CNN2Head_train_101age.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import CNN2Head_input2 as CNN2Head_input\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import BKNetStyle2 as BKNetStyle\n",
    "from const import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load smile image...................\n",
      "Done !\n",
      "Number of smile train data:  3000\n",
      "---------------------------------------------------------------\n",
      "Load gender image...................\n",
      "Done !\n",
      "Number of gender train data:  96000\n",
      "---------------------------------------------------------------\n",
      "Load age image...................\n",
      "Done !\n",
      "Number of age train data:  96000\n",
      "---------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "''' PREPARE DATA '''\n",
    "''' PREPARE DATA '''\n",
    "smile_train, smile_test = CNN2Head_input.getSmileImage()\n",
    "gender_train, gender_test = CNN2Head_input.getGenderImage()\n",
    "age_train, age_test = CNN2Head_input.getAgeImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "654\n",
      "33221\n",
      "33221\n",
      "2000\n"
     ]
    }
   ],
   "source": [
    "print(len(smile_test))\n",
    "print(len(gender_test))\n",
    "print(len(age_test))\n",
    "print(DECAY_STEP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(index, num_classes):\n",
    "    assert index < num_classes and index >= 0\n",
    "    tmp = np.zeros(num_classes, dtype=np.float32)\n",
    "    tmp[index] = 1.0\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-f3b231242595>:2: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please switch to tf.train.get_or_create_global_step\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "global_step = tf.contrib.framework.get_or_create_global_step()\n",
    "x, y_, mask = BKNetStyle.Input()\n",
    "y_smile_conv, y_gender_conv, y_age_conv, phase_train, keep_prob = BKNetStyle.BKNetModel(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device: /device:GPU:0\n",
      "Create new model\n",
      "OK\n",
      "Epoch: 1\n",
      "Learning rate: 0.010000\n",
      "Smile task train accuracy: 83.233872312052\n",
      "Gender task train accuracy: 79.91060265066267\n",
      "Age task train error: 24.048677\n",
      "Smile loss: 0.43748635\n",
      "Gender loss: 0.46560946\n",
      "Age loss: 4.041232\n",
      "Total loss: 5.692841. L2-loss: 0.74851364\n",
      "\n",
      "\n",
      "Epoch: 2\n",
      "Learning rate: 0.010000\n",
      "Smile task train accuracy: 91.50745223567071\n",
      "Gender task train accuracy: 86.52191127133302\n",
      "Age task train error: 23.758846\n",
      "Smile loss: 0.31835926\n",
      "Gender loss: 0.372616\n",
      "Age loss: 3.9625173\n",
      "Total loss: 5.011775. L2-loss: 0.3582825\n",
      "\n",
      "\n",
      "Epoch: 3\n",
      "Learning rate: 0.009500\n",
      "Smile task train accuracy: 92.27434897135808\n",
      "Gender task train accuracy: 87.21777092428394\n",
      "Age task train error: 23.71135\n",
      "Smile loss: 0.29424226\n",
      "Gender loss: 0.35999578\n",
      "Age loss: 3.932642\n",
      "Total loss: 4.94331. L2-loss: 0.35642964\n",
      "\n",
      "\n",
      "Epoch: 4\n",
      "Learning rate: 0.009025\n",
      "Smile task train accuracy: 92.67162337878838\n",
      "Gender task train accuracy: 87.76449996353521\n",
      "Age task train error: 23.69215\n",
      "Smile loss: 0.28880024\n",
      "Gender loss: 0.35232309\n",
      "Age loss: 3.9126985\n",
      "Total loss: 4.905427. L2-loss: 0.35160545\n",
      "\n",
      "\n",
      "Epoch: 5\n",
      "Learning rate: 0.008574\n",
      "Smile task train accuracy: 92.96882813802301\n",
      "Gender task train accuracy: 87.95023807787283\n",
      "Age task train error: 23.658587\n",
      "Smile loss: 0.27978364\n",
      "Gender loss: 0.34711146\n",
      "Age loss: 3.8932393\n",
      "Total loss: 4.8798323. L2-loss: 0.35969707\n",
      "\n",
      "\n",
      "Epoch: 6\n",
      "Learning rate: 0.008145\n",
      "Smile task train accuracy: 93.46847597772813\n",
      "Gender task train accuracy: 88.08526598737262\n",
      "Age task train error: 23.643494\n",
      "Smile loss: 0.27141088\n",
      "Gender loss: 0.3429699\n",
