 1024/ 6954]\n",
      "loss: 0.144341  [ 2048/ 6954]\n",
      "loss: 0.125300  [ 3072/ 6954]\n",
      "loss: 0.117579  [ 4096/ 6954]\n",
      "loss: 0.113501  [ 5120/ 6954]\n",
      "loss: 0.152920  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001246 \n",
      "\n",
      "Epoch 275\n",
      "-------------------------------\n",
      "loss: 0.124666  [    0/ 6954]\n",
      "loss: 0.102797  [ 1024/ 6954]\n",
      "loss: 0.117535  [ 2048/ 6954]\n",
      "loss: 0.130469  [ 3072/ 6954]\n",
      "loss: 0.125404  [ 4096/ 6954]\n",
      "loss: 0.099908  [ 5120/ 6954]\n",
      "loss: 0.112933  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001265 \n",
      "\n",
      "Epoch 276\n",
      "-------------------------------\n",
      "loss: 0.075248  [    0/ 6954]\n",
      "loss: 0.099655  [ 1024/ 6954]\n",
      "loss: 0.107940  [ 2048/ 6954]\n",
      "loss: 0.106937  [ 3072/ 6954]\n",
      "loss: 0.116738  [ 4096/ 6954]\n",
      "loss: 0.110583  [ 5120/ 6954]\n",
      "loss: 0.103972  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001263 \n",
      "\n",
      "Epoch 277\n",
      "-------------------------------\n",
      "loss: 0.115510  [    0/ 6954]\n",
      "loss: 0.110962  [ 1024/ 6954]\n",
      "loss: 0.096124  [ 2048/ 6954]\n",
      "loss: 0.109240  [ 3072/ 6954]\n",
      "loss: 0.104791  [ 4096/ 6954]\n",
      "loss: 0.083160  [ 5120/ 6954]\n",
      "loss: 0.114974  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001308 \n",
      "\n",
      "Epoch 278\n",
      "-------------------------------\n",
      "loss: 0.124161  [    0/ 6954]\n",
      "loss: 0.103230  [ 1024/ 6954]\n",
      "loss: 0.075699  [ 2048/ 6954]\n",
      "loss: 0.075477  [ 3072/ 6954]\n",
      "loss: 0.094222  [ 4096/ 6954]\n",
      "loss: 0.124858  [ 5120/ 6954]\n",
      "loss: 0.078590  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001288 \n",
      "\n",
      "Epoch 279\n",
      "-------------------------------\n",
      "loss: 0.079776  [    0/ 6954]\n",
      "loss: 0.073656  [ 1024/ 6954]\n",
      "loss: 0.102405  [ 2048/ 6954]\n",
      "loss: 0.099438  [ 3072/ 6954]\n",
      "loss: 0.092733  [ 4096/ 6954]\n",
      "loss: 0.113594  [ 5120/ 6954]\n",
      "loss: 0.098650  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001264 \n",
      "\n",
      "Epoch 280\n",
      "-------------------------------\n",
      "loss: 0.129977  [    0/ 6954]\n",
      "loss: 0.156065  [ 1024/ 6954]\n",
      "loss: 0.115985  [ 2048/ 6954]\n",
      "loss: 0.093131  [ 3072/ 6954]\n",
      "loss: 0.091771  [ 4096/ 6954]\n",
      "loss: 0.110963  [ 5120/ 6954]\n",
      "loss: 0.096784  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001256 \n",
      "\n",
      "Epoch 281\n",
      "-------------------------------\n",
      "loss: 0.107542  [    0/ 6954]\n",
      "loss: 0.125569  [ 1024/ 6954]\n",
      "loss: 0.113837  [ 2048/ 6954]\n",
      "loss: 0.119920  [ 3072/ 6954]\n",
      "loss: 0.129427  [ 4096/ 6954]\n",
      "loss: 0.095420  [ 5120/ 6954]\n",
      "loss: 0.106985  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001308 \n",
      "\n",
      "Epoch 282\n",
      "-------------------------------\n",
      "loss: 0.090833  [    0/ 6954]\n",
      "loss: 0.097750  [ 1024/ 6954]\n",
      "loss: 0.100982  [ 2048/ 6954]\n",
      "loss: 0.098979  [ 3072/ 6954]\n",
      "loss: 0.075974  [ 4096/ 6954]\n",
      "loss: 0.111078  [ 5120/ 6954]\n",
      "loss: 0.127654  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001256 \n",
      "\n",
      "Epoch 283\n",
      "-------------------------------\n",
      "loss: 0.104392  [    0/ 6954]\n",
      "loss: 0.088002  [ 1024/ 6954]\n",
      "loss: 0.145357  [ 2048/ 6954]\n",
      "loss: 0.081688  [ 3072/ 6954]\n",
      "loss: 0.103070  [ 4096/ 6954]\n",
      "loss: 0.095265  [ 5120/ 6954]\n",
      "loss: 0.112558  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001301 \n",
      "\n",
      "Epoch 284\n",
      "-------------------------------\n",
      "loss: 0.143261  [    0/ 6954]\n",
      "loss: 0.105772  [ 1024/ 6954]\n",
      "loss: 0.100099  [ 2048/ 6954]\n",
      "loss: 0.117948  [ 3072/ 6954]\n",
      "loss: 0.081850  [ 4096/ 6954]\n",
      "loss: 0.145621  [ 5120/ 6954]\n",
      "loss: 0.118829  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.9%, Avg loss: 0.001245 \n",
      "\n",
      "Epoch 285\n",
      "-------------------------------\n",
      "loss: 0.106459  [    0/ 6954]\n",
      "loss: 0.098259  [ 1024/ 6954]\n",
      "loss: 0.120679  [ 2048/ 6954]\n",
      "loss: 0.098135  [ 3072/ 6954]\n",
      "loss: 0.138975  [ 4096/ 6954]\n",
      "loss: 0.107240  [ 5120/ 6954]\n",
      "loss: 0.101560  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001270 \n",
      "\n",
      "Epoch 286\n",
      "-------------------------------\n",
      "loss: 0.107923  [    0/ 6954]\n",
      "loss: 0.105374  [ 1024/ 6954]\n",
      "loss: 0.120745  [ 2048/ 6954]\n",
      "loss: 0.114274  [ 3072/ 6954]\n",
      "loss: 0.120393  [ 4096/ 6954]\n",
      "loss: 0.124545  [ 5120/ 6954]\n",
      "loss: 0.127865  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001287 \n",
      "\n",
      "Epoch 287\n",
      "-------------------------------\n",
      "loss: 0.105384  [    0/ 6954]\n",
      "loss: 0.086479  [ 1024/ 6954]\n",
      "loss: 0.110364  [ 2048/ 6954]\n",
      "loss: 0.138042  [ 3072/ 6954]\n",
      "loss: 0.091427  [ 4096/ 6954]\n",
      "loss: 0.137332  [ 5120/ 6954]\n",
      "loss: 0.118831  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001282 \n",
      "\n",
      "Epoch 288\n",
      "-------------------------------\n",
      "loss: 0.121153  [    0/ 6954]\n",
      "loss: 0.085712  [ 1024/ 6954]\n",
      "loss: 0.093956  [ 2048/ 6954]\n",
      "loss: 0.094177  [ 3072/ 6954]\n",
      "loss: 0.097154  [ 4096/ 6954]\n",
      "loss: 0.136825  [ 5120/ 6954]\n",
      "loss: 0.083095  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001306 \n",
      "\n",
      "Epoch 289\n",
      "-------------------------------\n",
      "loss: 0.103591  [    0/ 6954]\n",
      "loss: 0.092248  [ 1024/ 6954]\n",
      "loss: 0.085359  [ 2048/ 6954]\n",
      "loss: 0.101051  [ 3072/ 6954]\n",
      "loss: 0.124519  [ 4096/ 6954]\n",
      "loss: 0.095911  [ 5120/ 6954]\n",
      "loss: 0.113162  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001312 \n",
      "\n",
      "Epoch 290\n",
      "-------------------------------\n",
      "loss: 0.106997  [    0/ 6954]\n",
      "loss: 0.074088  [ 1024/ 6954]\n",
      "loss: 0.081512  [ 2048/ 6954]\n",
      "loss: 0.112857  [ 3072/ 6954]\n",
      "loss: 0.122898  [ 4096/ 6954]\n",
      "loss: 0.092212  [ 5120/ 6954]\n",
      "loss: 0.106744  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001301 \n",
      "\n",
      "Epoch 291\n",
      "-------------------------------\n",
      "loss: 0.091376  [    0/ 6954]\n",
      "loss: 0.123396  [ 1024/ 6954]\n",
      "loss: 0.070134  [ 2048/ 6954]\n",
      "loss: 0.099675  [ 3072/ 6954]\n",
      "loss: 0.104465  [ 4096/ 6954]\n",
      "loss: 0.107737  [ 5120/ 6954]\n",
      "loss: 0.082918  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001292 \n",
      "\n",
      "Epoch 292\n",
      "-------------------------------\n",
      "loss: 0.087675  [    0/ 6954]\n",
      "loss: 0.094768  [ 1024/ 6954]\n",
      "loss: 0.102629  [ 2048/ 6954]\n",
      "loss: 0.108833  [ 3072/ 6954]\n",
      "loss: 0.104739  [ 4096/ 6954]\n",
      "loss: 0.093929  [ 5120/ 6954]\n",
      "loss: 0.133422  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001311 \n",
      "\n",
      "Epoch 293\n",
      "-------------------------------\n",
      "loss: 0.072234  [    0/ 6954]\n",
      "loss: 0.134552  [ 1024/ 6954]\n",
      "loss: 0.069419  [ 2048/ 6954]\n",
      "loss: 0.121853  [ 3072/ 6954]\n",
      "loss: 0.113480  [ 4096/ 6954]\n",
      "loss: 0.089609  [ 5120/ 6954]\n",
      "loss: 0.111173  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001300 \n",
      "\n",
      "Epoch 294\n",
      "-------------------------------\n",
      "loss: 0.111321  [    0/ 6954]\n",
      "loss: 0.114183  [ 1024/ 6954]\n",
      "loss: 0.147388  [ 2048/ 6954]\n",
      "loss: 0.085920  [ 3072/ 6954]\n",
      "loss: 0.092878  [ 4096/ 6954]\n",
      "loss: 0.051724  [ 5120/ 6954]\n",
      "loss: 0.113355  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001247 \n",
      "\n",
      "Epoch 295\n",
      "-------------------------------\n",
      "loss: 0.072237  [    0/ 6954]\n",
      "loss: 0.101683  [ 1024/ 6954]\n",
      "loss: 0.091038  [ 2048/ 6954]\n",
      "loss: 0.104038  [ 3072/ 6954]\n",
      "loss: 0.093992  [ 4096/ 6954]\n",
      "loss: 0.109995  [ 5120/ 6954]\n",
      "loss: 0.133518  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001239 \n",
      "\n",
      "Epoch 296\n",
      "-------------------------------\n",
      "loss: 0.101378  [    0/ 6954]\n",
      "loss: 0.099578  [ 1024/ 6954]\n",
      "loss: 0.100535  [ 2048/ 6954]\n",
      "loss: 0.103631  [ 3072/ 6954]\n",
      "loss: 0.098130  [ 4096/ 6954]\n",
      "loss: 0.097886  [ 5120/ 6954]\n",
      "loss: 0.108045  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001311 \n",
      "\n",
      "Epoch 297\n",
      "-------------------------------\n",
      "loss: 0.078385  [    0/ 6954]\n",
      "loss: 0.124017  [ 1024/ 6954]\n",
      "loss: 0.126987  [ 2048/ 6954]\n",
      "loss: 0.079159  [ 3072/ 6954]\n",
      "loss: 0.072797  [ 4096/ 6954]\n",
      "loss: 0.151240  [ 5120/ 6954]\n",
      "loss: 0.119202  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.8%, Avg loss: 0.001235 \n",
      "\n",
      "Epoch 298\n",
      "-------------------------------\n",
      "loss: 0.109091  [    0/ 6954]\n",
      "loss: 0.127068  [ 1024/ 6954]\n",
      "loss: 0.102879  [ 2048/ 6954]\n",
      "loss: 0.072016  [ 3072/ 6954]\n",
      "loss: 0.109607  [ 4096/ 6954]\n",
      "loss: 0.084658  [ 5120/ 6954]\n",
      "loss: 0.107478  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001274 \n",
      "\n",
      "Epoch 299\n",
      "-------------------------------\n",
      "loss: 0.095034  [    0/ 6954]\n",
      "loss: 0.092392  [ 1024/ 6954]\n",
      "loss: 0.090338  [ 2048/ 6954]\n",
      "loss: 0.099077  [ 3072/ 6954]\n",
      "loss: 0.079342  [ 4096/ 6954]\n",
      "loss: 0.121862  [ 5120/ 6954]\n",
      "loss: 0.091819  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001312 \n",
      "\n",
      "Epoch 300\n",
      "-------------------------------\n",
      "loss: 0.085188  [    0/ 6954]\n",
      "loss: 0.102068  [ 1024/ 6954]\n",
      "loss: 0.092166  [ 2048/ 6954]\n",
      "loss: 0.069839  [ 3072/ 6954]\n",
      "loss: 0.066825  [ 4096/ 6954]\n",
      "loss: 0.106715  [ 5120/ 6954]\n",
      "loss: 0.157398  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001285 \n",
      "\n",
      "Epoch 301\n",
      "-------------------------------\n",
      "loss: 0.083182  [    0/ 6954]\n",
      "loss: 0.103432  [ 1024/ 6954]\n",
      "loss: 0.089037  [ 2048/ 6954]\n",
      "loss: 0.094465  [ 3072/ 6954]\n",
      "loss: 0.127720  [ 4096/ 6954]\n",
      "loss: 0.108104  [ 5120/ 6954]\n",
      "loss: 0.131461  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001302 \n",
      "\n",
      "Epoch 302\n",
      "-------------------------------\n",
      "loss: 0.088824  [    0/ 6954]\n",
      "loss: 0.091478  [ 1024/ 6954]\n",
      "loss: 0.118142  [ 2048/ 6954]\n",
      "loss: 0.131179  [ 3072/ 6954]\n",
      "loss: 0.129244  [ 4096/ 6954]\n",
      "loss: 0.098622  [ 5120/ 6954]\n",
      "loss: 0.095867  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001299 \n",
      "\n",
      "Epoch 303\n",
      "-------------------------------\n",
      "loss: 0.090871  [    0/ 6954]\n",
      "loss: 0.108673  [ 1024/ 6954]\n",
      "loss: 0.085387  [ 2048/ 6954]\n",
      "loss: 0.113280  [ 3072/ 6954]\n",
      "loss: 0.087708  [ 4096/ 6954]\n",
      "loss: 0.099625  [ 5120/ 6954]\n",
      "loss: 0.100432  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001264 \n",
      "\n",
      "Epoch 304\n",
      "-------------------------------\n",
      "loss: 0.115716  [    0/ 6954]\n",
      "loss: 0.114706  [ 1024/ 6954]\n",
      "loss: 0.128002  [ 2048/ 6954]\n",
      "loss: 0.047330  [ 3072/ 6954]\n",
      "loss: 0.089021  [ 4096/ 6954]\n",
      "loss: 0.102997  [ 5120/ 6954]\n",
      "loss: 0.085263  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.4%, Avg loss: 0.001211 \n",
      "\n",
      "Epoch 305\n",
      "-------------------------------\n",
      "loss: 0.096234  [    0/ 6954]\n",
      "loss: 0.107805  [ 1024/ 6954]\n",
      "loss: 0.071540  [ 2048/ 6954]\n",
      "loss: 0.076438  [ 3072/ 6954]\n",
      "loss: 0.068229  [ 4096/ 6954]\n",
      "loss: 0.077409  [ 5120/ 6954]\n",
      "loss: 0.094925  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001340 \n",
      "\n",
      "Epoch 306\n",
      "-------------------------------\n",
      "loss: 0.087190  [    0/ 6954]\n",
      "loss: 0.107044  [ 1024/ 6954]\n",
      "loss: 0.088005  [ 2048/ 6954]\n",
      "loss: 0.077488  [ 3072/ 6954]\n",
      "loss: 0.087710  [ 4096/ 6954]\n",
      "loss: 0.080787  [ 5120/ 6954]\n",
      "loss: 0.103407  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001331 \n",
      "\n",
      "Epoch 307\n",
      "-------------------------------\n",
      "loss: 0.071915  [    0/ 6954]\n",
      "loss: 0.109265  [ 1024/ 6954]\n",
      "loss: 0.088031  [ 2048/ 6954]\n",
      "loss: 0.117589  [ 3072/ 6954]\n",
      "loss: 0.086068  [ 4096/ 6954]\n",
      "loss: 0.085833  [ 5120/ 6954]\n",
      "loss: 0.111020  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001324 \n",
      "\n",
      "Epoch 308\n",
      "-------------------------------\n",
      "loss: 0.104318  [    0/ 6954]\n",
      "loss: 0.116249  [ 1024/ 6954]\n",
      "loss: 0.067647  [ 2048/ 6954]\n",
      "loss: 0.097537  [ 3072/ 6954]\n",
      "loss: 0.090423  [ 4096/ 6954]\n",
      "loss: 0.153548  [ 5120/ 6954]\n",
      "loss: 0.102237  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001321 \n",
      "\n",
      "Epoch 309\n",
      "-------------------------------\n",
      "loss: 0.057032  [    0/ 6954]\n",
      "loss: 0.101161  [ 1024/ 6954]\n",
      "loss: 0.128977  [ 2048/ 6954]\n",
      "loss: 0.100076  [ 3072/ 6954]\n",
      "loss: 0.128584  [ 4096/ 6954]\n",
      "loss: 0.090805  [ 5120/ 6954]\n",
      "loss: 0.085494  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001274 \n",
      "\n",
      "Epoch 310\n",
      "-------------------------------\n",
      "loss: 0.095845  [    0/ 6954]\n",
      "loss: 0.067481  [ 1024/ 6954]\n",
      "loss: 0.111307  [ 2048/ 6954]\n",
      "loss: 0.119234  [ 3072/ 6954]\n",
      "loss: 0.133122  [ 4096/ 6954]\n",
      "loss: 0.113249  [ 5120/ 6954]\n",
      "loss: 0.101500  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001306 \n",
      "\n",
      "Epoch 311\n",
      "-------------------------------\n",
      "loss: 0.113427  [    0/ 6954]\n",
      "loss: 0.097385  [ 1024/ 6954]\n",
      "loss: 0.120001  [ 2048/ 6954]\n",
      "loss: 0.117361  [ 3072/ 6954]\n",
      "loss: 0.102619  [ 4096/ 6954]\n",
      "loss: 0.095544  [ 5120/ 6954]\n",
      "loss: 0.131504  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001268 \n",
      "\n",
      "Epoch 312\n",
      "-------------------------------\n",
      "loss: 0.099643  [    0/ 6954]\n",
      "loss: 0.112277  [ 1024/ 6954]\n",
      "loss: 0.106250  [ 2048/ 6954]\n",
      "loss: 0.114275  [ 3072/ 6954]\n",
      "loss: 0.083498  [ 4096/ 6954]\n",
      "loss: 0.079738  [ 5120/ 6954]\n",
      "loss: 0.078311  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001385 \n",
      "\n",
      "Epoch 313\n",
      "-------------------------------\n",
      "loss: 0.146403  [    0/ 6954]\n",
      "loss: 0.098025  [ 1024/ 6954]\n",
      "loss: 0.096820  [ 2048/ 6954]\n",
      "loss: 0.111767  [ 3072/ 6954]\n",
      "loss: 0.085601  [ 4096/ 6954]\n",
      "loss: 0.086667  [ 5120/ 6954]\n",
      "loss: 0.070519  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001290 \n",
      "\n",
      "Epoch 314\n",
      "-------------------------------\n",
      "loss: 0.102863  [    0/ 6954]\n",
      "loss: 0.082052  [ 1024/ 6954]\n",
      "loss: 0.125133  [ 2048/ 6954]\n",
      "loss: 0.078918  [ 3072/ 6954]\n",
      "loss: 0.106901  [ 4096/ 6954]\n",
      "loss: 0.108364  [ 5120/ 6954]\n",
      "loss: 0.108344  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001301 \n",
      "\n",
      "Epoch 315\n",
      "-------------------------------\n",
      "loss: 0.103826  [    0/ 6954]\n",
      "loss: 0.105176  [ 1024/ 6954]\n",
      "loss: 0.117382  [ 2048/ 6954]\n",
      "loss: 0.121550  [ 3072/ 6954]\n",
