1128 lines (1127 with data), 650.4 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "import sys\n",
    "import pydicom\n",
    "import random\n",
    "import re\n",
    "import scipy\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "plt.set_cmap('gray')\n",
    "%matplotlib inline\n",
    "## Seeding \n",
    "seed = 2019\n",
    "random.seed = seed\n",
    "np.random.seed = seed\n",
    "tf.seed = seed\n",
    "\n",
    "\n",
    "IMG_DTYPE = np.float\n",
    "SEG_DTYPE = np.uint8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author IBBM\n",
    "- Date 1/3/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/IBBM/Cascaded-FCN\n",
    "\"\"\"\n",
    "def imshow(*args,**kwargs):\n",
    "    cmap = kwargs.get('cmap', 'gray')\n",
    "    title= kwargs.get('title','')\n",
    "    if len(args)==0:\n",
    "        raise ValueError(\"No images given to imshow\")\n",
    "    elif len(args)==1:\n",
    "        plt.title(title)\n",
    "        plt.imshow(args[0], interpolation='none')\n",
    "    else:\n",
    "        n=len(args)\n",
    "        if type(cmap)==str:\n",
    "            cmap = [cmap]*n\n",
    "        if type(title)==str:\n",
    "            title= [title]*n\n",
    "        plt.figure(figsize=(n*5,10))\n",
    "        for i in range(n):\n",
    "            plt.subplot(1,n,i+1)\n",
    "            plt.title(title[i])\n",
    "            plt.imshow(args[i], cmap[i])\n",
    "    plt.show()\n",
    "    \n",
    "def normalize_image(img):\n",
    "    \"\"\" Normalize image values to [0,1] \"\"\"\n",
    "    min_, max_ = float(np.min(img)), float(np.max(img))\n",
    "    return (img - min_) / (max_ - min_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author IBBM\n",
    "- Date 1/3/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/IBBM/Cascaded-FCN\n",
    "\"\"\"\n",
    "def step1_preprocess_img_slice(img_slc):\n",
    "    \"\"\"\n",
    "    Preprocesses the image 3d volumes by performing the following :\n",
    "    1- Set pixels with hounsfield value great than 1200, to zero.\n",
    "    2- Clip all hounsfield values to the range [-100, 400]\n",
    "    3- Apply Histogram Equalization\n",
    "    \"\"\"    \n",
    "    img_slc[img_slc>1200] = 0\n",
    "    img_slc   = np.clip(img_slc, -100, 400)\n",
    "    img_slc = normalize_image(img_slc)\n",
    "\n",
    "    \n",
    "    img_slc = img_slc * 255\n",
    "    img_slc = img_slc.astype('uint8')\n",
    "    img_slc = cv2.equalizeHist(img_slc)\n",
    "    img_slc = normalize_image(img_slc)\n",
    "    return img_slc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGen(keras.utils.Sequence):\n",
    "    def __init__(self, ids, path, batch_size=8, image_size=128):\n",
    "        self.ids = ids\n",
    "        self.path = path\n",
    "        self.batch_size = batch_size\n",
    "        self.image_size = image_size\n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __load__(self, id_name):\n",
    "        patient_id = id_name.split('_')\n",
    "        image_path = os.path.join(self.path,\"patients\", id_name)\n",
    "        mask_path = os.path.join(self.path,\"masks\")\n",
    "        all_masks = os.listdir(mask_path)\n",
    "        dicom_image = pydicom.dcmread(image_path)\n",
    "        image = step1_preprocess_img_slice(dicom_image.pixel_array)\n",
    "        image = normalize_image(image)\n",
    "        image = np.array(Image.fromarray(image).resize([image_size, image_size])).astype(IMG_DTYPE)\n",
    "        \n",
    "        mask = pydicom.dcmread(os.path.join(mask_path,patient_id[0]+'_liver' , id_name)).pixel_array\n",
    "        mask = mask/255.0\n",
    "        mask = np.clip(mask, 0, 1)\n",
    "        mask = np.array(Image.fromarray(mask).resize([image_size, image_size])).astype(IMG_DTYPE)\n",
    "        mask = mask[:, :, np.newaxis]\n",
    "        return image, mask\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        if(index+1)*self.batch_size > len(self.ids):\n",
    "            self.batch_size = len(self.ids) - index*self.batch_size\n",
    "        \n",
    "        files_batch = self.ids[index*self.batch_size : (index+1)*self.batch_size]\n",
    "\n",
    "        image = []\n",
    "        mask  = []\n",
    "    \n",
    "        for id_name in files_batch:\n",
    "            _img, _mask = self.__load__(id_name)\n",
    "            _img = np.stack((_img,)*3, axis=-1)\n",
    "            image.append(_img)\n",
    "            mask.append(_mask)\n",
    "        \n",
    "        image = np.array(image)\n",
    "        mask  = np.array(mask)\n",
    "\n",
    "        return image, mask\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        pass\n",
    "    \n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.ids)/float(self.batch_size)))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20275\n"
     ]
    }
   ],
   "source": [
    "image_size = 256\n",
    "train_path = \"train\"\n",
    "batch_size = 8\n",
    "epochs = 20\n",
    "\n",
    "## Training Ids\n",
    "images = []\n",
    "for file in os.listdir(os.path.join(train_path, \"patients\")):\n",
    "        images.append(file)\n",
    "print(len(images))\n",
    "\n",
    "val_data_size = len(images)//5\n",
    "\n",
    "valid_ids = images[:val_data_size]\n",
    "train_ids = images[val_data_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = DataGen(train_ids, train_path, batch_size=batch_size, image_size=image_size)\n",
    "x, y = gen.__getitem__(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x6e3049a02fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jjDHGGGOMMcaYNeJlZTWMmmIKQbJUpmXLlmFLKNw3XeqyKlRkDOnpipY1yTCp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\nxmyeZPThqzrp2LFj9sILL6z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69thjgcSOeeONN4CKKyTJyJHBIvtoxowZYd3IvJDBMHDg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\n9sDIEqftttsupU48SK5Zs8bOyYMtn7FMKjs72x7SqSfl4SGWSZNly5b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LBsZxsSvIENWz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iQMhfr6+rRsWLypjxI3VPg3xgTxXcrLy+1N/tFHH23nkGT2zKpVqyyeCfAmneNicpSW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ZsmX2rT+WCCbHlClTJKV+I86/ySnCJsCMadKkSbEmFN/UkzM0ePBgOw8MCY755ptvSorsm+OO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\nCJzdYFqZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e304abea710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = random.randint(0, len(x)-1)\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.imshow(x[r])\n",