      "Age loss: 3.8779697\n",
      "Total loss: 4.845989. L2-loss: 0.35363898\n",
      "\n",
      "\n",
      "Epoch: 7\n",
      "Learning rate: 0.007738\n",
      "Smile task train accuracy: 93.53763046450365\n",
      "Gender task train accuracy: 88.2575402406626\n",
      "Age task train error: 23.626926\n",
      "Smile loss: 0.26756993\n",
      "Gender loss: 0.33904094\n",
      "Age loss: 3.8692617\n",
      "Total loss: 4.824275. L2-loss: 0.3484022\n",
      "\n",
      "\n",
      "Epoch: 8\n",
      "Learning rate: 0.007351\n",
      "Smile task train accuracy: 93.69603627029369\n",
      "Gender task train accuracy: 88.28941473122649\n",
      "Age task train error: 23.592245\n",
      "Smile loss: 0.2642053\n",
      "Gender loss: 0.33697575\n",
      "Age loss: 3.8564987\n",
      "Total loss: 4.8044105. L2-loss: 0.3467303\n",
      "\n",
      "\n",
      "Epoch: 9\n",
      "Learning rate: 0.007351\n",
      "Smile task train accuracy: 93.85210375408415\n",
      "Gender task train accuracy: 88.53787886681184\n",
      "Age task train error: 23.573595\n",
      "Smile loss: 0.25686234\n",
      "Gender loss: 0.3333881\n",
      "Age loss: 3.8442261\n",
      "Total loss: 4.777973. L2-loss: 0.3434965\n",
      "\n",
      "\n",
      "Epoch: 10\n",
      "Learning rate: 0.006983\n",
      "Smile task train accuracy: 94.00426837401628\n",
      "Gender task train accuracy: 88.70297246330003\n",
      "Age task train error: 23.553947\n",
      "Smile loss: 0.25327146\n",
      "Gender loss: 0.3304612\n",
      "Age loss: 3.8341894\n",
      "Total loss: 4.7618213. L2-loss: 0.34389946\n",
      "\n",
      "\n",
      "Epoch: 11\n",
      "Learning rate: 0.006634\n",
      "Smile task train accuracy: 94.04194312006135\n",
      "Gender task train accuracy: 88.69220029590115\n",
      "Age task train error: 23.536097\n",
      "Smile loss: 0.25210315\n",
      "Gender loss: 0.32790476\n",
      "Age loss: 3.8275528\n",
      "Total loss: 4.7551193. L2-loss: 0.34755883\n",
      "\n",
      "\n",
      "Epoch: 12\n",
      "Learning rate: 0.006302\n",
      "Smile task train accuracy: 94.14941494149414\n",
      "Gender task train accuracy: 88.71339119200675\n",
      "Age task train error: 23.518448\n",
      "Smile loss: 0.25062644\n",
      "Gender loss: 0.32530537\n",
      "Age loss: 3.820357\n",
      "Total loss: 4.7380548. L2-loss: 0.3417663\n",
      "\n",
      "\n",
      "Epoch: 13\n",
      "Learning rate: 0.005987\n",
      "Smile task train accuracy: 94.28847692718058\n",
      "Gender task train accuracy: 88.87569155752821\n",
      "Age task train error: 23.499077\n",
      "Smile loss: 0.245074\n",
      "Gender loss: 0.32298404\n",
      "Age loss: 3.8112051\n",
      "Total loss: 4.7192855. L2-loss: 0.34002265\n",
      "\n",
      "\n",
      "Epoch: 14\n",
      "Learning rate: 0.005688\n",
      "Smile task train accuracy: 94.42555177702208\n",
      "Gender task train accuracy: 88.9578861822501\n",
      "Age task train error: 23.487658\n",
      "Smile loss: 0.24272643\n",
      "Gender loss: 0.32204962\n",
      "Age loss: 3.807482\n",
      "Total loss: 4.7132688. L2-loss: 0.34101066\n",
      "\n",
      "\n",
      "Epoch: 15\n",
      "Learning rate: 0.005404\n",
      "Smile task train accuracy: 94.37535424932484\n",
      "Gender task train accuracy: 89.05895100960637\n",
      "Age task train error: 23.47439\n",
      "Smile loss: 0.2418905\n",
      "Gender loss: 0.3188678\n",
      "Age loss: 3.7951586\n",
      "Total loss: 4.6940494. L2-loss: 0.33813208\n",
      "\n",
      "\n",
      "Epoch: 16\n",
      "Learning rate: 0.005133\n",
      "Smile task train accuracy: 94.64541726402828\n",
      "Gender task train accuracy: 89.13720709738588\n",
      "Age task train error: 23.45718\n",
      "Smile loss: 0.23559943\n",
      "Gender loss: 0.31750247\n",
      "Age loss: 3.7863142\n",
      "Total loss: 4.67788. L2-loss: 0.33846387\n",
      "\n",
      "\n",
      "Epoch: 17\n",
      "Learning rate: 0.005133\n",
      "Smile task train accuracy: 94.81177686639325\n",
      "Gender task train accuracy: 89.04807552045345\n",
      "Age task train error: 23.442104\n",
      "Smile loss: 0.23178655\n",
      "Gender loss: 0.3163338\n",
      "Age loss: 3.7797644\n",
      "Total loss: 4.667841. L2-loss: 0.33995652\n",
