mongoose54/UNet_Segmentation.ipynb

## UNet_Segmentation.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device 3 failed:\n",
      "Bad device number 3. Only 1 devices available.\n",
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is disabled, cuDNN 5105)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import libraries\n",
    "import numpy as np\n",
    "import lasagne\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import os\n",
    "import sys\n",
    "import fnmatch\n",
    "from Unet import *\n",
    "import cPickle\n",
    "from time import sleep\n",
    "from generators import batch_generator, threaded_generator, random_crop_generator, center_crop_generator\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from LMDBLoader import read_lmdb_images, read_lmdb_labels, read_lmdb\n",
    "from keras.utils import np_utils\n",
    "import os.path\n",
    "import nibabel as nib\n",
    "import scipy\n",
    "from scipy.stats import threshold\n",
    "from data_preparation import *\n",
    "import itertools\n",
    "import wget\n",
    "from itertools import islice\n",
    "from scipy.misc import imresize, imrotate\n",
    "\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "\n",
    "np.random.seed(123)\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: timestamping does nothing in combination with -O. See the manual\n",
      "for details.\n",
      "\n",
      "--2017-03-17 20:16:18--  https://www.dropbox.com/s/2vjan6mbcf2fsit/target.npy?dl=0\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 108.160.172.206\n",
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      "Resolving dl.dropboxusercontent.com (dl.dropboxusercontent.com)... 45.58.75.165\n",
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      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 461373536 (440M) [text/plain]\n",
      "Saving to: ‘target.npy’\n",
      "\n",
      "100%[======================================>] 461,373,536 15.9MB/s   in 34s    \n",
      "\n",
      "2017-03-17 20:16:59 (12.8 MB/s) - ‘target.npy’ saved [461373536/461373536]\n",
      "\n",
      "WARNING: timestamping does nothing in combination with -O. See the manual\n",
      "for details.\n",
      "\n",
      "--2017-03-17 20:16:59--  https://www.dropbox.com/s/0wljupgym34uwuu/names.npy?dl=0\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 108.160.172.206\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|108.160.172.206|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://dl.dropboxusercontent.com/content_link/kgi8pr3aVsRT578fOVnqDQHqNqdCMoncxbWFcd0BzuDe49nK4RyQK6DoDWTHBqSi/file [following]\n",
      "--2017-03-17 20:17:04--  https://dl.dropboxusercontent.com/content_link/kgi8pr3aVsRT578fOVnqDQHqNqdCMoncxbWFcd0BzuDe49nK4RyQK6DoDWTHBqSi/file\n",
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      "HTTP request sent, awaiting response... 200 OK\n",
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      "Saving to: ‘names.npy’\n",
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      "2017-03-17 20:17:05 (942 MB/s) - ‘names.npy’ saved [7340/7340]\n",
      "\n",
      "Data shape  (220, 1, 128, 128, 128)\n",
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d03d87250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Training/Evaluation parameters. You can change these parameters\n",
    "BATCH_SIZE = 1\n",
    "N_EPOCHS = 10\n",
    "N_BATCHES_PER_EPOCH = 100\n",
    "PATCH_SIZE = 128\n",
    " \n",
    "\n",
    "\n",
    "\n",
    "# if os.path.exists(\"/nfs-raid/data/Alex/BRATS2015_Training/data.npy\") == False:\n",
    "#     folder = '/nfs-raid/data/Alex/BRATS2015_Training/HGG/'\n",