    "print(r)\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.imshow(np.reshape(y[r], (image_size, image_size)), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author nikhilroxtomar\n",
    "- Date 10/4/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/nikhilroxtomar/Deep-Residual-Unet\n",
    "\"\"\"\n",
    "def bn_act(x, act=True):\n",
    "    x = keras.layers.BatchNormalization()(x)\n",
    "    if act == True:\n",
    "        x = keras.layers.Activation(\"relu\")(x)\n",
    "    return x\n",
    "\n",
    "def conv_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    conv = bn_act(x)\n",
    "    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)\n",
    "    return conv\n",
    "\n",
    "def stem(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)\n",
    "    conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
    "    \n",
    "    shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
    "    shortcut = bn_act(shortcut, act=False)\n",
    "    \n",
    "    output = keras.layers.Add()([conv, shortcut])\n",
    "    return output\n",
    "\n",
    "def residual_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    res = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
    "    res = conv_block(res, filters, kernel_size=kernel_size, padding=padding, strides=1)\n",
    "    \n",
    "    shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
    "    shortcut = bn_act(shortcut, act=False)\n",
    "    \n",
    "    output = keras.layers.Add()([shortcut, res])\n",
    "    return output\n",
    "\n",
    "def upsample_concat_block(x, xskip):\n",
    "    u = keras.layers.UpSampling2D((2, 2))(x)\n",
    "    c = keras.layers.Concatenate()([u, xskip])\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResUNet():\n",
    "    f = [16, 32, 64, 128, 256]\n",
    "    inputs = keras.layers.Input((image_size, image_size, 3))\n",
    "    \n",
    "    ## Encoder\n",
    "    e0 = inputs\n",
    "    e1 = stem(e0, f[0])\n",
    "    e2 = residual_block(e1, f[1], strides=2)\n",
    "    e3 = residual_block(e2, f[2], strides=2)\n",
    "    e4 = residual_block(e3, f[3], strides=2)\n",
    "    e5 = residual_block(e4, f[4], strides=2)\n",
    "    \n",
    "    ## Bridge\n",
    "    b0 = conv_block(e5, f[4], strides=1)\n",
    "    b1 = conv_block(b0, f[4], strides=1)\n",
    "    \n",
    "    ## Decoder\n",
    "    u1 = upsample_concat_block(b1, e4)\n",
    "    d1 = residual_block(u1, f[4])\n",
    "    \n",
    "    u2 = upsample_concat_block(d1, e3)\n",
    "    d2 = residual_block(u2, f[3])\n",
    "    \n",
    "    u3 = upsample_concat_block(d2, e2)\n",
    "    d3 = residual_block(u3, f[2])\n",
    "    \n",
    "    u4 = upsample_concat_block(d3, e1)\n",
    "    d4 = residual_block(u4, f[1])\n",
    "    \n",
    "    outputs = keras.layers.Conv2D(1, (1, 1), padding=\"same\", activation=\"sigmoid\")(d4)\n",
    "    model = keras.models.Model(inputs, outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "smooth = 1.\n",
    "\n",
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = tf.layers.flatten(y_true)\n",
    "    y_pred_f = tf.layers.flatten(y_pred)\n",
    "    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return 1.0 - dice_coef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 256, 16) 448         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 256, 256, 16) 64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 256, 256, 16) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 256, 256, 16) 64          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 256, 256, 16) 2320        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 256, 256, 16) 64          conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 256, 256, 16) 0           conv2d_2[0][0]                   \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 256, 256, 16) 64          add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 256, 256, 16) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 32) 4640        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 128, 128, 32) 128         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 128, 128, 32) 544         add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 128, 128, 32) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 128, 128, 32) 128         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 128, 32) 9248        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 128, 128, 32) 0           batch_normalization_5[0][0]      \n",
      "                                                                 conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 128, 128, 32) 128         add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 128, 128, 32) 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 64, 64, 64)   18496       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 64, 64, 64)   256         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 64, 64)   2112        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 64, 64, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 64, 64, 64)   256         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 64, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 64, 64, 64)   0           batch_normalization_8[0][0]      \n",
