Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Activation, Reshape, Dropout\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import UpSampling2D\n",
    "from keras.layers import Deconvolution2D\n",
    "from keras.layers import Conv2DTranspose\n",
    "from keras.layers import Concatenate\n",
    "from keras.layers import Lambda \n",
    "from keras.utils import to_categorical\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras.layers import Reshape\n",
    "\n",
    "from keras import backend as K\n",
    "from keras import regularizers, optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import ReduceLROnPlateau, CSVLogger,EarlyStopping,ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io as scio\n",
    "import numpy as np    \n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import re\n",
    "from scipy.misc import imsave\n",
    "from scipy import ndimage, misc\n",
    "from numpy import unravel_index\n",
    "from operator import sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def atoi(text) : \n",
    "    return int(text) if text.isdigit() else text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def natural_keys(text) :\n",
    "    return [atoi(c) for c in re.split('(\\d+)', text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "root_path = \"\"\n",
    "filenames = []\n",
    "for root, dirnames, filenames in os.walk(\"Dataset/Train_images/\"):\n",
    "    filenames.sort(key = natural_keys)\n",
    "    rootpath = root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['image_1.png', 'image_2.png', 'image_3.png', 'image_4.png', 'image_5.png', 'image_6.png', 'image_7.png', 'image_8.png', 'image_9.png', 'image_10.png', 'image_11.png', 'image_12.png', 'image_13.png', 'image_14.png', 'image_15.png', 'image_16.png', 'image_17.png', 'image_18.png', 'image_19.png', 'image_20.png', 'image_21.png', 'image_22.png', 'image_23.png', 'image_24.png', 'image_25.png', 'image_26.png', 'image_27.png', 'image_28.png', 'image_29.png', 'image_30.png', 'image_31.png', 'image_32.png', 'image_33.png', 'image_34.png', 'image_35.png', 'image_36.png', 'image_37.png', 'image_38.png', 'image_39.png', 'image_40.png', 'image_41.png', 'image_42.png', 'image_43.png', 'image_44.png', 'image_45.png', 'image_46.png', 'image_47.png', 'image_48.png', 'image_49.png', 'image_50.png', 'image_51.png', 'image_52.png', 'image_53.png', 'image_54.png', 'image_55.png', 'image_56.png', 'image_57.png', 'image_58.png', 'image_59.png', 'image_60.png', 'image_61.png', 'image_62.png', 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'image_704.png', 'image_705.png', 'image_706.png', 'image_707.png', 'image_708.png', 'image_709.png', 'image_710.png', 'image_711.png', 'image_712.png', 'image_713.png', 'image_714.png', 'image_715.png', 'image_716.png', 'image_717.png', 'image_718.png', 'image_719.png', 'image_720.png', 'image_721.png', 'image_722.png', 'image_723.png', 'image_724.png', 'image_725.png', 'image_726.png', 'image_727.png', 'image_728.png', 'image_729.png', 'image_730.png', 'image_731.png', 'image_732.png', 'image_733.png', 'image_734.png', 'image_735.png', 'image_736.png', 'image_737.png', 'image_738.png', 'image_739.png', 'image_740.png', 'image_741.png', 'image_742.png', 'image_743.png', 'image_744.png', 'image_745.png', 'image_746.png', 'image_747.png', 'image_748.png', 'image_749.png', 'image_750.png', 'image_751.png', 'image_752.png', 'image_753.png', 'image_754.png', 'image_755.png', 'image_756.png', 'image_757.png', 'image_758.png', 'image_759.png', 'image_760.png', 'image_761.png', 'image_762.png', 'image_763.png', 'image_764.png', 'image_765.png', 'image_766.png', 'image_767.png', 'image_768.png', 'image_769.png', 'image_770.png']\n"
     ]
    }
   ],
   "source": [
    "print(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_1.png\n",
      "image_2.png\n",
      "image_3.png\n",
      "image_4.png\n",
      "image_5.png\n",
      "image_6.png\n",
      "image_7.png\n",
      "image_8.png\n",
      "image_9.png\n",
      "image_10.png\n",
      "image_11.png\n",
      "image_12.png\n",
      "image_13.png\n",
      "image_14.png\n",
      "image_15.png\n",
      "image_16.png\n",
      "image_17.png\n",
      "image_18.png\n",
      "image_19.png\n",
      "image_20.png\n",
      "image_21.png\n",
      "image_22.png\n",
      "image_23.png\n",
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      "image_26.png\n",
      "image_27.png\n",
      "image_28.png\n",
      "image_29.png\n",
      "image_30.png\n",
      "image_31.png\n",
      "image_32.png\n",
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      "image_39.png\n",
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      "image_47.png\n",