    "#     images, target, names = load_mha(folder, 128, 128)\n",
    "#     np.save(\"/nfs-raid/data/Alex/BRATS2015_Training/names.npy\", names)\n",
    "#     np.save(\"/nfs-raid/data/Alex/BRATS2015_Training/data.npy\", images)\n",
    "#     np.save(\"/nfs-raid/data/Alex/BRATS2015_Training/target.npy\", target)\n",
    "\n",
    "if os.path.exists(\"names.npy\") == False:\n",
    "    #!wget -N https://www.dropbox.com/s/h4no2pm970im82k/data.npy?dl=0 -O data.npy\n",
    "    !wget -N https://www.dropbox.com/s/2vjan6mbcf2fsit/target.npy?dl=0 -O target.npy\n",
    "    !wget -N https://www.dropbox.com/s/0wljupgym34uwuu/names.npy?dl=0 -O names.npy\n",
    "        \n",
    "names = np.load(\"names.npy\")\n",
    "data_train = np.load(\"data.npy\").astype('float32')\n",
    "target_train = np.load(\"target.npy\").astype('uint8')\n",
    "\n",
    "\n",
    "target_train[target_train>0]=1\n",
    "\n",
    "print \"Data shape \", data_train.shape\n",
    "print \"Target shape \", target_train.shape\n",
    "print \"Names length \", len(names)\n",
    "\n",
    "\n",
    "print \"Target max intensity \", np.max(target_train)\n",
    "\n",
    "mean = np.mean(data_train)  # mean for data centering\n",
    "std = np.std(data_train)  # std for data normalization\n",
    "\n",
    "data_train -= mean\n",
    "data_train /= std\n",
    "\n",
    "total_foreground4 = 0\n",
    "total_foreground3 = 0\n",
    "total_foreground2 = 0\n",
    "total_foreground1 = 0\n",
    "total_background = 0\n",
    "\n",
    "total_foreground4 += np.sum(target_train == 4)\n",
    "total_foreground3 += np.sum(target_train == 3)\n",
    "total_foreground2 += np.sum(target_train == 2)\n",
    "total_foreground1 += np.sum(target_train == 1)\n",
    "total_background += np.sum(target_train == 0)\n",
    "\n",
    "\n",
    "print \"total_background= \", total_background, \" total_foreground1 \", total_foreground1, \" total_foreground2 \", total_foreground2\n",
    "\n",
    "plt.hist(target_train.ravel())  \n",
    "plt.title(\"Histogram with 'auto' bins\")\n",
    "plt.show()\n",
    "\n",
    "names_valid = names[201:220]\n",
    "data_valid = data_train[201:220]\n",
    "target_valid = target_train[201:220]\n",
    "data_test = data_valid\n",
    "target_test = target_valid\n",
    "data_train = data_train[0:200]\n",
    "target_train = target_train[0:200]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_train.shape  (200, 1, 128, 128, 128)\n",
      "data_valid.shape  (200, 1, 128, 128, 128)\n",
      "Name shape   (19,)\n",
      "Name:   VSD.Brain.XX.O.MR_Flair.35804.mha\n"
     ]
    },
    {
     "data": {
      "image/png": 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0+oy+o8byVAwCHIH3KJk8SRW1RNhCVIJzc3PJEfC73/1Oyw0rGmNM5VjRLDIo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\n223dfvvtfd9Zyfz3WNEYYyr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d6c6dad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARoAAAEWCAYAAAC5cVjBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAKvxCzj35AErbIfXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d60083d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_background=  2069732  total_foreground  27420.0\n",
      "(200, 1, 128, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "#Display the original image and segmentation image for sanity check\n",
    "print \"data_train.shape \", data_train.shape\n",
    "print \"data_valid.shape \", target_train.shape\n",
    "print \"Name shape  \", names_valid.shape\n",
    "\n",
    "print \"Name:  \", names_valid[12]\n",
