GAZEL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "target=\"/special/jbpark/TabS6LData/Joonbeom/train_dataset/\"\n",
    "\n",
    "left_eye_right_top = np.load(target+\"left_eye_right_top.npy\")\n",
    "left_eye_left_bottom = np.load(target+\"left_eye_left_bottom.npy\")\n",
    "right_eye_right_top = np.load(target+\"right_eye_right_top.npy\")\n",
    "right_eye_left_bottom = np.load(target+\"right_eye_left_bottom.npy\")\n",
    "\n",
    "\n",
    "left_eye_right_top[:,1] = left_eye_right_top[:,1] - left_eye_left_bottom[:,1]\n",
    "left_eye_right_top[:,0] = left_eye_left_bottom[:,0] - left_eye_right_top[:,0]\n",
    "\n",
    "right_eye_right_top[:,1] = right_eye_right_top[:,1] - right_eye_left_bottom[:,1]\n",
    "right_eye_right_top[:,0] = right_eye_left_bottom[:,0] - right_eye_right_top[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[236.05314, 236.0531 ],\n",
       "       [230.8075 , 230.8074 ],\n",
       "       [251.79   , 251.7901 ],\n",
       "       ...,\n",
       "       [220.31616, 220.3162 ],\n",
       "       [230.8075 , 230.8074 ],\n",
       "       [230.80749, 230.8074 ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_eye_size = left_eye_right_top\n",
    "left_eye_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[230.80755, 230.80744],\n",
       "       [230.80745, 230.8074 ],\n",
       "       [246.54435, 246.5444 ],\n",
       "       ...,\n",
       "       [230.80752, 230.8075 ],\n",
       "       [225.56189, 225.562  ],\n",
       "       [225.56195, 225.5619 ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_eye_size = right_eye_right_top\n",
    "right_eye_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "left_eye (InputLayer)           [(None, 64, 64, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "right_eye (InputLayer)          [(None, 64, 64, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 64, 32)   320         left_eye[0][0]                   \n",
      "                                                                 right_eye[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D) (None, 32, 32, 32)   0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling2D) (None, 32, 32, 32)   0           conv2d_6[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 32, 64)   18496       max_pooling2d_12[0][0]           \n",
      "                                                                 max_pooling2d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D) (None, 16, 16, 64)   0           conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling2D) (None, 16, 16, 64)   0           conv2d_7[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 64)   36928       max_pooling2d_13[0][0]           \n",
      "                                                                 max_pooling2d_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D) (None, 8, 8, 64)     0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling2D) (None, 8, 8, 64)     0           conv2d_8[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_12 (Flatten)            (None, 4096)         0           max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten_13 (Flatten)            (None, 4096)         0           max_pooling2d_17[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "left_eye_size (InputLayer)      [(None, 1, 1, 2)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "right_eye_size (InputLayer)     [(None, 1, 1, 2)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 8192)         0           flatten_12[0][0]                 \n",
      "                                                                 flatten_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "euler (InputLayer)              [(None, 1, 1, 3)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "facepos (InputLayer)            [(None, 1, 1, 2)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_16 (Flatten)            (None, 2)            0           left_eye_size[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_17 (Flatten)            (None, 2)            0           right_eye_size[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 64)           524352      concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_15 (Flatten)            (None, 3)            0           euler[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_14 (Flatten)            (None, 2)            0           facepos[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 4)            0           flatten_16[0][0]                 \n",