      "\n",
      "\n",
      "Epoch: 18\n",
      "Learning rate: 0.004877\n",
      "Smile task train accuracy: 94.9124854142357\n",
      "Gender task train accuracy: 89.21594165147174\n",
      "Age task train error: 23.433146\n",
      "Smile loss: 0.22714381\n",
      "Gender loss: 0.31259078\n",
      "Age loss: 3.7756944\n",
      "Total loss: 4.655228. L2-loss: 0.3397992\n",
      "\n",
      "\n",
      "Epoch: 19\n",
      "Learning rate: 0.004633\n",
      "Smile task train accuracy: 95.12536676447053\n",
      "Gender task train accuracy: 89.3217336945197\n",
      "Age task train error: 23.422224\n",
      "Smile loss: 0.22276558\n",
      "Gender loss: 0.31164047\n",
      "Age loss: 3.771169\n",
      "Total loss: 4.644997. L2-loss: 0.33942208\n",
      "\n",
      "\n",
      "Epoch: 20\n",
      "Learning rate: 0.004401\n",
      "Smile task train accuracy: 94.96599546606214\n",
      "Gender task train accuracy: 89.38495681256968\n",
      "Age task train error: 23.409416\n",
      "Smile loss: 0.2228109\n",
      "Gender loss: 0.30885252\n",
      "Age loss: 3.763971\n",
      "Total loss: 4.633934. L2-loss: 0.33829945\n",
      "\n",
      "\n",
      "Epoch: 21\n",
      "Learning rate: 0.004181\n",
      "Smile task train accuracy: 95.24904980996199\n",
      "Gender task train accuracy: 89.36214549167516\n",
      "Age task train error: 23.39938\n",
      "Smile loss: 0.21867621\n",
      "Gender loss: 0.3086736\n",
      "Age loss: 3.7595024\n",
      "Total loss: 4.6233826. L2-loss: 0.3365304\n",
      "\n",
      "\n",
      "Epoch: 22\n",
      "Learning rate: 0.003972\n",
      "Smile task train accuracy: 95.4186255876763\n",
      "Gender task train accuracy: 89.30193790372995\n",
      "Age task train error: 23.388802\n",
      "Smile loss: 0.21168143\n",
      "Gender loss: 0.30790547\n",
      "Age loss: 3.7527711\n",
      "Total loss: 4.603345. L2-loss: 0.33098677\n",
      "\n",
      "\n",
      "Epoch: 23\n",
      "Learning rate: 0.003774\n",
      "Smile task train accuracy: 95.46242581849704\n",
      "Gender task train accuracy: 89.44620995050795\n",
      "Age task train error: 23.378265\n",
      "Smile loss: 0.21278732\n",
      "Gender loss: 0.30528778\n",
      "Age loss: 3.744967\n",
      "Total loss: 4.595853. L2-loss: 0.33281076\n",
      "\n",
      "\n",
      "Epoch: 24\n",
      "Learning rate: 0.003774\n",
      "Smile task train accuracy: 95.44545212056549\n",
      "Gender task train accuracy: 89.41100471988081\n",
      "Age task train error: 23.368265\n",
      "Smile loss: 0.20955189\n",
      "Gender loss: 0.30289066\n",
      "Age loss: 3.7425153\n",
      "Total loss: 4.586658. L2-loss: 0.33170027\n",
      "\n",
      "\n",
      "Epoch: 25\n",
      "Learning rate: 0.003585\n",
      "Smile task train accuracy: 95.55925987664611\n",
      "Gender task train accuracy: 89.50729831945907\n",
      "Age task train error: 23.357946\n",
      "Smile loss: 0.20648031\n",
      "Gender loss: 0.30211073\n",
      "Age loss: 3.7365484\n",
      "Total loss: 4.5748105. L2-loss: 0.32967123\n",
      "\n",
      "\n",
      "Epoch: 26\n",
      "Learning rate: 0.003406\n",
      "Smile task train accuracy: 95.6453602747491\n",
      "Gender task train accuracy: 89.42246579909771\n",
      "Age task train error: 23.349476\n",
      "Smile loss: 0.20329009\n",
      "Gender loss: 0.30215225\n",
      "Age loss: 3.734774\n",
      "Total loss: 4.5711184. L2-loss: 0.33090222\n",
      "\n",
      "\n",
      "Epoch: 27\n",
      "Learning rate: 0.003235\n",
      "Smile task train accuracy: 95.67623429009568\n",
      "Gender task train accuracy: 89.39477604942645\n",
      "Age task train error: 23.34445\n",
      "Smile loss: 0.20197505\n",
      "Gender loss: 0.300272\n",
      "Age loss: 3.7315435\n",
      "Total loss: 4.5645857. L2-loss: 0.33079484\n",
      "\n",
      "\n",
      "Epoch: 28\n",
      "Learning rate: 0.003074\n",
      "Smile task train accuracy: 95.97932988831472\n",
      "Gender task train accuracy: 89.60968595661325\n",
      "Age task train error: 23.335075\n",
      "Smile loss: 0.1952603\n",
      "Gender loss: 0.29881743\n",
      "Age loss: 3.722881\n",
      "Total loss: 4.5414658. L2-loss: 0.32450703\n",
      "\n",
      "\n",
      "Epoch: 29\n",