      "                                                                 conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 64, 64, 64)   256         add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 64, 64, 64)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 32, 32, 128)  73856       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 32, 32, 128)  512         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 32, 128)  8320        add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 32, 32, 128)  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 128)  512         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 128)  147584      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 32, 32, 128)  0           batch_normalization_11[0][0]     \n",
      "                                                                 conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 32, 32, 128)  512         add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 32, 32, 128)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 256)  295168      activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 256)  1024        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 256)  33024       add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 16, 256)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 256)  1024        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 256)  590080      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 16, 16, 256)  0           batch_normalization_14[0][0]     \n",
      "                                                                 conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 256)  1024        add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 16, 16, 256)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 256)  590080      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 256)  1024        conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 16, 16, 256)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 256)  590080      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 32, 32, 256)  0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 32, 32, 384)  0           up_sampling2d_1[0][0]            \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 32, 32, 384)  1536        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 32, 32, 384)  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 256)  884992      activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 32, 32, 256)  1024        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 32, 32, 256)  98560       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 32, 32, 256)  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 32, 32, 256)  1024        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 256)  590080      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 32, 32, 256)  0           batch_normalization_19[0][0]     \n",
      "                                                                 conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 64, 64, 256)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 64, 64, 320)  0           up_sampling2d_2[0][0]            \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 64, 64, 320)  1280        concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 64, 64, 320)  0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 64, 64, 128)  368768      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 64, 64, 128)  512         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 64, 64, 128)  41088       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 64, 64, 128)  0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 64, 64, 128)  512         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 64, 64, 128)  147584      activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 64, 64, 128)  0           batch_normalization_22[0][0]     \n",
      "                                                                 conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 128, 128, 128 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 128, 128, 160 0           up_sampling2d_3[0][0]            \n",
      "                                                                 add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 128, 128, 160 640         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 128, 128, 160 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 128, 128, 64) 92224       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 128, 128, 64) 256         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 128, 128, 64) 10304       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 128, 128, 64) 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 128, 128, 64) 256         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 128, 128, 64) 36928       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 128, 128, 64) 0           batch_normalization_25[0][0]     \n",
      "                                                                 conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 256, 256, 64) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 256, 256, 80) 0           up_sampling2d_4[0][0]            \n",
      "                                                                 add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 256, 256, 80) 320         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 256, 256, 80) 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 256, 256, 32) 23072       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 256, 256, 32) 128         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 256, 256, 32) 2592        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 256, 256, 32) 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 256, 256, 32) 128         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 256, 256, 32) 9248        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 256, 256, 32) 0           batch_normalization_28[0][0]     \n",
      "                                                                 conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 256, 256, 1)  33          add_9[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 4,723,057\n",