      "loss: 0.096278  [ 4096/ 6954]\n",
      "loss: 0.113172  [ 5120/ 6954]\n",
      "loss: 0.079367  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001344 \n",
      "\n",
      "Epoch 316\n",
      "-------------------------------\n",
      "loss: 0.097131  [    0/ 6954]\n",
      "loss: 0.155566  [ 1024/ 6954]\n",
      "loss: 0.079510  [ 2048/ 6954]\n",
      "loss: 0.082200  [ 3072/ 6954]\n",
      "loss: 0.111634  [ 4096/ 6954]\n",
      "loss: 0.096741  [ 5120/ 6954]\n",
      "loss: 0.144342  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001290 \n",
      "\n",
      "Epoch 317\n",
      "-------------------------------\n",
      "loss: 0.110026  [    0/ 6954]\n",
      "loss: 0.117682  [ 1024/ 6954]\n",
      "loss: 0.083246  [ 2048/ 6954]\n",
      "loss: 0.106891  [ 3072/ 6954]\n",
      "loss: 0.112075  [ 4096/ 6954]\n",
      "loss: 0.110191  [ 5120/ 6954]\n",
      "loss: 0.108800  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001326 \n",
      "\n",
      "Epoch 318\n",
      "-------------------------------\n",
      "loss: 0.132219  [    0/ 6954]\n",
      "loss: 0.122080  [ 1024/ 6954]\n",
      "loss: 0.098729  [ 2048/ 6954]\n",
      "loss: 0.070645  [ 3072/ 6954]\n",
      "loss: 0.124184  [ 4096/ 6954]\n",
      "loss: 0.098930  [ 5120/ 6954]\n",
      "loss: 0.099597  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001337 \n",
      "\n",
      "Epoch 319\n",
      "-------------------------------\n",
      "loss: 0.089400  [    0/ 6954]\n",
      "loss: 0.127010  [ 1024/ 6954]\n",
      "loss: 0.117373  [ 2048/ 6954]\n",
      "loss: 0.094620  [ 3072/ 6954]\n",
      "loss: 0.072220  [ 4096/ 6954]\n",
      "loss: 0.123961  [ 5120/ 6954]\n",
      "loss: 0.108134  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001328 \n",
      "\n",
      "Epoch 320\n",
      "-------------------------------\n",
      "loss: 0.097819  [    0/ 6954]\n",
      "loss: 0.096370  [ 1024/ 6954]\n",
      "loss: 0.090381  [ 2048/ 6954]\n",
      "loss: 0.118981  [ 3072/ 6954]\n",
      "loss: 0.104068  [ 4096/ 6954]\n",
      "loss: 0.119751  [ 5120/ 6954]\n",
      "loss: 0.093813  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001331 \n",
      "\n",
      "Epoch 321\n",
      "-------------------------------\n",
      "loss: 0.101780  [    0/ 6954]\n",
      "loss: 0.095799  [ 1024/ 6954]\n",
      "loss: 0.073572  [ 2048/ 6954]\n",
      "loss: 0.094286  [ 3072/ 6954]\n",
      "loss: 0.072727  [ 4096/ 6954]\n",
      "loss: 0.098153  [ 5120/ 6954]\n",
      "loss: 0.101500  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001320 \n",
      "\n",
      "Epoch 322\n",
      "-------------------------------\n",
      "loss: 0.083060  [    0/ 6954]\n",
      "loss: 0.111182  [ 1024/ 6954]\n",
      "loss: 0.085225  [ 2048/ 6954]\n",
      "loss: 0.114047  [ 3072/ 6954]\n",
      "loss: 0.095229  [ 4096/ 6954]\n",
      "loss: 0.110120  [ 5120/ 6954]\n",
      "loss: 0.100737  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001319 \n",
      "\n",
      "Epoch 323\n",
      "-------------------------------\n",
      "loss: 0.094855  [    0/ 6954]\n",
      "loss: 0.114720  [ 1024/ 6954]\n",
      "loss: 0.089493  [ 2048/ 6954]\n",
      "loss: 0.093716  [ 3072/ 6954]\n",
      "loss: 0.105378  [ 4096/ 6954]\n",
      "loss: 0.167348  [ 5120/ 6954]\n",
      "loss: 0.096924  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001315 \n",
      "\n",
      "Epoch 324\n",
      "-------------------------------\n",
      "loss: 0.095599  [    0/ 6954]\n",
      "loss: 0.097606  [ 1024/ 6954]\n",
      "loss: 0.102862  [ 2048/ 6954]\n",
      "loss: 0.101967  [ 3072/ 6954]\n",
      "loss: 0.099299  [ 4096/ 6954]\n",
      "loss: 0.120267  [ 5120/ 6954]\n",
      "loss: 0.115335  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001273 \n",
      "\n",
      "Epoch 325\n",
      "-------------------------------\n",
      "loss: 0.103462  [    0/ 6954]\n",
      "loss: 0.089381  [ 1024/ 6954]\n",
      "loss: 0.113646  [ 2048/ 6954]\n",
      "loss: 0.106928  [ 3072/ 6954]\n",
      "loss: 0.120587  [ 4096/ 6954]\n",
      "loss: 0.070941  [ 5120/ 6954]\n",
      "loss: 0.120689  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001298 \n",
      "\n",
      "Epoch 326\n",
      "-------------------------------\n",
      "loss: 0.100875  [    0/ 6954]\n",
      "loss: 0.085069  [ 1024/ 6954]\n",
      "loss: 0.099690  [ 2048/ 6954]\n",
      "loss: 0.098379  [ 3072/ 6954]\n",
      "loss: 0.088816  [ 4096/ 6954]\n",
      "loss: 0.118863  [ 5120/ 6954]\n",
      "loss: 0.125608  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001328 \n",
      "\n",
      "Epoch 327\n",
      "-------------------------------\n",
      "loss: 0.109008  [    0/ 6954]\n",
      "loss: 0.082127  [ 1024/ 6954]\n",
      "loss: 0.084175  [ 2048/ 6954]\n",
      "loss: 0.095383  [ 3072/ 6954]\n",
      "loss: 0.081497  [ 4096/ 6954]\n",
      "loss: 0.134472  [ 5120/ 6954]\n",
      "loss: 0.120889  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001323 \n",
      "\n",
      "Epoch 328\n",
      "-------------------------------\n",
      "loss: 0.104226  [    0/ 6954]\n",
      "loss: 0.110872  [ 1024/ 6954]\n",
      "loss: 0.099054  [ 2048/ 6954]\n",
      "loss: 0.096295  [ 3072/ 6954]\n",
      "loss: 0.070787  [ 4096/ 6954]\n",
      "loss: 0.105620  [ 5120/ 6954]\n",
      "loss: 0.110921  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001355 \n",
      "\n",
      "Epoch 329\n",
      "-------------------------------\n",
      "loss: 0.096848  [    0/ 6954]\n",
      "loss: 0.075815  [ 1024/ 6954]\n",
      "loss: 0.118693  [ 2048/ 6954]\n",
      "loss: 0.103915  [ 3072/ 6954]\n",
      "loss: 0.096150  [ 4096/ 6954]\n",
      "loss: 0.115979  [ 5120/ 6954]\n",
      "loss: 0.125336  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001312 \n",
      "\n",
      "Epoch 330\n",
      "-------------------------------\n",
      "loss: 0.086292  [    0/ 6954]\n",
      "loss: 0.083030  [ 1024/ 6954]\n",
      "loss: 0.103851  [ 2048/ 6954]\n",
      "loss: 0.056256  [ 3072/ 6954]\n",
      "loss: 0.098526  [ 4096/ 6954]\n",
      "loss: 0.087685  [ 5120/ 6954]\n",
      "loss: 0.085364  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001306 \n",
      "\n",
      "Epoch 331\n",
      "-------------------------------\n",
      "loss: 0.090795  [    0/ 6954]\n",
      "loss: 0.095533  [ 1024/ 6954]\n",
      "loss: 0.081118  [ 2048/ 6954]\n",
      "loss: 0.091744  [ 3072/ 6954]\n",
      "loss: 0.123435  [ 4096/ 6954]\n",
      "loss: 0.097619  [ 5120/ 6954]\n",
      "loss: 0.131288  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001329 \n",
      "\n",
      "Epoch 332\n",
      "-------------------------------\n",
      "loss: 0.090628  [    0/ 6954]\n",
      "loss: 0.110470  [ 1024/ 6954]\n",
      "loss: 0.088087  [ 2048/ 6954]\n",
      "loss: 0.101230  [ 3072/ 6954]\n",
      "loss: 0.124535  [ 4096/ 6954]\n",
      "loss: 0.082840  [ 5120/ 6954]\n",
      "loss: 0.095556  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001268 \n",
      "\n",
      "Epoch 333\n",
      "-------------------------------\n",
      "loss: 0.126912  [    0/ 6954]\n",
      "loss: 0.082554  [ 1024/ 6954]\n",
      "loss: 0.107961  [ 2048/ 6954]\n",
      "loss: 0.128491  [ 3072/ 6954]\n",
      "loss: 0.094481  [ 4096/ 6954]\n",
      "loss: 0.089315  [ 5120/ 6954]\n",
      "loss: 0.109374  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001336 \n",
      "\n",
      "Epoch 334\n",
      "-------------------------------\n",
      "loss: 0.099638  [    0/ 6954]\n",
      "loss: 0.081075  [ 1024/ 6954]\n",
      "loss: 0.126502  [ 2048/ 6954]\n",
      "loss: 0.116910  [ 3072/ 6954]\n",
      "loss: 0.090147  [ 4096/ 6954]\n",
      "loss: 0.068009  [ 5120/ 6954]\n",
      "loss: 0.094984  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001303 \n",
      "\n",
      "Epoch 335\n",
      "-------------------------------\n",
      "loss: 0.072974  [    0/ 6954]\n",
      "loss: 0.098699  [ 1024/ 6954]\n",
      "loss: 0.112922  [ 2048/ 6954]\n",
      "loss: 0.068974  [ 3072/ 6954]\n",
      "loss: 0.085876  [ 4096/ 6954]\n",
      "loss: 0.108474  [ 5120/ 6954]\n",
      "loss: 0.088277  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001355 \n",
      "\n",
      "Epoch 336\n",
      "-------------------------------\n",
      "loss: 0.112969  [    0/ 6954]\n",
      "loss: 0.131223  [ 1024/ 6954]\n",
      "loss: 0.087133  [ 2048/ 6954]\n",
      "loss: 0.076617  [ 3072/ 6954]\n",
      "loss: 0.107022  [ 4096/ 6954]\n",
      "loss: 0.077005  [ 5120/ 6954]\n",
      "loss: 0.106535  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001332 \n",
      "\n",
      "Epoch 337\n",
      "-------------------------------\n",
      "loss: 0.096091  [    0/ 6954]\n",
      "loss: 0.101283  [ 1024/ 6954]\n",
      "loss: 0.110181  [ 2048/ 6954]\n",
      "loss: 0.097715  [ 3072/ 6954]\n",
      "loss: 0.091532  [ 4096/ 6954]\n",
      "loss: 0.076200  [ 5120/ 6954]\n",
      "loss: 0.096532  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001347 \n",
      "\n",
      "Epoch 338\n",
      "-------------------------------\n",
      "loss: 0.110744  [    0/ 6954]\n",
      "loss: 0.094760  [ 1024/ 6954]\n",
      "loss: 0.099048  [ 2048/ 6954]\n",
      "loss: 0.110059  [ 3072/ 6954]\n",
      "loss: 0.080221  [ 4096/ 6954]\n",
      "loss: 0.133309  [ 5120/ 6954]\n",
      "loss: 0.072598  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001320 \n",
      "\n",
      "Epoch 339\n",
      "-------------------------------\n",
      "loss: 0.087333  [    0/ 6954]\n",
      "loss: 0.125598  [ 1024/ 6954]\n",
      "loss: 0.094823  [ 2048/ 6954]\n",
      "loss: 0.089508  [ 3072/ 6954]\n",
      "loss: 0.116141  [ 4096/ 6954]\n",
      "loss: 0.108455  [ 5120/ 6954]\n",
      "loss: 0.086794  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001263 \n",
      "\n",
      "Epoch 340\n",
      "-------------------------------\n",
      "loss: 0.095603  [    0/ 6954]\n",
      "loss: 0.097543  [ 1024/ 6954]\n",
      "loss: 0.094598  [ 2048/ 6954]\n",
      "loss: 0.104593  [ 3072/ 6954]\n",
      "loss: 0.089230  [ 4096/ 6954]\n",
      "loss: 0.097031  [ 5120/ 6954]\n",
      "loss: 0.098497  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.001246 \n",
      "\n",
      "Epoch 341\n",
      "-------------------------------\n",
      "loss: 0.082969  [    0/ 6954]\n",
      "loss: 0.105170  [ 1024/ 6954]\n",
      "loss: 0.087738  [ 2048/ 6954]\n",
      "loss: 0.112994  [ 3072/ 6954]\n",
      "loss: 0.100972  [ 4096/ 6954]\n",
      "loss: 0.100558  [ 5120/ 6954]\n",
      "loss: 0.077476  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001290 \n",
      "\n",
      "Epoch 342\n",
      "-------------------------------\n",
      "loss: 0.090945  [    0/ 6954]\n",
      "loss: 0.076285  [ 1024/ 6954]\n",
      "loss: 0.082799  [ 2048/ 6954]\n",
      "loss: 0.085593  [ 3072/ 6954]\n",
      "loss: 0.105917  [ 4096/ 6954]\n",
      "loss: 0.083386  [ 5120/ 6954]\n",
      "loss: 0.090159  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001306 \n",
      "\n",
      "Epoch 343\n",
      "-------------------------------\n",
      "loss: 0.080088  [    0/ 6954]\n",
      "loss: 0.097739  [ 1024/ 6954]\n",
      "loss: 0.054745  [ 2048/ 6954]\n",
      "loss: 0.101283  [ 3072/ 6954]\n",
      "loss: 0.097586  [ 4096/ 6954]\n",
      "loss: 0.106071  [ 5120/ 6954]\n",
      "loss: 0.108255  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001325 \n",
      "\n",
      "Epoch 344\n",
      "-------------------------------\n",
      "loss: 0.111866  [    0/ 6954]\n",
      "loss: 0.127007  [ 1024/ 6954]\n",
      "loss: 0.104340  [ 2048/ 6954]\n",
      "loss: 0.078661  [ 3072/ 6954]\n",
      "loss: 0.095565  [ 4096/ 6954]\n",
      "loss: 0.089473  [ 5120/ 6954]\n",
      "loss: 0.098345  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001309 \n",
      "\n",
      "Epoch 345\n",
      "-------------------------------\n",
      "loss: 0.107637  [    0/ 6954]\n",
      "loss: 0.095782  [ 1024/ 6954]\n",
      "loss: 0.096211  [ 2048/ 6954]\n",
      "loss: 0.100486  [ 3072/ 6954]\n",
      "loss: 0.076507  [ 4096/ 6954]\n",
      "loss: 0.091381  [ 5120/ 6954]\n",
      "loss: 0.086937  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001305 \n",
      "\n",
      "Epoch 346\n",
      "-------------------------------\n",
      "loss: 0.117591  [    0/ 6954]\n",
      "loss: 0.106665  [ 1024/ 6954]\n",
      "loss: 0.074605  [ 2048/ 6954]\n",
      "loss: 0.098363  [ 3072/ 6954]\n",
      "loss: 0.094382  [ 4096/ 6954]\n",
      "loss: 0.099267  [ 5120/ 6954]\n",
      "loss: 0.114093  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001289 \n",
      "\n",
      "Epoch 347\n",
      "-------------------------------\n",
      "loss: 0.057684  [    0/ 6954]\n",
      "loss: 0.080078  [ 1024/ 6954]\n",
      "loss: 0.098666  [ 2048/ 6954]\n",
      "loss: 0.091817  [ 3072/ 6954]\n",
      "loss: 0.095288  [ 4096/ 6954]\n",
      "loss: 0.122504  [ 5120/ 6954]\n",
      "loss: 0.095902  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.6%, Avg loss: 0.001308 \n",
      "\n",
      "Epoch 348\n",
      "-------------------------------\n",
      "loss: 0.109034  [    0/ 6954]\n",
      "loss: 0.110332  [ 1024/ 6954]\n",
      "loss: 0.093297  [ 2048/ 6954]\n",
      "loss: 0.110969  [ 3072/ 6954]\n",
      "loss: 0.087024  [ 4096/ 6954]\n",
      "loss: 0.094097  [ 5120/ 6954]\n",
      "loss: 0.112240  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001282 \n",
      "\n",
      "Epoch 349\n",
      "-------------------------------\n",
      "loss: 0.130520  [    0/ 6954]\n",
      "loss: 0.096192  [ 1024/ 6954]\n",
      "loss: 0.096082  [ 2048/ 6954]\n",
      "loss: 0.090599  [ 3072/ 6954]\n",
      "loss: 0.095939  [ 4096/ 6954]\n",
      "loss: 0.109657  [ 5120/ 6954]\n",
      "loss: 0.114388  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001331 \n",
      "\n",
      "Epoch 350\n",
      "-------------------------------\n",
      "loss: 0.121713  [    0/ 6954]\n",
      "loss: 0.107464  [ 1024/ 6954]\n",
      "loss: 0.078375  [ 2048/ 6954]\n",
      "loss: 0.120567  [ 3072/ 6954]\n",
      "loss: 0.142831  [ 4096/ 6954]\n",
      "loss: 0.094662  [ 5120/ 6954]\n",
      "loss: 0.096263  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001328 \n",
      "\n",
      "Epoch 351\n",
      "-------------------------------\n",
      "loss: 0.101598  [    0/ 6954]\n",
      "loss: 0.111760  [ 1024/ 6954]\n",
      "loss: 0.108462  [ 2048/ 6954]\n",
      "loss: 0.082051  [ 3072/ 6954]\n",
      "loss: 0.093843  [ 4096/ 6954]\n",
      "loss: 0.094253  [ 5120/ 6954]\n",
      "loss: 0.072079  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001390 \n",
      "\n",
      "Epoch 352\n",
      "-------------------------------\n",
      "loss: 0.104160  [    0/ 6954]\n",
      "loss: 0.096392  [ 1024/ 6954]\n",
      "loss: 0.081733  [ 2048/ 6954]\n",
      "loss: 0.122673  [ 3072/ 6954]\n",
      "loss: 0.101923  [ 4096/ 6954]\n",
      "loss: 0.092169  [ 5120/ 6954]\n",
      "loss: 0.085164  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001312 \n",
      "\n",
      "Epoch 353\n",
      "-------------------------------\n",
      "loss: 0.060262  [    0/ 6954]\n",
      "loss: 0.102945  [ 1024/ 6954]\n",
      "loss: 0.073920  [ 2048/ 6954]\n",
      "loss: 0.120407  [ 3072/ 6954]\n",
      "loss: 0.078018  [ 4096/ 6954]\n",
      "loss: 0.094967  [ 5120/ 6954]\n",
      "loss: 0.085217  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001281 \n",
      "\n",
      "Epoch 354\n",
      "-------------------------------\n",
      "loss: 0.071162  [    0/ 6954]\n",
      "loss: 0.086566  [ 1024/ 6954]\n",
      "loss: 0.108263  [ 2048/ 6954]\n",
      "loss: 0.098948  [ 3072/ 6954]\n",
      "loss: 0.082528  [ 4096/ 6954]\n",
      "loss: 0.113520  [ 5120/ 6954]\n",
      "loss: 0.087398  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001271 \n",
      "\n",
      "Epoch 355\n",
      "-------------------------------\n",
      "loss: 0.083858  [    0/ 6954]\n",
      "loss: 0.081902  [ 1024/ 6954]\n",
      "loss: 0.093808  [ 2048/ 6954]\n",
      "loss: 0.062332  [ 3072/ 6954]\n",
      "loss: 0.072279  [ 4096/ 6954]\n",
      "loss: 0.096782  [ 5120/ 6954]\n",
      "loss: 0.075316  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.9%, Avg loss: 0.001382 \n",
      "\n",
      "Epoch 356\n",
      "-------------------------------\n",
      "loss: 0.107470  [    0/ 6954]\n",
      "loss: 0.054635  [ 1024/ 6954]\n",
      "loss: 0.066047  [ 2048/ 6954]\n",
      "loss: 0.068109  [ 3072/ 6954]\n",
      "loss: 0.132139  [ 4096/ 6954]\n",
      "loss: 0.116386  [ 5120/ 6954]\n",