      "                                                                 flatten_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 16)           1040        dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 9)            0           flatten_15[0][0]                 \n",
      "                                                                 flatten_14[0][0]                 \n",
      "                                                                 concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 16)           0           dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 16)           160         concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)    (None, 32)           0           dropout_2[0][0]                  \n",
      "                                                                 dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 16)           528         concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "pred (Dense)                    (None, 2)            34          dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 2)            0           pred[0][0]                       \n",
      "                                                                 flatten_14[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 581,858\n",
      "Trainable params: 581,858\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Train on 4091 samples, validate on 455 samples\n",
      "Epoch 1/1000\n",
      "4091/4091 [==============================] - 2s 573us/sample - loss: 795.0425 - val_loss: 784.1378\n",
      "Epoch 2/1000\n",
      "4091/4091 [==============================] - 1s 346us/sample - loss: 758.2288 - val_loss: 761.7167\n",
      "Epoch 3/1000\n",
      "4091/4091 [==============================] - 1s 326us/sample - loss: 652.3380 - val_loss: 520.0112\n",
      "Epoch 4/1000\n",
      "4091/4091 [==============================] - 1s 327us/sample - loss: 487.2900 - val_loss: 471.7740\n",
      "Epoch 5/1000\n",
      "4091/4091 [==============================] - 1s 330us/sample - loss: 447.9268 - val_loss: 463.4483\n",
      "Epoch 6/1000\n",
      "4091/4091 [==============================] - 1s 327us/sample - loss: 421.6344 - val_loss: 415.5681\n",
      "Epoch 7/1000\n",
      "4091/4091 [==============================] - 1s 333us/sample - loss: 412.1954 - val_loss: 385.7020\n",
      "Epoch 8/1000\n",
      "4091/4091 [==============================] - 1s 334us/sample - loss: 372.1230 - val_loss: 316.0144\n",
      "Epoch 9/1000\n",
      "4091/4091 [==============================] - 1s 337us/sample - loss: 268.1036 - val_loss: 222.1573\n",
      "Epoch 10/1000\n",
      "4091/4091 [==============================] - 1s 334us/sample - loss: 193.8019 - val_loss: 203.4684\n",
      "Epoch 11/1000\n",
      "4091/4091 [==============================] - 1s 336us/sample - loss: 155.0336 - val_loss: 142.7758\n",
      "Epoch 12/1000\n",
      "4091/4091 [==============================] - 1s 333us/sample - loss: 145.9115 - val_loss: 122.4036\n",
      "Epoch 13/1000\n",
      "4091/4091 [==============================] - 1s 312us/sample - loss: 127.0046 - val_loss: 136.8705\n",
      "Epoch 14/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 123.3501 - val_loss: 131.2531\n",
      "Epoch 15/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 117.0970 - val_loss: 131.0158\n",
      "Epoch 16/1000\n",
      "4091/4091 [==============================] - 1s 332us/sample - loss: 111.4964 - val_loss: 110.3825\n",
      "Epoch 17/1000\n",
      "4091/4091 [==============================] - 1s 335us/sample - loss: 109.0983 - val_loss: 102.0083\n",
      "Epoch 18/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 110.6208 - val_loss: 103.2242\n",
      "Epoch 19/1000\n",
      "4091/4091 [==============================] - 1s 311us/sample - loss: 103.8414 - val_loss: 106.6854\n",
      "Epoch 20/1000\n",
      "4091/4091 [==============================] - 1s 312us/sample - loss: 101.4423 - val_loss: 113.7135\n",
      "Epoch 21/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 99.1239 - val_loss: 124.8725\n",
      "Epoch 22/1000\n",
      "4091/4091 [==============================] - 1s 336us/sample - loss: 95.5660 - val_loss: 98.7649\n",
      "Epoch 23/1000\n",
      "4091/4091 [==============================] - 1s 342us/sample - loss: 102.4671 - val_loss: 102.9940\n",
      "Epoch 24/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 95.2080 - val_loss: 123.2929\n",
      "Epoch 25/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 98.7420 - val_loss: 143.5991\n",
      "Epoch 26/1000\n",
      "4091/4091 [==============================] - 1s 337us/sample - loss: 93.3007 - val_loss: 91.2800\n",