      "Learning rate: 0.002920\n",
      "Smile task train accuracy: 95.94238655686327\n",
      "Gender task train accuracy: 89.56377691882938\n",
      "Age task train error: 23.325943\n",
      "Smile loss: 0.19610779\n",
      "Gender loss: 0.2973213\n",
      "Age loss: 3.7194173\n",
      "Total loss: 4.5374627. L2-loss: 0.324616\n",
      "\n",
      "\n",
      "Epoch: 30\n",
      "Learning rate: 0.002774\n",
      "Smile task train accuracy: 96.09588584383543\n",
      "Gender task train accuracy: 89.6122027964742\n",
      "Age task train error: 23.317411\n",
      "Smile loss: 0.18479048\n",
      "Gender loss: 0.29589316\n",
      "Age loss: 3.7131317\n",
      "Total loss: 4.518279. L2-loss: 0.32446352\n",
      "\n",
      "\n",
      "Epoch: 31\n",
      "Learning rate: 0.002635\n",
      "Smile task train accuracy: 96.15859148354396\n",
      "Gender task train accuracy: 89.61400218784185\n",
      "Age task train error: 23.310923\n",
      "Smile loss: 0.1862988\n",
      "Gender loss: 0.29592815\n",
      "Age loss: 3.710858\n",
      "Total loss: 4.518752. L2-loss: 0.325667\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 32\n",
      "Learning rate: 0.002635\n",
      "Smile task train accuracy: 96.41952260301373\n",
      "Gender task train accuracy: 89.73733837610311\n",
      "Age task train error: 23.30334\n",
      "Smile loss: 0.17936909\n",
      "Gender loss: 0.29311857\n",
      "Age loss: 3.7037084\n",
      "Total loss: 4.5016875. L2-loss: 0.32549134\n",
      "\n",
      "\n",
      "Epoch: 33\n",
      "Learning rate: 0.002503\n",
      "Smile task train accuracy: 96.54919481212283\n",
      "Gender task train accuracy: 89.80255274811148\n",
      "Age task train error: 23.29705\n",
      "Smile loss: 0.17559023\n",
      "Gender loss: 0.29152653\n",
      "Age loss: 3.704306\n",
      "Total loss: 4.498239. L2-loss: 0.32681602\n",
      "\n",
      "\n",
      "Epoch: 34\n",
      "Learning rate: 0.002378\n",
      "Smile task train accuracy: 96.67944657442908\n",
      "Gender task train accuracy: 89.71607189372233\n",
      "Age task train error: 23.29075\n",
      "Smile loss: 0.17123285\n",
      "Gender loss: 0.29244134\n",
      "Age loss: 3.70178\n",
      "Total loss: 4.4960494. L2-loss: 0.33059493\n",
      "\n",
      "\n",
      "Epoch: 35\n",
      "Learning rate: 0.002259\n",
      "Smile task train accuracy: 96.76569637557934\n",
      "Gender task train accuracy: 89.67004573726597\n",
      "Age task train error: 23.284492\n",
      "Smile loss: 0.16738145\n",
      "Gender loss: 0.29203266\n",
      "Age loss: 3.6949065\n",
      "Total loss: 4.4842296. L2-loss: 0.32990915\n",
      "\n",
      "\n",
      "Epoch: 36\n",
      "Learning rate: 0.002146\n",
      "Smile task train accuracy: 96.97596105758011\n",
      "Gender task train accuracy: 89.82007230447059\n",
      "Age task train error: 23.27662\n",
      "Smile loss: 0.16167665\n",
      "Gender loss: 0.2892924\n",
      "Age loss: 3.6933312\n",
      "Total loss: 4.476865. L2-loss: 0.33256438\n",
      "\n",
      "\n",
      "Epoch: 37\n",
      "Learning rate: 0.002039\n",
      "Smile task train accuracy: 97.25954325720953\n",
      "Gender task train accuracy: 89.73618954343704\n",
      "Age task train error: 23.270641\n",
      "Smile loss: 0.15578493\n",
      "Gender loss: 0.2901112\n",
      "Age loss: 3.6895435\n",
      "Total loss: 4.4732003. L2-loss: 0.33776066\n",
      "\n",
      "\n",
      "Epoch: 38\n",
      "Learning rate: 0.001937\n",
      "Smile task train accuracy: 97.77303640485398\n",
      "Gender task train accuracy: 89.79306895617563\n",
      "Age task train error: 23.264599\n",
      "Smile loss: 0.14340931\n",
      "Gender loss: 0.28824407\n",
      "Age loss: 3.6840754\n",
      "Total loss: 4.4558616. L2-loss: 0.3401328\n",
      "\n",
      "\n",
      "Epoch: 39\n",
      "Learning rate: 0.001937\n",
      "Smile task train accuracy: 98.0628813389791\n",
      "Gender task train accuracy: 89.6954478677183\n",
      "Age task train error: 23.259811\n",
      "Smile loss: 0.13727099\n",
      "Gender loss: 0.28825915\n",
      "Age loss: 3.6822145\n",
      "Total loss: 4.4555407. L2-loss: 0.34779567\n",
      "\n",
      "\n",
      "Epoch: 40\n",
      "Learning rate: 0.001840\n",