      "loss: 0.079536  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001307 \n",
      "\n",
      "Epoch 357\n",
      "-------------------------------\n",
      "loss: 0.068669  [    0/ 6954]\n",
      "loss: 0.141973  [ 1024/ 6954]\n",
      "loss: 0.097923  [ 2048/ 6954]\n",
      "loss: 0.106278  [ 3072/ 6954]\n",
      "loss: 0.100376  [ 4096/ 6954]\n",
      "loss: 0.087803  [ 5120/ 6954]\n",
      "loss: 0.096010  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001337 \n",
      "\n",
      "Epoch 358\n",
      "-------------------------------\n",
      "loss: 0.093866  [    0/ 6954]\n",
      "loss: 0.086703  [ 1024/ 6954]\n",
      "loss: 0.110295  [ 2048/ 6954]\n",
      "loss: 0.089772  [ 3072/ 6954]\n",
      "loss: 0.089350  [ 4096/ 6954]\n",
      "loss: 0.106540  [ 5120/ 6954]\n",
      "loss: 0.103761  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001332 \n",
      "\n",
      "Epoch 359\n",
      "-------------------------------\n",
      "loss: 0.072085  [    0/ 6954]\n",
      "loss: 0.119725  [ 1024/ 6954]\n",
      "loss: 0.072395  [ 2048/ 6954]\n",
      "loss: 0.071801  [ 3072/ 6954]\n",
      "loss: 0.096553  [ 4096/ 6954]\n",
      "loss: 0.082565  [ 5120/ 6954]\n",
      "loss: 0.077903  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001322 \n",
      "\n",
      "Epoch 360\n",
      "-------------------------------\n",
      "loss: 0.076653  [    0/ 6954]\n",
      "loss: 0.064229  [ 1024/ 6954]\n",
      "loss: 0.073910  [ 2048/ 6954]\n",
      "loss: 0.094372  [ 3072/ 6954]\n",
      "loss: 0.138494  [ 4096/ 6954]\n",
      "loss: 0.079899  [ 5120/ 6954]\n",
      "loss: 0.123838  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001332 \n",
      "\n",
      "Epoch 361\n",
      "-------------------------------\n",
      "loss: 0.118150  [    0/ 6954]\n",
      "loss: 0.089626  [ 1024/ 6954]\n",
      "loss: 0.087032  [ 2048/ 6954]\n",
      "loss: 0.097618  [ 3072/ 6954]\n",
      "loss: 0.066847  [ 4096/ 6954]\n",
      "loss: 0.097873  [ 5120/ 6954]\n",
      "loss: 0.126780  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001430 \n",
      "\n",
      "Epoch 362\n",
      "-------------------------------\n",
      "loss: 0.094721  [    0/ 6954]\n",
      "loss: 0.110113  [ 1024/ 6954]\n",
      "loss: 0.094968  [ 2048/ 6954]\n",
      "loss: 0.090366  [ 3072/ 6954]\n",
      "loss: 0.120703  [ 4096/ 6954]\n",
      "loss: 0.104811  [ 5120/ 6954]\n",
      "loss: 0.067670  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.7%, Avg loss: 0.001322 \n",
      "\n",
      "Epoch 363\n",
      "-------------------------------\n",
      "loss: 0.091638  [    0/ 6954]\n",
      "loss: 0.097082  [ 1024/ 6954]\n",
      "loss: 0.086643  [ 2048/ 6954]\n",
      "loss: 0.112662  [ 3072/ 6954]\n",
      "loss: 0.068946  [ 4096/ 6954]\n",
      "loss: 0.083791  [ 5120/ 6954]\n",
      "loss: 0.097885  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001330 \n",
      "\n",
      "Epoch 364\n",
      "-------------------------------\n",
      "loss: 0.080769  [    0/ 6954]\n",
      "loss: 0.082985  [ 1024/ 6954]\n",
      "loss: 0.099087  [ 2048/ 6954]\n",
      "loss: 0.068444  [ 3072/ 6954]\n",
      "loss: 0.068002  [ 4096/ 6954]\n",
      "loss: 0.106729  [ 5120/ 6954]\n",
      "loss: 0.053097  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001332 \n",
      "\n",
      "Epoch 365\n",
      "-------------------------------\n",
      "loss: 0.055352  [    0/ 6954]\n",
      "loss: 0.111017  [ 1024/ 6954]\n",
      "loss: 0.101328  [ 2048/ 6954]\n",
      "loss: 0.098057  [ 3072/ 6954]\n",
      "loss: 0.057790  [ 4096/ 6954]\n",
      "loss: 0.099912  [ 5120/ 6954]\n",
      "loss: 0.069802  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001317 \n",
      "\n",
      "Epoch 366\n",
      "-------------------------------\n",
      "loss: 0.077675  [    0/ 6954]\n",
      "loss: 0.081142  [ 1024/ 6954]\n",
      "loss: 0.096356  [ 2048/ 6954]\n",
      "loss: 0.093695  [ 3072/ 6954]\n",
      "loss: 0.104399  [ 4096/ 6954]\n",
      "loss: 0.096398  [ 5120/ 6954]\n",
      "loss: 0.115465  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001343 \n",
      "\n",
      "Epoch 367\n",
      "-------------------------------\n",
      "loss: 0.100198  [    0/ 6954]\n",
      "loss: 0.127355  [ 1024/ 6954]\n",
      "loss: 0.098968  [ 2048/ 6954]\n",
      "loss: 0.087669  [ 3072/ 6954]\n",
      "loss: 0.097498  [ 4096/ 6954]\n",
      "loss: 0.135167  [ 5120/ 6954]\n",
      "loss: 0.100449  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001321 \n",
      "\n",
      "Epoch 368\n",
      "-------------------------------\n",
      "loss: 0.089002  [    0/ 6954]\n",
      "loss: 0.116436  [ 1024/ 6954]\n",
      "loss: 0.102097  [ 2048/ 6954]\n",
      "loss: 0.097054  [ 3072/ 6954]\n",
      "loss: 0.090042  [ 4096/ 6954]\n",
      "loss: 0.090291  [ 5120/ 6954]\n",
      "loss: 0.076603  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.5%, Avg loss: 0.001319 \n",
      "\n",
      "Epoch 369\n",
      "-------------------------------\n",
      "loss: 0.118622  [    0/ 6954]\n",
      "loss: 0.080445  [ 1024/ 6954]\n",
      "loss: 0.112513  [ 2048/ 6954]\n",
      "loss: 0.082171  [ 3072/ 6954]\n",
      "loss: 0.113762  [ 4096/ 6954]\n",
      "loss: 0.105227  [ 5120/ 6954]\n",
      "loss: 0.091292  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001322 \n",
      "\n",
      "Epoch 370\n",
      "-------------------------------\n",
      "loss: 0.098720  [    0/ 6954]\n",
      "loss: 0.091192  [ 1024/ 6954]\n",
      "loss: 0.071538  [ 2048/ 6954]\n",
      "loss: 0.110889  [ 3072/ 6954]\n",
      "loss: 0.090211  [ 4096/ 6954]\n",
      "loss: 0.083041  [ 5120/ 6954]\n",
      "loss: 0.116379  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001379 \n",
      "\n",
      "Epoch 371\n",
      "-------------------------------\n",
      "loss: 0.089309  [    0/ 6954]\n",
      "loss: 0.048825  [ 1024/ 6954]\n",
      "loss: 0.094019  [ 2048/ 6954]\n",
      "loss: 0.115982  [ 3072/ 6954]\n",
      "loss: 0.096632  [ 4096/ 6954]\n",
      "loss: 0.082232  [ 5120/ 6954]\n",
      "loss: 0.088503  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001388 \n",
      "\n",
      "Epoch 372\n",
      "-------------------------------\n",
      "loss: 0.139604  [    0/ 6954]\n",
      "loss: 0.080452  [ 1024/ 6954]\n",
      "loss: 0.068452  [ 2048/ 6954]\n",
      "loss: 0.107944  [ 3072/ 6954]\n",
      "loss: 0.098477  [ 4096/ 6954]\n",
      "loss: 0.101065  [ 5120/ 6954]\n",
      "loss: 0.074171  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001335 \n",
      "\n",
      "Epoch 373\n",
      "-------------------------------\n",
      "loss: 0.072373  [    0/ 6954]\n",
      "loss: 0.092015  [ 1024/ 6954]\n",
      "loss: 0.085442  [ 2048/ 6954]\n",
      "loss: 0.073549  [ 3072/ 6954]\n",
      "loss: 0.078317  [ 4096/ 6954]\n",
      "loss: 0.096627  [ 5120/ 6954]\n",
      "loss: 0.103224  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001379 \n",
      "\n",
      "Epoch 374\n",
      "-------------------------------\n",
      "loss: 0.061882  [    0/ 6954]\n",
      "loss: 0.114653  [ 1024/ 6954]\n",
      "loss: 0.082018  [ 2048/ 6954]\n",
      "loss: 0.086720  [ 3072/ 6954]\n",
      "loss: 0.071437  [ 4096/ 6954]\n",
      "loss: 0.106466  [ 5120/ 6954]\n",
      "loss: 0.091591  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001328 \n",
      "\n",
      "Epoch 375\n",
      "-------------------------------\n",
      "loss: 0.094306  [    0/ 6954]\n",
      "loss: 0.096004  [ 1024/ 6954]\n",
      "loss: 0.098424  [ 2048/ 6954]\n",
      "loss: 0.067787  [ 3072/ 6954]\n",
      "loss: 0.109470  [ 4096/ 6954]\n",
      "loss: 0.127703  [ 5120/ 6954]\n",
      "loss: 0.100915  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001415 \n",
      "\n",
      "Epoch 376\n",
      "-------------------------------\n",
      "loss: 0.081022  [    0/ 6954]\n",
      "loss: 0.108667  [ 1024/ 6954]\n",
      "loss: 0.101901  [ 2048/ 6954]\n",
      "loss: 0.075074  [ 3072/ 6954]\n",
      "loss: 0.072412  [ 4096/ 6954]\n",
      "loss: 0.094169  [ 5120/ 6954]\n",
      "loss: 0.067711  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.3%, Avg loss: 0.001347 \n",
      "\n",
      "Epoch 377\n",
      "-------------------------------\n",
      "loss: 0.105975  [    0/ 6954]\n",
      "loss: 0.116785  [ 1024/ 6954]\n",
      "loss: 0.060541  [ 2048/ 6954]\n",
      "loss: 0.102359  [ 3072/ 6954]\n",
      "loss: 0.089968  [ 4096/ 6954]\n",
      "loss: 0.070424  [ 5120/ 6954]\n",
      "loss: 0.104936  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001343 \n",
      "\n",
      "Epoch 378\n",
      "-------------------------------\n",
      "loss: 0.078365  [    0/ 6954]\n",
      "loss: 0.083972  [ 1024/ 6954]\n",
      "loss: 0.067031  [ 2048/ 6954]\n",
      "loss: 0.101750  [ 3072/ 6954]\n",
      "loss: 0.092574  [ 4096/ 6954]\n",
      "loss: 0.094719  [ 5120/ 6954]\n",
      "loss: 0.089680  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001348 \n",
      "\n",
      "Epoch 379\n",
      "-------------------------------\n",
      "loss: 0.085718  [    0/ 6954]\n",
      "loss: 0.083968  [ 1024/ 6954]\n",
      "loss: 0.097724  [ 2048/ 6954]\n",
      "loss: 0.078305  [ 3072/ 6954]\n",
      "loss: 0.102054  [ 4096/ 6954]\n",
      "loss: 0.106841  [ 5120/ 6954]\n",
      "loss: 0.095327  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001319 \n",
      "\n",
      "Epoch 380\n",
      "-------------------------------\n",
      "loss: 0.050716  [    0/ 6954]\n",
      "loss: 0.046000  [ 1024/ 6954]\n",
      "loss: 0.091003  [ 2048/ 6954]\n",
      "loss: 0.089111  [ 3072/ 6954]\n",
      "loss: 0.072991  [ 4096/ 6954]\n",
      "loss: 0.080820  [ 5120/ 6954]\n",
      "loss: 0.078398  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001366 \n",
      "\n",
      "Epoch 381\n",
      "-------------------------------\n",
      "loss: 0.157761  [    0/ 6954]\n",
      "loss: 0.093060  [ 1024/ 6954]\n",
      "loss: 0.092537  [ 2048/ 6954]\n",
      "loss: 0.078819  [ 3072/ 6954]\n",
      "loss: 0.091563  [ 4096/ 6954]\n",
      "loss: 0.091332  [ 5120/ 6954]\n",
      "loss: 0.077931  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001350 \n",
      "\n",
      "Epoch 382\n",
      "-------------------------------\n",
      "loss: 0.106000  [    0/ 6954]\n",
      "loss: 0.086114  [ 1024/ 6954]\n",
      "loss: 0.081930  [ 2048/ 6954]\n",
      "loss: 0.076715  [ 3072/ 6954]\n",
      "loss: 0.098211  [ 4096/ 6954]\n",
      "loss: 0.092394  [ 5120/ 6954]\n",
      "loss: 0.089513  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001356 \n",
      "\n",
      "Epoch 383\n",
      "-------------------------------\n",
      "loss: 0.071244  [    0/ 6954]\n",
      "loss: 0.086476  [ 1024/ 6954]\n",
      "loss: 0.073227  [ 2048/ 6954]\n",
      "loss: 0.086176  [ 3072/ 6954]\n",
      "loss: 0.078785  [ 4096/ 6954]\n",
      "loss: 0.093260  [ 5120/ 6954]\n",
      "loss: 0.085940  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001406 \n",
      "\n",
      "Epoch 384\n",
      "-------------------------------\n",
      "loss: 0.097967  [    0/ 6954]\n",
      "loss: 0.070972  [ 1024/ 6954]\n",
      "loss: 0.100840  [ 2048/ 6954]\n",
      "loss: 0.085425  [ 3072/ 6954]\n",
      "loss: 0.082883  [ 4096/ 6954]\n",
      "loss: 0.101810  [ 5120/ 6954]\n",
      "loss: 0.087411  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001401 \n",
      "\n",
      "Epoch 385\n",
      "-------------------------------\n",
      "loss: 0.078857  [    0/ 6954]\n",
      "loss: 0.122704  [ 1024/ 6954]\n",
      "loss: 0.077907  [ 2048/ 6954]\n",
      "loss: 0.101886  [ 3072/ 6954]\n",
      "loss: 0.085070  [ 4096/ 6954]\n",
      "loss: 0.100121  [ 5120/ 6954]\n",
      "loss: 0.086742  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001386 \n",
      "\n",
      "Epoch 386\n",
      "-------------------------------\n",
      "loss: 0.082333  [    0/ 6954]\n",
      "loss: 0.089223  [ 1024/ 6954]\n",
      "loss: 0.107870  [ 2048/ 6954]\n",
      "loss: 0.073071  [ 3072/ 6954]\n",
      "loss: 0.088275  [ 4096/ 6954]\n",
      "loss: 0.068597  [ 5120/ 6954]\n",
      "loss: 0.104372  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001340 \n",
      "\n",
      "Epoch 387\n",
      "-------------------------------\n",
      "loss: 0.083273  [    0/ 6954]\n",
      "loss: 0.091596  [ 1024/ 6954]\n",
      "loss: 0.070629  [ 2048/ 6954]\n",
      "loss: 0.120541  [ 3072/ 6954]\n",
      "loss: 0.106096  [ 4096/ 6954]\n",
      "loss: 0.110518  [ 5120/ 6954]\n",
      "loss: 0.116417  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001356 \n",
      "\n",
      "Epoch 388\n",
      "-------------------------------\n",
      "loss: 0.061151  [    0/ 6954]\n",
      "loss: 0.079732  [ 1024/ 6954]\n",
      "loss: 0.085953  [ 2048/ 6954]\n",
      "loss: 0.089503  [ 3072/ 6954]\n",
      "loss: 0.094577  [ 4096/ 6954]\n",
      "loss: 0.082759  [ 5120/ 6954]\n",
      "loss: 0.079614  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001299 \n",
      "\n",
      "Epoch 389\n",
      "-------------------------------\n",
      "loss: 0.074573  [    0/ 6954]\n",
      "loss: 0.075783  [ 1024/ 6954]\n",
      "loss: 0.124989  [ 2048/ 6954]\n",
      "loss: 0.079868  [ 3072/ 6954]\n",
      "loss: 0.066818  [ 4096/ 6954]\n",
      "loss: 0.085481  [ 5120/ 6954]\n",
      "loss: 0.107174  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001396 \n",
      "\n",
      "Epoch 390\n",
      "-------------------------------\n",
      "loss: 0.093055  [    0/ 6954]\n",
      "loss: 0.095219  [ 1024/ 6954]\n",
      "loss: 0.095704  [ 2048/ 6954]\n",
      "loss: 0.084682  [ 3072/ 6954]\n",
      "loss: 0.085330  [ 4096/ 6954]\n",
      "loss: 0.080021  [ 5120/ 6954]\n",
      "loss: 0.110759  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001351 \n",
      "\n",
      "Epoch 391\n",
      "-------------------------------\n",
      "loss: 0.134396  [    0/ 6954]\n",
      "loss: 0.118184  [ 1024/ 6954]\n",
      "loss: 0.105780  [ 2048/ 6954]\n",
      "loss: 0.110529  [ 3072/ 6954]\n",
      "loss: 0.104700  [ 4096/ 6954]\n",
      "loss: 0.092054  [ 5120/ 6954]\n",
      "loss: 0.087893  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001374 \n",
      "\n",
      "Epoch 392\n",
      "-------------------------------\n",
      "loss: 0.113991  [    0/ 6954]\n",
      "loss: 0.111179  [ 1024/ 6954]\n",
      "loss: 0.110168  [ 2048/ 6954]\n",
      "loss: 0.066482  [ 3072/ 6954]\n",
      "loss: 0.105416  [ 4096/ 6954]\n",
      "loss: 0.102979  [ 5120/ 6954]\n",
      "loss: 0.062335  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001377 \n",
      "\n",
      "Epoch 393\n",
      "-------------------------------\n",
      "loss: 0.089199  [    0/ 6954]\n",
      "loss: 0.083369  [ 1024/ 6954]\n",
      "loss: 0.063046  [ 2048/ 6954]\n",
      "loss: 0.075455  [ 3072/ 6954]\n",
      "loss: 0.082550  [ 4096/ 6954]\n",
      "loss: 0.087322  [ 5120/ 6954]\n",
      "loss: 0.091494  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001371 \n",
      "\n",
      "Epoch 394\n",
      "-------------------------------\n",
      "loss: 0.084681  [    0/ 6954]\n",
      "loss: 0.074014  [ 1024/ 6954]\n",
      "loss: 0.110169  [ 2048/ 6954]\n",
      "loss: 0.080569  [ 3072/ 6954]\n",
      "loss: 0.089435  [ 4096/ 6954]\n",
      "loss: 0.117955  [ 5120/ 6954]\n",
      "loss: 0.075048  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001371 \n",
      "\n",
      "Epoch 395\n",
      "-------------------------------\n",
      "loss: 0.058569  [    0/ 6954]\n",
      "loss: 0.056499  [ 1024/ 6954]\n",
      "loss: 0.113416  [ 2048/ 6954]\n",
      "loss: 0.088590  [ 3072/ 6954]\n",
      "loss: 0.074471  [ 4096/ 6954]\n",
      "loss: 0.081597  [ 5120/ 6954]\n",
      "loss: 0.096046  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001344 \n",
      "\n",
      "Epoch 396\n",
      "-------------------------------\n",
      "loss: 0.065409  [    0/ 6954]\n",
      "loss: 0.087893  [ 1024/ 6954]\n",
      "loss: 0.089550  [ 2048/ 6954]\n",
      "loss: 0.095467  [ 3072/ 6954]\n",
      "loss: 0.111766  [ 4096/ 6954]\n",
      "loss: 0.104970  [ 5120/ 6954]\n",
      "loss: 0.084917  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001381 \n",
      "\n",
      "Epoch 397\n",
      "-------------------------------\n",
      "loss: 0.106812  [    0/ 6954]\n",
      "loss: 0.094953  [ 1024/ 6954]\n",
      "loss: 0.080841  [ 2048/ 6954]\n",
      "loss: 0.120337  [ 3072/ 6954]\n",
      "loss: 0.110524  [ 4096/ 6954]\n",
      "loss: 0.073379  [ 5120/ 6954]\n",
      "loss: 0.083169  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001341 \n",
      "\n",
      "Epoch 398\n",