      "Epoch 27/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 99.3198 - val_loss: 95.2536\n",
      "Epoch 28/1000\n",
      "4091/4091 [==============================] - 1s 320us/sample - loss: 89.9358 - val_loss: 100.2747\n",
      "Epoch 29/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 89.0546 - val_loss: 94.4197\n",
      "Epoch 30/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 91.7951 - val_loss: 92.1840\n",
      "Epoch 31/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 89.7937 - val_loss: 111.4058\n",
      "Epoch 32/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 88.6358 - val_loss: 113.8003\n",
      "Epoch 33/1000\n",
      "4091/4091 [==============================] - 1s 321us/sample - loss: 84.9154 - val_loss: 92.4621\n",
      "Epoch 34/1000\n",
      "4091/4091 [==============================] - 1s 310us/sample - loss: 85.9968 - val_loss: 92.1079\n",
      "Epoch 35/1000\n",
      "4091/4091 [==============================] - 1s 337us/sample - loss: 79.6069 - val_loss: 85.9891\n",
      "Epoch 36/1000\n",
      "4091/4091 [==============================] - 1s 335us/sample - loss: 87.0025 - val_loss: 82.5499\n",
      "Epoch 37/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 81.1065 - val_loss: 89.4928\n",
      "Epoch 38/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 83.0719 - val_loss: 90.9398\n",
      "Epoch 39/1000\n",
      "4091/4091 [==============================] - 1s 333us/sample - loss: 80.9742 - val_loss: 77.8190\n",
      "Epoch 40/1000\n",
      "4091/4091 [==============================] - 1s 312us/sample - loss: 79.4269 - val_loss: 80.7973\n",
      "Epoch 41/1000\n",
      "4091/4091 [==============================] - 1s 310us/sample - loss: 80.6976 - val_loss: 81.6428\n",
      "Epoch 42/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 73.5831 - val_loss: 83.2436\n",
      "Epoch 43/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 78.4440 - val_loss: 114.7423\n",
      "Epoch 44/1000\n",
      "4091/4091 [==============================] - 1s 332us/sample - loss: 79.4910 - val_loss: 77.2816\n",
      "Epoch 45/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 78.7024 - val_loss: 98.9016\n",
      "Epoch 46/1000\n",
      "4091/4091 [==============================] - 1s 339us/sample - loss: 74.9479 - val_loss: 76.1489\n",
      "Epoch 47/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 78.2271 - val_loss: 87.7332\n",
      "Epoch 48/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 76.1163 - val_loss: 96.1843\n",
      "Epoch 49/1000\n",
      "4091/4091 [==============================] - 1s 333us/sample - loss: 76.6485 - val_loss: 73.6764\n",
      "Epoch 50/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 82.8055 - val_loss: 78.6878\n",
      "Epoch 51/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 75.9778 - val_loss: 90.5706\n",
      "Epoch 52/1000\n",
      "4091/4091 [==============================] - 1s 311us/sample - loss: 71.6627 - val_loss: 94.1232\n",
      "Epoch 53/1000\n",
      "4091/4091 [==============================] - 1s 310us/sample - loss: 71.7148 - val_loss: 96.3474\n",
      "Epoch 54/1000\n",
      "4091/4091 [==============================] - 1s 332us/sample - loss: 76.0827 - val_loss: 69.2618\n",
      "Epoch 55/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 68.8446 - val_loss: 88.7648\n",
      "Epoch 56/1000\n",
      "4091/4091 [==============================] - 1s 327us/sample - loss: 70.0802 - val_loss: 89.6406\n",
      "Epoch 57/1000\n",
      "4091/4091 [==============================] - 1s 332us/sample - loss: 73.6199 - val_loss: 73.7182\n",
      "Epoch 58/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 69.7119 - val_loss: 80.4004\n",
      "Epoch 59/1000\n",
      "4091/4091 [==============================] - 1s 339us/sample - loss: 71.7848 - val_loss: 73.3057\n",
      "Epoch 60/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 67.9022 - val_loss: 73.5835\n",
      "Epoch 61/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 69.1101 - val_loss: 95.0744\n",
      "Epoch 62/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 69.4795 - val_loss: 76.9887\n",
      "Epoch 63/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 65.1887 - val_loss: 71.6816\n",
      "Epoch 64/1000\n",
      "4091/4091 [==============================] - 1s 326us/sample - loss: 66.1863 - val_loss: 80.5970\n",
      "Epoch 65/1000\n",
      "4091/4091 [==============================] - 1s 319us/sample - loss: 66.4925 - val_loss: 80.0452\n",
      "Epoch 66/1000\n",