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      "image_49.png\n",
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      "image_52.png\n",
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     ]
    }
   ],
   "source": [
    "images = []\n",
    "for filename in filenames :\n",
    "    filepath = os.path.join(root,filename)\n",
    "    image = ndimage.imread(filepath, mode = \"L\")\n",
    "    images.append(image)\n",
    "    print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe3f290e610>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bG0EWrprJCCsKViSruCZ8G1WxVA4lIx7OscIT4ABTqAz2orKQCQ+CoO2c+zzw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\nNdo82fMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3f2edf2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[0],cmap = \"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = np.load('resized_labels.npy')\n",
    "labels_list = []\n",
    "for i in range(len(labels)):\n",
    "    labels_list.append(labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "770"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(labels_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels = np.zeros((770,496,64,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(labels_list)) :\n",
    "    for j in range(496) :\n",
    "        for k in range(64):\n",
    "            if(labels_list[i][j][k] == 0):\n",
    "                train_labels[i][j][k][0] = 1\n",
    "            if(labels_list[i][j][k] == 1):\n",
    "                train_labels[i][j][k][1] = 1\n",
    "            if(labels_list[i][j][k] == 2):\n",
    "                train_labels[i][j][k][2] = 1\n",
    "            if(labels_list[i][j][k] == 3):\n",
    "                train_labels[i][j][k][3] = 1\n",
    "            if(labels_list[i][j][k] == 4):\n",
    "                train_labels[i][j][k][4] = 1\n",
    "            if(labels_list[i][j][k] == 5):\n",
    "                train_labels[i][j][k][5] = 1\n",
    "            if(labels_list[i][j][k] == 6):\n",
    "                train_labels[i][j][k][6] = 1\n",
    "            if(labels_list[i][j][k] == 7):\n",
    "                train_labels[i][j][k][7] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(770, 496, 64, 8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "770\n",
      "(496, 64, 1)\n",
      "[4, 7, 9, 10, 13, 15, 17, 18, 20, 22, 25, 26, 30, 31, 33, 34, 35, 38, 40, 41, 42, 43, 44, 46, 49, 51, 52, 53, 54, 56, 57, 58, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 85, 89, 91, 92, 93, 94, 95, 97, 98, 100, 103, 104, 105, 107, 111, 113, 115, 117, 118, 119, 121, 122, 124, 126, 127, 130, 132, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 155, 157, 158, 159, 160, 161, 163, 164, 165, 169, 170, 171, 173, 176, 178, 179, 180, 181, 182, 183, 184, 185, 186, 190, 193, 194, 195, 196, 197, 200, 202, 203, 204, 205, 207, 210, 211, 212, 213, 214, 215, 216, 218, 220, 222, 223, 224, 225, 227, 229, 231, 232, 233, 236, 238, 239, 240, 246, 247, 249, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 267, 268, 269, 270, 275, 276, 277, 278, 279, 280, 281, 283, 284, 287, 288, 291, 292, 295, 297, 298, 299, 302, 303, 304, 306, 307, 308, 309, 313, 314, 317, 318, 319, 321, 322, 323, 324, 325, 327, 328, 329, 330, 332, 333, 334, 335, 336, 337, 339, 344, 345, 346, 347, 348, 349, 350, 351, 353, 354, 356, 357, 358, 360, 361, 363, 365, 367, 368, 369, 371, 372, 373, 374, 377, 378, 379, 381, 382, 383, 384, 386, 388, 389, 390, 391, 392, 393, 394, 395, 396, 398, 399, 400, 402, 403, 404, 405, 406, 407, 409, 411, 412, 415, 417, 418, 420, 421, 422, 423, 424, 426, 428, 430, 431, 432, 433, 434, 435, 437, 438, 439, 442, 443, 445, 446, 447, 448, 449, 450, 454, 455, 456, 457, 459, 461, 462, 463, 464, 465, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 480, 482, 485, 486, 487, 488, 491, 492, 493, 494, 496, 497, 498, 500, 501, 504, 505, 507, 508, 509, 510, 512, 513, 514, 515, 516, 518, 520, 522, 523, 524, 525, 526, 528, 529, 530, 531, 534, 535, 536, 538, 542, 543, 546, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 561, 565, 566, 570, 571, 576, 580, 581, 583, 584, 585, 586, 587, 590, 591, 592, 593, 594, 596, 597, 598, 599, 600, 601, 603, 604, 609, 610, 611, 612, 613, 617, 619, 620, 622, 623, 624, 625, 626, 627, 629, 630, 631, 632, 634, 635, 638, 644, 645, 646, 647, 648, 650, 651, 652, 653, 657, 658, 659, 661, 662, 663, 665, 667, 668, 669, 670, 672, 674, 676, 677, 678, 679, 680, 681, 685, 686, 688, 689, 691, 692, 693, 695, 696, 697, 699, 700, 704, 705, 706, 707, 709, 711, 713, 714, 715, 716, 719, 721, 722, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 736, 739, 740, 742, 744, 745, 746, 747, 750, 752, 753, 754, 755, 756, 758, 761, 763, 764, 766, 768, 769]\n",