      "Smile task train accuracy: 98.52651931859853\n",
      "Gender task train accuracy: 89.74639493206635\n",
      "Age task train error: 23.253763\n",
      "Smile loss: 0.12947287\n",
      "Gender loss: 0.2871487\n",
      "Age loss: 3.678667\n",
      "Total loss: 4.448024. L2-loss: 0.35273525\n",
      "\n",
      "\n",
      "Epoch: 41\n",
      "Learning rate: 0.001748\n",
      "Smile task train accuracy: 98.75291763921307\n",
      "Gender task train accuracy: 89.75192490023859\n",
      "Age task train error: 23.248241\n",
      "Smile loss: 0.12208921\n",
      "Gender loss: 0.28666562\n",
      "Age loss: 3.6761293\n",
      "Total loss: 4.442487. L2-loss: 0.3576029\n",
      "\n",
      "\n",
      "Epoch: 42\n",
      "Learning rate: 0.001661\n",
      "Smile task train accuracy: 98.93659577305154\n",
      "Gender task train accuracy: 89.6740716042179\n",
      "Age task train error: 23.244574\n",
      "Smile loss: 0.11140607\n",
      "Gender loss: 0.285787\n",
      "Age loss: 3.670048\n",
      "Total loss: 4.4253097. L2-loss: 0.35806853\n",
      "\n",
      "\n",
      "Epoch: 43\n",
      "Learning rate: 0.001578\n",
      "Smile task train accuracy: 99.24994999666644\n",
      "Gender task train accuracy: 89.79902272324732\n",
      "Age task train error: 23.240492\n",
      "Smile loss: 0.10568354\n",
      "Gender loss: 0.28568527\n",
      "Age loss: 3.671595\n",
      "Total loss: 4.421627. L2-loss: 0.3586631\n",
      "\n",
      "\n",
      "Epoch: 44\n",
      "Learning rate: 0.001499\n",
      "Smile task train accuracy: 99.44661132779945\n",
      "Gender task train accuracy: 89.73504068683123\n",
      "Age task train error: 23.234741\n",
      "Smile loss: 0.09549095\n",
      "Gender loss: 0.28417382\n",
      "Age loss: 3.6645925\n",
      "Total loss: 4.4022756. L2-loss: 0.35801828\n",
      "\n",
      "\n",
      "Epoch: 45\n",
      "Learning rate: 0.001424\n",
      "Smile task train accuracy: 99.39652585603307\n",
      "Gender task train accuracy: 89.75868464375768\n",
      "Age task train error: 23.229353\n",
      "Smile loss: 0.09478423\n",
      "Gender loss: 0.28344947\n",
      "Age loss: 3.6623082\n",
      "Total loss: 4.3977284. L2-loss: 0.35718647\n",
      "\n",
      "\n",
      "Epoch: 46\n",
      "Learning rate: 0.001353\n",
      "Smile task train accuracy: 99.41994199419942\n",
      "Gender task train accuracy: 89.79162325484475\n",
      "Age task train error: 23.223434\n",
      "Smile loss: 0.09083513\n",
      "Gender loss: 0.28185815\n",
      "Age loss: 3.6593602\n",
      "Total loss: 4.3892655. L2-loss: 0.3572121\n",
      "\n",
      "\n",
      "Epoch: 47\n",
      "Learning rate: 0.001353\n",
      "Smile task train accuracy: 99.56323142066482\n",
      "Gender task train accuracy: 89.88611854923575\n",
      "Age task train error: 23.218277\n",
      "Smile loss: 0.08016126\n",
      "Gender loss: 0.28062895\n",
      "Age loss: 3.6527035\n",
      "Total loss: 4.367969. L2-loss: 0.35447487\n",
      "\n",
      "\n",
      "Epoch: 48\n",
      "Learning rate: 0.001285\n",
      "Smile task train accuracy: 99.59317060157396\n",
      "Gender task train accuracy: 89.8106956440203\n",
      "Age task train error: 23.213343\n",
      "Smile loss: 0.08280443\n",
      "Gender loss: 0.28095284\n",
      "Age loss: 3.6507328\n",
      "Total loss: 4.3671155. L2-loss: 0.35262537\n",
      "\n",
      "\n",
      "Epoch: 49\n",
      "Learning rate: 0.001221\n",
      "Smile task train accuracy: 99.66992298202914\n",
      "Gender task train accuracy: 89.80319021473\n",
      "Age task train error: 23.209139\n",
      "Smile loss: 0.07209571\n",
      "Gender loss: 0.27922896\n",
      "Age loss: 3.646579\n",
      "Total loss: 4.348823. L2-loss: 0.35091954\n",
      "\n",
      "\n",
      "Epoch: 50\n",
      "Learning rate: 0.001160\n",
      "Smile task train accuracy: 99.65659798626392\n",
      "Gender task train accuracy: 89.80412585955408\n",
      "Age task train error: 23.204313\n",
      "Smile loss: 0.0716353\n",
      "Gender loss: 0.27825513\n",
      "Age loss: 3.641277\n",
      "Total loss: 4.3383164. L2-loss: 0.3471486\n",
      "\n",
      "\n",
      "Epoch: 51\n",
      "Learning rate: 0.001102\n",
      "Smile task train accuracy: 99.70996132817709\n",