      "Trainable params: 4,715,761\n",
      "Non-trainable params: 7,296\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = ResUNet()\n",
    "adam = keras.optimizers.Adam()\n",
    "model.compile(optimizer=adam, loss=dice_coef_loss, metrics=[\"acc\", dice_coef])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 996s 491ms/step - loss: 0.1306 - acc: 0.9736 - dice_coef: 0.8694 - val_loss: 0.1069 - val_acc: 0.9758 - val_dice_coef: 0.8931\n",
      "\n",
      "Epoch 2/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 736s 363ms/step - loss: 0.0991 - acc: 0.9791 - dice_coef: 0.9009 - val_loss: 0.1570 - val_acc: 0.9700 - val_dice_coef: 0.8430\n",
      "\n",
      "Epoch 3/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0980 - acc: 0.9791 - dice_coef: 0.9020 - val_loss: 0.0977 - val_acc: 0.9784 - val_dice_coef: 0.9023\n",
      "\n",
      "Epoch 4/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0768 - acc: 0.9822 - dice_coef: 0.9232 - val_loss: 0.0947 - val_acc: 0.9783 - val_dice_coef: 0.9053\n",
      "\n",
      "Epoch 5/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0733 - acc: 0.9826 - dice_coef: 0.9267 - val_loss: 0.1571 - val_acc: 0.9675 - val_dice_coef: 0.8429\n",
      "\n",
      "Epoch 6/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 735s 362ms/step - loss: 0.0662 - acc: 0.9833 - dice_coef: 0.9338 - val_loss: 0.9792 - val_acc: 0.9092 - val_dice_coef: 0.0208\n",
      "\n",
      "Epoch 7/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0626 - acc: 0.9835 - dice_coef: 0.9374 - val_loss: 0.1453 - val_acc: 0.9722 - val_dice_coef: 0.8547\n",
      "\n",
      "Epoch 8/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0538 - acc: 0.9845 - dice_coef: 0.9462 - val_loss: 0.0602 - val_acc: 0.9834 - val_dice_coef: 0.9398\n",
      "\n",
      "Epoch 9/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0499 - acc: 0.9849 - dice_coef: 0.9501 - val_loss: 0.0481 - val_acc: 0.9845 - val_dice_coef: 0.9519\n",
      "\n",
      "Epoch 10/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0428 - acc: 0.9859 - dice_coef: 0.9572 - val_loss: 0.0832 - val_acc: 0.9798 - val_dice_coef: 0.9168\n",
      "\n",
      "Epoch 11/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 733s 362ms/step - loss: 0.0394 - acc: 0.9862 - dice_coef: 0.9606 - val_loss: 0.1244 - val_acc: 0.9721 - val_dice_coef: 0.8756\n",
      "\n",
      "Epoch 12/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0359 - acc: 0.9866 - dice_coef: 0.9641 - val_loss: 0.0668 - val_acc: 0.9818 - val_dice_coef: 0.9332\n",
      "\n",
      "Epoch 13/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0305 - acc: 0.9871 - dice_coef: 0.9695 - val_loss: 0.0412 - val_acc: 0.9853 - val_dice_coef: 0.9588\n",
      "\n",
      "Epoch 14/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0313 - acc: 0.9871 - dice_coef: 0.9687 - val_loss: 0.0451 - val_acc: 0.9848 - val_dice_coef: 0.9549\n",
      "\n",
      "Epoch 15/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0292 - acc: 0.9873 - dice_coef: 0.9708 - val_loss: 0.2057 - val_acc: 0.9647 - val_dice_coef: 0.7943\n",
      "\n",
      "Epoch 16/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 734s 362ms/step - loss: 0.0277 - acc: 0.9875 - dice_coef: 0.9723 - val_loss: 0.0263 - val_acc: 0.9873 - val_dice_coef: 0.9737\n",
      "\n",
      "Epoch 17/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0282 - acc: 0.9874 - dice_coef: 0.9718 - val_loss: 0.0264 - val_acc: 0.9873 - val_dice_coef: 0.9736\n",
      "\n",
      "Epoch 18/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0246 - acc: 0.9879 - dice_coef: 0.9754 - val_loss: 0.0248 - val_acc: 0.9875 - val_dice_coef: 0.9752\n",
      "\n",
      "Epoch 19/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.0240 - acc: 0.9879 - dice_coef: 0.9760 - val_loss: 0.0268 - val_acc: 0.9873 - val_dice_coef: 0.9732\n",
      "\n",
      "Epoch 20/20\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 733s 362ms/step - loss: 0.0242 - acc: 0.9879 - dice_coef: 0.9758 - val_loss: 0.0224 - val_acc: 0.9878 - val_dice_coef: 0.9776\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x6e30442c04e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os.environ ['CUDA_VISIBLE_DEVICES'] = '1' \n",
    "train_gen = DataGen(train_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "valid_gen = DataGen(valid_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "\n",
    "train_steps = len(train_ids)//batch_size\n",
    "valid_steps = len(valid_ids)//batch_size\n",
    "# with tf.device('/gpu:0'):\n",
    "model.fit_generator(train_gen, validation_data=valid_gen, steps_per_epoch=train_steps, validation_steps=valid_steps, \n",
    "                    epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('liver_model_final_resunet.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "model.save_weights(\"liver_model_weights_final_resunet.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del model\n",
    "model.save(os.path.join('models','liver_model_final_resunet.h5')s)  # creates a HDF5 file 'my_model.h5'\n",
    "\n",
    "model = keras.models.load_model(os.path.join('models','liver_model_final_resunet.h5'), compile= False)\n",
    "# model.summary()\n",
    "valid_gen = DataGen(valid_ids, train_path, image_size=image_size, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 128, 128, 3) (8, 128, 128, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = valid_gen.__getitem__(7)\n",
    "result = model.predict(x)\n",
    "\n",
    "result = result > 0.5\n",
    "print(x.shape, result.shape)\n",
    "\n",