      "[0, 1, 2, 3, 5, 6, 8, 11, 12, 14, 16, 19, 21, 23, 24, 27, 28, 29, 32, 36, 37, 39, 45, 47, 48, 50, 55, 59, 60, 64, 78, 81, 82, 83, 84, 86, 87, 88, 90, 96, 99, 101, 102, 106, 108, 109, 110, 112, 114, 116, 120, 123, 125, 128, 129, 131, 133, 135, 140, 147, 154, 156, 162, 166, 167, 168, 172, 174, 175, 177, 187, 188, 189, 191, 192, 198, 199, 201, 206, 208, 209, 217, 219, 221, 226, 228, 230, 234, 235, 237, 241, 242, 243, 244, 245, 248, 250, 251, 252, 254, 259, 265, 266, 271, 272, 273, 274, 282, 285, 286, 289, 290, 293, 294, 296, 300, 301, 305, 310, 311, 312, 315, 316, 320, 326, 331, 338, 340, 341, 342, 343, 352, 355, 359, 362, 364, 366, 370, 375, 376, 380, 385, 387, 397, 401, 408, 410, 413, 414, 416, 419, 425, 427, 429, 436, 440, 441, 444, 451, 452, 453, 458, 460, 466, 467, 468, 479, 481, 483, 484, 489, 490, 495, 499, 502, 503, 506, 511, 517, 519, 521, 527, 532, 533, 537, 539, 540, 541, 544, 545, 547, 560, 562, 563, 564, 567, 568, 569, 572, 573, 574, 575, 577, 578, 579, 582, 588, 589, 595, 602, 605, 606, 607, 608, 614, 615, 616, 618, 621, 628, 633, 636, 637, 639, 640, 641, 642, 643, 649, 654, 655, 656, 660, 664, 666, 671, 673, 675, 682, 683, 684, 687, 690, 694, 698, 701, 702, 703, 708, 710, 712, 717, 718, 720, 723, 724, 735, 737, 738, 741, 743, 748, 749, 751, 757, 759, 760, 762, 765, 767]\n"
     ]
    }
   ],
   "source": [
    "images=np.array(images)\n",
    "print(images.shape[0])\n",
    "images = images.reshape(images.shape[0],496,64,1)\n",
    "\n",
    "print(images[0].shape)\n",
    "train_indices = np.random.choice(770,500,replace = False)\n",
    "print(sorted(train_indices))\n",
    "train_images_random = []\n",
    "train_labels_random = []\n",
    "\n",
    "for i in train_indices:\n",
    "    train_images_random.append(images[i])\n",
    "    train_labels_random.append(train_labels[i])\n",
    "\n",
    "test_indices = [x for x in range(770) if x not in train_indices]\n",
    "print(test_indices)\n",
    "test_images = []\n",
    "test_labels = []\n",
    "for i in test_indices:\n",
    "    test_images.append(images[i])\n",
    "    test_labels.append(train_labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_images = np.array(train_images_random)\n",
    "train_labels = np.array(train_labels_random)\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_images = train_images.astype('float32')\n",
    "train_labels = train_labels.astype('float32')\n",
    "test_images = test_images.astype('float32')\n",
    "test_labels = test_labels.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 496, 64, 1)\n",
      "(270, 496, 64, 1)\n",
      "(500, 496, 64, 8)\n"
     ]
    }
   ],
   "source": [
    "print(train_images.shape)\n",
    "print(test_images.shape)\n",
    "print(train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_shape = 496*64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_decay = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_frontend(input_width,input_height) :\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(64,(3,3),activation='relu',padding = 'same',name = 'conv1_1',input_shape =(input_width, input_height, 3)))\n",
    "    model.add(Conv2D(64,(3,3),activation='relu',padding = 'same',name = 'conv1_2'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2),name = 'pool_1'))\n",
    "\n",
    "    \n",
    "    model.add(Conv2D(128,(3,3),activation='relu',padding = 'same',name = 'conv2_1'))\n",
    "    model.add(Conv2D(128,(3,3),activation='relu',padding = 'same',name = 'conv2_2'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2),name = 'pool_2'))\n",
    "    \n",
    "    model.add(Conv2D(256,(3,3),activation='relu',padding = 'same',name = 'conv3_1'))\n",
    "    model.add(Conv2D(256,(3,3),activation='relu',padding = 'same',name = 'conv3_2'))\n",
    "    model.add(Conv2D(256,(3,3),activation='relu',padding = 'same',name = 'conv3_3'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2),name = 'pool_3'))\n",
    "    \n",
    "    model.add(Conv2D(512,(3,3),activation='relu',padding = 'same',name = 'conv4_1'))\n",
    "    model.add(Conv2D(512,(3,3),activation='relu',padding = 'same',name = 'conv4_2'))\n",
    "    model.add(Conv2D(512,(3,3),activation='relu',padding = 'same',name = 'conv4_3'))\n",
    "    \n",
    "    model.add(Conv2D(512,(3,3),activation='relu',dilation_rate= (2,2), padding = 'same',name = 'conv5_1'))\n",
    "    model.add(Conv2D(512,(3,3),activation='relu',dilation_rate= (2,2), padding = 'same',name = 'conv5_2'))\n",
    "    model.add(Conv2D(512,(3,3),activation='relu',dilation_rate= (2,2), padding = 'same',name = 'conv5_3'))\n",
    "    \n",
    "    \n",