      "Gender task train accuracy: 89.80682670667667\n",
      "Age task train error: 23.199785\n",
      "Smile loss: 0.067791685\n",
      "Gender loss: 0.27850738\n",
      "Age loss: 3.6388285\n",
      "Total loss: 4.325531. L2-loss: 0.34040356\n",
      "\n",
      "\n",
      "Epoch: 52\n",
      "Learning rate: 0.001047\n",
      "Smile task train accuracy: 99.6799466577763\n",
      "Gender task train accuracy: 89.78545602317367\n",
      "Age task train error: 23.195444\n",
      "Smile loss: 0.06817419\n",
      "Gender loss: 0.27867386\n",
      "Age loss: 3.633382\n",
      "Total loss: 4.3194237. L2-loss: 0.3391939\n",
      "\n",
      "\n",
      "Epoch: 53\n",
      "Learning rate: 0.000994\n",
      "Smile task train accuracy: 99.76995399079816\n",
      "Gender task train accuracy: 89.84892685976244\n",
      "Age task train error: 23.191109\n",
      "Smile loss: 0.06354076\n",
      "Gender loss: 0.27693808\n",
      "Age loss: 3.632822\n",
      "Total loss: 4.312162. L2-loss: 0.3388612\n",
      "\n",
      "\n",
      "Epoch: 54\n",
      "Learning rate: 0.000994\n",
      "Smile task train accuracy: 99.74990829970989\n",
      "Gender task train accuracy: 89.88789798299717\n",
      "Age task train error: 23.185577\n",
      "Smile loss: 0.06337772\n",
      "Gender loss: 0.2749593\n",
      "Age loss: 3.6293452\n",
      "Total loss: 4.3028183. L2-loss: 0.3351361\n",
      "\n",
      "\n",
      "Epoch: 55\n",
      "Learning rate: 0.000945\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-64ecaf74aa72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    134\u001b[0m             feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n\u001b[1;32m    135\u001b[0m                        \u001b[0mphase_train\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                        keep_prob: 1})\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;34m'''--------------------------------------- DEBUG -----------------------------------------------------'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1324\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "smile_loss, gender_loss, age_loss, l2_loss, loss = BKNetStyle.selective_loss(y_smile_conv, y_gender_conv,\n",
    "                                                                             y_age_conv, y_, mask)\n",
    "if tf.test.gpu_device_name():\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\n",
    "else:\n",
    "    print(\"Please install GPU version of TF\")\n",
    "    \n",
    "train_step = BKNetStyle.train_op(loss, global_step)\n",
    "\n",
    "smile_mask = tf.get_collection('smile_mask')[0]\n",
    "gender_mask = tf.get_collection('gender_mask')[0]\n",
    "age_mask = tf.get_collection('age_mask')[0]\n",
    "\n",
    "y_smile = tf.get_collection('y_smile')[0]\n",
    "y_gender = tf.get_collection('y_gender')[0]\n",
    "y_age = tf.get_collection('y_age')[0]\n",
    "\n",
    "smile_correct_prediction = tf.equal(tf.argmax(y_smile_conv, 1), tf.argmax(y_smile, 1))\n",
    "gender_correct_prediction = tf.equal(tf.argmax(y_gender_conv, 1), tf.argmax(y_gender, 1))\n",
    "# age_correct_prediction = tf.equal(tf.argmax(y_age_conv, 1), tf.argmax(y_age, 1))\n",
    "\n",
    "smile_true_pred = tf.reduce_sum(tf.cast(smile_correct_prediction, dtype=tf.float32) * smile_mask)\n",
    "gender_true_pred = tf.reduce_sum(tf.cast(gender_correct_prediction, dtype=tf.float32) * gender_mask)\n",
    "# age_true_pred = tf.reduce_sum(tf.cast(age_correct_prediction, dtype=tf.float32) * age_mask)\n",
    "age_mae, update_op = tf.metrics.mean_absolute_error(\n",
    "    tf.argmax(y_age, 1), tf.argmax(y_age_conv, 1), name=\"age_mae\")\n",
    "running_vars = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=\"age_mae\")\n",
    "running_vars_initializer = tf.variables_initializer(var_list=running_vars)\n",
    "sess.run(running_vars_initializer)\n",
    "\n",
    "train_data = []\n",
    "# Mask: Smile -> 0, Gender -> 1, Age -> 2\n",
    "for i in range(len(smile_train) * 10):\n",
    "    img = (smile_train[i % 3000][0] - 128) / 255.0\n",
    "    label = smile_train[i % 3000][1]\n",
    "    train_data.append((img, one_hot(label, 101), 0.0))\n",
    "for i in range(len(gender_train)):\n",
    "    img = (gender_train[i][0] - 128) / 255.0\n",