      "-------------------------------\n",
      "loss: 0.115874  [    0/ 6954]\n",
      "loss: 0.103869  [ 1024/ 6954]\n",
      "loss: 0.088726  [ 2048/ 6954]\n",
      "loss: 0.078886  [ 3072/ 6954]\n",
      "loss: 0.104650  [ 4096/ 6954]\n",
      "loss: 0.078904  [ 5120/ 6954]\n",
      "loss: 0.115013  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001336 \n",
      "\n",
      "Epoch 399\n",
      "-------------------------------\n",
      "loss: 0.102905  [    0/ 6954]\n",
      "loss: 0.066375  [ 1024/ 6954]\n",
      "loss: 0.092817  [ 2048/ 6954]\n",
      "loss: 0.090042  [ 3072/ 6954]\n",
      "loss: 0.082444  [ 4096/ 6954]\n",
      "loss: 0.102241  [ 5120/ 6954]\n",
      "loss: 0.079834  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001346 \n",
      "\n",
      "Epoch 400\n",
      "-------------------------------\n",
      "loss: 0.080985  [    0/ 6954]\n",
      "loss: 0.092131  [ 1024/ 6954]\n",
      "loss: 0.083673  [ 2048/ 6954]\n",
      "loss: 0.084435  [ 3072/ 6954]\n",
      "loss: 0.095555  [ 4096/ 6954]\n",
      "loss: 0.114955  [ 5120/ 6954]\n",
      "loss: 0.087307  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001389 \n",
      "\n",
      "Epoch 401\n",
      "-------------------------------\n",
      "loss: 0.097524  [    0/ 6954]\n",
      "loss: 0.090219  [ 1024/ 6954]\n",
      "loss: 0.098740  [ 2048/ 6954]\n",
      "loss: 0.096345  [ 3072/ 6954]\n",
      "loss: 0.089490  [ 4096/ 6954]\n",
      "loss: 0.090598  [ 5120/ 6954]\n",
      "loss: 0.095359  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.2%, Avg loss: 0.001333 \n",
      "\n",
      "Epoch 402\n",
      "-------------------------------\n",
      "loss: 0.097333  [    0/ 6954]\n",
      "loss: 0.072752  [ 1024/ 6954]\n",
      "loss: 0.085557  [ 2048/ 6954]\n",
      "loss: 0.102229  [ 3072/ 6954]\n",
      "loss: 0.091249  [ 4096/ 6954]\n",
      "loss: 0.083008  [ 5120/ 6954]\n",
      "loss: 0.099062  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001360 \n",
      "\n",
      "Epoch 403\n",
      "-------------------------------\n",
      "loss: 0.090408  [    0/ 6954]\n",
      "loss: 0.074547  [ 1024/ 6954]\n",
      "loss: 0.096026  [ 2048/ 6954]\n",
      "loss: 0.090249  [ 3072/ 6954]\n",
      "loss: 0.092562  [ 4096/ 6954]\n",
      "loss: 0.110333  [ 5120/ 6954]\n",
      "loss: 0.091184  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001403 \n",
      "\n",
      "Epoch 404\n",
      "-------------------------------\n",
      "loss: 0.079835  [    0/ 6954]\n",
      "loss: 0.075309  [ 1024/ 6954]\n",
      "loss: 0.057348  [ 2048/ 6954]\n",
      "loss: 0.090325  [ 3072/ 6954]\n",
      "loss: 0.099652  [ 4096/ 6954]\n",
      "loss: 0.102809  [ 5120/ 6954]\n",
      "loss: 0.067495  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001364 \n",
      "\n",
      "Epoch 405\n",
      "-------------------------------\n",
      "loss: 0.105992  [    0/ 6954]\n",
      "loss: 0.064635  [ 1024/ 6954]\n",
      "loss: 0.082243  [ 2048/ 6954]\n",
      "loss: 0.082590  [ 3072/ 6954]\n",
      "loss: 0.114265  [ 4096/ 6954]\n",
      "loss: 0.072597  [ 5120/ 6954]\n",
      "loss: 0.117838  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001352 \n",
      "\n",
      "Epoch 406\n",
      "-------------------------------\n",
      "loss: 0.071255  [    0/ 6954]\n",
      "loss: 0.069941  [ 1024/ 6954]\n",
      "loss: 0.097050  [ 2048/ 6954]\n",
      "loss: 0.107379  [ 3072/ 6954]\n",
      "loss: 0.057431  [ 4096/ 6954]\n",
      "loss: 0.095583  [ 5120/ 6954]\n",
      "loss: 0.092361  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001393 \n",
      "\n",
      "Epoch 407\n",
      "-------------------------------\n",
      "loss: 0.093461  [    0/ 6954]\n",
      "loss: 0.091968  [ 1024/ 6954]\n",
      "loss: 0.095192  [ 2048/ 6954]\n",
      "loss: 0.077227  [ 3072/ 6954]\n",
      "loss: 0.089351  [ 4096/ 6954]\n",
      "loss: 0.060869  [ 5120/ 6954]\n",
      "loss: 0.075037  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001389 \n",
      "\n",
      "Epoch 408\n",
      "-------------------------------\n",
      "loss: 0.062350  [    0/ 6954]\n",
      "loss: 0.059890  [ 1024/ 6954]\n",
      "loss: 0.076617  [ 2048/ 6954]\n",
      "loss: 0.094519  [ 3072/ 6954]\n",
      "loss: 0.100364  [ 4096/ 6954]\n",
      "loss: 0.094577  [ 5120/ 6954]\n",
      "loss: 0.124002  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001464 \n",
      "\n",
      "Epoch 409\n",
      "-------------------------------\n",
      "loss: 0.104089  [    0/ 6954]\n",
      "loss: 0.132940  [ 1024/ 6954]\n",
      "loss: 0.095165  [ 2048/ 6954]\n",
      "loss: 0.127436  [ 3072/ 6954]\n",
      "loss: 0.066849  [ 4096/ 6954]\n",
      "loss: 0.104057  [ 5120/ 6954]\n",
      "loss: 0.069713  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001363 \n",
      "\n",
      "Epoch 410\n",
      "-------------------------------\n",
      "loss: 0.076831  [    0/ 6954]\n",
      "loss: 0.102711  [ 1024/ 6954]\n",
      "loss: 0.067020  [ 2048/ 6954]\n",
      "loss: 0.083848  [ 3072/ 6954]\n",
      "loss: 0.081911  [ 4096/ 6954]\n",
      "loss: 0.087259  [ 5120/ 6954]\n",
      "loss: 0.069700  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001382 \n",
      "\n",
      "Epoch 411\n",
      "-------------------------------\n",
      "loss: 0.070969  [    0/ 6954]\n",
      "loss: 0.076581  [ 1024/ 6954]\n",
      "loss: 0.077459  [ 2048/ 6954]\n",
      "loss: 0.083771  [ 3072/ 6954]\n",
      "loss: 0.098743  [ 4096/ 6954]\n",
      "loss: 0.063865  [ 5120/ 6954]\n",
      "loss: 0.069859  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001384 \n",
      "\n",
      "Epoch 412\n",
      "-------------------------------\n",
      "loss: 0.078406  [    0/ 6954]\n",
      "loss: 0.095388  [ 1024/ 6954]\n",
      "loss: 0.096264  [ 2048/ 6954]\n",
      "loss: 0.109259  [ 3072/ 6954]\n",
      "loss: 0.077380  [ 4096/ 6954]\n",
      "loss: 0.079477  [ 5120/ 6954]\n",
      "loss: 0.062442  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.1%, Avg loss: 0.001327 \n",
      "\n",
      "Epoch 413\n",
      "-------------------------------\n",
      "loss: 0.080904  [    0/ 6954]\n",
      "loss: 0.096461  [ 1024/ 6954]\n",
      "loss: 0.072963  [ 2048/ 6954]\n",
      "loss: 0.071659  [ 3072/ 6954]\n",
      "loss: 0.079339  [ 4096/ 6954]\n",
      "loss: 0.090973  [ 5120/ 6954]\n",
      "loss: 0.084876  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001371 \n",
      "\n",
      "Epoch 414\n",
      "-------------------------------\n",
      "loss: 0.076979  [    0/ 6954]\n",
      "loss: 0.101153  [ 1024/ 6954]\n",
      "loss: 0.060172  [ 2048/ 6954]\n",
      "loss: 0.086641  [ 3072/ 6954]\n",
      "loss: 0.053744  [ 4096/ 6954]\n",
      "loss: 0.107838  [ 5120/ 6954]\n",
      "loss: 0.091472  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001383 \n",
      "\n",
      "Epoch 415\n",
      "-------------------------------\n",
      "loss: 0.072780  [    0/ 6954]\n",
      "loss: 0.062828  [ 1024/ 6954]\n",
      "loss: 0.051564  [ 2048/ 6954]\n",
      "loss: 0.099431  [ 3072/ 6954]\n",
      "loss: 0.084517  [ 4096/ 6954]\n",
      "loss: 0.085809  [ 5120/ 6954]\n",
      "loss: 0.080009  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001437 \n",
      "\n",
      "Epoch 416\n",
      "-------------------------------\n",
      "loss: 0.080976  [    0/ 6954]\n",
      "loss: 0.074600  [ 1024/ 6954]\n",
      "loss: 0.061910  [ 2048/ 6954]\n",
      "loss: 0.068556  [ 3072/ 6954]\n",
      "loss: 0.068647  [ 4096/ 6954]\n",
      "loss: 0.069675  [ 5120/ 6954]\n",
      "loss: 0.084575  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001416 \n",
      "\n",
      "Epoch 417\n",
      "-------------------------------\n",
      "loss: 0.112850  [    0/ 6954]\n",
      "loss: 0.100531  [ 1024/ 6954]\n",
      "loss: 0.084419  [ 2048/ 6954]\n",
      "loss: 0.071794  [ 3072/ 6954]\n",
      "loss: 0.101991  [ 4096/ 6954]\n",
      "loss: 0.099344  [ 5120/ 6954]\n",
      "loss: 0.066854  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001414 \n",
      "\n",
      "Epoch 418\n",
      "-------------------------------\n",
      "loss: 0.088057  [    0/ 6954]\n",
      "loss: 0.086969  [ 1024/ 6954]\n",
      "loss: 0.082055  [ 2048/ 6954]\n",
      "loss: 0.073816  [ 3072/ 6954]\n",
      "loss: 0.103417  [ 4096/ 6954]\n",
      "loss: 0.088955  [ 5120/ 6954]\n",
      "loss: 0.081285  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001395 \n",
      "\n",
      "Epoch 419\n",
      "-------------------------------\n",
      "loss: 0.070103  [    0/ 6954]\n",
      "loss: 0.117340  [ 1024/ 6954]\n",
      "loss: 0.089085  [ 2048/ 6954]\n",
      "loss: 0.098828  [ 3072/ 6954]\n",
      "loss: 0.067119  [ 4096/ 6954]\n",
      "loss: 0.071230  [ 5120/ 6954]\n",
      "loss: 0.083051  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.0%, Avg loss: 0.001416 \n",
      "\n",
      "Epoch 420\n",
      "-------------------------------\n",
      "loss: 0.101008  [    0/ 6954]\n",
      "loss: 0.127350  [ 1024/ 6954]\n",
      "loss: 0.110194  [ 2048/ 6954]\n",
      "loss: 0.143470  [ 3072/ 6954]\n",
      "loss: 0.106465  [ 4096/ 6954]\n",
      "loss: 0.117984  [ 5120/ 6954]\n",
      "loss: 0.078344  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001379 \n",
      "\n",
      "Epoch 421\n",
      "-------------------------------\n",
      "loss: 0.091013  [    0/ 6954]\n",
      "loss: 0.073756  [ 1024/ 6954]\n",
      "loss: 0.074191  [ 2048/ 6954]\n",
      "loss: 0.095714  [ 3072/ 6954]\n",
      "loss: 0.087635  [ 4096/ 6954]\n",
      "loss: 0.072625  [ 5120/ 6954]\n",
      "loss: 0.096534  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001389 \n",
      "\n",
      "Epoch 422\n",
      "-------------------------------\n",
      "loss: 0.074489  [    0/ 6954]\n",
      "loss: 0.065993  [ 1024/ 6954]\n",
      "loss: 0.112122  [ 2048/ 6954]\n",
      "loss: 0.110387  [ 3072/ 6954]\n",
      "loss: 0.114973  [ 4096/ 6954]\n",
      "loss: 0.079833  [ 5120/ 6954]\n",
      "loss: 0.054699  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001348 \n",
      "\n",
      "Epoch 423\n",
      "-------------------------------\n",
      "loss: 0.062359  [    0/ 6954]\n",
      "loss: 0.053773  [ 1024/ 6954]\n",
      "loss: 0.078211  [ 2048/ 6954]\n",
      "loss: 0.076214  [ 3072/ 6954]\n",
      "loss: 0.082998  [ 4096/ 6954]\n",
      "loss: 0.114434  [ 5120/ 6954]\n",
      "loss: 0.072557  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001380 \n",
      "\n",
      "Epoch 424\n",
      "-------------------------------\n",
      "loss: 0.082315  [    0/ 6954]\n",
      "loss: 0.094399  [ 1024/ 6954]\n",
      "loss: 0.074993  [ 2048/ 6954]\n",
      "loss: 0.099953  [ 3072/ 6954]\n",
      "loss: 0.077403  [ 4096/ 6954]\n",
      "loss: 0.066518  [ 5120/ 6954]\n",
      "loss: 0.074747  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001343 \n",
      "\n",
      "Epoch 425\n",
      "-------------------------------\n",
      "loss: 0.090010  [    0/ 6954]\n",
      "loss: 0.071886  [ 1024/ 6954]\n",
      "loss: 0.064520  [ 2048/ 6954]\n",
      "loss: 0.099080  [ 3072/ 6954]\n",
      "loss: 0.106354  [ 4096/ 6954]\n",
      "loss: 0.058820  [ 5120/ 6954]\n",
      "loss: 0.094377  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.3%, Avg loss: 0.001474 \n",
      "\n",
      "Epoch 426\n",
      "-------------------------------\n",
      "loss: 0.097821  [    0/ 6954]\n",
      "loss: 0.101127  [ 1024/ 6954]\n",
      "loss: 0.099788  [ 2048/ 6954]\n",
      "loss: 0.059384  [ 3072/ 6954]\n",
      "loss: 0.078461  [ 4096/ 6954]\n",
      "loss: 0.074852  [ 5120/ 6954]\n",
      "loss: 0.080544  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.9%, Avg loss: 0.001379 \n",
      "\n",
      "Epoch 427\n",
      "-------------------------------\n",
      "loss: 0.074664  [    0/ 6954]\n",
      "loss: 0.086725  [ 1024/ 6954]\n",
      "loss: 0.084032  [ 2048/ 6954]\n",
      "loss: 0.086972  [ 3072/ 6954]\n",
      "loss: 0.086734  [ 4096/ 6954]\n",
      "loss: 0.072432  [ 5120/ 6954]\n",
      "loss: 0.058518  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001389 \n",
      "\n",
      "Epoch 428\n",
      "-------------------------------\n",
      "loss: 0.065300  [    0/ 6954]\n",
      "loss: 0.078408  [ 1024/ 6954]\n",
      "loss: 0.106599  [ 2048/ 6954]\n",
      "loss: 0.089769  [ 3072/ 6954]\n",
      "loss: 0.095487  [ 4096/ 6954]\n",
      "loss: 0.109092  [ 5120/ 6954]\n",
      "loss: 0.095080  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001382 \n",
      "\n",
      "Epoch 429\n",
      "-------------------------------\n",
      "loss: 0.087855  [    0/ 6954]\n",
      "loss: 0.076298  [ 1024/ 6954]\n",
      "loss: 0.100558  [ 2048/ 6954]\n",
      "loss: 0.084863  [ 3072/ 6954]\n",
      "loss: 0.090044  [ 4096/ 6954]\n",
      "loss: 0.086550  [ 5120/ 6954]\n",
      "loss: 0.089205  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001429 \n",
      "\n",
      "Epoch 430\n",
      "-------------------------------\n",
      "loss: 0.068912  [    0/ 6954]\n",
      "loss: 0.097004  [ 1024/ 6954]\n",
      "loss: 0.067284  [ 2048/ 6954]\n",
      "loss: 0.104853  [ 3072/ 6954]\n",
      "loss: 0.069886  [ 4096/ 6954]\n",
      "loss: 0.102818  [ 5120/ 6954]\n",
      "loss: 0.091306  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001417 \n",
      "\n",
      "Epoch 431\n",
      "-------------------------------\n",
      "loss: 0.117247  [    0/ 6954]\n",
      "loss: 0.071966  [ 1024/ 6954]\n",
      "loss: 0.072088  [ 2048/ 6954]\n",
      "loss: 0.105700  [ 3072/ 6954]\n",
      "loss: 0.110964  [ 4096/ 6954]\n",
      "loss: 0.100166  [ 5120/ 6954]\n",
      "loss: 0.091848  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001329 \n",
      "\n",
      "Epoch 432\n",
      "-------------------------------\n",
      "loss: 0.083159  [    0/ 6954]\n",
      "loss: 0.109699  [ 1024/ 6954]\n",
      "loss: 0.091052  [ 2048/ 6954]\n",
      "loss: 0.075290  [ 3072/ 6954]\n",
      "loss: 0.077632  [ 4096/ 6954]\n",
      "loss: 0.072214  [ 5120/ 6954]\n",
      "loss: 0.079577  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001376 \n",
      "\n",
      "Epoch 433\n",
      "-------------------------------\n",
      "loss: 0.079440  [    0/ 6954]\n",
      "loss: 0.102587  [ 1024/ 6954]\n",
      "loss: 0.083686  [ 2048/ 6954]\n",
      "loss: 0.085558  [ 3072/ 6954]\n",
      "loss: 0.109496  [ 4096/ 6954]\n",
      "loss: 0.094644  [ 5120/ 6954]\n",
      "loss: 0.063924  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001435 \n",
      "\n",
      "Epoch 434\n",
      "-------------------------------\n",
      "loss: 0.074177  [    0/ 6954]\n",
      "loss: 0.089109  [ 1024/ 6954]\n",
      "loss: 0.066978  [ 2048/ 6954]\n",
      "loss: 0.070420  [ 3072/ 6954]\n",
      "loss: 0.102554  [ 4096/ 6954]\n",
      "loss: 0.085346  [ 5120/ 6954]\n",
      "loss: 0.100498  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001414 \n",
      "\n",
      "Epoch 435\n",
      "-------------------------------\n",
      "loss: 0.079444  [    0/ 6954]\n",
      "loss: 0.084087  [ 1024/ 6954]\n",
      "loss: 0.078806  [ 2048/ 6954]\n",
      "loss: 0.092513  [ 3072/ 6954]\n",
      "loss: 0.096776  [ 4096/ 6954]\n",
      "loss: 0.093979  [ 5120/ 6954]\n",
      "loss: 0.100672  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001421 \n",
      "\n",
      "Epoch 436\n",
      "-------------------------------\n",
      "loss: 0.067629  [    0/ 6954]\n",
      "loss: 0.095235  [ 1024/ 6954]\n",
      "loss: 0.083219  [ 2048/ 6954]\n",
      "loss: 0.099639  [ 3072/ 6954]\n",
      "loss: 0.062774  [ 4096/ 6954]\n",
      "loss: 0.124753  [ 5120/ 6954]\n",
      "loss: 0.054232  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001411 \n",
      "\n",
      "Epoch 437\n",
      "-------------------------------\n",
      "loss: 0.083719  [    0/ 6954]\n",
      "loss: 0.128394  [ 1024/ 6954]\n",
      "loss: 0.116878  [ 2048/ 6954]\n",
      "loss: 0.171964  [ 3072/ 6954]\n",
      "loss: 0.119492  [ 4096/ 6954]\n",
      "loss: 0.102535  [ 5120/ 6954]\n",
      "loss: 0.101149  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001360 \n",
      "\n",
      "Epoch 438\n",
      "-------------------------------\n",
      "loss: 0.114848  [    0/ 6954]\n",
      "loss: 0.096624  [ 1024/ 6954]\n",
      "loss: 0.104686  [ 2048/ 6954]\n",
      "loss: 0.097147  [ 3072/ 6954]\n",
      "loss: 0.061953  [ 4096/ 6954]\n",
      "loss: 0.091781  [ 5120/ 6954]\n",
      "loss: 0.077979  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001392 \n",
      "\n",
      "Epoch 439\n",
      "-------------------------------\n",
      "loss: 0.076213  [    0/ 6954]\n",