    "imshow(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8d531d3898>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.imshow(np.reshape(y[1]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.imshow(np.reshape(result[1]*255, (image_size, image_size)), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/matplotlib/pyplot.py:528: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KADCeTjgcKqWcnuTPk7ys1npvkjcmeWySi5IcTPLabtdNfvxBrcC1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\nDgEAAAA0TDgEAAAA0LD/B45PNdw/h9CoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e3014158240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eNBdH4jVndZoOtoMPPnjAkXSeYWWMPLni4LThXIG963e+5ljgQDi/GCxL2QMA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\nUxwCAAAA6DDFIQAAAIAOUxw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e30141084a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AwCQSCk0mp5UBAAAANMzkEDATuiMQv/ALv5Ak+f3f//0+y2nSi170ojz00EN9\nlwEAwAQyMTTZTA4BAAAANKxMwmJcpZT+iwCm2sLCQpJkeXk5iYUGx+GRRx5JkrzlLW9JkrzxjW88\nuP8Zq+tqrfv6LgJGSa8IDJM+cfxMDfWr1nrcO8DkEAAAAEDDrDkEzITuEurdVcu6q5V129m+lZWV\nJBv79EUvelGS5Nprr+2tJgAAJpeJoenhXRMAAABAw0wOATOhmxjqplq6ySG2p9aapaWlJMnNN9+c\nJHn2s5+dxFQWAABHZmJo+ujsAQAAABpmcgiYCfPz80mS3/3d302S/OIv/mIS0y3btbS0lH/9139N\nknzP93zPId8znQUAwGYmhqaXS9kDM+Hw08km4bltVniRn3guZc/M0ysCw6RPHD794mRzKXsAAAAA\njum44VAp5exSykdLKZ8updxUSvm5wfbHlVL+qZTyucHHxw62l1LKW0opt5RSPlVKec6o/xEAa2tr\nWVtby+Mf//g8/vGPP/g1J2Z5eTnLy8t54IEH8sADD6SUkl27dmXXrl19lwZMKL0iMG1KKSZdhqDb\nj/blbNjK5NBKkjfUWp+e5HlJLi2lPCPJ5Uk+Ums9N8lHBl8nyfcnOXfw3yVJ3jb0qgEAmBR6RQCY\ncsddkLrWekeSOwaff6OU8ukke5NcmOTFg5u9O8n/TfJLg+1/XtdP5Pz3UsrppZQ9g98DMFL33HNP\nko01iLpzyh3ROLZHHnkkSfKf//mfSZLnP//5SdYX+u4uZQ9wJHpFgLboq2fTCa05VEp5SpLvTPIf\nSZ7QvYgPPp45uNneJF/e9GMHBtsO/12XlFKuLaVce+JlAwAwafSKADCdtnwp+1LKY5L8bZKfr7Xe\nf4y08EjfeNRy8LXWK5JcMfjdlosHhuL0008/5GtHNo6tW5dp9+7dSTYmhjqrq6tjrwmYTnpFgNmm\nr55tW5ocKqUsZP3F/i9qre8bbL6zlLJn8P09Se4abD+Q5OxNP/6kJLcPp1wAACaNXhEApttWrlZW\nkrwjyadrrW/e9K33J7l48PnFSa7etP11gytRPC/Jfc4hB8bFlbVOzNzcXObm5g5eaWJhYSELCwt9\nlwVMEb0iwGxzRbI2bOW0shck+dEkN5RSrh9s+5Ukv5fkqlLKjyf5UpIfGnzvg0lenuSWJA8l+bGh\nVgwAwCTRKwLAlCvdlXx6LcJ55MCQdc9trla2odsXy8vLSZK77747e/eurwG7c+fOQ743Ca8NbNl1\ntdZ9fRcBo6RXBEZBv3Ns+ufZUWs97p255QWpAabB4aeUeVHbsLKykmQjCNq7d2/m5tbPLhYKAQCt\n0O8cm/65TSd0KXsAAAAAZovoR0PUAAAMx0lEQVTJIWCmnHrqqX2XMHG6o2PdQtPd0aC5ubmDl7IH\nAADaZXIIAAAAoGEmh4CZ8rWvfa3vEibG6upqkmR+fj7JxsRQt+bQ0tJSP4UBADBRrDOEySEAAACA\nhpkcApghtdaDVx774he/mCR5znOek2RjzaHu+wAAAInJIQAAAICmmRwCplp3fnQ3FXPmmWf2WU7v\nlpaW8rGPfSxJcsEFFyTZ2EfdVcsAACCx1hAbyiS8WSil9F8EMBO657TuEu1zc7M5INn9Ow9/Qf/k\nJz+Z8847r4+S6M91tdZ9fRcBo6RXBIZpEt4D90UY1KZa63Hv+Nl81wQAAADAljitDJhqu3btSpK8\n+tWvTpKsrKwkSXbsmO2nt8XFxSTJ7t27kzgKBADAsekXORaTQwAAAAANs+YQMNW6NYVWV1d7rmR0\nNj9PLy0tJUn+6I/+KEnyS7/0S73UxESx5hAzT68IDNMkvAceB5NCdKw5BAAAAMAxzfaiHMDM665K\nNsu6aaGlpaU87WlPS5LcddddfZYEAMCEMSnEdpgcAgAAAGiYySFgqp1++ulJkvvvvz9Jctppp/VZ\nzlB158N/8IMfTJK86lWvmvmrsAEAcGJMDDEMJocAAAAAGuYQNDDV7r333iTTOzHUTQcd6YjPxRdf\nnCS56qqrDm5bWVkZT2EAAEws00IMm3AIoEebX9i/7du+LUny2c9+9pDvtXK5VQAAjk0oxKg4rQwA\nAACgYSaHAHpw+OlkL33pS/OFL3zhiLcBAGD7prG3MinEuJgcAgAAAGiYySFgas3NzeUDH/hAkmRp\naSlJsnPnzj5LOqrDJ4X++7//O0ly7rnnJkkWFhYsNg0AwEGmhhgnk0MAAAAADTM5BEyttbW17Nix\n/jQ2qRNDi4uLSZI//MM/TJJcfvnlR7zdysrKVJ4HDwDAcJgUok8mhwAAAAAaVibhSHUppf8igKm0\nb9++JMk111yT5NFr+/ThkUceSbI+2fTc5z43SXLTTTf1Vg8z77pa676+i4BR0isCwzAJ732PxMQQ\no1ZrPe6DzGllwNQqpRxc2Pnzn/98kuTss89Osr7A8yhsDp+6z7uPd911V5KNU8fe8573jKQGAACm\nlzCISeS0MgAAAICGOa0MmCl33HFHkuSJT3xikvVTu5L1y95vx+rq6iG/b2FhITfccEOS5JWvfGWS\n5Ctf+UqS5KGHHtrW34IT5LQyZp5eERimcb4HNiXEJNjKaWUmhwAAAAAaZs0hYCZ0k0F79uxJkuzd\nuzdJcuDAgSTJ8vLywaNE3W27KaCVlZUk69NA3W26RaXvvvvuJMmrXvWqJMnnPve5JOvTQYf/HgAA\nJl83zTOsCSLTQcwCk0MAAAAADTM5BMyEw6d3vvrVrybZOJLzkpe8JE95ylOSJO985zuTJM985jOT\nJN/6rd+aJPmHf/iHnHbaaUmSc889N0nyiU98Yst/EwCA6WHiBzaYHAIAAABomKuVATNp2OeSwwRz\ntTJmnl4RAE6eq5UBAAAAcEzWHAJmkokhAACArTE5BAAAANAw4RAAAABAw4RDAAAAAA0TDgEAAAA0\nTDgEAAAA0DDhEAAAAEDDhEMAAAAADRMOAQAAADRMOAQAAADQMOEQAAAAQMOEQwAAAAANEw4BAAAA\nNEw4BAAAANAw4RAAAABAw4RDAAAAAA0TDgEAAAA0TDgEAAAA0DDhEAAAAEDDhEMAAAAADRMOAQAA\nADRMOAQAAADQMOEQAAAAQMOEQwAAAAANEw4BAAAANEw4BAAAANAw4RAAAABAw4RDAAAAAA0TDgEA\nAAA0TDgEAAAA0DDhEAAAAEDDhEMAAAAADRMOAQAAADRMOAQAAADQMOEQAAAAQMOEQwAAAAANEw4B\nAAAANEw4BAAAANAw4RAAAABAw4RDAAAAAA0TDgEAAAA0TDgEAAAA0DDhEAAAAEDDjhsOlVLOLqV8\ntJTy6VLKTaWUnxts//VSym2llOsH/71808/8cinlllLKzaWU/aP8BwAA0B+9IgBMv1JrPfYNStmT\nZE+t9ROllG9Ocl2SVyZ5dZIHaq3/87DbPyPJXyZ5bpKzknw4yf9Xa109xt84dhEAwNFcV2vd13cR\ntEuvCACTrdZajneb404O1VrvqLV+YvD5N5J8OsneY/zIhUneW2tdrLV+IcktWX/xBwBgxugVAWD6\nndCaQ6WUpyT5ziT/Mdh0WSnlU6WUK0spjx1s25vky5t+7ECO0CCUUi4ppVxbSrn2hKsGAGDi6BUB\nYDptORwqpTwmyd8m+fla6/1J3pbkqUnOS3JHkj/obnqEH3/UKHCt9Ypa6z6j8AAA00+vCADTa0vh\nUCllIesv9n9Ra31fktRa76y1rtZa15L8WTbGgQ8kOXvTjz8pye3DKxkAgEmiVwSA6baVq5WVJO9I\n8ula65s3bd+z6WYXJblx8Pn7k7ymlLKrlHJOknOTfHx4JQMAMCn0igAw/XZs4TYvSPKjSW4opVw/\n2PYrSV5bSjkv62PAtyb5ySSptd5USrkqyX8lWUly6bGuPgEAwFTTKwLAlDvupezHUoTLkwLAyXIp\ne2aeXhEATt5QLmUPAAAAwOwSDgEAAAA0TDgEAAAA0DDhEAAAAEDDtnK1snG4O8mDg4+M3hmxr8fF\nvh4f+3o87Ofx2eq+/h+jLgQmgF5xvDzXj499PT729XjYz+Mz1F5xIq5WliSllGtdbWU87Ovxsa/H\nx74eD/t5fOxrOJT/J8bHvh4f+3p87OvxsJ/HZ9j72mllAAAAAA0TDgEAAAA0bJLCoSv6LqAh9vX4\n2NfjY1+Ph/08PvY1HMr/E+NjX4+PfT0+9vV42M/jM9R9PTFrDgEAAAAwfpM0OQQAAADAmAmHAAAA\nABo2EeFQKeVlpZSbSym3lFIu77ueWVJKubWUckMp5fpSyrWDbY8rpfxTKeVzg4+P7bvOaVRKubKU\nclcp5cZN2464b8u6twwe458qpTynv8qnz1H29a+XUm4bPLavL6W8fNP3fnmwr28upezvp+rpVEo5\nu5Ty0VLKp0spN5VSfm6w3WN7iI6xnz2u4Qj0iqOjVxwdveL46BXHR684Hn30ir2HQ6WU+ST/f5Lv\nT/KMJK8tpTyj36pmzktqrefVWvcNvr48yUdqrecm+cjga07cu5K87LBtR9u335/k3MF/lyR525hq\nnBXvyqP3dZL8r8Fj+7xa6weTZPD88Zokzxz8zFsHzzNszUqSN9Ran57keUkuHexTj+3hOtp+Tjyu\n4RB6xbHQK47Gu6JXHJd3Ra84LnrF8Rh7r9h7OJTkuUluqbV+vta6lOS9SS7suaZZd2GSdw8+f3eS\nV/ZYy9Sqtf5zkq8dtvlo+/bCJH9e1/17ktNLKXvGU+n0O8q+PpoLk7y31rpYa/1Ckluy/jzDFtRa\n76i1fmLw+TeSfDrJ3nhsD9Ux9vPReFzTMr3i+OkVh0CvOD56xfHRK45HH73iJIRDe5N8edPXB3Ls\nfzQnpib5P6WU60oplwy2PaHWekey/qBLcmZv1c2eo+1bj/PRuGwwnnrlppF3+3pISilPSfKdSf4j\nHtsjc9h+Tjyu4XAe/6OlVxwvr6fj5TV1hPSK4zGuXnESwqFyhG117FXMrhfUWp+T9XG+S0sp3913\nQY3yOB++tyV5apLzktyR5A8G2+3rISilPCbJ3yb5+Vrr/ce66RG22d9bdIT97HENj+bxP1p6xcng\ncT58XlNHSK84HuPsFSchHDqQ5OxNXz8pye091TJzaq23Dz7eleTvsj5admc3yjf4eFd/Fc6co+1b\nj/Mhq7XeWWtdrbWuJfmzbIxN2tfbVEpZyPqL0F/UWt832OyxPWRH2s8e13BEHv8jpFccO6+nY+I1\ndXT0iuMx7l5xEsKha5KcW0o5p5SyM+uLKL2/55pmQinl1FLKN3efJ3lpkhuzvn8vHtzs4iRX91Ph\nTDravn1/ktcNVut/XpL7urFLTs5h5ypflPXHdrK+r19TStlVSjkn64vffXzc9U2rUkpJ8o4kn661\nvnnTtzy2h+ho+9njGo5IrzgiesVeeD0dE6+po6FXHI8+esUd2yt5+2qtK6WUy5J8KMl8kitrrTf1\nXNaseEKSv1t/XGVHkvfUWv93KeWaJFeVUn48yZeS/FCPNU6tUspfJnlxkjNKKQeS/FqS38uR9+