    "    label = (int)(gender_train[i][1])\n",
    "    train_data.append((img, one_hot(label, 101), 1.0))\n",
    "for i in range(len(age_train)):\n",
    "    img = (age_train[i][0] - 128) / 255.0\n",
    "    label = (int)(age_train[i][1])\n",
    "    train_data.append((img, one_hot(label, 101), 2.0))\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "if not os.path.isfile('./save/current5/model-age101.ckpt.index'):\n",
    "    print('Create new model')\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print('OK')\n",
    "else:\n",
    "    print('Restoring existed model')\n",
    "    saver.restore(sess, './save/current5/model-age101.ckpt.index')\n",
    "    print('OK')\n",
    "\n",
    "loss_summary_placeholder = tf.placeholder(tf.float32)\n",
    "tf.summary.scalar('loss', loss_summary_placeholder)\n",
    "merge_summary = tf.summary.merge_all()\n",
    "writer = tf.summary.FileWriter(\"./summary/summary5/\", graph=tf.get_default_graph())\n",
    "\n",
    "learning_rate = tf.get_collection('learning_rate')[0]\n",
    "current_epoch = (int)(global_step.eval() / (len(train_data) // BATCH_SIZE))\n",
    "for epoch in range(current_epoch + 1, NUM_EPOCHS):\n",
    "    print('Epoch:', str(epoch))\n",
    "    np.random.shuffle(train_data)\n",
    "    train_img = []\n",
    "    train_label = []\n",
    "    train_mask = []\n",
    "\n",
    "    for i in range(len(train_data)):\n",
    "        train_img.append(train_data[i][0])\n",
    "        train_label.append(train_data[i][1])\n",
    "        train_mask.append(train_data[i][2])\n",
    "\n",
    "    number_batch = len(train_data) // BATCH_SIZE\n",
    "\n",
    "    avg_ttl = []\n",
    "    avg_rgl = []\n",
    "    avg_smile_loss = []\n",
    "    avg_gender_loss = []\n",
    "    avg_age_loss = []\n",
    "\n",
    "    smile_nb_true_pred = 0\n",
    "    gender_nb_true_pred = 0\n",
    "    age_nb_true_pred = 0\n",
    "\n",
    "    smile_nb_train = 0\n",
    "    gender_nb_train = 0\n",
    "    age_nb_train = 0\n",
    "\n",
    "    print(\"Learning rate: %f\" % learning_rate.eval())\n",
    "#     for batch in range(number_batch):\n",
    "    for batch in range(number_batch):\n",
    "#         if (batch+1)%1000==0:\n",
    "#             print('Training on batch {0}/{1}'.format(str(batch + 1), str(number_batch)))\n",
    "        top = batch * BATCH_SIZE\n",
    "        bot = min((batch + 1) * BATCH_SIZE, len(train_data))\n",
    "        batch_img = np.asarray(train_img[top:bot])\n",
    "        batch_label = np.asarray(train_label[top:bot])\n",
    "        batch_mask = np.asarray(train_mask[top:bot])\n",
    "\n",
    "        for i in range(BATCH_SIZE):\n",
    "            if batch_mask[i] == 0.0:\n",
    "                smile_nb_train += 1\n",
    "            else:\n",
    "                if batch_mask[i] == 1.0:\n",
    "                    gender_nb_train += 1                        \n",
    "                else:\n",
    "                    age_nb_train += 1\n",
    "\n",
    "        batch_img = CNN2Head_input.augmentation(batch_img, 48)\n",
    "\n",
    "        ttl, sml, gel, agl, l2l, _ = sess.run([loss, smile_loss, gender_loss, age_loss, l2_loss, train_step],\n",
    "                                              feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                         phase_train: True,\n",
    "                                                         keep_prob: 0.5})\n",
    "\n",
    "        smile_nb_true_pred += sess.run(smile_true_pred, feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                                   phase_train: True,\n",
    "                                                                   keep_prob: 0.5})\n",
    "\n",
    "        gender_nb_true_pred += sess.run(gender_true_pred,\n",
    "                                        feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                   phase_train: True,\n",
    "                                                   keep_prob: 0.5})\n",
    "\n",
    "#         age_nb_true_pred += sess.run(age_true_pred,\n",
    "#                                          feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "#                                                     phase_train: True,\n",