    "    model.add(Conv2D(4096,(7,7),dilation_rate= (4,4), padding = \"same\", activation='relu', name = \"fc6\"))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Conv2D(4096,(1,1),activation='relu',padding = \"same\",name = \"fc7\"))\n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    model.add(Conv2D(8,(1,1),activation='linear',name = 'fc-final'))\n",
    "    \n",
    "    return model\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = get_frontend(512,512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1_1 (Conv2D)             (None, 512, 512, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv1_2 (Conv2D)             (None, 512, 512, 64)      36928     \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 256, 256, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2_1 (Conv2D)             (None, 256, 256, 128)     73856     \n",
      "_________________________________________________________________\n",
      "conv2_2 (Conv2D)             (None, 256, 256, 128)     147584    \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 128, 128, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv3_1 (Conv2D)             (None, 128, 128, 256)     295168    \n",
      "_________________________________________________________________\n",
      "conv3_2 (Conv2D)             (None, 128, 128, 256)     590080    \n",
      "_________________________________________________________________\n",
      "conv3_3 (Conv2D)             (None, 128, 128, 256)     590080    \n",
      "_________________________________________________________________\n",
      "pool_3 (MaxPooling2D)        (None, 64, 64, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv4_1 (Conv2D)             (None, 64, 64, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv4_2 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv4_3 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_1 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_2 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_3 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "fc6 (Conv2D)                 (None, 64, 64, 4096)      102764544 \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 64, 64, 4096)      0         \n",
      "_________________________________________________________________\n",
      "fc7 (Conv2D)                 (None, 64, 64, 4096)      16781312  \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 64, 64, 4096)      0         \n",
      "_________________________________________________________________\n",
      "fc-final (Conv2D)            (None, 64, 64, 8)         32776     \n",
      "=================================================================\n",
      "Total params: 134,293,320.0\n",
      "Trainable params: 134,293,320.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_softmax(model) :\n",
    "    \n",
    "    _, curr_width, curr_height, curr_channels = model.layers[-1].output_shape\n",
    "    \n",
    "  #  model.add(Reshape((data_shape,8),input_shape = (496,64,8)))\n",
    "    model.add(Activation('softmax'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = get_frontend(512,512)\n",
    "model = add_softmax(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1_1 (Conv2D)             (None, 512, 512, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv1_2 (Conv2D)             (None, 512, 512, 64)      36928     \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 256, 256, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2_1 (Conv2D)             (None, 256, 256, 128)     73856     \n",
      "_________________________________________________________________\n",
      "conv2_2 (Conv2D)             (None, 256, 256, 128)     147584    \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 128, 128, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv3_1 (Conv2D)             (None, 128, 128, 256)     295168    \n",
      "_________________________________________________________________\n",
      "conv3_2 (Conv2D)             (None, 128, 128, 256)     590080    \n",
      "_________________________________________________________________\n",
      "conv3_3 (Conv2D)             (None, 128, 128, 256)     590080    \n",
      "_________________________________________________________________\n",
      "pool_3 (MaxPooling2D)        (None, 64, 64, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv4_1 (Conv2D)             (None, 64, 64, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv4_2 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv4_3 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_1 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_2 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "conv5_3 (Conv2D)             (None, 64, 64, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "fc6 (Conv2D)                 (None, 64, 64, 4096)      102764544 \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 64, 64, 4096)      0         \n",