    "\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.imshow(data_valid[12][0][50], cmap = cm.Greys_r, interpolation='none')\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.imshow(target_test[12][0][50], cmap = cm.Greys_r, interpolation='none')\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "total_foreground=0\n",
    "total_background=0\n",
    "total_foreground += np.sum(target_test[12])\n",
    "total_background += np.sum(target_test[12] == 0)\n",
    "print \"total_background= \", total_background, \" total_foreground \", total_foreground\n",
    "\n",
    "print target_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "net['reshapeSeg']  (2, 2097152)\n",
      "Net output shape (1, 2, 128, 128, 128)\n",
      "output_layer_for_loss (2097152, 2)\n"
     ]
    }
   ],
   "source": [
    " \n",
    "#Build network by passing specific parameters. See UNet.py for architecture\n",
    "net = build_UNet(n_input_channels=1, BATCH_SIZE=BATCH_SIZE, num_output_classes=2, pad='same',\n",
    "                     nonlinearity=lasagne.nonlinearities.elu, input_dim=(PATCH_SIZE, PATCH_SIZE),depth=128,\n",
    "                     base_n_filters=24,kernel_size=3, do_dropout=True)\n",
    "\n",
    "#Point network's output layer to output_layer_for_loss parameter parameter for later use\n",
    "output_layer_for_loss = net[\"output_flattened\"]\n",
    "\n",
    "\n",
    "#Some sanity check to see shape information for specific layers' outputs\n",
    "print \"Net output shape\", net['output_segmentation'].output_shape\n",
    "print \"output_layer_for_loss\", output_layer_for_loss.output_shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#Class distribution between the two classes\n",
    "class_frequencies = np.array([np.sum(target_train == 0) ,\n",
    "                              np.sum(target_train == 1)  ])\n",
    "\n",
    "# We are taking the log here because we want the net to focus more on the foreground pixels but not too much (otherwise\n",
    "# it would not be penalized enough for missclassifying terrain pixels which results in too many false positives)\n",
    "class_weights = np.log(class_frequencies[[1,0]])\n",
    "class_weights = class_weights / np.sum(class_weights) * 2.\n",
    "class_weights = class_weights.astype(np.float32)\n",
    "\n",
    "\n",
    "#Tensors\n",
    "x_sym = T.TensorType('float32',(False,)*5) ('inputs') #Input tensor (i.e. images)\n",
    "seg_sym = T.ivector() #Target tensor (i.e. segmented images)\n",
    "w_sym = T.vector() #Weight tensor for initializtion (i.e. see class_weights above)\n",
    "\n",
    "#Add some weight decay for more regulization to the loss function\n",
    "l2_loss = lasagne.regularization.regularize_network_params(output_layer_for_loss, lasagne.regularization.l2) * 1e-4\n",
    "#This is prediction function\n",
    "prediction_train = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=False)\n",
    "# This is the main loss function. We could use a binary loss but I stuck with categorical crossentropy so that less code has to be changed if your\n",
    "# application has more than two classes\n",
    "loss = lasagne.objectives.categorical_crossentropy(prediction_train, seg_sym)\n",
    "# We multiply our loss by a weight map. In this example the weight map only increases the loss for foreground pixels and\n",
    "# decreases the loss for other pixels. We do this to ensure that the network puts more focus on getting the foreground right\n",
    "loss *= w_sym\n",
    "loss = loss.mean()\n",
    "loss += l2_loss\n",
    "\n",
    "#This is the accuracy function\n",
    "acc_train = T.mean(T.eq(T.argmax(prediction_train, axis=1), seg_sym), dtype=theano.config.floatX)\n",
    "\n",
    "\n",