      "4091/4091 [==============================] - 1s 334us/sample - loss: 66.6113 - val_loss: 69.1772\n",
      "Epoch 67/1000\n",
      "4091/4091 [==============================] - 1s 332us/sample - loss: 64.6292 - val_loss: 67.7958\n",
      "Epoch 68/1000\n",
      "4091/4091 [==============================] - 1s 331us/sample - loss: 70.5303 - val_loss: 63.8710\n",
      "Epoch 69/1000\n",
      "4091/4091 [==============================] - 1s 310us/sample - loss: 66.2809 - val_loss: 65.9917\n",
      "Epoch 70/1000\n",
      "4091/4091 [==============================] - 1s 312us/sample - loss: 66.7388 - val_loss: 69.9570\n",
      "Epoch 71/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 62.3338 - val_loss: 73.1659\n",
      "Epoch 72/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 61.2209 - val_loss: 67.7041\n",
      "Epoch 73/1000\n",
      "4091/4091 [==============================] - 1s 331us/sample - loss: 65.7375 - val_loss: 63.4689\n",
      "Epoch 74/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 66.9699 - val_loss: 63.5672\n",
      "Epoch 75/1000\n",
      "4091/4091 [==============================] - 1s 336us/sample - loss: 59.5526 - val_loss: 69.4968\n",
      "Epoch 76/1000\n",
      "4091/4091 [==============================] - 1s 331us/sample - loss: 60.8402 - val_loss: 72.8034\n",
      "Epoch 77/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 63.1051 - val_loss: 64.0705\n",
      "Epoch 78/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 63.0003 - val_loss: 74.7629\n",
      "Epoch 79/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 59.8304 - val_loss: 68.9519\n",
      "Epoch 80/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 62.0825 - val_loss: 71.9425\n",
      "Epoch 81/1000\n",
      "4091/4091 [==============================] - 1s 345us/sample - loss: 59.3622 - val_loss: 59.1258\n",
      "Epoch 82/1000\n",
      "4091/4091 [==============================] - 1s 311us/sample - loss: 58.7216 - val_loss: 64.5296\n",
      "Epoch 83/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 60.3087 - val_loss: 65.5825\n",
      "Epoch 84/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 58.4394 - val_loss: 70.6622\n",
      "Epoch 85/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 61.3564 - val_loss: 63.5469\n",
      "Epoch 86/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 57.7237 - val_loss: 71.6362\n",
      "Epoch 87/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 61.5075 - val_loss: 60.4142\n",
      "Epoch 88/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 56.6568 - val_loss: 75.0548\n",
      "Epoch 89/1000\n",
      "4091/4091 [==============================] - 1s 322us/sample - loss: 59.6209 - val_loss: 71.6449\n",
      "Epoch 90/1000\n",
      "4091/4091 [==============================] - 1s 316us/sample - loss: 57.8768 - val_loss: 65.3240\n",
      "Epoch 91/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 56.6220 - val_loss: 66.4331\n",
      "Epoch 92/1000\n",
      "4091/4091 [==============================] - 1s 340us/sample - loss: 60.1460 - val_loss: 57.7953\n",
      "Epoch 93/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 55.4984 - val_loss: 61.2460\n",
      "Epoch 94/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 57.9378 - val_loss: 91.5359\n",
      "Epoch 95/1000\n",
      "4091/4091 [==============================] - 1s 335us/sample - loss: 60.2874 - val_loss: 76.0026\n",
      "Epoch 96/1000\n",
      "4091/4091 [==============================] - 1s 321us/sample - loss: 56.7340 - val_loss: 70.4812\n",
      "Epoch 97/1000\n",
      "4091/4091 [==============================] - 1s 343us/sample - loss: 56.8556 - val_loss: 54.8174\n",
      "Epoch 98/1000\n",
      "4091/4091 [==============================] - 1s 328us/sample - loss: 54.6021 - val_loss: 58.2182\n",
      "Epoch 99/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 53.7191 - val_loss: 61.7332\n",
      "Epoch 100/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 56.2742 - val_loss: 63.2534\n",
      "Epoch 101/1000\n",
      "4091/4091 [==============================] - 1s 335us/sample - loss: 54.3492 - val_loss: 54.6667\n",
      "Epoch 102/1000\n",
      "4091/4091 [==============================] - 1s 314us/sample - loss: 55.1336 - val_loss: 56.5772\n",
      "Epoch 103/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 54.9620 - val_loss: 67.9488\n",
      "Epoch 104/1000\n",
      "4091/4091 [==============================] - 1s 343us/sample - loss: 52.4127 - val_loss: 52.4814\n",