      "loss: 0.094870  [ 1024/ 6954]\n",
      "loss: 0.083549  [ 2048/ 6954]\n",
      "loss: 0.106253  [ 3072/ 6954]\n",
      "loss: 0.083545  [ 4096/ 6954]\n",
      "loss: 0.092157  [ 5120/ 6954]\n",
      "loss: 0.096265  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001377 \n",
      "\n",
      "Epoch 440\n",
      "-------------------------------\n",
      "loss: 0.084808  [    0/ 6954]\n",
      "loss: 0.092597  [ 1024/ 6954]\n",
      "loss: 0.099113  [ 2048/ 6954]\n",
      "loss: 0.084898  [ 3072/ 6954]\n",
      "loss: 0.084464  [ 4096/ 6954]\n",
      "loss: 0.061881  [ 5120/ 6954]\n",
      "loss: 0.087431  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001419 \n",
      "\n",
      "Epoch 441\n",
      "-------------------------------\n",
      "loss: 0.066673  [    0/ 6954]\n",
      "loss: 0.074406  [ 1024/ 6954]\n",
      "loss: 0.079163  [ 2048/ 6954]\n",
      "loss: 0.078445  [ 3072/ 6954]\n",
      "loss: 0.071469  [ 4096/ 6954]\n",
      "loss: 0.070654  [ 5120/ 6954]\n",
      "loss: 0.096907  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001424 \n",
      "\n",
      "Epoch 442\n",
      "-------------------------------\n",
      "loss: 0.089765  [    0/ 6954]\n",
      "loss: 0.098044  [ 1024/ 6954]\n",
      "loss: 0.064344  [ 2048/ 6954]\n",
      "loss: 0.075122  [ 3072/ 6954]\n",
      "loss: 0.072718  [ 4096/ 6954]\n",
      "loss: 0.086783  [ 5120/ 6954]\n",
      "loss: 0.095810  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001429 \n",
      "\n",
      "Epoch 443\n",
      "-------------------------------\n",
      "loss: 0.073549  [    0/ 6954]\n",
      "loss: 0.097791  [ 1024/ 6954]\n",
      "loss: 0.097934  [ 2048/ 6954]\n",
      "loss: 0.113499  [ 3072/ 6954]\n",
      "loss: 0.107795  [ 4096/ 6954]\n",
      "loss: 0.117888  [ 5120/ 6954]\n",
      "loss: 0.075131  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.1%, Avg loss: 0.001529 \n",
      "\n",
      "Epoch 444\n",
      "-------------------------------\n",
      "loss: 0.071694  [    0/ 6954]\n",
      "loss: 0.073670  [ 1024/ 6954]\n",
      "loss: 0.096769  [ 2048/ 6954]\n",
      "loss: 0.070919  [ 3072/ 6954]\n",
      "loss: 0.088764  [ 4096/ 6954]\n",
      "loss: 0.105207  [ 5120/ 6954]\n",
      "loss: 0.086868  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001412 \n",
      "\n",
      "Epoch 445\n",
      "-------------------------------\n",
      "loss: 0.061384  [    0/ 6954]\n",
      "loss: 0.107953  [ 1024/ 6954]\n",
      "loss: 0.101514  [ 2048/ 6954]\n",
      "loss: 0.070840  [ 3072/ 6954]\n",
      "loss: 0.077350  [ 4096/ 6954]\n",
      "loss: 0.076318  [ 5120/ 6954]\n",
      "loss: 0.116132  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001381 \n",
      "\n",
      "Epoch 446\n",
      "-------------------------------\n",
      "loss: 0.102777  [    0/ 6954]\n",
      "loss: 0.060691  [ 1024/ 6954]\n",
      "loss: 0.092297  [ 2048/ 6954]\n",
      "loss: 0.083205  [ 3072/ 6954]\n",
      "loss: 0.105239  [ 4096/ 6954]\n",
      "loss: 0.059311  [ 5120/ 6954]\n",
      "loss: 0.093427  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001462 \n",
      "\n",
      "Epoch 447\n",
      "-------------------------------\n",
      "loss: 0.073570  [    0/ 6954]\n",
      "loss: 0.079211  [ 1024/ 6954]\n",
      "loss: 0.080400  [ 2048/ 6954]\n",
      "loss: 0.058395  [ 3072/ 6954]\n",
      "loss: 0.062444  [ 4096/ 6954]\n",
      "loss: 0.112862  [ 5120/ 6954]\n",
      "loss: 0.103581  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001401 \n",
      "\n",
      "Epoch 448\n",
      "-------------------------------\n",
      "loss: 0.069798  [    0/ 6954]\n",
      "loss: 0.076477  [ 1024/ 6954]\n",
      "loss: 0.088949  [ 2048/ 6954]\n",
      "loss: 0.067751  [ 3072/ 6954]\n",
      "loss: 0.064679  [ 4096/ 6954]\n",
      "loss: 0.114260  [ 5120/ 6954]\n",
      "loss: 0.094106  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001407 \n",
      "\n",
      "Epoch 449\n",
      "-------------------------------\n",
      "loss: 0.056628  [    0/ 6954]\n",
      "loss: 0.093958  [ 1024/ 6954]\n",
      "loss: 0.068439  [ 2048/ 6954]\n",
      "loss: 0.066857  [ 3072/ 6954]\n",
      "loss: 0.101277  [ 4096/ 6954]\n",
      "loss: 0.083053  [ 5120/ 6954]\n",
      "loss: 0.104777  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001434 \n",
      "\n",
      "Epoch 450\n",
      "-------------------------------\n",
      "loss: 0.086402  [    0/ 6954]\n",
      "loss: 0.075812  [ 1024/ 6954]\n",
      "loss: 0.076662  [ 2048/ 6954]\n",
      "loss: 0.080610  [ 3072/ 6954]\n",
      "loss: 0.100598  [ 4096/ 6954]\n",
      "loss: 0.082135  [ 5120/ 6954]\n",
      "loss: 0.088335  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001384 \n",
      "\n",
      "Epoch 451\n",
      "-------------------------------\n",
      "loss: 0.085175  [    0/ 6954]\n",
      "loss: 0.123824  [ 1024/ 6954]\n",
      "loss: 0.113238  [ 2048/ 6954]\n",
      "loss: 0.069196  [ 3072/ 6954]\n",
      "loss: 0.089770  [ 4096/ 6954]\n",
      "loss: 0.090352  [ 5120/ 6954]\n",
      "loss: 0.077973  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001364 \n",
      "\n",
      "Epoch 452\n",
      "-------------------------------\n",
      "loss: 0.079526  [    0/ 6954]\n",
      "loss: 0.092987  [ 1024/ 6954]\n",
      "loss: 0.109808  [ 2048/ 6954]\n",
      "loss: 0.074119  [ 3072/ 6954]\n",
      "loss: 0.129308  [ 4096/ 6954]\n",
      "loss: 0.118616  [ 5120/ 6954]\n",
      "loss: 0.105638  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001447 \n",
      "\n",
      "Epoch 453\n",
      "-------------------------------\n",
      "loss: 0.047241  [    0/ 6954]\n",
      "loss: 0.094121  [ 1024/ 6954]\n",
      "loss: 0.116808  [ 2048/ 6954]\n",
      "loss: 0.080926  [ 3072/ 6954]\n",
      "loss: 0.098287  [ 4096/ 6954]\n",
      "loss: 0.112620  [ 5120/ 6954]\n",
      "loss: 0.070838  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001428 \n",
      "\n",
      "Epoch 454\n",
      "-------------------------------\n",
      "loss: 0.106605  [    0/ 6954]\n",
      "loss: 0.079157  [ 1024/ 6954]\n",
      "loss: 0.077805  [ 2048/ 6954]\n",
      "loss: 0.110660  [ 3072/ 6954]\n",
      "loss: 0.064483  [ 4096/ 6954]\n",
      "loss: 0.092328  [ 5120/ 6954]\n",
      "loss: 0.101467  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001393 \n",
      "\n",
      "Epoch 455\n",
      "-------------------------------\n",
      "loss: 0.084878  [    0/ 6954]\n",
      "loss: 0.063472  [ 1024/ 6954]\n",
      "loss: 0.083143  [ 2048/ 6954]\n",
      "loss: 0.063607  [ 3072/ 6954]\n",
      "loss: 0.099155  [ 4096/ 6954]\n",
      "loss: 0.093717  [ 5120/ 6954]\n",
      "loss: 0.074704  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.7%, Avg loss: 0.001454 \n",
      "\n",
      "Epoch 456\n",
      "-------------------------------\n",
      "loss: 0.065294  [    0/ 6954]\n",
      "loss: 0.065703  [ 1024/ 6954]\n",
      "loss: 0.059892  [ 2048/ 6954]\n",
      "loss: 0.089522  [ 3072/ 6954]\n",
      "loss: 0.085196  [ 4096/ 6954]\n",
      "loss: 0.069269  [ 5120/ 6954]\n",
      "loss: 0.082463  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001441 \n",
      "\n",
      "Epoch 457\n",
      "-------------------------------\n",
      "loss: 0.078025  [    0/ 6954]\n",
      "loss: 0.080757  [ 1024/ 6954]\n",
      "loss: 0.098082  [ 2048/ 6954]\n",
      "loss: 0.094870  [ 3072/ 6954]\n",
      "loss: 0.132075  [ 4096/ 6954]\n",
      "loss: 0.072121  [ 5120/ 6954]\n",
      "loss: 0.064637  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001408 \n",
      "\n",
      "Epoch 458\n",
      "-------------------------------\n",
      "loss: 0.092170  [    0/ 6954]\n",
      "loss: 0.049922  [ 1024/ 6954]\n",
      "loss: 0.082629  [ 2048/ 6954]\n",
      "loss: 0.067598  [ 3072/ 6954]\n",
      "loss: 0.075397  [ 4096/ 6954]\n",
      "loss: 0.077373  [ 5120/ 6954]\n",
      "loss: 0.071704  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001472 \n",
      "\n",
      "Epoch 459\n",
      "-------------------------------\n",
      "loss: 0.099944  [    0/ 6954]\n",
      "loss: 0.073988  [ 1024/ 6954]\n",
      "loss: 0.060017  [ 2048/ 6954]\n",
      "loss: 0.105544  [ 3072/ 6954]\n",
      "loss: 0.076643  [ 4096/ 6954]\n",
      "loss: 0.068281  [ 5120/ 6954]\n",
      "loss: 0.102969  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001390 \n",
      "\n",
      "Epoch 460\n",
      "-------------------------------\n",
      "loss: 0.107740  [    0/ 6954]\n",
      "loss: 0.100860  [ 1024/ 6954]\n",
      "loss: 0.062826  [ 2048/ 6954]\n",
      "loss: 0.080448  [ 3072/ 6954]\n",
      "loss: 0.047775  [ 4096/ 6954]\n",
      "loss: 0.100256  [ 5120/ 6954]\n",
      "loss: 0.080631  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001372 \n",
      "\n",
      "Epoch 461\n",
      "-------------------------------\n",
      "loss: 0.058831  [    0/ 6954]\n",
      "loss: 0.075797  [ 1024/ 6954]\n",
      "loss: 0.067347  [ 2048/ 6954]\n",
      "loss: 0.071713  [ 3072/ 6954]\n",
      "loss: 0.099166  [ 4096/ 6954]\n",
      "loss: 0.091291  [ 5120/ 6954]\n",
      "loss: 0.098772  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001438 \n",
      "\n",
      "Epoch 462\n",
      "-------------------------------\n",
      "loss: 0.057309  [    0/ 6954]\n",
      "loss: 0.105353  [ 1024/ 6954]\n",
      "loss: 0.052807  [ 2048/ 6954]\n",
      "loss: 0.053833  [ 3072/ 6954]\n",
      "loss: 0.076975  [ 4096/ 6954]\n",
      "loss: 0.083716  [ 5120/ 6954]\n",
      "loss: 0.093717  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001461 \n",
      "\n",
      "Epoch 463\n",
      "-------------------------------\n",
      "loss: 0.086689  [    0/ 6954]\n",
      "loss: 0.085032  [ 1024/ 6954]\n",
      "loss: 0.088886  [ 2048/ 6954]\n",
      "loss: 0.090535  [ 3072/ 6954]\n",
      "loss: 0.096019  [ 4096/ 6954]\n",
      "loss: 0.075564  [ 5120/ 6954]\n",
      "loss: 0.077238  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001463 \n",
      "\n",
      "Epoch 464\n",
      "-------------------------------\n",
      "loss: 0.085691  [    0/ 6954]\n",
      "loss: 0.080667  [ 1024/ 6954]\n",
      "loss: 0.053804  [ 2048/ 6954]\n",
      "loss: 0.085100  [ 3072/ 6954]\n",
      "loss: 0.068631  [ 4096/ 6954]\n",
      "loss: 0.085120  [ 5120/ 6954]\n",
      "loss: 0.068965  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001412 \n",
      "\n",
      "Epoch 465\n",
      "-------------------------------\n",
      "loss: 0.096947  [    0/ 6954]\n",
      "loss: 0.076321  [ 1024/ 6954]\n",
      "loss: 0.087370  [ 2048/ 6954]\n",
      "loss: 0.068042  [ 3072/ 6954]\n",
      "loss: 0.056664  [ 4096/ 6954]\n",
      "loss: 0.073979  [ 5120/ 6954]\n",
      "loss: 0.095306  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001450 \n",
      "\n",
      "Epoch 466\n",
      "-------------------------------\n",
      "loss: 0.069494  [    0/ 6954]\n",
      "loss: 0.089780  [ 1024/ 6954]\n",
      "loss: 0.065278  [ 2048/ 6954]\n",
      "loss: 0.068599  [ 3072/ 6954]\n",
      "loss: 0.085906  [ 4096/ 6954]\n",
      "loss: 0.080533  [ 5120/ 6954]\n",
      "loss: 0.084738  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001463 \n",
      "\n",
      "Epoch 467\n",
      "-------------------------------\n",
      "loss: 0.068325  [    0/ 6954]\n",
      "loss: 0.083684  [ 1024/ 6954]\n",
      "loss: 0.061031  [ 2048/ 6954]\n",
      "loss: 0.070966  [ 3072/ 6954]\n",
      "loss: 0.063695  [ 4096/ 6954]\n",
      "loss: 0.073261  [ 5120/ 6954]\n",
      "loss: 0.077893  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001397 \n",
      "\n",
      "Epoch 468\n",
      "-------------------------------\n",
      "loss: 0.090665  [    0/ 6954]\n",
      "loss: 0.089840  [ 1024/ 6954]\n",
      "loss: 0.113203  [ 2048/ 6954]\n",
      "loss: 0.097479  [ 3072/ 6954]\n",
      "loss: 0.087455  [ 4096/ 6954]\n",
      "loss: 0.080241  [ 5120/ 6954]\n",
      "loss: 0.083847  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 74.0%, Avg loss: 0.001543 \n",
      "\n",
      "Epoch 469\n",
      "-------------------------------\n",
      "loss: 0.121973  [    0/ 6954]\n",
      "loss: 0.115205  [ 1024/ 6954]\n",
      "loss: 0.101824  [ 2048/ 6954]\n",
      "loss: 0.081708  [ 3072/ 6954]\n",
      "loss: 0.089885  [ 4096/ 6954]\n",
      "loss: 0.062674  [ 5120/ 6954]\n",
      "loss: 0.084797  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001370 \n",
      "\n",
      "Epoch 470\n",
      "-------------------------------\n",
      "loss: 0.090655  [    0/ 6954]\n",
      "loss: 0.082342  [ 1024/ 6954]\n",
      "loss: 0.108607  [ 2048/ 6954]\n",
      "loss: 0.060008  [ 3072/ 6954]\n",
      "loss: 0.073230  [ 4096/ 6954]\n",
      "loss: 0.086607  [ 5120/ 6954]\n",
      "loss: 0.083363  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001398 \n",
      "\n",
      "Epoch 471\n",
      "-------------------------------\n",
      "loss: 0.053959  [    0/ 6954]\n",
      "loss: 0.066655  [ 1024/ 6954]\n",
      "loss: 0.062641  [ 2048/ 6954]\n",
      "loss: 0.067881  [ 3072/ 6954]\n",
      "loss: 0.048725  [ 4096/ 6954]\n",
      "loss: 0.105587  [ 5120/ 6954]\n",
      "loss: 0.121312  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001411 \n",
      "\n",
      "Epoch 472\n",
      "-------------------------------\n",
      "loss: 0.078258  [    0/ 6954]\n",
      "loss: 0.098840  [ 1024/ 6954]\n",
      "loss: 0.074311  [ 2048/ 6954]\n",
      "loss: 0.081254  [ 3072/ 6954]\n",
      "loss: 0.080351  [ 4096/ 6954]\n",
      "loss: 0.076391  [ 5120/ 6954]\n",
      "loss: 0.082513  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001390 \n",
      "\n",
      "Epoch 473\n",
      "-------------------------------\n",
      "loss: 0.047038  [    0/ 6954]\n",
      "loss: 0.060464  [ 1024/ 6954]\n",
      "loss: 0.106156  [ 2048/ 6954]\n",
      "loss: 0.093893  [ 3072/ 6954]\n",
      "loss: 0.069445  [ 4096/ 6954]\n",
      "loss: 0.091149  [ 5120/ 6954]\n",
      "loss: 0.074702  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001450 \n",
      "\n",
      "Epoch 474\n",
      "-------------------------------\n",
      "loss: 0.090607  [    0/ 6954]\n",
      "loss: 0.068411  [ 1024/ 6954]\n",
      "loss: 0.076980  [ 2048/ 6954]\n",
      "loss: 0.065916  [ 3072/ 6954]\n",
      "loss: 0.081420  [ 4096/ 6954]\n",
      "loss: 0.039842  [ 5120/ 6954]\n",
      "loss: 0.081702  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001427 \n",
      "\n",
      "Epoch 475\n",
      "-------------------------------\n",
      "loss: 0.085479  [    0/ 6954]\n",
      "loss: 0.046824  [ 1024/ 6954]\n",
      "loss: 0.050205  [ 2048/ 6954]\n",
      "loss: 0.064507  [ 3072/ 6954]\n",
      "loss: 0.119773  [ 4096/ 6954]\n",
      "loss: 0.100282  [ 5120/ 6954]\n",
      "loss: 0.060155  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001448 \n",
      "\n",
      "Epoch 476\n",
      "-------------------------------\n",
      "loss: 0.103978  [    0/ 6954]\n",
      "loss: 0.085411  [ 1024/ 6954]\n",
      "loss: 0.127614  [ 2048/ 6954]\n",
      "loss: 0.082989  [ 3072/ 6954]\n",
      "loss: 0.093839  [ 4096/ 6954]\n",
      "loss: 0.090047  [ 5120/ 6954]\n",
      "loss: 0.065890  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.7%, Avg loss: 0.001492 \n",
      "\n",
      "Epoch 477\n",
      "-------------------------------\n",
      "loss: 0.082743  [    0/ 6954]\n",
      "loss: 0.099635  [ 1024/ 6954]\n",
      "loss: 0.098058  [ 2048/ 6954]\n",
      "loss: 0.113658  [ 3072/ 6954]\n",
      "loss: 0.106991  [ 4096/ 6954]\n",
      "loss: 0.060671  [ 5120/ 6954]\n",
      "loss: 0.072349  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001439 \n",
      "\n",
      "Epoch 478\n",
      "-------------------------------\n",
      "loss: 0.045535  [    0/ 6954]\n",
      "loss: 0.081972  [ 1024/ 6954]\n",
      "loss: 0.112333  [ 2048/ 6954]\n",
      "loss: 0.099585  [ 3072/ 6954]\n",
      "loss: 0.092337  [ 4096/ 6954]\n",
      "loss: 0.078867  [ 5120/ 6954]\n",
      "loss: 0.090532  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001439 \n",
      "\n",
      "Epoch 479\n",
      "-------------------------------\n",
      "loss: 0.111544  [    0/ 6954]\n",
      "loss: 0.090665  [ 1024/ 6954]\n",
      "loss: 0.048699  [ 2048/ 6954]\n",
      "loss: 0.073293  [ 3072/ 6954]\n",
      "loss: 0.108577  [ 4096/ 6954]\n",
      "loss: 0.080348  [ 5120/ 6954]\n",
      "loss: 0.109246  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001419 \n",
      "\n",
      "Epoch 480\n",
      "-------------------------------\n",
      "loss: 0.072458  [    0/ 6954]\n",
      "loss: 0.068066  [ 1024/ 6954]\n",