    "#                                                     keep_prob: 0.5})\n",
    "\n",
    "        sess.run(update_op,\n",
    "            feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                       phase_train: False,\n",
    "                       keep_prob: 1})\n",
    "\n",
    "        '''--------------------------------------- DEBUG -----------------------------------------------------'''\n",
    "        '''\n",
    "        sm_mask, em_mask, ge_mask = sess.run([smile_mask, gender_mask, age_mask],\n",
    "                                             feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                        phase_train: True,\n",
    "                                                        keep_prob: 0.5})\n",
    "        print('Smile mask: ', sm_mask)\n",
    "        print('Gender mask', ge_mask)\n",
    "        print('Age mask', ag_mask)\n",
    "        print('Batch mask', batch_mask)\n",
    "\n",
    "        y_true_sm, y_true_ge, y_true_ag = sess.run([y_smile, y_gender, y_age],\n",
    "                                                   feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                              phase_train: True,\n",
    "                                                              keep_prob: 0.5})\n",
    "        print('Smile label', y_true_sm)\n",
    "        print('Gender label', y_true_ge)\n",
    "        print('Age label', y_true_ag)\n",
    "        print('Batch label', batch_label)\n",
    "\n",
    "        y_conv_sm, y_conv_ge, y_conv_ag = sess.run([y_smile_conv, y_gender_conv, y_age_conv],\n",
    "                                                   feed_dict={x: batch_img, y_: batch_label, mask: batch_mask,\n",
    "                                                              phase_train: True,\n",
    "                                                              keep_prob: 0.5})\n",
    "        print('Smile conv', y_conv_sm)\n",
    "        print('Gender conv', y_conv_ge)\n",
    "        print('Age conv', y_conv_ag)\n",
    "\n",
    "        '''\n",
    "        '''---------------------------------- END OF DEBUG ----------------------------------------------------'''\n",
    "\n",
    "        avg_ttl.append(ttl)\n",
    "        avg_smile_loss.append(sml)\n",
    "        avg_gender_loss.append(gel)\n",
    "        avg_age_loss.append(agl)\n",
    "\n",
    "        avg_rgl.append(l2l)\n",
    "\n",
    "    smile_train_accuracy = smile_nb_true_pred * 1.0 / smile_nb_train\n",
    "    gender_train_accuracy = gender_nb_true_pred * 1.0 / gender_nb_train\n",
    "    age_train_accuracy = age_nb_true_pred * 1.0 / age_nb_train\n",
    "\n",
    "    avg_smile_loss = np.average(avg_smile_loss)\n",
    "    avg_gender_loss = np.average(avg_gender_loss)\n",
    "    avg_age_loss = np.average(avg_age_loss)\n",
    "\n",
    "    avg_rgl = np.average(avg_rgl)\n",
    "    avg_ttl = np.average(avg_ttl)\n",
    "\n",
    "    summary = sess.run(merge_summary, feed_dict={loss_summary_placeholder: avg_ttl})\n",
    "    writer.add_summary(summary, global_step=epoch)\n",
    "\n",
    "    with open('log5.csv', 'a') as f:\n",
    "        # epochs, smile_train_accuracy, gender_train_accuracy, age_train_accuracy,\n",
    "        # avg_smile_loss, avg_gender_loss, avg_age_loss, avg_ttl, avg_rgl\n",
    "        f.write('{0},{1},{2},{3},{4},{5},{6},{7},{8}\\n'.format(epoch, smile_train_accuracy, gender_train_accuracy, age_train_accuracy, avg_smile_loss, avg_gender_loss, avg_age_loss, avg_ttl, avg_rgl))\n",
    "\n",
    "    print('Smile task train accuracy: ' + str(smile_train_accuracy * 100))\n",
    "    print('Gender task train accuracy: ' + str(gender_train_accuracy * 100))\n",
    "    age_train_error = sess.run(age_mae)\n",
    "    print('Age task train error: ' + str(age_train_error))\n",
    "\n",
    "    print('Smile loss: ' + str(avg_smile_loss))\n",
    "    print('Gender loss: ' + str(avg_gender_loss))\n",
    "    print('Age loss: ' + str(avg_age_loss))\n",
    "    print('Total loss: ' + str(avg_ttl) + '. L2-loss: ' + str(avg_rgl))\n",
    "\n",
    "    print('\\n')\n",
    "    \n",
    "    saver.save(sess, './save/current5/model-age101.ckpt.index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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