      "_________________________________________________________________\n",
      "fc7 (Conv2D)                 (None, 64, 64, 4096)      16781312  \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 64, 64, 4096)      0         \n",
      "_________________________________________________________________\n",
      "fc-final (Conv2D)            (None, 64, 64, 8)         32776     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64, 64, 8)         0         \n",
      "=================================================================\n",
      "Total params: 134,293,320.0\n",
      "Trainable params: 134,293,320.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_context(model,no_of_classes) :\n",
    "    \n",
    "    model.add(Conv2D(no_of_classes*2,(3,3),padding = \"same\",activation = 'relu', name = \"ct_conv1_1\"))\n",
    "    model.add(Conv2D(no_of_classes*2,(3,3),padding = \"same\",activation = 'relu', name = \"ct_conv1_2\"))\n",
    "\n",
    "    model.add(Conv2D(no_of_classes*4,(3,3),padding = \"same\",dilation_rate = (2,2),activation = 'relu', name = \"ct_conv2_1\"))\n",
    "    model.add(Conv2D(no_of_classes*8,(3,3),padding = \"same\",dilation_rate = (4,4),activation = 'relu', name = \"ct_conv3_1\"))\n",
    "    model.add(Conv2D(no_of_classes*16,(3,3),padding = 'same', dilation_rate = (8,8),activation = 'relu', name = \"ct_conv4_1\"))\n",
    "    model.add(Conv2D(no_of_classes*32,(3,3), padding = 'same',dilation_rate = (16,16),activation = 'relu', name = \"ct_conv5_1\"))\n",
    "    \n",
    "    model.add(Conv2D(no_of_classes*32,(3,3),padding = 'same', activation = 'relu', name = \"ct_fc1\"))\n",
    "    \n",
    "    model.add(Deconvolution2D(no_of_classes, kernel_size = (3,3), strides = (2,2), activation = \"relu\", name = \"ct_deconv_1\", padding = \"same\"))\n",
    "    model.add(Deconvolution2D(no_of_classes, kernel_size = (3,3), strides = (2,2),activation = \"relu\", name = \"ct_deconv_2\", padding = \"same\"))\n",
    "    model.add(Deconvolution2D(no_of_classes, kernel_size = (3,3), strides = (2,2),activation = \"relu\", name = \"ct_deconv_3\", padding = \"same\"))\n",
    "    model.add(Conv2D(no_of_classes,(1,1),activation = 'relu', name = \"ct_final\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = get_frontend(496,64)\n",
    "model = add_context(model,8)\n",
    "model = add_softmax(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1_1 (Conv2D)             (None, 496, 64, 64)       1792      \n",
      "_________________________________________________________________\n",
      "conv1_2 (Conv2D)             (None, 496, 64, 64)       36928     \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 248, 32, 64)       0         \n",
      "_________________________________________________________________\n",
      "conv2_1 (Conv2D)             (None, 248, 32, 128)      73856     \n",
      "_________________________________________________________________\n",
      "conv2_2 (Conv2D)             (None, 248, 32, 128)      147584    \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 124, 16, 128)      0         \n",
      "_________________________________________________________________\n",
      "conv3_1 (Conv2D)             (None, 124, 16, 256)      295168    \n",
      "_________________________________________________________________\n",
      "conv3_2 (Conv2D)             (None, 124, 16, 256)      590080    \n",
      "_________________________________________________________________\n",
      "conv3_3 (Conv2D)             (None, 124, 16, 256)      590080    \n",
      "_________________________________________________________________\n",
      "pool_3 (MaxPooling2D)        (None, 62, 8, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv4_1 (Conv2D)             (None, 62, 8, 512)        1180160   \n",
      "_________________________________________________________________\n",
      "conv4_2 (Conv2D)             (None, 62, 8, 512)        2359808   \n",
      "_________________________________________________________________\n",
      "conv4_3 (Conv2D)             (None, 62, 8, 512)        2359808   \n",
      "_________________________________________________________________\n",
      "conv5_1 (Conv2D)             (None, 62, 8, 512)        2359808   \n",
      "_________________________________________________________________\n",
      "conv5_2 (Conv2D)             (None, 62, 8, 512)        2359808   \n",