      "loss: 0.092129  [ 2048/ 6954]\n",
      "loss: 0.080591  [ 3072/ 6954]\n",
      "loss: 0.054317  [ 4096/ 6954]\n",
      "loss: 0.083496  [ 5120/ 6954]\n",
      "loss: 0.080135  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001437 \n",
      "\n",
      "Epoch 481\n",
      "-------------------------------\n",
      "loss: 0.050699  [    0/ 6954]\n",
      "loss: 0.086272  [ 1024/ 6954]\n",
      "loss: 0.103975  [ 2048/ 6954]\n",
      "loss: 0.106433  [ 3072/ 6954]\n",
      "loss: 0.110390  [ 4096/ 6954]\n",
      "loss: 0.118563  [ 5120/ 6954]\n",
      "loss: 0.102039  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001377 \n",
      "\n",
      "Epoch 482\n",
      "-------------------------------\n",
      "loss: 0.047834  [    0/ 6954]\n",
      "loss: 0.089124  [ 1024/ 6954]\n",
      "loss: 0.081298  [ 2048/ 6954]\n",
      "loss: 0.107419  [ 3072/ 6954]\n",
      "loss: 0.115891  [ 4096/ 6954]\n",
      "loss: 0.109680  [ 5120/ 6954]\n",
      "loss: 0.077979  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001400 \n",
      "\n",
      "Epoch 483\n",
      "-------------------------------\n",
      "loss: 0.099738  [    0/ 6954]\n",
      "loss: 0.072831  [ 1024/ 6954]\n",
      "loss: 0.066100  [ 2048/ 6954]\n",
      "loss: 0.072611  [ 3072/ 6954]\n",
      "loss: 0.083524  [ 4096/ 6954]\n",
      "loss: 0.108257  [ 5120/ 6954]\n",
      "loss: 0.094460  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.0%, Avg loss: 0.001344 \n",
      "\n",
      "Epoch 484\n",
      "-------------------------------\n",
      "loss: 0.078915  [    0/ 6954]\n",
      "loss: 0.094170  [ 1024/ 6954]\n",
      "loss: 0.066517  [ 2048/ 6954]\n",
      "loss: 0.064077  [ 3072/ 6954]\n",
      "loss: 0.068239  [ 4096/ 6954]\n",
      "loss: 0.076603  [ 5120/ 6954]\n",
      "loss: 0.078589  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001421 \n",
      "\n",
      "Epoch 485\n",
      "-------------------------------\n",
      "loss: 0.078841  [    0/ 6954]\n",
      "loss: 0.099378  [ 1024/ 6954]\n",
      "loss: 0.077834  [ 2048/ 6954]\n",
      "loss: 0.053427  [ 3072/ 6954]\n",
      "loss: 0.097842  [ 4096/ 6954]\n",
      "loss: 0.075953  [ 5120/ 6954]\n",
      "loss: 0.089834  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001376 \n",
      "\n",
      "Epoch 486\n",
      "-------------------------------\n",
      "loss: 0.083407  [    0/ 6954]\n",
      "loss: 0.075381  [ 1024/ 6954]\n",
      "loss: 0.099614  [ 2048/ 6954]\n",
      "loss: 0.060250  [ 3072/ 6954]\n",
      "loss: 0.085997  [ 4096/ 6954]\n",
      "loss: 0.077215  [ 5120/ 6954]\n",
      "loss: 0.081770  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001419 \n",
      "\n",
      "Epoch 487\n",
      "-------------------------------\n",
      "loss: 0.084376  [    0/ 6954]\n",
      "loss: 0.083356  [ 1024/ 6954]\n",
      "loss: 0.102466  [ 2048/ 6954]\n",
      "loss: 0.089934  [ 3072/ 6954]\n",
      "loss: 0.086733  [ 4096/ 6954]\n",
      "loss: 0.071896  [ 5120/ 6954]\n",
      "loss: 0.097879  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001393 \n",
      "\n",
      "Epoch 488\n",
      "-------------------------------\n",
      "loss: 0.081604  [    0/ 6954]\n",
      "loss: 0.056346  [ 1024/ 6954]\n",
      "loss: 0.104480  [ 2048/ 6954]\n",
      "loss: 0.068946  [ 3072/ 6954]\n",
      "loss: 0.088371  [ 4096/ 6954]\n",
      "loss: 0.057017  [ 5120/ 6954]\n",
      "loss: 0.064134  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001452 \n",
      "\n",
      "Epoch 489\n",
      "-------------------------------\n",
      "loss: 0.070458  [    0/ 6954]\n",
      "loss: 0.071195  [ 1024/ 6954]\n",
      "loss: 0.070728  [ 2048/ 6954]\n",
      "loss: 0.088228  [ 3072/ 6954]\n",
      "loss: 0.077342  [ 4096/ 6954]\n",
      "loss: 0.104254  [ 5120/ 6954]\n",
      "loss: 0.116926  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001375 \n",
      "\n",
      "Epoch 490\n",
      "-------------------------------\n",
      "loss: 0.083845  [    0/ 6954]\n",
      "loss: 0.064237  [ 1024/ 6954]\n",
      "loss: 0.060960  [ 2048/ 6954]\n",
      "loss: 0.082954  [ 3072/ 6954]\n",
      "loss: 0.088624  [ 4096/ 6954]\n",
      "loss: 0.099592  [ 5120/ 6954]\n",
      "loss: 0.090460  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001441 \n",
      "\n",
      "Epoch 491\n",
      "-------------------------------\n",
      "loss: 0.047436  [    0/ 6954]\n",
      "loss: 0.035429  [ 1024/ 6954]\n",
      "loss: 0.073391  [ 2048/ 6954]\n",
      "loss: 0.063744  [ 3072/ 6954]\n",
      "loss: 0.072341  [ 4096/ 6954]\n",
      "loss: 0.086601  [ 5120/ 6954]\n",
      "loss: 0.106439  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001427 \n",
      "\n",
      "Epoch 492\n",
      "-------------------------------\n",
      "loss: 0.065784  [    0/ 6954]\n",
      "loss: 0.083124  [ 1024/ 6954]\n",
      "loss: 0.084590  [ 2048/ 6954]\n",
      "loss: 0.092998  [ 3072/ 6954]\n",
      "loss: 0.103421  [ 4096/ 6954]\n",
      "loss: 0.073335  [ 5120/ 6954]\n",
      "loss: 0.067227  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001422 \n",
      "\n",
      "Epoch 493\n",
      "-------------------------------\n",
      "loss: 0.090477  [    0/ 6954]\n",
      "loss: 0.079449  [ 1024/ 6954]\n",
      "loss: 0.084566  [ 2048/ 6954]\n",
      "loss: 0.071882  [ 3072/ 6954]\n",
      "loss: 0.092196  [ 4096/ 6954]\n",
      "loss: 0.083396  [ 5120/ 6954]\n",
      "loss: 0.078086  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.0%, Avg loss: 0.001509 \n",
      "\n",
      "Epoch 494\n",
      "-------------------------------\n",
      "loss: 0.077966  [    0/ 6954]\n",
      "loss: 0.065729  [ 1024/ 6954]\n",
      "loss: 0.088998  [ 2048/ 6954]\n",
      "loss: 0.072630  [ 3072/ 6954]\n",
      "loss: 0.055544  [ 4096/ 6954]\n",
      "loss: 0.084150  [ 5120/ 6954]\n",
      "loss: 0.091114  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.2%, Avg loss: 0.001530 \n",
      "\n",
      "Epoch 495\n",
      "-------------------------------\n",
      "loss: 0.056651  [    0/ 6954]\n",
      "loss: 0.080092  [ 1024/ 6954]\n",
      "loss: 0.086260  [ 2048/ 6954]\n",
      "loss: 0.068678  [ 3072/ 6954]\n",
      "loss: 0.061321  [ 4096/ 6954]\n",
      "loss: 0.105820  [ 5120/ 6954]\n",
      "loss: 0.121890  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.7%, Avg loss: 0.001413 \n",
      "\n",
      "Epoch 496\n",
      "-------------------------------\n",
      "loss: 0.091997  [    0/ 6954]\n",
      "loss: 0.088006  [ 1024/ 6954]\n",
      "loss: 0.088051  [ 2048/ 6954]\n",
      "loss: 0.065791  [ 3072/ 6954]\n",
      "loss: 0.077713  [ 4096/ 6954]\n",
      "loss: 0.078313  [ 5120/ 6954]\n",
      "loss: 0.089002  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001387 \n",
      "\n",
      "Epoch 497\n",
      "-------------------------------\n",
      "loss: 0.088137  [    0/ 6954]\n",
      "loss: 0.085443  [ 1024/ 6954]\n",
      "loss: 0.058250  [ 2048/ 6954]\n",
      "loss: 0.049316  [ 3072/ 6954]\n",
      "loss: 0.074014  [ 4096/ 6954]\n",
      "loss: 0.064237  [ 5120/ 6954]\n",
      "loss: 0.111241  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.0%, Avg loss: 0.001474 \n",
      "\n",
      "Epoch 498\n",
      "-------------------------------\n",
      "loss: 0.052112  [    0/ 6954]\n",
      "loss: 0.078148  [ 1024/ 6954]\n",
      "loss: 0.066844  [ 2048/ 6954]\n",
      "loss: 0.055560  [ 3072/ 6954]\n",
      "loss: 0.115409  [ 4096/ 6954]\n",
      "loss: 0.071593  [ 5120/ 6954]\n",
      "loss: 0.073832  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001425 \n",
      "\n",
      "Epoch 499\n",
      "-------------------------------\n",
      "loss: 0.098405  [    0/ 6954]\n",
      "loss: 0.083756  [ 1024/ 6954]\n",
      "loss: 0.072351  [ 2048/ 6954]\n",
      "loss: 0.058407  [ 3072/ 6954]\n",
      "loss: 0.078815  [ 4096/ 6954]\n",
      "loss: 0.064356  [ 5120/ 6954]\n",
      "loss: 0.087099  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001433 \n",
      "\n",
      "Epoch 500\n",
      "-------------------------------\n",
      "loss: 0.074694  [    0/ 6954]\n",
      "loss: 0.094911  [ 1024/ 6954]\n",
      "loss: 0.084174  [ 2048/ 6954]\n",
      "loss: 0.072370  [ 3072/ 6954]\n",
      "loss: 0.102862  [ 4096/ 6954]\n",
      "loss: 0.095032  [ 5120/ 6954]\n",
      "loss: 0.062959  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001453 \n",
      "\n",
      "Epoch 501\n",
      "-------------------------------\n",
      "loss: 0.081135  [    0/ 6954]\n",
      "loss: 0.070699  [ 1024/ 6954]\n",
      "loss: 0.085726  [ 2048/ 6954]\n",
      "loss: 0.092674  [ 3072/ 6954]\n",
      "loss: 0.067796  [ 4096/ 6954]\n",
      "loss: 0.076683  [ 5120/ 6954]\n",
      "loss: 0.078796  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001432 \n",
      "\n",
      "Epoch 502\n",
      "-------------------------------\n",
      "loss: 0.110958  [    0/ 6954]\n",
      "loss: 0.101933  [ 1024/ 6954]\n",
      "loss: 0.058239  [ 2048/ 6954]\n",
      "loss: 0.078595  [ 3072/ 6954]\n",
      "loss: 0.079044  [ 4096/ 6954]\n",
      "loss: 0.071922  [ 5120/ 6954]\n",
      "loss: 0.088217  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.0%, Avg loss: 0.001476 \n",
      "\n",
      "Epoch 503\n",
      "-------------------------------\n",
      "loss: 0.053647  [    0/ 6954]\n",
      "loss: 0.102919  [ 1024/ 6954]\n",
      "loss: 0.108865  [ 2048/ 6954]\n",
      "loss: 0.098348  [ 3072/ 6954]\n",
      "loss: 0.077399  [ 4096/ 6954]\n",
      "loss: 0.062469  [ 5120/ 6954]\n",
      "loss: 0.101203  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001452 \n",
      "\n",
      "Epoch 504\n",
      "-------------------------------\n",
      "loss: 0.104559  [    0/ 6954]\n",
      "loss: 0.056085  [ 1024/ 6954]\n",
      "loss: 0.073138  [ 2048/ 6954]\n",
      "loss: 0.058997  [ 3072/ 6954]\n",
      "loss: 0.075048  [ 4096/ 6954]\n",
      "loss: 0.073471  [ 5120/ 6954]\n",
      "loss: 0.090853  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001406 \n",
      "\n",
      "Epoch 505\n",
      "-------------------------------\n",
      "loss: 0.087527  [    0/ 6954]\n",
      "loss: 0.059635  [ 1024/ 6954]\n",
      "loss: 0.061357  [ 2048/ 6954]\n",
      "loss: 0.066881  [ 3072/ 6954]\n",
      "loss: 0.080223  [ 4096/ 6954]\n",
      "loss: 0.072742  [ 5120/ 6954]\n",
      "loss: 0.111124  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001419 \n",
      "\n",
      "Epoch 506\n",
      "-------------------------------\n",
      "loss: 0.084674  [    0/ 6954]\n",
      "loss: 0.089629  [ 1024/ 6954]\n",
      "loss: 0.064393  [ 2048/ 6954]\n",
      "loss: 0.078941  [ 3072/ 6954]\n",
      "loss: 0.045842  [ 4096/ 6954]\n",
      "loss: 0.065279  [ 5120/ 6954]\n",
      "loss: 0.096471  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001400 \n",
      "\n",
      "Epoch 507\n",
      "-------------------------------\n",
      "loss: 0.055745  [    0/ 6954]\n",
      "loss: 0.082031  [ 1024/ 6954]\n",
      "loss: 0.050369  [ 2048/ 6954]\n",
      "loss: 0.074236  [ 3072/ 6954]\n",
      "loss: 0.071353  [ 4096/ 6954]\n",
      "loss: 0.099568  [ 5120/ 6954]\n",
      "loss: 0.085229  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.6%, Avg loss: 0.001482 \n",
      "\n",
      "Epoch 508\n",
      "-------------------------------\n",
      "loss: 0.063220  [    0/ 6954]\n",
      "loss: 0.060935  [ 1024/ 6954]\n",
      "loss: 0.075513  [ 2048/ 6954]\n",
      "loss: 0.082182  [ 3072/ 6954]\n",
      "loss: 0.078861  [ 4096/ 6954]\n",
      "loss: 0.072448  [ 5120/ 6954]\n",
      "loss: 0.075906  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001388 \n",
      "\n",
      "Epoch 509\n",
      "-------------------------------\n",
      "loss: 0.073967  [    0/ 6954]\n",
      "loss: 0.086935  [ 1024/ 6954]\n",
      "loss: 0.066849  [ 2048/ 6954]\n",
      "loss: 0.104857  [ 3072/ 6954]\n",
      "loss: 0.094845  [ 4096/ 6954]\n",
      "loss: 0.072507  [ 5120/ 6954]\n",
      "loss: 0.071851  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001396 \n",
      "\n",
      "Epoch 510\n",
      "-------------------------------\n",
      "loss: 0.067940  [    0/ 6954]\n",
      "loss: 0.058735  [ 1024/ 6954]\n",
      "loss: 0.111315  [ 2048/ 6954]\n",
      "loss: 0.095023  [ 3072/ 6954]\n",
      "loss: 0.110813  [ 4096/ 6954]\n",
      "loss: 0.093290  [ 5120/ 6954]\n",
      "loss: 0.061608  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001435 \n",
      "\n",
      "Epoch 511\n",
      "-------------------------------\n",
      "loss: 0.076420  [    0/ 6954]\n",
      "loss: 0.070838  [ 1024/ 6954]\n",
      "loss: 0.067024  [ 2048/ 6954]\n",
      "loss: 0.075724  [ 3072/ 6954]\n",
      "loss: 0.073340  [ 4096/ 6954]\n",
      "loss: 0.099403  [ 5120/ 6954]\n",
      "loss: 0.079703  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001452 \n",
      "\n",
      "Epoch 512\n",
      "-------------------------------\n",
      "loss: 0.063869  [    0/ 6954]\n",
      "loss: 0.071743  [ 1024/ 6954]\n",
      "loss: 0.070308  [ 2048/ 6954]\n",
      "loss: 0.056453  [ 3072/ 6954]\n",
      "loss: 0.084473  [ 4096/ 6954]\n",
      "loss: 0.082000  [ 5120/ 6954]\n",
      "loss: 0.086660  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.8%, Avg loss: 0.001394 \n",
      "\n",
      "Epoch 513\n",
      "-------------------------------\n",
      "loss: 0.077114  [    0/ 6954]\n",
      "loss: 0.101821  [ 1024/ 6954]\n",
      "loss: 0.069554  [ 2048/ 6954]\n",
      "loss: 0.050746  [ 3072/ 6954]\n",
      "loss: 0.098527  [ 4096/ 6954]\n",
      "loss: 0.087390  [ 5120/ 6954]\n",
      "loss: 0.087925  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.6%, Avg loss: 0.001485 \n",
      "\n",
      "Epoch 514\n",
      "-------------------------------\n",
      "loss: 0.087226  [    0/ 6954]\n",
      "loss: 0.074530  [ 1024/ 6954]\n",
      "loss: 0.115875  [ 2048/ 6954]\n",
      "loss: 0.074930  [ 3072/ 6954]\n",
      "loss: 0.104635  [ 4096/ 6954]\n",
      "loss: 0.103897  [ 5120/ 6954]\n",
      "loss: 0.099152  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001435 \n",
      "\n",
      "Epoch 515\n",
      "-------------------------------\n",
      "loss: 0.093843  [    0/ 6954]\n",
      "loss: 0.098044  [ 1024/ 6954]\n",
      "loss: 0.083553  [ 2048/ 6954]\n",
      "loss: 0.064529  [ 3072/ 6954]\n",
      "loss: 0.092463  [ 4096/ 6954]\n",
      "loss: 0.086446  [ 5120/ 6954]\n",
      "loss: 0.088633  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.2%, Avg loss: 0.001423 \n",
      "\n",
      "Epoch 516\n",
      "-------------------------------\n",
      "loss: 0.083212  [    0/ 6954]\n",
      "loss: 0.090588  [ 1024/ 6954]\n",
      "loss: 0.084774  [ 2048/ 6954]\n",
      "loss: 0.085379  [ 3072/ 6954]\n",
      "loss: 0.075610  [ 4096/ 6954]\n",
      "loss: 0.072889  [ 5120/ 6954]\n",
      "loss: 0.096924  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001416 \n",
      "\n",
      "Epoch 517\n",
      "-------------------------------\n",
      "loss: 0.058485  [    0/ 6954]\n",
      "loss: 0.069624  [ 1024/ 6954]\n",
      "loss: 0.070881  [ 2048/ 6954]\n",
      "loss: 0.093677  [ 3072/ 6954]\n",
      "loss: 0.106834  [ 4096/ 6954]\n",
      "loss: 0.098008  [ 5120/ 6954]\n",
      "loss: 0.092582  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001364 \n",
      "\n",
      "Epoch 518\n",
      "-------------------------------\n",
      "loss: 0.067973  [    0/ 6954]\n",
      "loss: 0.082441  [ 1024/ 6954]\n",
      "loss: 0.066750  [ 2048/ 6954]\n",
      "loss: 0.068088  [ 3072/ 6954]\n",
      "loss: 0.091818  [ 4096/ 6954]\n",
      "loss: 0.083144  [ 5120/ 6954]\n",
      "loss: 0.066500  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.0%, Avg loss: 0.001474 \n",
      "\n",
      "Epoch 519\n",
      "-------------------------------\n",
      "loss: 0.060980  [    0/ 6954]\n",
      "loss: 0.062051  [ 1024/ 6954]\n",
      "loss: 0.065730  [ 2048/ 6954]\n",
      "loss: 0.066531  [ 3072/ 6954]\n",
      "loss: 0.068016  [ 4096/ 6954]\n",
      "loss: 0.060147  [ 5120/ 6954]\n",
      "loss: 0.097083  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001412 \n",
      "\n",
      "Epoch 520\n",
      "-------------------------------\n",
      "loss: 0.066499  [    0/ 6954]\n",
      "loss: 0.083390  [ 1024/ 6954]\n",
      "loss: 0.088458  [ 2048/ 6954]\n",
      "loss: 0.065696  [ 3072/ 6954]\n",
      "loss: 0.061817  [ 4096/ 6954]\n",
      "loss: 0.096616  [ 5120/ 6954]\n",
      "loss: 0.073995  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001470 \n",
      "\n",
      "Epoch 521\n",
      "-------------------------------\n",
      "loss: 0.142687  [    0/ 6954]\n",
      "loss: 0.083324  [ 1024/ 6954]\n",