      "Epoch 105/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 53.7358 - val_loss: 54.7304\n",
      "Epoch 106/1000\n",
      "4091/4091 [==============================] - 1s 312us/sample - loss: 54.6894 - val_loss: 56.6055\n",
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      "Epoch 431/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 26.0278 - val_loss: 34.1306\n",
      "Epoch 432/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 24.2375 - val_loss: 34.1869\n",
      "Epoch 433/1000\n",
      "4091/4091 [==============================] - 1s 321us/sample - loss: 24.8810 - val_loss: 33.8923\n",
      "Epoch 434/1000\n",
      "4091/4091 [==============================] - 1s 322us/sample - loss: 24.3441 - val_loss: 36.0966\n",
      "Epoch 435/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 25.2340 - val_loss: 36.3956\n",
      "Epoch 436/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 25.6747 - val_loss: 37.6175\n",
      "Epoch 437/1000\n",
      "4091/4091 [==============================] - 1s 321us/sample - loss: 24.5453 - val_loss: 39.2973\n",
      "Epoch 438/1000\n",
      "4091/4091 [==============================] - 1s 325us/sample - loss: 26.7685 - val_loss: 34.1117\n",
      "Epoch 439/1000\n",
      "4091/4091 [==============================] - 1s 327us/sample - loss: 25.0311 - val_loss: 34.6542\n",
      "Epoch 440/1000\n",
      "4091/4091 [==============================] - 1s 317us/sample - loss: 25.0282 - val_loss: 36.9352\n",
      "Epoch 441/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 26.1532 - val_loss: 35.0692\n",
      "Epoch 442/1000\n",
      "4091/4091 [==============================] - 1s 315us/sample - loss: 24.3442 - val_loss: 35.5764\n",
      "Epoch 443/1000\n",
      "4091/4091 [==============================] - 1s 321us/sample - loss: 27.2497 - val_loss: 36.9058\n",
      "Epoch 444/1000\n",
      "4091/4091 [==============================] - 1s 323us/sample - loss: 27.6859 - val_loss: 36.5020\n",
      "Epoch 445/1000\n",
      "4091/4091 [==============================] - 1s 313us/sample - loss: 26.0469 - val_loss: 39.7122\n",
      "Epoch 446/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 25.9345 - val_loss: 37.0411\n",
      "Epoch 447/1000\n",
      "4091/4091 [==============================] - 1s 318us/sample - loss: 24.9404 - val_loss: 33.9000\n",
      "Epoch 448/1000\n",
      "4091/4091 [==============================] - 1s 319us/sample - loss: 24.7789 - val_loss: 41.3486\n",
      "Epoch 449/1000\n",
      "4091/4091 [==============================] - 1s 323us/sample - loss: 25.1309 - val_loss: 33.6697\n",
      "Epoch 00449: early stopping\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import glob\n",
    "import os\n",
    "import random\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import concatenate, ZeroPadding2D, Add, add, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.initializers import Constant\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.utils import multi_gpu_model\n",
    "import gc\n",
    "from pathlib import Path\n",
    "\n",
    "dropout_rate=0\n",
    "resolution = 64\n",
    "channels = 1\n",
    "target=\"/special/jbpark/TabS6LData/Joonbeom/train_dataset/\"\n",
    "model_dir = \"/special/jbpark/EvalModel\"\n",
    "\n",
    "def custom_loss(y_true, y_pred): \n",
    "    #euclidean loss\n",
    "    return K.sqrt(K.sum(K.square(y_pred - y_true), axis=-1)) # /205.7\n",
    "    #squared loss\n",
    "    #return K.sum(K.square(y_pred - y_true), axis=-1) \n",
    "    \n",
    "SConv1 = Conv2D(32, (3, 3), padding = 'same', activation = 'relu')\n",
    "SConv2 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu')\n",
    "SConv3 = Conv2D(64, (3, 3), padding = 'same', activation = 'relu')\n",
    "\n",
    "# Left Eye\n",
    "input1 = Input(shape=(resolution, resolution,channels), name='left_eye')\n",
    "x = SConv1(input1)\n",
    "x = MaxPooling2D(pool_size = (2, 2))(x)\n",
    "\n",
    "x = SConv2(x)\n",
    "x = MaxPooling2D(pool_size = (2, 2))(x)\n",
    "\n",
    "x = SConv3(x)\n",
    "left_eye = MaxPooling2D(pool_size = (2, 2))(x)\n",
    "left_eye = Flatten()(left_eye)\n",
    "\n",
    "# Right Eye\n",
    "input2 = Input(shape=(resolution, resolution,channels), name='right_eye')\n",
    "x = SConv1(input2)\n",
    "x = MaxPooling2D(pool_size = (2, 2))(x)\n",
    "\n",
    "x = SConv2(x)\n",
    "x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)\n",
    "\n",
    "x = SConv3(x)\n",
    "right_eye = MaxPooling2D(pool_size = (2, 2), padding='same')(x)\n",
    "right_eye = Flatten()(right_eye)\n",
    "\n",
    "# Eyes\n",