    "#Repeat the same code for the testing phase. This phase is deterministic. The parameters (i.e. weights, biases) do not update during test phase\n",
    "\n",
    "#This is prediction function\n",
    "prediction_test = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=True)\n",
    "# This is the main loss function. We could use a binary loss but I stuck with categorical crossentropy so that less code has to be changed if your\n",
    "# application has more than two classes\n",
    "loss_val = lasagne.objectives.categorical_crossentropy(prediction_test, seg_sym)\n",
    "# We multiply our loss by a weight map. In this example the weight map only increases the loss for foreground pixels and\n",
    "# decreases the loss for other pixels. We do this to ensure that the network puts more focus on getting the foreground right\n",
    "loss_val *= w_sym\n",
    "loss_val = loss_val.mean()\n",
    "loss_val += l2_loss\n",
    "\n",
    "#This is the accuracy function\n",
    "acc = T.mean(T.eq(T.argmax(prediction_test, axis=1), seg_sym), dtype=theano.config.floatX)\n",
    "\n",
    "#Use params to point to the network parameters\n",
    "params = lasagne.layers.get_all_params(output_layer_for_loss, trainable=True)\n",
    "\n",
    "#Learning rate has to be a shared variable because we decrease it with every epoch\n",
    "learning_rate = theano.shared(np.float32(0.001))\n",
    "\n",
    "#Use adam optimizer to update the parameters based on loss and learning rate\n",
    "updates = lasagne.updates.adam(loss, params, learning_rate=learning_rate)\n",
    "\n",
    "#Create a convenience function get_segmentation to get the segmentation\n",
    "seg_output = lasagne.layers.get_output(net[\"output_segmentation\"], x_sym, deterministic=True)\n",
    "get_segmentation = theano.function([x_sym], seg_output)\n",
    "\n",
    "\n",
    "\n",
    "#This is the training function. It takes 3 inputs: x_sym, seg_sym, w_sym and it outputs loss, acc_train and it calls updates to update the network\n",
    "train_fn = theano.function([x_sym, seg_sym, w_sym], [loss, acc_train], updates=updates, allow_input_downcast=True)\n",
    "\n",
    "#This is the testing/validation function. It takes 3 inputs: x_sym, seg_sym, w_sym and it outputs loss_val, acc\n",
    "val_fn = theano.function([x_sym, seg_sym, w_sym], [loss_val, acc],  allow_input_downcast=True)\n",
    "\n",
    "#We need this for calculating the AUC score\n",
    "get_class_probas = theano.function([x_sym], prediction_test)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started experiment\n",
      "epoch:  0 \n",
      "train accuracy:  0.946915  train loss:  16.1475\n",
      "val accuracy:  0.94241  val loss:  0.282434 auc:  0.954520172075\n",
      "epoch:  1 \n",
      "train accuracy:  0.981476  train loss:  0.257852\n",
      "val accuracy:  0.985988  val loss:  0.244424 auc:  0.974039193784\n",
      "epoch:  2 \n",
      "train accuracy:  0.983318  train loss:  0.248513\n",
      "val accuracy:  0.985218  val loss:  0.241975 auc:  0.975818158418\n",
      "epoch:  3 \n",
      "train accuracy:  0.984407  train loss:  0.246029\n",
      "val accuracy:  0.986744  val loss:  0.240007 auc:  0.977746604927\n",
      "epoch:  4 \n",
      "train accuracy:  0.98474  train loss:  0.241789\n",
      "val accuracy:  0.986814  val loss:  0.238244 auc:  0.978098466776\n",
      "epoch:  5 \n",
      "train accuracy:  0.984629  train loss:  0.240846\n",
      "val accuracy:  0.984349  val loss:  0.239883 auc:  0.977449660034\n",
      "epoch:  6 \n",
      "train accuracy:  0.984994  train loss:  0.238735\n",
      "val accuracy:  0.986878  val loss:  0.233186 auc:  0.97881310894\n",
      "epoch:  7 \n",