      "loss: 0.060271  [ 2048/ 6954]\n",
      "loss: 0.072573  [ 3072/ 6954]\n",
      "loss: 0.090282  [ 4096/ 6954]\n",
      "loss: 0.076158  [ 5120/ 6954]\n",
      "loss: 0.068780  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001437 \n",
      "\n",
      "Epoch 522\n",
      "-------------------------------\n",
      "loss: 0.070548  [    0/ 6954]\n",
      "loss: 0.063539  [ 1024/ 6954]\n",
      "loss: 0.092125  [ 2048/ 6954]\n",
      "loss: 0.075335  [ 3072/ 6954]\n",
      "loss: 0.058567  [ 4096/ 6954]\n",
      "loss: 0.091945  [ 5120/ 6954]\n",
      "loss: 0.065598  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001473 \n",
      "\n",
      "Epoch 523\n",
      "-------------------------------\n",
      "loss: 0.059631  [    0/ 6954]\n",
      "loss: 0.089671  [ 1024/ 6954]\n",
      "loss: 0.111871  [ 2048/ 6954]\n",
      "loss: 0.070801  [ 3072/ 6954]\n",
      "loss: 0.072455  [ 4096/ 6954]\n",
      "loss: 0.094718  [ 5120/ 6954]\n",
      "loss: 0.123433  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001425 \n",
      "\n",
      "Epoch 524\n",
      "-------------------------------\n",
      "loss: 0.118633  [    0/ 6954]\n",
      "loss: 0.082932  [ 1024/ 6954]\n",
      "loss: 0.088258  [ 2048/ 6954]\n",
      "loss: 0.105124  [ 3072/ 6954]\n",
      "loss: 0.078095  [ 4096/ 6954]\n",
      "loss: 0.090028  [ 5120/ 6954]\n",
      "loss: 0.079897  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001374 \n",
      "\n",
      "Epoch 525\n",
      "-------------------------------\n",
      "loss: 0.074845  [    0/ 6954]\n",
      "loss: 0.087593  [ 1024/ 6954]\n",
      "loss: 0.082814  [ 2048/ 6954]\n",
      "loss: 0.081245  [ 3072/ 6954]\n",
      "loss: 0.084918  [ 4096/ 6954]\n",
      "loss: 0.074658  [ 5120/ 6954]\n",
      "loss: 0.062045  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001396 \n",
      "\n",
      "Epoch 526\n",
      "-------------------------------\n",
      "loss: 0.052831  [    0/ 6954]\n",
      "loss: 0.081063  [ 1024/ 6954]\n",
      "loss: 0.079518  [ 2048/ 6954]\n",
      "loss: 0.085283  [ 3072/ 6954]\n",
      "loss: 0.081398  [ 4096/ 6954]\n",
      "loss: 0.086052  [ 5120/ 6954]\n",
      "loss: 0.094347  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001433 \n",
      "\n",
      "Epoch 527\n",
      "-------------------------------\n",
      "loss: 0.078833  [    0/ 6954]\n",
      "loss: 0.069180  [ 1024/ 6954]\n",
      "loss: 0.077958  [ 2048/ 6954]\n",
      "loss: 0.096330  [ 3072/ 6954]\n",
      "loss: 0.073992  [ 4096/ 6954]\n",
      "loss: 0.083410  [ 5120/ 6954]\n",
      "loss: 0.074536  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.4%, Avg loss: 0.001489 \n",
      "\n",
      "Epoch 528\n",
      "-------------------------------\n",
      "loss: 0.062607  [    0/ 6954]\n",
      "loss: 0.081028  [ 1024/ 6954]\n",
      "loss: 0.083045  [ 2048/ 6954]\n",
      "loss: 0.083784  [ 3072/ 6954]\n",
      "loss: 0.081069  [ 4096/ 6954]\n",
      "loss: 0.059935  [ 5120/ 6954]\n",
      "loss: 0.078669  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001397 \n",
      "\n",
      "Epoch 529\n",
      "-------------------------------\n",
      "loss: 0.079082  [    0/ 6954]\n",
      "loss: 0.081604  [ 1024/ 6954]\n",
      "loss: 0.091708  [ 2048/ 6954]\n",
      "loss: 0.060030  [ 3072/ 6954]\n",
      "loss: 0.077607  [ 4096/ 6954]\n",
      "loss: 0.094189  [ 5120/ 6954]\n",
      "loss: 0.062551  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001430 \n",
      "\n",
      "Epoch 530\n",
      "-------------------------------\n",
      "loss: 0.064070  [    0/ 6954]\n",
      "loss: 0.045553  [ 1024/ 6954]\n",
      "loss: 0.084832  [ 2048/ 6954]\n",
      "loss: 0.068720  [ 3072/ 6954]\n",
      "loss: 0.084079  [ 4096/ 6954]\n",
      "loss: 0.086551  [ 5120/ 6954]\n",
      "loss: 0.085454  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001479 \n",
      "\n",
      "Epoch 531\n",
      "-------------------------------\n",
      "loss: 0.081046  [    0/ 6954]\n",
      "loss: 0.092595  [ 1024/ 6954]\n",
      "loss: 0.067995  [ 2048/ 6954]\n",
      "loss: 0.064206  [ 3072/ 6954]\n",
      "loss: 0.070053  [ 4096/ 6954]\n",
      "loss: 0.084483  [ 5120/ 6954]\n",
      "loss: 0.099193  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001413 \n",
      "\n",
      "Epoch 532\n",
      "-------------------------------\n",
      "loss: 0.062223  [    0/ 6954]\n",
      "loss: 0.114204  [ 1024/ 6954]\n",
      "loss: 0.066712  [ 2048/ 6954]\n",
      "loss: 0.081532  [ 3072/ 6954]\n",
      "loss: 0.060654  [ 4096/ 6954]\n",
      "loss: 0.073238  [ 5120/ 6954]\n",
      "loss: 0.085833  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001428 \n",
      "\n",
      "Epoch 533\n",
      "-------------------------------\n",
      "loss: 0.067607  [    0/ 6954]\n",
      "loss: 0.087956  [ 1024/ 6954]\n",
      "loss: 0.049666  [ 2048/ 6954]\n",
      "loss: 0.053928  [ 3072/ 6954]\n",
      "loss: 0.092839  [ 4096/ 6954]\n",
      "loss: 0.093504  [ 5120/ 6954]\n",
      "loss: 0.068317  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001477 \n",
      "\n",
      "Epoch 534\n",
      "-------------------------------\n",
      "loss: 0.078037  [    0/ 6954]\n",
      "loss: 0.059239  [ 1024/ 6954]\n",
      "loss: 0.082644  [ 2048/ 6954]\n",
      "loss: 0.068850  [ 3072/ 6954]\n",
      "loss: 0.102761  [ 4096/ 6954]\n",
      "loss: 0.081290  [ 5120/ 6954]\n",
      "loss: 0.101134  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001454 \n",
      "\n",
      "Epoch 535\n",
      "-------------------------------\n",
      "loss: 0.079930  [    0/ 6954]\n",
      "loss: 0.069560  [ 1024/ 6954]\n",
      "loss: 0.078492  [ 2048/ 6954]\n",
      "loss: 0.067487  [ 3072/ 6954]\n",
      "loss: 0.059157  [ 4096/ 6954]\n",
      "loss: 0.093302  [ 5120/ 6954]\n",
      "loss: 0.086891  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001463 \n",
      "\n",
      "Epoch 536\n",
      "-------------------------------\n",
      "loss: 0.067240  [    0/ 6954]\n",
      "loss: 0.112697  [ 1024/ 6954]\n",
      "loss: 0.084378  [ 2048/ 6954]\n",
      "loss: 0.065108  [ 3072/ 6954]\n",
      "loss: 0.086907  [ 4096/ 6954]\n",
      "loss: 0.060262  [ 5120/ 6954]\n",
      "loss: 0.092592  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001428 \n",
      "\n",
      "Epoch 537\n",
      "-------------------------------\n",
      "loss: 0.058896  [    0/ 6954]\n",
      "loss: 0.087762  [ 1024/ 6954]\n",
      "loss: 0.088271  [ 2048/ 6954]\n",
      "loss: 0.086431  [ 3072/ 6954]\n",
      "loss: 0.088381  [ 4096/ 6954]\n",
      "loss: 0.099808  [ 5120/ 6954]\n",
      "loss: 0.090975  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001461 \n",
      "\n",
      "Epoch 538\n",
      "-------------------------------\n",
      "loss: 0.071030  [    0/ 6954]\n",
      "loss: 0.076835  [ 1024/ 6954]\n",
      "loss: 0.102448  [ 2048/ 6954]\n",
      "loss: 0.084348  [ 3072/ 6954]\n",
      "loss: 0.070919  [ 4096/ 6954]\n",
      "loss: 0.100102  [ 5120/ 6954]\n",
      "loss: 0.065632  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001423 \n",
      "\n",
      "Epoch 539\n",
      "-------------------------------\n",
      "loss: 0.061753  [    0/ 6954]\n",
      "loss: 0.084750  [ 1024/ 6954]\n",
      "loss: 0.096840  [ 2048/ 6954]\n",
      "loss: 0.094664  [ 3072/ 6954]\n",
      "loss: 0.066721  [ 4096/ 6954]\n",
      "loss: 0.078151  [ 5120/ 6954]\n",
      "loss: 0.094697  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.0%, Avg loss: 0.001519 \n",
      "\n",
      "Epoch 540\n",
      "-------------------------------\n",
      "loss: 0.084352  [    0/ 6954]\n",
      "loss: 0.079132  [ 1024/ 6954]\n",
      "loss: 0.087040  [ 2048/ 6954]\n",
      "loss: 0.078078  [ 3072/ 6954]\n",
      "loss: 0.090841  [ 4096/ 6954]\n",
      "loss: 0.091277  [ 5120/ 6954]\n",
      "loss: 0.053487  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001458 \n",
      "\n",
      "Epoch 541\n",
      "-------------------------------\n",
      "loss: 0.056192  [    0/ 6954]\n",
      "loss: 0.112110  [ 1024/ 6954]\n",
      "loss: 0.084268  [ 2048/ 6954]\n",
      "loss: 0.080544  [ 3072/ 6954]\n",
      "loss: 0.050618  [ 4096/ 6954]\n",
      "loss: 0.071924  [ 5120/ 6954]\n",
      "loss: 0.075772  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 0.001447 \n",
      "\n",
      "Epoch 542\n",
      "-------------------------------\n",
      "loss: 0.064750  [    0/ 6954]\n",
      "loss: 0.081776  [ 1024/ 6954]\n",
      "loss: 0.054585  [ 2048/ 6954]\n",
      "loss: 0.077832  [ 3072/ 6954]\n",
      "loss: 0.123895  [ 4096/ 6954]\n",
      "loss: 0.073395  [ 5120/ 6954]\n",
      "loss: 0.084398  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.1%, Avg loss: 0.001417 \n",
      "\n",
      "Epoch 543\n",
      "-------------------------------\n",
      "loss: 0.081663  [    0/ 6954]\n",
      "loss: 0.060724  [ 1024/ 6954]\n",
      "loss: 0.056652  [ 2048/ 6954]\n",
      "loss: 0.086393  [ 3072/ 6954]\n",
      "loss: 0.070032  [ 4096/ 6954]\n",
      "loss: 0.046667  [ 5120/ 6954]\n",
      "loss: 0.127532  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 73.5%, Avg loss: 0.001632 \n",
      "\n",
      "Epoch 544\n",
      "-------------------------------\n",
      "loss: 0.130816  [    0/ 6954]\n",
      "loss: 0.086262  [ 1024/ 6954]\n",
      "loss: 0.089939  [ 2048/ 6954]\n",
      "loss: 0.076129  [ 3072/ 6954]\n",
      "loss: 0.056017  [ 4096/ 6954]\n",
      "loss: 0.060152  [ 5120/ 6954]\n",
      "loss: 0.098098  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001430 \n",
      "\n",
      "Epoch 545\n",
      "-------------------------------\n",
      "loss: 0.045752  [    0/ 6954]\n",
      "loss: 0.061104  [ 1024/ 6954]\n",
      "loss: 0.074978  [ 2048/ 6954]\n",
      "loss: 0.092698  [ 3072/ 6954]\n",
      "loss: 0.069116  [ 4096/ 6954]\n",
      "loss: 0.077259  [ 5120/ 6954]\n",
      "loss: 0.087425  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001441 \n",
      "\n",
      "Epoch 546\n",
      "-------------------------------\n",
      "loss: 0.086054  [    0/ 6954]\n",
      "loss: 0.080603  [ 1024/ 6954]\n",
      "loss: 0.078191  [ 2048/ 6954]\n",
      "loss: 0.085794  [ 3072/ 6954]\n",
      "loss: 0.073818  [ 4096/ 6954]\n",
      "loss: 0.073501  [ 5120/ 6954]\n",
      "loss: 0.062102  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001411 \n",
      "\n",
      "Epoch 547\n",
      "-------------------------------\n",
      "loss: 0.077443  [    0/ 6954]\n",
      "loss: 0.091390  [ 1024/ 6954]\n",
      "loss: 0.093028  [ 2048/ 6954]\n",
      "loss: 0.079273  [ 3072/ 6954]\n",
      "loss: 0.076399  [ 4096/ 6954]\n",
      "loss: 0.062527  [ 5120/ 6954]\n",
      "loss: 0.086733  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001465 \n",
      "\n",
      "Epoch 548\n",
      "-------------------------------\n",
      "loss: 0.069540  [    0/ 6954]\n",
      "loss: 0.089903  [ 1024/ 6954]\n",
      "loss: 0.067040  [ 2048/ 6954]\n",
      "loss: 0.064989  [ 3072/ 6954]\n",
      "loss: 0.073618  [ 4096/ 6954]\n",
      "loss: 0.074596  [ 5120/ 6954]\n",
      "loss: 0.080557  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001479 \n",
      "\n",
      "Epoch 549\n",
      "-------------------------------\n",
      "loss: 0.075907  [    0/ 6954]\n",
      "loss: 0.109685  [ 1024/ 6954]\n",
      "loss: 0.080754  [ 2048/ 6954]\n",
      "loss: 0.069713  [ 3072/ 6954]\n",
      "loss: 0.057030  [ 4096/ 6954]\n",
      "loss: 0.075909  [ 5120/ 6954]\n",
      "loss: 0.078687  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001412 \n",
      "\n",
      "Epoch 550\n",
      "-------------------------------\n",
      "loss: 0.091522  [    0/ 6954]\n",
      "loss: 0.063495  [ 1024/ 6954]\n",
      "loss: 0.059992  [ 2048/ 6954]\n",
      "loss: 0.075888  [ 3072/ 6954]\n",
      "loss: 0.054900  [ 4096/ 6954]\n",
      "loss: 0.096871  [ 5120/ 6954]\n",
      "loss: 0.074289  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001456 \n",
      "\n",
      "Epoch 551\n",
      "-------------------------------\n",
      "loss: 0.087536  [    0/ 6954]\n",
      "loss: 0.051487  [ 1024/ 6954]\n",
      "loss: 0.076813  [ 2048/ 6954]\n",
      "loss: 0.050472  [ 3072/ 6954]\n",
      "loss: 0.076909  [ 4096/ 6954]\n",
      "loss: 0.059963  [ 5120/ 6954]\n",
      "loss: 0.064635  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.7%, Avg loss: 0.001473 \n",
      "\n",
      "Epoch 552\n",
      "-------------------------------\n",
      "loss: 0.066795  [    0/ 6954]\n",
      "loss: 0.109068  [ 1024/ 6954]\n",
      "loss: 0.076710  [ 2048/ 6954]\n",
      "loss: 0.101487  [ 3072/ 6954]\n",
      "loss: 0.087910  [ 4096/ 6954]\n",
      "loss: 0.078052  [ 5120/ 6954]\n",
      "loss: 0.096304  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001386 \n",
      "\n",
      "Epoch 553\n",
      "-------------------------------\n",
      "loss: 0.094931  [    0/ 6954]\n",
      "loss: 0.063894  [ 1024/ 6954]\n",
      "loss: 0.096050  [ 2048/ 6954]\n",
      "loss: 0.108505  [ 3072/ 6954]\n",
      "loss: 0.079113  [ 4096/ 6954]\n",
      "loss: 0.071535  [ 5120/ 6954]\n",
      "loss: 0.071934  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.4%, Avg loss: 0.001488 \n",
      "\n",
      "Epoch 554\n",
      "-------------------------------\n",
      "loss: 0.074721  [    0/ 6954]\n",
      "loss: 0.085323  [ 1024/ 6954]\n",
      "loss: 0.093523  [ 2048/ 6954]\n",
      "loss: 0.076580  [ 3072/ 6954]\n",
      "loss: 0.089501  [ 4096/ 6954]\n",
      "loss: 0.061868  [ 5120/ 6954]\n",
      "loss: 0.078305  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001432 \n",
      "\n",
      "Epoch 555\n",
      "-------------------------------\n",
      "loss: 0.067288  [    0/ 6954]\n",
      "loss: 0.071033  [ 1024/ 6954]\n",
      "loss: 0.097933  [ 2048/ 6954]\n",
      "loss: 0.074235  [ 3072/ 6954]\n",
      "loss: 0.086883  [ 4096/ 6954]\n",
      "loss: 0.089005  [ 5120/ 6954]\n",
      "loss: 0.062674  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001399 \n",
      "\n",
      "Epoch 556\n",
      "-------------------------------\n",
      "loss: 0.068532  [    0/ 6954]\n",
      "loss: 0.081083  [ 1024/ 6954]\n",
      "loss: 0.102409  [ 2048/ 6954]\n",
      "loss: 0.075156  [ 3072/ 6954]\n",
      "loss: 0.067274  [ 4096/ 6954]\n",
      "loss: 0.091545  [ 5120/ 6954]\n",
      "loss: 0.079445  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.6%, Avg loss: 0.001415 \n",
      "\n",
      "Epoch 557\n",
      "-------------------------------\n",
      "loss: 0.076245  [    0/ 6954]\n",
      "loss: 0.054648  [ 1024/ 6954]\n",
      "loss: 0.122861  [ 2048/ 6954]\n",
      "loss: 0.079520  [ 3072/ 6954]\n",
      "loss: 0.052546  [ 4096/ 6954]\n",
      "loss: 0.061758  [ 5120/ 6954]\n",
      "loss: 0.072568  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001465 \n",
      "\n",
      "Epoch 558\n",
      "-------------------------------\n",
      "loss: 0.096224  [    0/ 6954]\n",
      "loss: 0.075552  [ 1024/ 6954]\n",
      "loss: 0.066793  [ 2048/ 6954]\n",
      "loss: 0.067620  [ 3072/ 6954]\n",
      "loss: 0.072976  [ 4096/ 6954]\n",
      "loss: 0.051870  [ 5120/ 6954]\n",
      "loss: 0.100156  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001414 \n",
      "\n",
      "Epoch 559\n",
      "-------------------------------\n",
      "loss: 0.086691  [    0/ 6954]\n",
      "loss: 0.062363  [ 1024/ 6954]\n",
      "loss: 0.085179  [ 2048/ 6954]\n",
      "loss: 0.055558  [ 3072/ 6954]\n",
      "loss: 0.088190  [ 4096/ 6954]\n",
      "loss: 0.062182  [ 5120/ 6954]\n",
      "loss: 0.086206  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001359 \n",
      "\n",
      "Epoch 560\n",
      "-------------------------------\n",
      "loss: 0.076041  [    0/ 6954]\n",
      "loss: 0.058472  [ 1024/ 6954]\n",
      "loss: 0.059182  [ 2048/ 6954]\n",
      "loss: 0.077591  [ 3072/ 6954]\n",
      "loss: 0.094183  [ 4096/ 6954]\n",
      "loss: 0.073274  [ 5120/ 6954]\n",
      "loss: 0.071615  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001368 \n",
      "\n",
      "Epoch 561\n",
      "-------------------------------\n",
      "loss: 0.078738  [    0/ 6954]\n",
      "loss: 0.078344  [ 1024/ 6954]\n",
      "loss: 0.060489  [ 2048/ 6954]\n",
      "loss: 0.121224  [ 3072/ 6954]\n",
      "loss: 0.086814  [ 4096/ 6954]\n",
      "loss: 0.065226  [ 5120/ 6954]\n",
      "loss: 0.094555  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001444 \n",
      "\n",
      "Epoch 562\n",
      "-------------------------------\n",
      "loss: 0.067332  [    0/ 6954]\n",
      "loss: 0.108335  [ 1024/ 6954]\n",