    "eyes = concatenate([left_eye, right_eye])\n",
    "fc1 = Dense(64, activation='relu')(eyes)\n",
    "fc2 = Dense(16, activation='relu')(fc1)\n",
    "fc2 = Dropout(rate=dropout_rate)(fc2)\n",
    "\n",
    "# Facepos\n",
    "input4 = Input(shape=(1, 1, 2), name='facepos')\n",
    "facepos = Flatten()(input4)\n",
    "\n",
    "#Euler\n",
    "input3 = Input(shape=(1, 1, 3), name='euler')\n",
    "euler = Flatten()(input3)\n",
    "\n",
    "# Eye size\n",
    "input5 = Input(shape=(1, 1, 2), name='left_eye_size')\n",
    "input6 = Input(shape=(1, 1, 2), name='right_eye_size')\n",
    "left_eye_size = Flatten()(input5)\n",
    "right_eye_size = Flatten()(input6)\n",
    "eye_sizes = concatenate([left_eye_size, right_eye_size])\n",
    "\n",
    "head_pose = concatenate([euler, facepos, eye_sizes])\n",
    "fc_f1 = Dense(16, activation='relu')(head_pose)\n",
    "\n",
    "# FC2, FC3\n",
    "fc2 = concatenate([fc2, fc_f1])\n",
    "fc2 = Dense(16, activation='relu')(fc2)\n",
    "fc3 = Dense(2, activation='linear', name='pred')(fc2)\n",
    "\n",
    "fc3 = add([fc3,facepos])\n",
    "pred = fc3\n",
    "\n",
    "model = Model(inputs=[input1, input2, input3, input4, input5, input6], outputs=[pred])\n",
    "\n",
    "tf.distribute.MirroredStrategy()\n",
    "\n",
    "model.compile(loss=custom_loss, optimizer=Adam(lr=1e-3))\n",
    "model.summary()\n",
    "\n",
    "gaze_point = np.load(target+\"gaze_point.npy\").astype(float)\n",
    "left_eye = np.load(target+\"left_eye.npy\").reshape(-1,resolution,resolution,channels)\n",
    "right_eye = np.load(target+\"right_eye.npy\").reshape(-1,resolution,resolution,channels)\n",
    "euler = np.load(target+\"euler.npy\").reshape(-1,1,1,3)\n",
    "facepos = np.load(target+\"facepos.npy\").reshape(-1,1,1,2)\n",
    "left_eye_right_top = np.load(target+\"left_eye_right_top.npy\")\n",
    "left_eye_left_bottom = np.load(target+\"left_eye_left_bottom.npy\")\n",
    "right_eye_right_top = np.load(target+\"right_eye_right_top.npy\")\n",
    "right_eye_left_bottom = np.load(target+\"right_eye_left_bottom.npy\")\n",
    "\n",
    "\n",
    "left_eye_right_top[:,1] = left_eye_right_top[:,1] - left_eye_left_bottom[:,1]\n",
    "left_eye_right_top[:,0] = left_eye_left_bottom[:,0] - left_eye_right_top[:,0]\n",
    "\n",
    "right_eye_right_top[:,1] = right_eye_right_top[:,1] - right_eye_left_bottom[:,1]\n",
    "right_eye_right_top[:,0] = right_eye_left_bottom[:,0] - right_eye_right_top[:,0]\n",
    "\n",
    "left_eye_size = left_eye_right_top.reshape(-1,1,1,2)\n",
    "right_eye_size = left_eye_left_bottom.reshape(-1,1,1,2)\n",
    "\n",
    "epoch = 1000\n",
    "Path(model_dir+'/checkpoint').mkdir(parents=True, exist_ok=True)\n",
    "mc = ModelCheckpoint(model_dir+'/checkpoint/gazel_shared_ver.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n",
    "#hist = model.fit([left_eye,right_eye, euler, facepos],gaze_point, validation_split=0.1,epochs=epoch, callbacks=[es, mc])\n",
    "hist = model.fit([left_eye, right_eye, euler, facepos, left_eye_size, right_eye_size,],gaze_point, validation_split=0.1,epochs=epoch, callbacks=[es, mc,reduce_lr])\n",
    "\n",
    "Path(model_dir+'/Lmodels').mkdir(parents=True, exist_ok=True)\n",
    "model.save(model_dir+'/Lmodels/gazel_shared_ver.h5')\n",
    "\n",
    "K.clear_session()\n",
    "%xdel -n gaze_point\n",
    "%xdel -n left_eye\n",
    "%xdel -n right_eye\n",
    "%xdel -n euler\n",
    "%xdel -n facepos\n",
    "%xdel -n left_eye_right_top\n",
    "%xdel -n left_eye_left_bottom\n",
    "%xdel -n right_eye_right_top\n",
    "%xdel -n right_eye_left_bottom\n",
    "\n",
    "gc.collect()\n",
    "%reset -f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorFlow Lite Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ConverterError",
     "evalue": "See console for info.\n/bin/sh: 1: toco_from_protos: not found\n\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mConverterError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-68c152c5e8dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Convert the model.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFLiteConverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_keras_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mtflite_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# Save the TF Lite model.