      "train accuracy:  0.984591  train loss:  0.236606\n",
      "val accuracy:  0.9849  val loss:  0.234146 auc:  0.97817358713\n",
      "epoch:  8 \n",
      "train accuracy:  0.984747  train loss:  0.235468\n",
      "val accuracy:  0.987019  val loss:  0.230162 auc:  0.978428275306\n",
      "epoch:  9 \n",
      "train accuracy:  0.984881  train loss:  0.232456\n",
      "val accuracy:  0.985607  val loss:  0.230317 auc:  0.978493411344\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print \"Started experiment\"\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    train_generator = batch_generator(data_train, target_train, BATCH_SIZE, shuffle=True)\n",
    "\n",
    "    #Training phase\n",
    "    losses_train = []\n",
    "    n_batches = 0\n",
    "    accuracies_train = []\n",
    "    for data, target in train_generator:\n",
    "        target_flat = target.ravel()\n",
    "        loss, acc = train_fn(data.astype(np.float32), target_flat,class_weights[target_flat])\n",
    "        losses_train.append(loss)\n",
    "        accuracies_train.append(acc)\n",
    "        n_batches += 1\n",
    "        if n_batches > N_BATCHES_PER_EPOCH:\n",
    "            break\n",
    "    print \"epoch: \", epoch, \"\\ntrain accuracy: \", np.mean(accuracies_train), \" train loss: \", np.mean(losses_train)\n",
    "\n",
    "    losses_val = []\n",
    "    accuracies_val = []\n",
    "    auc_val = []\n",
    "    dice = []\n",
    "    #Testing phase\n",
    "    validation_generator = batch_generator(data_valid, target_valid, BATCH_SIZE, shuffle=False)\n",
    "    for data, target in validation_generator:\n",
    "        target_flat = target.ravel()\n",
    "        loss, acc = val_fn(data.astype(np.float32), target_flat, class_weights[target_flat])\n",
    "        losses_val.append(loss)\n",
    "        accuracies_val.append(acc)\n",
    "        auc_val.append(roc_auc_score(target_flat, get_class_probas(data)[:, 1]))\n",
    "    print \"val accuracy: \", np.mean(accuracies_val), \" val loss: \", np.mean(losses_val), \"auc: \", np.mean(auc_val)\n",
    "    learning_rate *= 0.2\n",
    "     # save trained parameters after each epoch so that we get a snapshot of the network\n",
    "    with open(\"UNet_params_ep%03.0f.pkl\"%epoch, 'w') as f:\n",
    "        cPickle.dump(lasagne.layers.get_all_param_values(output_layer_for_loss), f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
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HHkuX8QwelnbluG336/o824HA2oHJ9t133965ASxYsGDC/urzzvVN9+968LLs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\n7Pr164EuMX3BC14wvm1SziS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c908e8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S37Q1tcyZqzrP0676fpPMt9fN86TcaQfAiBEjgDKf9oYbbgiUZDp15ak/T2Jf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\nJEldMpkeRI4//vjOvzM3dBLIX/3qVwC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b9812cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HPvShrmVXFzmmv/3bv+08N3v2bADuueceoNR/JznOfNDpIVAfRT3nNe9N0v3U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\naeBMpiVJkiRJaslkehVIEpz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DSor+wAMPAKU+ut6bQJIkSdKqYzItSZIkSVJLo2EUR0er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\nW75GinMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97fb6510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pU6YA7c85o5pnFHdJmojGqo12xG5J0u5mMi1JkiRJUk8m0+NcXW9c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\nXQ5eVrYB2oHIyvQY2tQ7+0+iDG0SntfS9pyjPfb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97e2e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