0H\nk7w86wuDPZTkx8Ze8BQ7yr5+cSnlvKyPS96a5CeTpNZ6UynlqiT/lfVV/i+tta72UfeUekGSH01y\nQynl+sG2X4nH9rAdbT+/1uMaDqVXHCm94gjpFcdHrzhWesXxGHuvWGp1uh8AAABAqybhtDIAAAAA\neiIcAgAAAGiYcAgAAACgYcIhAAAAgIYJhwAAAAAaJhwCAAAAaJhwCAAAAKBh/w9v1UT4w//0iAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef0638c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef0bea9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef0e661d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DnkcCAABsBpVDAAAAAA2THAIAAABomOQQAAAAQMP0HIIp1/Ua6pYb/8AHPpAkufTSSzM/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\nkNRar621XlRrveg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec211c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef0dcc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PTpJs3rx5aGNifbZu3ZokmZ6eHvJIgBHlamVMPLVi/43CewKOjM4hYDmuVgYA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef0673c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D++LWqMFHx5xDAAAAAKzLnEPAVNu2bSkDP+ecc7KwsJAkmZmZSZLlr7dvX3oqrLUunzXq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\nABomHAIAAABo2LGsVjYK/zv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef82d82e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E7ycAo9O9PnktgvEgFGIlbisDAAAAaJjOIfpmbm4uycIy98DgdFdgu+6gv/iL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\nADRMOAQAAADQsHEKh64Z9QA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e30140e0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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cXFz5mU3voaa/UVOFs2/fvpXxHKwy50c/+lEOO+ywJMnRRx+98vVJcvfdd3dlzE972tOS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\nkxwCAAAAaDHJIQAAAIAW+/8BUg38oC10oMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec5e2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sqDhUSjlt2aevTPLNxY8/neTVpZTpUsrTkpyV5F+OL8S2TU9PZ3p6On/yJ39i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\nMMUhAAAAgIYpDgEAAAA07P8HxYkQPJY+ZqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec557048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jpME94hIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec450438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bsxKOl84hAAAAgIbpHAKOan5+3l5DPXrc4x6XJDnttNOSJA888ECf4QAAbMq+\niDD+FIeANTMzM0mS5z73uUkOHWlPvxSFAACAQfLODwAAAKBhxywOlVLeU0q5p5TyuXXXfrWUclcp\n5ebV/16w7mtvLqXcVkq5tZTyQ4MKHNh5CwsLWVhYyLve9a68613vyq5dmgsB2JpcEQDG33Y6h65I\ncuEm1y+tte5d/e8vk6SU8p1JLk7yP1e/5/dLKdM7FSwAACPnisgVAWCsHbMtoNb6d6WUx2/z512U\n5Opa68EkXyml3JbkaUn+4YQjBIZm7969SZJzzz2350hIkhtuuCFJcsoppyRJ9u/f32c4AJuSKwLA\n+DuZPYdeX0r5l9VW4kesXntskjvW3efO1WtHKKVcUkq5oZRyw0nEAADAaJIrAsCYONHi0GVJnpBk\nb5K7k/z26vWyyX03Pdew1np5rXVfrXXfCcYA7LBXvvKVeeUrX9l3GM2rtabWmn379mXfvn1rtwHG\niFwRGiBH6U8pJaVs9pQKJ+aEikO11q/XWpdqrctJ/jAr7cDJyuzPOevuenaSr51ciAAAjBO5IgCM\nlxM6iqiU8uha692rN1+cpDud4tok7y+lvDPJY5Kcl+QzJx0lMBRPetKT+g6BZG0W6Dd+4zcOuw0w\nLuSKADBejlkcKqV8IMkFSb6tlHJnkl9JckEpZW9W2oC/muSnk6TW+vlSygeTfCHJYpLX1VqXBhM6\nAAB9kysCwPgro7BGtJTSfxDQsN27dyc5dBrW8vJykmRq6mT2rOdkXXnllUmSV73qVUliTT9Hc6M9\nWZh0ckUYLXKS/uks53jUWo/5gDmhZWXAZDlw4MBhtxWF+tElWt2L/e/8zu8cdh0AAGAQvAMEAAAA\naJjOIWhY1yH0yEc+MkmysLCQJJmZmektppZtbA+++eabe4oEAGBzOpphMukcAgAAAGiYziFo1Pou\nlV//9V9PomOob13n1rOf/ewkydzcXJLk4MGDvcUEAMDosBE1g6JzCAAAAKBhjrIHMj8/n0Tn0Kgw\nI8RxcpQ9E0+uCKNjFN4/tkyeyInYzlH2OocAAAAAGmbPIWhYN/OgY2g0fNd3fVcSew0BAKNHx1C/\ndAwxaIpD0LBTTjklSXLPPfckSc4666w+w2naO9/5ztxyyy1JkqWlpZ6jAQAAWmJZGQAAAEDDdA5B\nw/bv358kax0rOof6c/nll2vXBgDgMJaTMSw6hwAAAAAapnMIGjY9PZ0k+exnP5sk+YEf+IE+w2nS\na1/72iTJv//7v2d5ebnnaAAAgBbpHAIAAABoWBmFPS5KKf0HAQ3atWulefCpT31qkuTv//7vkyRT\nU+rGgzI/P58k+djHPpYk+dG3daRgAAAQlElEQVQf/dE+w2Ey3Fhr3dd3EDBIckXo3yi8b2yN/YbY\nKbXWYz6YvAMEAAAAaJg9h6Bhi4uLSZKLL76450gm39LSUpLk4YcfTqJjCAAAGB06hwAAAAAaZs8h\nYM0oPB9MqoWFhSTJm9/85iTJpZdemuTQmBt7ToI9h5h4ckUYHXKWwbPXEDttO3sOKQ4BmZubS5L8\nzM/8TJLkt37rt5LYmHonHDx4MEly3333JUke9ahH9RkOk0lxiIknV4TRMQrvHyeVohCDYkNqAAAA\nALakcwg4wvXXX58kedaznpXp6el+gxljCwsLmZmZSXJoJqj7OArPvUwMnUNMPLkijA45zODoHGJQ\ndA4BAAAAsCWdQ8BR1VozPz+fJJmdne05mtG38fm0lLI2A9R1EHUbU8MO0jnExJMrwugYhfePk0bH\nEIOmcwgAAACALe3qOwBg9HRdQqWUtdkhHUTHtnHW54wzzlg7Ca47tQwAABIdQ4wWnUMAAAAADdM5\nBByh6xKanp5em9G4/fbbkyTnnHNOkkOdMF1nDIdcdNFFSZL9+/evjSUAwCRY31m+nc4XexQdSccQ\no0hxCDiq5eXltc+f8IQnJDm0ofKXvvSlJMnjH//4JMmuXe09nXRj0W02/epXvzpJcu211/YWEwDA\noB1PceNY922teKQwxKiyrAwAAACgYY6yB07KZZddliR5zWtec0QnzSRbXl7O1NRKfd1x9fTMUfZM\nPLkiTLZReE86aDqG6JOj7AEAAADYks4hYMd85CMfSZI84xnPSJLs3r07yaGZkknYvHpxcTFJ8gd/\n8Ad5y1vekuTQBt6Oq6cnOoeYeHJFaMMovDfdaTqGGAU6hwAAAADYks4hYGDOOuusJMnXv/71I762\ntLSUJJmenk5y5EzRyc6yHM8Rq9vRdQV1/5bHPe5xO/JzYQfoHGLiyRWBQRjke2EdQ4wSnUMAAAAA\nbEnnELBjuhmS7nmlO81reXn5iPse7blnY0fRZt+zfiam6+jpvtbtc9Tp9gjatWvXNv8VWzMLxAjS\nOcTEkysCg7YT74vliYwqnUMAAAAAbGlnptIBcuSMy2YdQ92JZXv27EmSPPTQQ0mS7/iO70iS/NIv\n/VKS5JJLLjniezfOxiwuLuZ7vud7kiS33nrrYV97/vOfnyT5xV/8xSTJD/7gD651GW08Ne3BBx9c\nuz4zM5Mkufvuu5Mkb3rTm5IkH/rQh5Ic6kDqOpIAABh/R+v62aqjSKcQk8SyMmCkdC+yU1NT+dZv\n/dYkyf3335/kUEGmK+DMzc2tFXa244//+I+TJD/xEz+RJPnnf/7nJMnevXuPGc8oPFfCUVhWxsST\nKwLAibOsDAAAAIAt6RwCRt7JdO+s/97Z2dnDrnW65WYn+7ugJzqHmHhyRQA4cTqHAAAAANiSDamB\nkXcyXTzrv3d+fn6gvwsAAGAc6RwCAAAAaJjiEAAAAEDDFIcAAAAAGqY4BAAAANAwxSEAAACAhikO\nAQAAADRMcQgAAACgYYpDAAAAAA1THAIAAABomOIQAAAAQMMUhwAAAAAapjgEAAAA0DDFIQAAAICG\nKQ4BAAAANExxCAAAAKBhikMAAAAADVMcAgAAAGiY4hAAAABAwxSHAAAAABqmOAQAAADQMMUhAAAA\ngIYpDgEAAAA0THEIAAAAoGGKQwAAAAANUxwCAAAAaJjiEAAAAEDDFIcAAAAAGqY4BAAAANAwxSEA\nAACAhikOAQAAADRMcQgAAACgYYpDAAAAAA1THAIAAABomOIQAAAAQMMUhwAAAAAapjgEAAAA0DDF\nIQAAAICGHbM4VEo5p5TyN6WUW0opny+l/Nzq9TNLKZ8opXxp9eMjVq+XUsrvlVJuK6X8Synl/EH/\nIwAA6IdcEQDG33Y6hxaT/EKt9X8keXqS15VSvjPJm5J8stZ6XpJPrt5OkucnOW/1v0uSXLbjUQMA\nMCrkigAw5o5ZHKq13l1rvWn18weS3JLksUkuSnLl6t2uTPKi1c8vSvLeuuLTSc4opTx6xyMHAKB3\nckUAGH/HtedQKeXxSZ6S5B+TPKrWeneykhQkOWv1bo9Ncse6b7tz9drGn3VJKeWGUsoNxx82AACj\nRq4IAONp13bvWErZk+RPk/x8rfX+UspR77rJtXrEhVovT3L56s8+4usAAIwPuSIAjK9tdQ6VUmay\n8mL/vlrrNauXv961AK9+vGf1+p1Jzln37Wcn+drOhAsAwKiRKwLAeNvOaWUlyR8luaXW+s51X7o2\nyStWP39Fkj9bd/1/rZ5E8fQk93UtxQAATBa5IgCMv1Lr1l26pZRnJflUkn9Nsrx6+S1ZWUv+wST/\nV5Lbk/xYrfW/VxOE/53kwiQPJ3lVrXXLteJahQHghN1Ya93XdxC0S64IAKOt1nrUtd6dYxaHhsEL\nPgCcMMUhJp5cEQBO3HaKQ8d1