      "_________________________________________________________________\n",
      "conv5_3 (Conv2D)             (None, 62, 8, 512)        2359808   \n",
      "_________________________________________________________________\n",
      "fc6 (Conv2D)                 (None, 62, 8, 4096)       102764544 \n",
      "_________________________________________________________________\n",
      "dropout_31 (Dropout)         (None, 62, 8, 4096)       0         \n",
      "_________________________________________________________________\n",
      "fc7 (Conv2D)                 (None, 62, 8, 4096)       16781312  \n",
      "_________________________________________________________________\n",
      "dropout_32 (Dropout)         (None, 62, 8, 4096)       0         \n",
      "_________________________________________________________________\n",
      "fc-final (Conv2D)            (None, 62, 8, 8)          32776     \n",
      "_________________________________________________________________\n",
      "ct_conv1_1 (Conv2D)          (None, 62, 8, 16)         1168      \n",
      "_________________________________________________________________\n",
      "ct_conv1_2 (Conv2D)          (None, 62, 8, 16)         2320      \n",
      "_________________________________________________________________\n",
      "ct_conv2_1 (Conv2D)          (None, 62, 8, 32)         4640      \n",
      "_________________________________________________________________\n",
      "ct_conv3_1 (Conv2D)          (None, 62, 8, 64)         18496     \n",
      "_________________________________________________________________\n",
      "ct_conv4_1 (Conv2D)          (None, 62, 8, 128)        73856     \n",
      "_________________________________________________________________\n",
      "ct_conv5_1 (Conv2D)          (None, 62, 8, 256)        295168    \n",
      "_________________________________________________________________\n",
      "ct_fc1 (Conv2D)              (None, 62, 8, 256)        590080    \n",
      "_________________________________________________________________\n",
      "ct_deconv_1 (Conv2DTranspose (None, 124, 16, 8)        18440     \n",
      "_________________________________________________________________\n",
      "ct_deconv_2 (Conv2DTranspose (None, 248, 32, 8)        584       \n",
      "_________________________________________________________________\n",
      "ct_deconv_3 (Conv2DTranspose (None, 496, 64, 8)        584       \n",
      "_________________________________________________________________\n",
      "ct_final (Conv2D)            (None, 496, 64, 8)        72        \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 496, 64, 8)        0         \n",
      "=================================================================\n",
      "Total params: 135,298,728.0\n",
      "Trainable params: 135,298,728.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model.png',show_shapes= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights_data = np.load(\"/home/iplab/Desktop/MajorProjectRelayNet/Multi-Scale-Context-Aggregation-by-Dilated-Convolutions/dilation8_pascal_voc.npy\", encoding='latin1').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-0e5a7aadd474>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mweights_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0mlayer_weights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweights_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m         \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer_weights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'weights'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlayer_weights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'biases'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[1;32melse\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36mset_weights\u001b[1;34m(self, weights)\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m         \u001b[0mweight_value_tuples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1148\u001b[1;33m         \u001b[0mparam_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1149\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mpv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36mbatch_get_value\u001b[1;34m(ops)\u001b[0m\n\u001b[0;32m   1945\u001b[0m     \"\"\"\n\u001b[0;32m   1946\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1947\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1948\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1949\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36mget_session\u001b[1;34m()\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_SESSION\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_MANUAL_VAR_INIT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         \u001b[0m_initialize_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_initialize_variables\u001b[1;34m()\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0muninitialized_variables\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[0msess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m         \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables_initializer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muninitialized_variables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    765\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 767\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    768\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    963\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m--> 965\u001b[1;33m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m    966\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1013\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m-> 1015\u001b[1;33m                            target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m   1016\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1022\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/iplab/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[0;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1004\u001b[1;33m                                  status, run_metadata)\n\u001b[0m\u001b[0;32m   1005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for layer in model.layers:\n",
    "    if layer.name in weights_data.keys():\n",
    "        layer_weights = weights_data[layer.name]\n",
    "        layer.set_weights((layer_weights['weights'],layer_weights['biases']))\n",
    "    else :\n",
    "        print(layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = np.load('weighted_cropped_images.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_rgb = np.array(Image.open(\"/home/guru/Desktop/Webp.net-resizeimage.jpg\")).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_rgb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean = [102.93, 111.36, 116.52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = image_rgb[:, :, ::-1] - mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " image_size = image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_width, input_height = 512, 512\n",
    "label_margin = 186\n",
    "net_in = np.zeros((1, input_height, input_width, 3), dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_height = input_height - 2 * label_margin\n",
    "output_width = input_width - 2 * label_margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mage = np.pad(image,\n",
    "                   ((label_margin, label_margin),\n",
    "                    (label_margin, label_margin),\n",
    "                    (0, 0)), 'reflect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "margins_h = (0, input_height - image.shape[0])\n",
    "margins_w = (0, input_width - image.shape[1])\n",
    "image = np.pad(image,(margins_h,margins_w,(0, 0)), 'reflect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net_in[0] = image\n",
    "prob = model.predict(net_in)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 21)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prob = np.pad(prob,((1,1),(1,1),(0,0)),'reflect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 66, 21)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def interp_map(prob, zoom, width, height):\n",
    "    zoom_prob = np.zeros((height, width, prob.shape[2]), dtype=np.float32)\n",
    "    for c in range(prob.shape[2]):\n",
    "        for h in range(height):\n",
    "            for w in range(width):\n",
    "                r0 = h // zoom\n",
    "                \n",
    "                r1 = r0 + 1\n",
    "                c0 = w // zoom\n",
    "                c1 = c0 + 1\n",
    "                rt = float(h) / zoom - r0\n",
    "                ct = float(w) / zoom - c0\n",
    "                #print(c0)\n",
    "                v0 = rt * prob[r1, c0, c] + (1 - rt) * prob[r0, c0, c]\n",
    "                v1 = rt * prob[r1, c1, c] + (1 - rt) * prob[r0, c1, c]\n",
    "                zoom_prob[h, w, c] = (1 - ct) * v0 + ct * v1\n",
    "    return zoom_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prob = interp_map(prob,8,512,512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction = np.argmax(prob, axis=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8eb84aa0d0>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8eb8516990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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