      "loss: 0.053123  [ 2048/ 6954]\n",
      "loss: 0.068841  [ 3072/ 6954]\n",
      "loss: 0.086107  [ 4096/ 6954]\n",
      "loss: 0.099476  [ 5120/ 6954]\n",
      "loss: 0.067492  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001488 \n",
      "\n",
      "Epoch 563\n",
      "-------------------------------\n",
      "loss: 0.076100  [    0/ 6954]\n",
      "loss: 0.057482  [ 1024/ 6954]\n",
      "loss: 0.052823  [ 2048/ 6954]\n",
      "loss: 0.066706  [ 3072/ 6954]\n",
      "loss: 0.086197  [ 4096/ 6954]\n",
      "loss: 0.065702  [ 5120/ 6954]\n",
      "loss: 0.055581  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001460 \n",
      "\n",
      "Epoch 564\n",
      "-------------------------------\n",
      "loss: 0.054171  [    0/ 6954]\n",
      "loss: 0.081807  [ 1024/ 6954]\n",
      "loss: 0.075906  [ 2048/ 6954]\n",
      "loss: 0.069301  [ 3072/ 6954]\n",
      "loss: 0.087606  [ 4096/ 6954]\n",
      "loss: 0.066700  [ 5120/ 6954]\n",
      "loss: 0.077884  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.4%, Avg loss: 0.001538 \n",
      "\n",
      "Epoch 565\n",
      "-------------------------------\n",
      "loss: 0.076938  [    0/ 6954]\n",
      "loss: 0.083194  [ 1024/ 6954]\n",
      "loss: 0.069000  [ 2048/ 6954]\n",
      "loss: 0.052748  [ 3072/ 6954]\n",
      "loss: 0.067709  [ 4096/ 6954]\n",
      "loss: 0.077210  [ 5120/ 6954]\n",
      "loss: 0.145729  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.4%, Avg loss: 0.001486 \n",
      "\n",
      "Epoch 566\n",
      "-------------------------------\n",
      "loss: 0.105176  [    0/ 6954]\n",
      "loss: 0.087936  [ 1024/ 6954]\n",
      "loss: 0.058583  [ 2048/ 6954]\n",
      "loss: 0.061231  [ 3072/ 6954]\n",
      "loss: 0.068130  [ 4096/ 6954]\n",
      "loss: 0.085240  [ 5120/ 6954]\n",
      "loss: 0.075459  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.5%, Avg loss: 0.001427 \n",
      "\n",
      "Epoch 567\n",
      "-------------------------------\n",
      "loss: 0.073489  [    0/ 6954]\n",
      "loss: 0.075294  [ 1024/ 6954]\n",
      "loss: 0.077879  [ 2048/ 6954]\n",
      "loss: 0.066092  [ 3072/ 6954]\n",
      "loss: 0.067779  [ 4096/ 6954]\n",
      "loss: 0.048001  [ 5120/ 6954]\n",
      "loss: 0.096793  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.5%, Avg loss: 0.001420 \n",
      "\n",
      "Epoch 568\n",
      "-------------------------------\n",
      "loss: 0.078069  [    0/ 6954]\n",
      "loss: 0.086380  [ 1024/ 6954]\n",
      "loss: 0.071356  [ 2048/ 6954]\n",
      "loss: 0.077280  [ 3072/ 6954]\n",
      "loss: 0.069078  [ 4096/ 6954]\n",
      "loss: 0.058181  [ 5120/ 6954]\n",
      "loss: 0.105992  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.4%, Avg loss: 0.001424 \n",
      "\n",
      "Epoch 569\n",
      "-------------------------------\n",
      "loss: 0.085619  [    0/ 6954]\n",
      "loss: 0.093516  [ 1024/ 6954]\n",
      "loss: 0.074368  [ 2048/ 6954]\n",
      "loss: 0.072760  [ 3072/ 6954]\n",
      "loss: 0.084907  [ 4096/ 6954]\n",
      "loss: 0.061116  [ 5120/ 6954]\n",
      "loss: 0.064308  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001412 \n",
      "\n",
      "Epoch 570\n",
      "-------------------------------\n",
      "loss: 0.062683  [    0/ 6954]\n",
      "loss: 0.082089  [ 1024/ 6954]\n",
      "loss: 0.063510  [ 2048/ 6954]\n",
      "loss: 0.062058  [ 3072/ 6954]\n",
      "loss: 0.082585  [ 4096/ 6954]\n",
      "loss: 0.080097  [ 5120/ 6954]\n",
      "loss: 0.088290  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.6%, Avg loss: 0.001457 \n",
      "\n",
      "Epoch 571\n",
      "-------------------------------\n",
      "loss: 0.093932  [    0/ 6954]\n",
      "loss: 0.073588  [ 1024/ 6954]\n",
      "loss: 0.072615  [ 2048/ 6954]\n",
      "loss: 0.076641  [ 3072/ 6954]\n",
      "loss: 0.060123  [ 4096/ 6954]\n",
      "loss: 0.057852  [ 5120/ 6954]\n",
      "loss: 0.073255  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.2%, Avg loss: 0.001392 \n",
      "\n",
      "Epoch 572\n",
      "-------------------------------\n",
      "loss: 0.067778  [    0/ 6954]\n",
      "loss: 0.085491  [ 1024/ 6954]\n",
      "loss: 0.108871  [ 2048/ 6954]\n",
      "loss: 0.082558  [ 3072/ 6954]\n",
      "loss: 0.100076  [ 4096/ 6954]\n",
      "loss: 0.113675  [ 5120/ 6954]\n",
      "loss: 0.116661  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.4%, Avg loss: 0.001496 \n",
      "\n",
      "Epoch 573\n",
      "-------------------------------\n",
      "loss: 0.064016  [    0/ 6954]\n",
      "loss: 0.116211  [ 1024/ 6954]\n",
      "loss: 0.062909  [ 2048/ 6954]\n",
      "loss: 0.094913  [ 3072/ 6954]\n",
      "loss: 0.084082  [ 4096/ 6954]\n",
      "loss: 0.085967  [ 5120/ 6954]\n",
      "loss: 0.080762  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.6%, Avg loss: 0.001409 \n",
      "\n",
      "Epoch 574\n",
      "-------------------------------\n",
      "loss: 0.074571  [    0/ 6954]\n",
      "loss: 0.052004  [ 1024/ 6954]\n",
      "loss: 0.083458  [ 2048/ 6954]\n",
      "loss: 0.095483  [ 3072/ 6954]\n",
      "loss: 0.082321  [ 4096/ 6954]\n",
      "loss: 0.110413  [ 5120/ 6954]\n",
      "loss: 0.066930  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.7%, Avg loss: 0.001485 \n",
      "\n",
      "Epoch 575\n",
      "-------------------------------\n",
      "loss: 0.100521  [    0/ 6954]\n",
      "loss: 0.081250  [ 1024/ 6954]\n",
      "loss: 0.079198  [ 2048/ 6954]\n",
      "loss: 0.066384  [ 3072/ 6954]\n",
      "loss: 0.071240  [ 4096/ 6954]\n",
      "loss: 0.059453  [ 5120/ 6954]\n",
      "loss: 0.077977  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001421 \n",
      "\n",
      "Epoch 576\n",
      "-------------------------------\n",
      "loss: 0.063351  [    0/ 6954]\n",
      "loss: 0.070937  [ 1024/ 6954]\n",
      "loss: 0.073947  [ 2048/ 6954]\n",
      "loss: 0.091348  [ 3072/ 6954]\n",
      "loss: 0.093980  [ 4096/ 6954]\n",
      "loss: 0.074607  [ 5120/ 6954]\n",
      "loss: 0.072182  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.0%, Avg loss: 0.001465 \n",
      "\n",
      "Epoch 577\n",
      "-------------------------------\n",
      "loss: 0.092652  [    0/ 6954]\n",
      "loss: 0.058981  [ 1024/ 6954]\n",
      "loss: 0.069655  [ 2048/ 6954]\n",
      "loss: 0.070466  [ 3072/ 6954]\n",
      "loss: 0.083243  [ 4096/ 6954]\n",
      "loss: 0.073341  [ 5120/ 6954]\n",
      "loss: 0.081263  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.1%, Avg loss: 0.001455 \n",
      "\n",
      "Epoch 578\n",
      "-------------------------------\n",
      "loss: 0.077829  [    0/ 6954]\n",
      "loss: 0.071298  [ 1024/ 6954]\n",
      "loss: 0.084830  [ 2048/ 6954]\n",
      "loss: 0.075860  [ 3072/ 6954]\n",
      "loss: 0.079904  [ 4096/ 6954]\n",
      "loss: 0.075009  [ 5120/ 6954]\n",
      "loss: 0.073929  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.8%, Avg loss: 0.001439 \n",
      "\n",
      "Epoch 579\n",
      "-------------------------------\n",
      "loss: 0.083120  [    0/ 6954]\n",
      "loss: 0.070047  [ 1024/ 6954]\n",
      "loss: 0.071114  [ 2048/ 6954]\n",
      "loss: 0.094575  [ 3072/ 6954]\n",
      "loss: 0.092821  [ 4096/ 6954]\n",
      "loss: 0.093351  [ 5120/ 6954]\n",
      "loss: 0.090254  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001479 \n",
      "\n",
      "Epoch 580\n",
      "-------------------------------\n",
      "loss: 0.049128  [    0/ 6954]\n",
      "loss: 0.086267  [ 1024/ 6954]\n",
      "loss: 0.070419  [ 2048/ 6954]\n",
      "loss: 0.086003  [ 3072/ 6954]\n",
      "loss: 0.086812  [ 4096/ 6954]\n",
      "loss: 0.046615  [ 5120/ 6954]\n",
      "loss: 0.071155  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.9%, Avg loss: 0.001451 \n",
      "\n",
      "Epoch 581\n",
      "-------------------------------\n",
      "loss: 0.074102  [    0/ 6954]\n",
      "loss: 0.064352  [ 1024/ 6954]\n",
      "loss: 0.091621  [ 2048/ 6954]\n",
      "loss: 0.069740  [ 3072/ 6954]\n",
      "loss: 0.081660  [ 4096/ 6954]\n",
      "loss: 0.068601  [ 5120/ 6954]\n",
      "loss: 0.086674  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.4%, Avg loss: 0.001401 \n",
      "\n",
      "Epoch 582\n",
      "-------------------------------\n",
      "loss: 0.044340  [    0/ 6954]\n",
      "loss: 0.076021  [ 1024/ 6954]\n",
      "loss: 0.053631  [ 2048/ 6954]\n",
      "loss: 0.080382  [ 3072/ 6954]\n",
      "loss: 0.062566  [ 4096/ 6954]\n",
      "loss: 0.064556  [ 5120/ 6954]\n",
      "loss: 0.082263  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.9%, Avg loss: 0.001494 \n",
      "\n",
      "Epoch 583\n",
      "-------------------------------\n",
      "loss: 0.055146  [    0/ 6954]\n",
      "loss: 0.053051  [ 1024/ 6954]\n",
      "loss: 0.068639  [ 2048/ 6954]\n",
      "loss: 0.050557  [ 3072/ 6954]\n",
      "loss: 0.062815  [ 4096/ 6954]\n",
      "loss: 0.080687  [ 5120/ 6954]\n",
      "loss: 0.053976  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 76.0%, Avg loss: 0.001476 \n",
      "\n",
      "Epoch 584\n",
      "-------------------------------\n",
      "loss: 0.054239  [    0/ 6954]\n",
      "loss: 0.058794  [ 1024/ 6954]\n",
      "loss: 0.080517  [ 2048/ 6954]\n",
      "loss: 0.048186  [ 3072/ 6954]\n",
      "loss: 0.102001  [ 4096/ 6954]\n",
      "loss: 0.052069  [ 5120/ 6954]\n",
      "loss: 0.064309  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 75.8%, Avg loss: 0.001507 \n",
      "\n",
      "Epoch 585\n",
      "-------------------------------\n",
      "loss: 0.053489  [    0/ 6954]\n",
      "loss: 0.060386  [ 1024/ 6954]\n",
      "loss: 0.074315  [ 2048/ 6954]\n",
      "loss: 0.073271  [ 3072/ 6954]\n",
      "loss: 0.061282  [ 4096/ 6954]\n",
      "loss: 0.095315  [ 5120/ 6954]\n",
      "loss: 0.070509  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 77.3%, Avg loss: 0.001409 \n",
      "\n",
      "Epoch 586\n",
      "-------------------------------\n",
      "loss: 0.053484  [    0/ 6954]\n",
      "loss: 0.095912  [ 1024/ 6954]\n",
      "loss: 0.070863  [ 2048/ 6954]\n",
      "loss: 0.050610  [ 3072/ 6954]\n",
      "loss: 0.111788  [ 4096/ 6954]\n",
      "loss: 0.097017  [ 5120/ 6954]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[1;32mIn [26]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m t \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(epochs):\n\u001B[0;32m      3\u001B[0m     \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mEpoch \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mt \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m-------------------------------\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m----> 4\u001B[0m     \u001B[43mtrain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_dataloader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mloss_fn\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptimizer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      5\u001B[0m     test(test_dataloader, model)\n\u001B[0;32m      6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDone!\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "Input \u001B[1;32mIn [24]\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(dataloader, model, loss_fn, optimizer)\u001B[0m\n\u001B[0;32m     11\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mzero_grad()\n\u001B[0;32m     12\u001B[0m loss\u001B[38;5;241m.\u001B[39mbackward()\n\u001B[1;32m---> 13\u001B[0m \u001B[43moptimizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstep\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     15\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m batch \u001B[38;5;241m%\u001B[39m \u001B[38;5;241m8\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m     16\u001B[0m     loss, current \u001B[38;5;241m=\u001B[39m loss\u001B[38;5;241m.\u001B[39mitem(), batch \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mlen\u001B[39m(X)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\study\\lib\\site-packages\\torch\\optim\\optimizer.py:88\u001B[0m, in \u001B[0;36mOptimizer._hook_for_profile.<locals>.profile_hook_step.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     86\u001B[0m profile_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOptimizer.step#\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m.step\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(obj\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m)\n\u001B[0;32m     87\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m torch\u001B[38;5;241m.\u001B[39mautograd\u001B[38;5;241m.\u001B[39mprofiler\u001B[38;5;241m.\u001B[39mrecord_function(profile_name):\n\u001B[1;32m---> 88\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\study\\lib\\site-packages\\adabound\\adabound.py:102\u001B[0m, in \u001B[0;36mAdaBound.step\u001B[1;34m(self, closure)\u001B[0m\n\u001B[0;32m    100\u001B[0m     denom \u001B[38;5;241m=\u001B[39m max_exp_avg_sq\u001B[38;5;241m.\u001B[39msqrt()\u001B[38;5;241m.\u001B[39madd_(group[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124meps\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m    101\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 102\u001B[0m     denom \u001B[38;5;241m=\u001B[39m \u001B[43mexp_avg_sq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msqrt\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39madd_(group[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124meps\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m    104\u001B[0m bias_correction1 \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m beta1 \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstep\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m    105\u001B[0m bias_correction2 \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m beta2 \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m state[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstep\u001B[39m\u001B[38;5;124m'\u001B[39m]\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "epochs = 1000\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t + 1}\\n-------------------------------\")\n",
    "    train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test(test_dataloader, model)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = pd.read_csv(\"data/test.csv\")\n",
    "training_data.fillna(0, inplace=True)\n",
    "seperated_columns = training_data['Cabin'].str.split('/', expand=True)\n",
    "training_data[['C1', 'C2', 'C3']] = seperated_columns\n",
    "seperated_columns = training_data['PassengerId'].str.split('_', expand=True)\n",
    "training_data[['PI1', 'PI2']] = seperated_columns.astype('int')\n",
    "seperated_columns = training_data['Name'].str.split(' ', expand=True)\n",
    "training_data['Last_name'] = seperated_columns[1]\n",
    "full_training_data = training_data\n",
    "training_data = full_training_data.drop(columns=['Cabin', 'Name'])\n",
    "cols = training_data.columns.tolist()\n",
    "print(cols)\n",
    "cols = cols[1:] + cols[:1]\n",
    "print(cols)\n",
    "training_data = training_data[cols]\n",
    "training_data.fillna(0, inplace=True)\n",
    "training_data = training_data.to_numpy()\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_with_mapped_value_for_names(column_number, map_name, data_set):\n",
    "    def name_only(key):\n",
    "        value = last_name_map.get(key)\n",
    "        if value is None:\n",
    "            return 0\n",
    "        else:\n",
    "            return value\n",
    "\n",
    "    data_set[:, column_number] = np.vectorize(name_only)(data_set[:, column_number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_mapped_value(0, planet_map, training_data)\n",
    "replace_with_mapped_value(1, TF_map, training_data)\n",
    "replace_with_mapped_value(2, destination_map, training_data)\n",
    "replace_with_mapped_value(4, TF_map, training_data)\n",
    "replace_with_mapped_value(10, cabin0, training_data)\n",
    "replace_with_mapped_value(12, cabin2, training_data)\n",
    "replace_with_mapped_value_for_names(15, last_name_map, training_data)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('model/ml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct = {v: k for k, v in TF_map.items()}\n",
    "dct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "for line in training_data:\n",
    "    current_line = torch.Tensor(line[:16].astype(float)).to(device)\n",
    "    pred = model(current_line)\n",
    "    result.append([line[16], dct[pred.argmax().item()]])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = [['PassengerId', 'Transported']] + result\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"result.csv\", result, delimiter=\",\", fmt='%s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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