WhkAAAAAk0VxCAAAAKBhikMAAAAADVMcAgAAAGiY4hAAAABAwxSH\nAAAAABqmOAQAAADQMMUhAAAAgIYpDgEAAAA0THEIAAAAoGGKQwAAAAANUxwCAAAAaJjiEAAAAEDD\nFIcAAAAAGqY4BAAAANAwxSEAAACAhikOAQAAADRMcQgAAACgYYpDAAAAAA1THAIAAABomOIQAAAA\nQMMUhwAAAAAapjgEAAAA0DDFIQAAAICGKQ4BAAAANExxCAAAAKBhikMAAAAADVMcAgAAAGiY4hAA\nAABAwxSHAAAAABqmOAQAAADQMMUhAAAAgIYpDgEAAAA0THEIAAAAoGGKQwAAAAANUxwCAAAAaJji\nEAAAAEDDFIcAAAAAGqY4BAAAANAwxSEAAACAhikOAQAAADRMcQgAAACgYYpDAAAAAA1THAIAAABo\nmOIQAAAAQMMUhwAAAAAapjgEAAAA0DDFIQAAAICGKQ4BAAAANExxCAAAAKBhikMAAAAADVMcAgAA\nAGiY4hAAAABAwxSHAAAAABqmOAQAAADQMMUhAAAAgIYpDgEAAAA0THEIAAAAoGGKQwAAAAANUxwC\nAAAAaJjiEAAAAEDDFIcAAAAAGqY4BAAAANAwxSEAAACAhikOAQAAADRMcQgAAACgYYpDAAAAAA1T\nHAIAAABomOIQAAAAQMMUhwAAAAAapjgEAAAA0DDFIQAAAICGKQ4BAAAANExxCAAAAKBhikMAAAAA\nDVMcAgAAAGiY4hAAAABAwxSHAAAAABqmOAQAAADQMMUhAAAAgIYpDgEAAAA0THEIAAAAoGHHLA6V\nUs4ppfxNKeWWUsrnSyk/t3r9V0spd5VSbl797wXrvufNpZTbSim3llJ+aJD/AAAA+iNXBIDxV2qt\nW9+hlEcneXSt9aZSyulJbkzyoiQvTfJgrfW3Ntz/O5N8IMnTkjwmyXVJnlhrXdrid2wdBABwNDfW\nWvf1HQTtkisCwGirtZZj3eeYnUO11rtrrTetfv5AkluSPHaLb7koydW11oO11q8kuS0rL/4AAEwY\nuSIAjL/j2nOolPL4JE9J8o+rl15fSvmXUsp7SimPWL322CR3rPu2O7NJglBKuaSUckMp5YbjjhoA\ngJEjVwSA8bTt4lApZU+SP03y87XW+5NcluQJSfYmuTvJb3d33eTbj2gFrrVeXmvdpxUeAGD8yRUB\nYHxtqzhUSpnJyov9+2qt1yRJrfXrtdalWutykj/MoXbgO5Ocs+7bz07ytZ0LGQCAUSJXBIDxtp3T\nykqSP0pyS631neuuP3rd3V6c5HOrn1+b5OJSylwp5dwk5yX5zM6FDADAqJArAsD427WN+zwzyU8m\n+ddSys2r196S5OWllL1ZaQP+apKfTpJa6+dLKR9M8oUki0let9XpEwAAjDW5IgCMuWMeZT+UIBxP\nCgAnylH2TDy5IgCcuB05yh4AAACAyaU4BAAAANAwxSEAAACAhikOAQAAADRsO6eVDcP/SfLQ6kcG\n79tirIfFWA+PsR4O4zw82x3rxw06EBgBcsXh8lw/PMZ6eIz1cBjn4dnRXHEkTitLklLKDU5bGQ5j\nPTzGeniM9XAY5+Ex1nA4/08Mj7EeHmM9PMZ6OIzz8Oz0WFtWBgAAANAwxSEAAACAho1ScejyvgNo\niLEeHmM9PMZ6OIzz8BhrOJz/J4bHWA+PsR4eYz0cxnl4dnSsR2bPIQAAAACGb5Q6hwAAAAAYMsUh\nAAAAgIaNRHGolHJhKeXWUsptpZQ39R3PJCmlfLWU8q+llJtLKTesXjuzlPKJUsqXVj8+ou84x1Ep\n5T2llHtKKZ9bd23TsS0rfm/1Mf4vpZTz+4t8/BxlrH+1lHLX6mP75lLKC9Z97c2rY31rKeWH+ol6\nPJVSziml/E0p5ZZSyudLKT+3et1jewdtMc4e17AJueLgyBUHR644PHLF4ZErDkcfuWLvxaFSynSS\ndyV5fpLvTPLyUsp39hvVxHl2rXVvrXXf6u03JflkrfW8JJ9cvc3xuyLJhRuuHW1sn5/kvNX/Lkly\n2ZBinBRX5MixTpJLVx/be2utf5kkq88fFyf5n6vf8/urzzNsz2KSX6i1/o8kT0/yutUx9djeWUcb\n58TjGg4jVxwKueJgXBG54rBcEbnisMgVh2PouWLvxaEkT0tyW631y7XW+SRXJ7mo55gm3UVJrlz9\n/MokL+oxlrFVa/27JP+94fLRxvaiJO+tKz6d5IxSyqOHE+n4O8pYH81FSa6utR6stX4lyW1ZeZ5h\nG2qtd9dab1r9/IEktyR5bDy2d9QW43w0Hte0TK44fHLFHSBXHB654vDIFYejj1xxFIpDj01yx7rb\nd2brfzTHpyb5eCnlxlLKJavXHlVrvTtZedAlOau36CbP0cbW43wwXr/anvqedS3vxnqHlFIen+Qp\nSf4xHtsDs2GcE49r2Mjjf7DkisPl9XS4vKYOkFxxOIaVK45Ccahscq0OPYrJ9cxa6/lZaed7XSnl\n/+k7oEZ5nO+8y5I8IcneJHcn+e3V68Z6B5RS9iT50yQ/X2u9f6u7bnLNeG/TJuPscQ1H8vgfLLni\naPA433leUwdIrjgcw8wVR6E4dGeSc9bdPjvJ13qKZeLUWr+2+vGeJB/OSmvZ17tWvtWP9/QX4cQ5\n2th6nO+wWuvXa61LtdblJH+YQ22TxvoklVJmsvIi9L5a6zWrlz22d9hm4+xxDZvy+B8gueLQeT0d\nEq+pgyNXHI5h54qjUBz6pyTnlVLOLaXMZmUTpWt7jmkilFJOK6Wc3n2e5HlJPpeV8X3F6t1ekeTP\n+olwIh1tbK9N8r9Wd+t/epL7urZLTsyGtcovzspjO1kZ64tLKXOllHOzsvndZ4Yd37gqpZQkf5Tk\nllrrO9d9yWN7Bx1tnD2uYVNyxQGRK/bC6+mQeE0dDLnicPSRK+46uZBPXq11sZTy+iQfSzKd5D21\n1s/3HNakeFSSD688rrIryftrrR8tpfxTkg+WUn4qye1JfqzHGMdWKeUDSS5I8m2llDuT/EqS38zm\nY/uXSV6QlY3BHk7yqqEHPMaOMtYXlFL2ZqVd8qtJfjpJaq2fL6V8MMkXsrLL/+tqrUt9xD2mnpnk\nJ5P8aynl5tVrb4nH9k472ji/3OMaDidXHCi54gDJFYdHrjhUcsXhGHquWGq13A8AAACgVaOwrAwA\nAACAnigOAQAAADRMcQgAAACgYYpDAAAAAA1THAIAAABomOIQAAAAQMMUhwAAAAAa9v8DLOq46A1p\n4QkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec57d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ErTgU3k8HxHtqf6gVB2MYteK2zQ1582qtM6WUNyb5QpKpJB+std4x\n5GFNihOTfHp+v8q2JB+ttX6+lPKVJNeVUv5bkn9Nct4Qxzi2SikfS3JGkqeVUu5OckWS/57l5/Zz\nSc7O/MJgjyf5xYEPeIytMNdnlFJOz3y75HeT/GqS1FrvKKVcl+TrmV/l/9Ja6+wwxj2mfibJf03y\nT6WU23rbLo99e6utNM8X2q/hSGrFvlIr9pFacXDUigOlVhyMgdeKpVan+wEAAAC0ahROKwMAAABg\nSIRDAAAAAA0TDgEAAAA0TDgEAAAA0DDhEAAAAEDDhEMAAAAADRMOAQAAADTs/wJjWn7hC1SMeAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec33c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VWg0H3GGb6Ry6Ksl567a9Icnnaq2nJfnc8r+T5JlJTlv+76Ik7+lNmAAADKmrIlcEgJF2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\nQ1lKAH4uyeeSnFZ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AAADAeFEcAgAAAGiY4hAAAABAwxSHAAAAABqmOAQAAADQMMUhAAAA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\n185nNOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec1f5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pe6ZKV+FgOBkcn+53adu2bX6fxp+l7Jl6+or0yzhc+8A00U+cDJayBwAAAGBD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fec0a8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kSZKkHvsNqPwHglYLgXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd87d4d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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76KOPJpm7/KwLxjorn8tG3qxnZmYWgqjjjjsuyegEbwAA/TLIBUI2UneuNZ5D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd870d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd8605ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gKas/AAAso1aEXnQOAQAAADSmc2hNqsrdJ9hKVoEAALiSGhF6Ew5BEw74AAAcpD4E9jmt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd84b8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd83be438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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AsFYc/mO2+P9llpmQGtZgZavw3Nzc4QmLh8uGp6QNJ7Vm8r3wwgtJkq1btyZJ\n7rnnnvz4j/94kiP/jwsLC0m0iTPRTEjNzFMrMu3UEdNNEMS0MyE1AAAAAKs6aThUVRdV1Veq6ltV\n9VBVvXew/MVV9ZdV9cjg67mD5VVVv1dVe6rqG1V1xWa/CdhsK08ZG3YNJdpLJ93y/7vh9/Pz85mf\nn89HPvKRfOQjH8nc3Fzm5uYOdw0lSx1DCwsLThcEOAm1IpycWnF6+T+jF2vpHDqU5AOttf+Y5Kok\n76mq1yT5UJK7WmuXJLlrcDtJrk1yyeDfzUk+seGjBgBgUqgVAWDKnXRylNbaE0meGHz/bFV9K8mF\nSa5LcvXgbp9O8tdJPjhYfntbOtR+T1WdU1XnD54HZs5wbprh1yuvXJr642tf+9rYxrQWrbXDR0L2\n79+fJDnjjDNOeP/hBN3DuZWef/75JMk555yTJDl48OBYJm4edvWsPKozHOcPf/jDJMlDDz2Ud7/7\n3UmSBx98MMmRuYZWe14AVqdWhFPjsvfAJDqlOYeq6pVJLk9yb5KXDT/EB19fOrjbhUkeW/awvYNl\nK5/r5qq6r6ruO/VhAwAwadSKADCd1nxZpao6K8mdSd7XWtu3yrmXx/vBMbF4a+22JLcNnltsztQa\nXs1q6Bvf+EaSpaNCwyNCwzmK5ubmkhx7pGijz2Ve3hU09NxzzyVJzjrrrGNe8+d//ueTJHfeeeea\nX2PYZTTsOvrN3/zN/Nqv/VqSY9/v8Qw7kbZsOXlGPXy+4WudeeaZSZbW/fA1nn322STJhz/84STJ\nxRdfnCS59dZbkyRPPvnkCccAwOlTK8KpmdYOorXUrdP2npYzxxC9WlPnUFVtz9KH/R+31j43WPy9\nqjp/8PPzkwz3vPYmuWjZw1+e5PGNGS4AAJNGrQgA061OlurWUnT66STPtNbet2z5/5fk6dba71TV\nh5K8uLX2/1bVTyW5Jclbk7whye+11l5/kteY3mgZVhgebdi+fXsOHjyYJPmHf/iHJMmP/diPHXXf\nYdfRanPfrGblfDvDeXaG8x8lycMPP5wkufbaa5Mkjz766DHPM5wraDje9dixY8fhxz/wwANJkte+\n9rVHjWv52P7lX/4lyZGOpuE6uOyyyw6Pe/h8P/3TP50keetb35ok+cM//MPDzzd8X3/7t3+bJPnB\nD35w3PEt7+SCGXN/a+3KcQ+CfqkVYeNMYq2yUZ00s/zeYNK11k66sa/ltLI3JbkxyTer6oHBsg8n\n+Z0kn62qdyX5TpK3D372hSx92O9J8nySXzjFcQMAMD3UigAw5U7aOTSSQTgaxIxaeS75sENn5VXC\nFhcX1zT3zsm8+c1vTpLcffeE09kXAAAKN0lEQVTdhzt0ht1Jo/hdX/l+h/MSPfbY0ryjb3jDG/Lt\nb387ydq6lVY+3/FuT8LfMBgznUPMPLUivdqoOkeHDPRtozqHgHVa+YE+DEK2b9+e5MgH9dve9rbD\nEyZfeOHSBVte//qlDvuf+qmfSpL84i/+4uFTrm6//fYkRyZhHj7f8tO3RhkKDa18reEYLrjggiTJ\n/Pz84Z8t/3655QHQyuc72W0AgFki1AFG5fRbFQAAAACYWk4rgzFYeXrU1q1bD59WNpyYeXh72A3U\nWjum22ZaL4EKbCinlTHz1IoAsH5rOa1M5xAAAABAx8w5BGNwvLl5hvPznGgunrU8DwAAAJwqnUMA\nAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAAQMeEQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAAAABAx4RD\nAAAAAB0TDgEAAAB0TDgEAAAA0DHhEAAAAEDHhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAAQMeE\nQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAAAABAx4RDAAAAAB0TDgEAAAB0TDgEAAAA0DHhEAAAAEDH\nhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAAQMeEQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAAAABA\nx4RDAAAAAB0TDgEAAAB0TDgEAAAA0DHhEAAAAEDHhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAA\nQMeEQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAAAABAx4RDAAAAAB0TDgEAAAB0TDgEAAAA0DHhEAAA\nAEDHhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAAQMeEQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAA\nAABAx4RDAAAAAB0TDgEAAAB0TDgEAAAA0DHhEAAAAEDHhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQ\nAAAAQMeEQwAAAAAdEw4BAAAAdEw4BAAAANAx4RAAAABAx4RDAAAAAB0TDgEAAAB0TDgEAAAA0DHh\nEAAAAEDHhEMAAAAAHRMOAQAAAHRMOAQAAADQMeEQAAAAQMeEQwAAAAAdO2k4VFUXVdVXqupbVfVQ\nVb13sPw3quq7VfXA4N9blz3mV6tqT1X9U1X91818AwAAjI9aEQCmX7XWVr9D1flJzm+tfb2qdiW5\nP8n1Sf5bkudaa7euuP9rkvxJktcnuSDJXyX5v1prC6u8xuqDAABO5P7W2pXjHgT9UisCwGRrrdXJ\n7nPSzqHW2hOtta8Pvn82ybeSXLjKQ65Lckdr7UBr7V+T7MnShz8AADNGrQgA0++U5hyqqlcmuTzJ\nvYNFt1TVN6rqk1V17mDZhUkeW/awvTlOgVBVN1fVfVV13ymPGgCAiaNWBIDptOZwqKrOSnJnkve1\n1vYl+USSVyfZneSJJL87vOtxHn5MK3Br7bbW2pVa4QEApp9aEQCm15rCoaranqUP+z9urX0uSVpr\n32utLbTWFpP8UY60A+9NctGyh788yeMbN2QAACaJWhEApttarlZWSf5nkm+11j6+bPn5y+72tiQP\nDr7/fJIbqmquql6V5JIkf79xQwYAYFKoFQFg+m1bw33elOTGJN+sqgcGyz6c5B1VtTtLbcCPJnl3\nkrTWHqqqzyb5xySHkrxntatPAAAw1dSKADDlTnop+5EMwuVJAWC9XMqemadWBID125BL2QMAAAAw\nu4RDAAAAAB0TDgEAAAB0TDgEAAAA0LG1XK1sFJ5K8sPBVzbfebGuR8W6Hh3rejSs59FZ67p+xWYP\nBCaAWnG0/K0fHet6dKzr0bCeR2dDa8WJuFpZklTVfa62MhrW9ehY16NjXY+G9Tw61jUcze/E6FjX\no2Ndj451PRrW8+hs9Lp2WhkAAABAx4RDAAAAAB2bpHDotnEPoCPW9ehY16NjXY+G9Tw61jUcze/E\n6FjXo2Ndj451PRrW8+hs6LqemDmHAAAAABi9SeocAgAAAGDEhEMAAAAAHZuIcKiq3lJV/1RVe6rq\nQ+Mezyypqker6ptV9UBV3TdY9uKq+suqemTw9dxxj3MaVdUnq+rJqnpw2bLjrtta8nuDbfwbVXXF\n+EY+fU6wrn+jqr472LYfqKq3LvvZrw7W9T9V1X8dz6inU1VdVFVfqapvVdVDVfXewXLb9gZaZT3b\nruE41IqbR624edSKo6NWHB214miMo1YcezhUVVuT/EGSa5O8Jsk7quo14x3VzPnPrbXdrbUrB7c/\nlOSu1tolSe4a3ObUfSrJW1YsO9G6vTbJJYN/Nyf5xIjGOCs+lWPXdZL898G2vbu19oUkGfz9uCHJ\nfxo85n8M/s6wNoeSfKC19h+TXJXkPYN1atveWCdaz4ntGo6iVhwJteLm+FTUiqPyqagVR0WtOBoj\nrxXHHg4leX2SPa21b7fWDia5I8l1Yx7TrLsuyacH3386yfVjHMvUaq19NckzKxafaN1el+T2tuSe\nJOdU1fmjGen0O8G6PpHrktzRWjvQWvvXJHuy9HeGNWitPdFa+/rg+2eTfCvJhbFtb6hV1vOJ2K7p\nmVpx9NSKG0CtODpqxdFRK47GOGrFSQiHLkzy2LLbe7P6m+bUtCRfqqr7q+rmwbKXtdaeSJY2uiQv\nHdvoZs+J1q3tfHPcMmhP/eSylnfreoNU1SuTXJ7k3ti2N82K9ZzYrmEl2//mUiuOls/T0fKZuonU\niqMxqlpxEsKhOs6yNvJRzK43tdauyFI733uq6v8e94A6ZTvfeJ9I8uoku5M8keR3B8ut6w1QVWcl\nuTPJ+1pr+1a763GWWd9rdJz1bLuGY9n+N5dacTLYzjeez9RNpFYcjVHWipMQDu1NctGy2y9P8viY\nxjJzWmuPD74+meTPstRa9r1hK9/g65PjG+HMOdG6tZ1vsNba91prC621xSR/lCNtk9b1aaqq7Vn6\nEPrj1trnBott2xvseOvZdg3HZfvfRGrFkfN5OiI+UzePWnE0Rl0rTkI49LUkl1TVq6pqR5YmUfr8\nmMc0E6rqzKraNfw+yX9J8mCW1u9Ng7vdlOTPxzPCmXSidfv5JO8czNZ/VZIfDNsuWZ8V5yq/LUvb\ndrK0rm+oqrmqelWWJr/7+1GPb1pVVSX5n0m+1Vr7+LIf2bY30InWs+0ajkutuEnUimPh83REfKZu\nDrXiaIyjVtx2ekM+fa21Q1V1S5IvJtma5JOttYfGPKxZ8bIkf7a0XWVbkv/VWvvfVfW1JJ+tqncl\n+U6St49xjFOrqv4kydVJzquqvUl+Pcnv5Pjr9gtJ3pqlicGeT/ILIx/wFDvBur66qnZnqV3y0STv\nTpLW2kNV9dkk/5ilWf7f01pbGMe4p9SbktyY5JtV9cBg2Ydj295oJ1rP77Bdw9HUiptKrbiJ1Iqj\no1YcKbXiaIy8VqzWnO4HAAAA0KtJOK0MAAAAgDERDgEAAAB0TDgEAAAA0DHhEAAAAEDHhEMAAAAA\nHRMOAQAAAHRMOAQAAADQsf8flbKiwUj+8JkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd83a7dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd82eb198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TK05PrbgGasXpqBWno1acxjZqxTmEQ9cnefLA55dz/C/N1RlJ/rGqHq6qO1bb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\nANCYcAgAAACgMeEQAAAAQGP/HwMZnIMKGtsZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd8215f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AgP6Znp5Okjz96U/P9u1eJsbdzMxMkuRxj3tckuQP/uAPkiRveMMbsn///iTmDAAAYFFX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\nPNJunW7uo2uvvTa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd815b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vt2cL+7w7yS8neaSqHp5s+80kH6yqa7PRBvxEkl9LkjHGo1V1b5JvJjme5PZz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\noLH/D6ZWG82VAOQpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2fd808e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e2ef84f4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 5, 1):\n",
    "    ## Dataset for prediction\n",
    "    x, y = valid_gen.__getitem__(i)\n",
    "    result = model.predict(x)\n",
    "    result = result > 0.4\n",
    "    \n",
    "    for i in range(len(result)):\n",
    "        fig = plt.figure(figsize=(20,20))\n",
    "        fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "        ax = fig.add_subplot(1, 2, 1)\n",
    "        ax.imshow(np.reshape(y[i]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "#         ax.imshow(x[i])\n",
    "        ax = fig.add_subplot(1, 2, 2)\n",
    "        ax.imshow(np.reshape(result[i]*255, (image_size, image_size)), cmap=\"gray\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}