\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/tensorflow20/lib/python3.7/site-packages/tensorflow_core/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0minput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m         \u001b[0moutput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         **converter_kwargs)\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_calibration_quantize\u001b[0m\u001b[0;34m(\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/tensorflow20/lib/python3.7/site-packages/tensorflow_core/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_impl\u001b[0;34m(input_data, input_tensors, output_tensors, enable_mlir_converter, *args, **kwargs)\u001b[0m\n\u001b[1;32m    447\u001b[0m       \u001b[0minput_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSerializeToString\u001b[0m\u001b[0;34m(\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[1;32m    448\u001b[0m       \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m       enable_mlir_converter=enable_mlir_converter)\n\u001b[0m\u001b[1;32m    450\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/tensorflow20/lib/python3.7/site-packages/tensorflow_core/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_protos\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m    198\u001b[0m       \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_try_convert_to_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m       \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_try_convert_to_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mConverterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"See console for info.\\n%s\\n%s\\n\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;31m# Must manually cleanup files.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mConverterError\u001b[0m: See console for info.\n/bin/sh: 1: toco_from_protos: not found\n\n\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from pathlib import Path\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "\n",
    "\n",
    "def custom_loss(y_true, y_pred): \n",
    "    #euclidean loss\n",
    "    return K.sqrt(K.sum(K.square(y_pred - y_true), axis=-1)) # /205.7\n",
    "    #squared loss\n",
    "    #return K.sum(K.square(y_pred - y_true), axis=-1) \n",
    "\n",
    "model_dir = \"/special/jbpark/EvalModel\"\n",
    "keras_file = model_dir+'/Lmodels/gazel_shared_ver.h5'\n",
    "# Convert to TensorFlow Lite model.\n",
    "model = load_model(keras_file,custom_objects={'custom_loss': custom_loss})\n",
    "\n",
    "# Convert the model.\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "# Save the TF Lite model.\n",
    "Path(model_dir+'/tflite').mkdir(parents=True, exist_ok=True)\n",
    "with tf.io.gfile.GFile(model_dir+'/tflite/gazel_shared_ver.tflite', 'wb') as f:\n",
    "    f.write(tflite_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### if you get error with toco_protos or toco run the above python code with terminal or convert whole file to .py file then run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### probably this error is due to my conda environment, if you use docker environment and latest tensorflow version 2.4.X you will not receive such error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "model_dir = \"/special/jbpark/EvalModel\"\n",
    "path = glob.glob(model_dir+'/tflite/gazel_shared_ver.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/special/jbpark/EvalModel/tflite/gazel_shared_ver.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'name': 'left_eye_right_top',\n",
       "  'index': 4,\n",
       "  'shape': array([1, 1, 1, 2], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)},\n",
       " {'name': 'right_eye_right_top',\n",
       "  'index': 54,\n",
       "  'shape': array([1, 1, 1, 2], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)},\n",
       " {'name': 'left_eye',\n",
       "  'index': 3,\n",
       "  'shape': array([ 1, 64, 64,  1], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)},\n",
       " {'name': 'facepos',\n",
       "  'index': 2,\n",
       "  'shape': array([1, 1, 1, 2], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)},\n",
       " {'name': 'right_eye',\n",
       "  'index': 53,\n",
       "  'shape': array([ 1, 64, 64,  1], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)},\n",
       " {'name': 'euler',\n",
       "  'index': 1,\n",
       "  'shape': array([1, 1, 1, 3], dtype=int32),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0)}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(path[0])\n",
    "tflite = tf.lite.Interpreter(model_path=path[0])\n",
    "tflite.get_input_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow 2.0",
   "language": "python",
   "name": "tensorflow20"
  },
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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