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le3uOrZ4aa39Ty+T3kN9L3b08v5t2ULg8pgu53bwlzYZ2oERwmixJkvbHZFqS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\nWrFp0yag636cdkA3BVTWTdfpPKZ7cx7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sUyuddtbJeFLqJPBJmzOKdxL5JNaXXnrpyHZq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\nSZIkSZIkSZI0T/wPQWLiWIgdyVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97ca7e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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el5yTtH/wwQcP7EdqmrONup12BPScw+xHzsvRRx89ue6zn/3sgTYyqveiRYsA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\n6+NJupxlUgef/aqnrGprpdu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97b35950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FXftLyp5zlmR+woQJXe2o1znqqKO62pr9Z/R0k2lJkiRpeDCZliRJkiSpTybT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\nk4Bk/0mi99xzT6Ak0e0puup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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b979ac610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RkuLsP+csiXxS8Pq9dqqtbPPZz342ABs2bAC65Bq65D0JeLa1adMmAL7xjW8M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\nAXTnOz+PQw89dHQb6c4uSZIk6elnMi1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97836350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p7j+/pHmDeC79G7wMdk+DJrnIPNO/pnmD8jd0j1CBJpn5F5r79VM0zQozK0B9HxnsXn06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\nXbvdTpfHjRvX2UauSTsJT5qfY03tdI4TYMmSJUC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b976b4dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZJODtWPC27fxcbrnllqnjlyRJknT8mExLkiRJkjSSyfQil8Ry9+7d86/VMbpJ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\nmUJd0Ke4SY2T7qStFO1KIt2mP5k2qya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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97522990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8u59kNDXVqdlNjXNdd520oE1bk8i29W319FJ5L/vPNrJsjjkJal13nGXr84Gu\nHjjTS+V61PXOOb82zUj9c9Ledtqv1IPX28172WaS+JxnW0s+6r06ta5lGrGk0dBdgyTPOf+83v48\nRl2nHHv2m2nDPvvZz448Dkna0dqa6bqnUu6lJtSSpLnKZFqSJEmSpJ5Mpue4I444AugSyIy4neR0\n3333BbpaW5icVmcE7KSrd91119DzJKWjpEY620pSkHQ3SWrSbhhOa6FLbJMi5/0ks0lZoatrzvaT\nSKSeLjXVOa577rlnYt0sm3rnqUbTbtP2Ot3N6NipP8755Xpn27nGSb2hS/ZTe57zzfZTK54EuU7k\n6zrBep2k+9l2Uptcj1wn6K5rjj0165I0V7RjSbSvb+k1SZLmEpNpSZIkSZJ6Mpme4zJX8tlnnw10\nSXQ72vOomuWkxdlGkui2/jcpZ53QJvlNjXTS1tRZJ1Vo63Tr7SXVzTJJTLPfzBFdz1Gdf7dzQCeZ\nTcqc46uTi6S4SbdzrZJmZ93sI8slMYYugW6vQ84p1yEpcJ2u5HxzzZJat9fwpptuArp0G7rrmmXa\nOvdc3xxHjr2eZzsjnZ9wwglDx3bGGWcAcPnllyNJO8JUsxtIkjTOTKYlSZIkSerJZHqOS1KZlDOJ\nZNLdUfW37ejZSVuTbiYRzfzSSVTrkbGTpua9pMqRhLStP66PKe+1c1Wnljfpbj2KeLaT88lc0ddf\nf/3I9+tEPvvNeSe1zajh7aix2X/S5/p8k14/8MADQJd6t+lKnS5PNeJ5jiuvZ926J0D2l2Ui28/r\n6VWQ1+sRwTNvdxL5XJs6eZekbWXUSNybW2a622vvi1Opx99wBHBJ0vZgMi1JkiRJUk8m03Nc5pNu\nU8/UQ4/6lj+pcZLRJAT1iNv1+0mk64Q22026mTQ5y+R4su6o2uEksqnljSSnSdlrbY1yUu0c+9FH\nHz1yufoY2lHDk1CndrpNhus0I6/lfHO92yQ+51An00nv23rn9Cpok/q6VnzJkiVDyyStz2jlOa48\nb3sm1MeU80+aXo/0LkmzbdQI3bNZEz3dbTlHtSRpezOZliRJkiSpJ5PpOS6JZF1XDF2q3M6lDN23\n80mIM690EtGkn0l7R42Mnf21CXD2k/Qz+6glic46SVHvvfdeoEtzcw4ZubuW/a5fvx7oUtycQ/ZR\nz/PcJr85nyTGSapTh57647rePOeXRDppb17P81yfOonJ+ba12UnEjzvuuKFzrFPtHFu2n23ceeed\nQJfiX3kkx/p1AAAc+UlEQVTllUPnWtcS5hrV51Pv52UvexkAl1xyCZI02+bSCN3522lCLUnalkym\nJUmSJEnqyca0JEmSJEk92c17jlu4cCHQdUnOwF/1oFkw3LU3XaTTZfjQQw8FusGrMt1Tuj9n2xmw\nCrqu0OlOnf3lMctmcK9sE7pu3elWl3XSBTCP6Y5dT8mVddMFPe/lHNppvzZu3Dixbrr1tYOX5dhy\nPdI1Ol3Ecw71e5lOKuu23enTdTrXrt5vtpHBwdoB0VauXAnAIYccMrHuvHnzqOUaLVq0COh+Dunu\nnUHFXvjCF06s0w4Sl/2203tJ0q7C7t6SpG3JZFqSJEmSpJ5Mpue4JLHtgF9JqJNC1tNaRdLdTK+V\nZZKYJkFtp9Cql0m6mwG35s+fD3TpatbNcqOOPfvJ86kGyIIu6U2KnGXrgbbq869T7byWdZI8J8VN\nit0mE/V5571cq6S5Ocb2OtfnkuQ522iT6fQMSApdH0cGZ0u6nZ/d9773PaAbiOzGG28EYPny5UD3\n84Auxc41zH6SzDjwmKTZNGpKrLkmf3fy90GSpNlkMi1JkiRJUk9+VTvH5Zv/pJttApCktH49qWZS\n1dQ/Z+qkFStWDG17VKqQb/GTriYRT31x1k26mvpr6Ka6SkKahDjrJMFNUpzktN5ekujsN0l1jr2d\nQqreTtbJeeXxJz/5CdDVEOe4UqddbyOJc563PQGybn3tpqpnT3qcZZNC1/Izys/5u9/97tCy1157\nLdCl7GeffTbQ1VLX+821yf7q5F+SZttcmhJrKu19WZKk2WAyLUmSJElSTybTc9ySJUuALsVNMpqE\neMGCBQCsWbNmYp2ku0k1kwSfeOKJwORRvUel3qndTUKb5CHPs812ZO76vSS+SbcPOOCAoXNLGlvX\nsmWd1GjnvYxifeCBBwJd+l6vm3XamuVco7yfxDzv18l0zjvXIil7Uu1ly5YNrVunvrmOeS1JyNFH\nHz30eo591apVE+sefvjhAPzt3/7t0HH88Ic/BOD73/8+AC9/+cuBLjH/0z/904ltPOtZzwLg2c9+\n9tAxjkNqJGl8jEOttCRJ24PJtCRJkiRJPZlMz1HtvMNJW9uUsR0hun4taXGS4qS7J5xwAgD/9E//\nBHQjc6cOGLq0Nols5jDO66lLTkJa1z1nO1m3rTfO8zzW6W7S46TWbdocWbd+PdvJfpMM5/yT5uda\n5Xn2VW8v9eXteSaZz2M9mnaS/uy/nec5c1fnOtTzTGe07iThSa9vueUWAE499VQAjjvuOADuuusu\nYHie66uuugrofr75fyUJ9QUXXADAu971LiSprzaRtteLJGlXZzItSZIkSVJPJtNzVJLgdevWAZNH\nwk4yOmqk5ryXlDUJdZLS1OOeeeaZAHzlK18BhlPWjASex3Zu6nbe5TqhaOus27rqdlTVeu7mdiTw\nJMSrV68e2m8S43qe6Tb5TqqdpDwJdM4l6rrrHGPOM4l0ttHOv510u34tqU32345afvDBBwNdugzw\nrW99C+iu3fr164e2+Za3vAWY/DOtr2WOJSN+JyFPrXqeS9J0TFUbPQ6JdO6Nba8mSZJmk8m0JEmS\nJEk9mUzPcUkbkxrn2/aknJnfuU532/mVk3LnG/qk29nWL/3SLwFwzTXXTGwjKW6S6dT7JinPuklS\nM8o2dGlx0t22ZjjpblLoOvVIIp1UNwlsnud4su06VY4ksEmv2/mXc63qWulok+/sJ9csxz6qhj3L\n5r0jjzxyaJkkxOkZcMUVV0ysmzQ51/Hnf/7nAXjVq14FdCOzZ7nMO13XqudaZfs5jtS55/gkqY9x\nSKJb7QwUJtSSpG3BZFqSJEmSpJ5sTEuSJEmS1JPdvOe42267Dei6TqdrcrpuZ4CuelCthQsXApO7\nQtddwaHrFhyZUgm6Lt8nn3wy0HUVTpfsdKXO80zHVe83209X5HQ/Ttf0DLKVLtXQdcVLd+5MEXXz\nzTcDkwcoq6fzyjVqjzXnnddzPOl2Xg/ilv1nP+lWna7qOdY8zzbr7eaYbr/9dgBOOeWUoePIz+eo\no46aWHf58uVD1yw/zzxmWrNc50ylVQ+Alv2nK35+DkuWLAHgfe97H5K0OXWX7nbgsXE0jl3UJUnj\nw2RakiRJkqSeTKbnuCSwSWZPOukkoBuoKslkPbhK0oQMuJVEOANxJSFNkpvl6sG8li1bBsANN9wA\nwEte8pKhbSVBzbf+eb0+liSkeZ50tR28K6/Xx5DEN9NHZVs59uyvnhorkhpn2cMPPxzoktokw20K\nXB9b9p/r8KMf/WhoG3nM4Da1JO4ZNGzDhg1Al1C/973vBWDBggUT6ySlzqBkOY5f/uVfBuCrX/0q\n0CX2Gczsta997cQ20psgg6XddNNNABx99NHA5CnJJGlzTHUlSdo8k2lJkiRJknoymZ7jVq1aBXS1\n0alVTl1u6n7zOnTJ9Lx58wBYvXr10DJJYjPdVl6vp1nK9lNPnAQ4z7NOnUhHEuHUF+d51k1ym1S9\nrstLqpqEeuXKlUCXIicRzvHVqXJbo51jbtOV7Dcpb/ZZn08ec2xZNlN0JeWtt91OfXXccccBXaqc\n6a6SWF955ZUT6z7zmc8Eupr4XLuvfOUrQPezTJr9whe+cGhf9TFu3LgR6FLz9DyQJEmSNHtMpiVJ\nkiRJ6slkekwkCc1I2Elkk4bWtbtJU1NvnHWS2CbBTUKblLdOiDOKdeqLM3p40uzUJUddb51jS4qc\n7eYcUv9cp8rtdpLIt2lzm3LX9dZJc9tRzLNu9p/3o36epDk1y+3o4kmmc73rOuS8144Inmu3YsUK\noKuPvuWWWybWzfXOzyLnku3nfHMco1L1Y445BoCvf/3rQFdvn+OSpKnk3rczjOBda8+rHl9EkqSZ\nMpmWJEmSJKknk+kxkXQ137InkU5CWc+VnNQ0j4sXLwa6GuKsk5Q3SXGdsqZ2N0l0XU9d7y+Pdfo5\n1UjbSVtTw53n9XKpxc780kkRUjOe887rda14O2925mLOaN55P/vNPg499NCJdXLNcp2TnmfZth69\nniM77+VaZQT20047DYBPfepTALzgBS8AutQfutQ655k0O/NPJ6lvE/LUYdfX5LLLLgM2P+K5JI2y\ns47gvbOelyRpxzKZliRJkiSpJ5PpMZH63yTGSUZH1X8lcU6amuf5Zj7Pk8JmzuosD12amcfsJyNR\np1Y326qT62w3qW2Ode3atUCXhI+aozlzImedWLJkydA5tPuHrkY8aW3mXc48z8cffzzQ1ScnVU9N\nOXTJcJZJ3XPOJXXIo0bzTpqcbSSpzjn92Z/9GQBnnHEG0F136JLpq6++GuhG7T7xxBMBePOb3wzA\nN7/5TaAb/Tujudf7y7ElVX//+9+PJI2ys9ZKS5K0PZhMS5IkSZLUk8n0mLjnnnsAuOGGG4AuTUg9\ncJ2QJj2NjNrdzhkd7dzN9bJ5r02R8zyJdV2vnH+vWbMG6Oquv/vd7w6tu+eeewLDqXZGCU8inJR1\n0aJFQJdIZy7l1BTDcP00dPM8JxE/7LDDgC7NzkjgqUOGLnk/5JBDhvYfOeakvkmj6/PIdpM833bb\nbQD8wi/8wtB1yPUBeN3rXgfAhz70IaBLiZKa55o+73nPA7oeCvVI5Om9kPOrf56SNErucbl/mFBL\nkjR9JtOSJEmSJPVkY1qSJEmSpJ7s5j1m0t03A0+le3c9iFa6Xu+7775A130vXYLTHTpdldMtuO4y\nnC7L2VYG4GrXzbZHHWO6d7fLZoCubDNdtwGOOOIIoBuALIOi5VzSZT3HVXfDzvZy7AcddBDQdQnP\nNcq2050xx1cfe9bJNclUVOk6na70mxsALOeVruunn346AOeffz4AH/zgByfWPffccwE477zzAFi6\ndCnQDaqWqbGOPfZYoJsirO5en2VHTTkmSaPkb8ioASElSdLmmUxLkiRJktSTyfSYSBKZhDYDYSUh\nTRoJXSKZZduBtpLcZhCrJKf1QGCRtDX7ycBkSUSzTp3u5lja5DvJcAbmWrx48dA2oUt6k+7mGLON\nTFmV5LaeGitpdY4pKXI7MFf2kYHJ6nQ928ix5polTc9x5Xmm+YIu4cljBidrBwt7z3veA8BVV101\nsW4GJ/voRz8KwGte8xoAfu3Xfm3oGK+//nqgG5CtTuZzLHfccQcwnFpL0ubUg1hKkqTpMZmWJEmS\nJKknk+kxkTQ3KXNSz0zhVE+HlbTypptuAro0NwnpUUcdBXQpbFLuOuXNv5NMZ5nUDqd2OmlG6pAB\n1q9fD3SJc7aVqaqSmGadOhF58pOfPLTf1B3n/BYsWAB0U4SdeOKJE+sm+d2wYQPQpcc5jiTmmWYs\n16OWa5djzHGknjDnkmPO8dbaZZNyp6dAegJkmiuAa665Buiu62c+8xmgS6CPP/54AJ7znOcMbfPS\nSy+d2MaKFSuArq78yCOPBODqq6+edIyStCvIPT1/7yRJmk0m05IkSZIk9WQyPSaSMieJzsjNSV2T\negJcccUVQJfmJhFOqpzkOMltm77WkgwnZa1H3q7XSaIKXRKQ7SYRSK1yUt+8nuXaf9fPkwSnhnnh\nwoUjl4fumiSpzvPUkrd12LXUeafuOml2+5iRyvNzqP/d1k5nv3nMNlKzDbB8+XIAvvjFLwLdqN23\n3HILACeffPLQ8/y861Hccy1ybKmzl6SptMntqHvqzqAeX0KSpNliMi1JkiRJUk8m02Mm6WZGbk5d\n7I033jixTGqGTznlFKBLj5M8ZA7lJBD77LPP0Pv1e0m3s9+2Rjiv1yNHt8lG3suySYpHzW/aJtH1\nKOH18+nUv2WZdr7lJBRJiOua7byWY2rnik7aWyfxkbry7Dc/h4zEnW3mGua6Q3dNcmz/8i//AnTz\nTqfuuZ7XGoZr5XNMSe//8i//ctIxStIoJreSJPVnMi1JkiRJUk/jMLGkX5dXMhJ30s5ly5YB8K1v\nfWtimdNPPx3o6orbuuck00k1s616vuVo637b1HnUiNz5d7vfJLLtfupEpK3fbuucM1J1Huta8cwr\nnXXbkcZTB51a6qTQdaqe9yJ15bmW7bZzLWvZRpLqHGOWTZ1zatjra3DttdcCsGrVKqAbxf3CCy8E\n4Bvf+AbQjeaeEbyhq8H+7Gc/O+mYdnHjcJ/bWt4fNSO5l7W9gsZdWwtu8j6lneMHPtrdwIIdfRCS\nxtZFwFu3tJDJtCRJkiRJPVkzPWZuvfVWAE466SSgS1Vf/OIXTyxz6KGHArBp0yagS0qT5madJMdJ\nJOra4tQmJ01uR+COpM51upta4aSq2U+S4KSqixcvHjou6FKRtq65Xga61DfzbsPkUbyTZqeWOOtk\nm9lHncQkvc5rSe+TIrdpe+qTobuOeS/XMEl01s3rdT149pck/MwzzwTg4osvBuDLX/4y0NVMp6Y6\nx1ufpyRNV+6D6TG0s43mbSItSdqWTKYlSZIkSerJxrQkSZIkST3ZzXtMZVqlu+++G+gG2apfSxft\neqArmDztVAbVygBe0HWFTrfudnqtdAXMY939O9NHpdt42808A3Klu3c9IFmWSZftdJluBzXL/nKc\n0HXjzjZyfukaneuRddOFO92w622k23rdfb1+nqmy6q7a2X66Xud5Bgl75jOfOXRd6muWZQ866CCg\n6yp/7rnnDm0zXeOzjbr7e30ekjQdo6YoHFd1l+7pTJ8oSdJMmUxLkiRJktSTyfSYSiKbAbiSZEI3\n8Fe+pU8SvHr1aqBLffN+0thRCW07bUoS1CybQa+SMgMcfvjhQJf8ZpCuNvnI/uuBs/Ja0vSsk0S4\nTbfrqamSWucYc+zZVjsQWd5vp8Oqzy/Hk54AK1euHNpHPVhP9pPUOIl7BkK7+eabh/aR1L1ep70m\nSbVPPPFEAG688cahY673f9lll006D0nanJ11ADIHHpMkbQ8m05IkSZIk9WQyPaYyVVKmyEqCCV39\ndOpvU9eb19vkNglyXX+cddopm2666SagS2qXLFkCDNcWZ90kHEl5k3zce++9QFdbXSe02V9bq5zU\nuZ0Sq54aKtvP/rPddmqutg4724QuIc5r7XRbU02dBd31TZqeZXM8Ob6cd53m53xWrVoFwCGHHDJ0\nHX7wgx8AcMsttwBdcm2dtKTZMM5Jbo7dOmlJ0vZmMi1JkiRJUk8m02Nu0aJFAKxfv37itbvuugvo\nkum2Bi61vUlbM9p1vVxqk5OmtiO+HnbYYUCX8tb10O3o4UkNsv0kxVk3STl0SW+WadPm1F+ndjnH\nDl0qkTS5rZnOMeb9qEfkzntJfnPsOdak29nmggULJtZNupxjzv7yPMeabaWWur42xxxzDND1BEhN\neM4lSXTONSm3JM3EqHR33OqnxzldlySNJ5NpSZIkSZJ6Mpkec1/60peAbrRn6NLMpNWpb26T2dTj\nJn1I+gld7XCS16uvvhqAhQsXAl2qnaS0TjCSDqRmOElHEtnst01ZYXIS3u5nzZo1AFx33XUAnH32\n2RPrJuFOTXbS7Wwr59TOYV3Xire10e2y2WbOt+4RkOucZdvkPXNT53k9v3ZS7STvmzZtGjrm7Kc9\nl4svvhhJmi317Aa5N49bQi1J0vZiMi1JkiRJUk8m0zuJ733vexP/Puqoo4BuBOhly5YBXaqax9ao\nWrnUE//whz8E4DnPeQ4Ad999NzB5jtJa6p/zXhLZJB9Jyus6tySv2W8S4vnz5wOw9957A11Ncb3f\nJMxt8p79JN1OnXHOtx45PMeWhDiPOd82qRm1buQ6tzXjOebUpdevtXO95vxzLbNORg6XpNlU91Cq\nx8KQJEmTmUxLkiRJktSTyfROaN999wW6JPbaa68FukR2+fLlQJfuJjGuR7VO8pmRwZOytnXQeb1O\nWZOipmY4dc7HHXfc0H7apLreblvPnKQ27//Kr/zK0LnB1HNC53iSbqd2OaNpj0rV81q2lXmnkzLn\nuOp1cw2y39RQp/456yTtqecGT2163su1aeeRTq30xz72sUnHLEmzqU6p5yLnl5Yk7Wgm05IkSZIk\n9WQyvRPKSNepnX7uc58LTK4VbiVBrZfNfM6LFy8eWjfpcpKLpLDQpdVZJuu0iXFer9PdJK9JcTPf\nc9L2JBEZ/bo+5uy3TbdzjKPmta63CV0NdI4180m3qXM7H3Yt59OmyknOk/bXc2SnNjvn3+4/55na\nbUnaVbWJtPNLS5J2FJNpSZIkSZJ6sjEtSZIkSVJPdvPeid16660AHHnkkUDXJTldiNM1Ll2pa+lO\nfPvttwNdN+t7770X6LpDpwt13f043ajTrTldxjO4VtvtOl266/fymEHS0mU83Z6vuOIKoBu4C7ru\n1Vk2g4bled5Pl+rso+5m3l6TPF+/fv3QOWXddPeul811rs8LYPXq1UB3/bMt6LpxZ6C1DJKWx5zL\nwoULkaTZlnta7kHjwO7dkqQdzWRakiRJkqSeTKZ3AUkzk34mGU36mtSzTqiT5mYQswymlXQ16Wub\nPtfLZrCuPCapTQLSJuT1dtsUOwOcJanNNusBwLK9dsqtLNum3lE/z/lkgLO9994bGE6goUvm62PP\ndUx6nvfy+oEHHjh0bjkX6FL6HHOuf1L0nNMll1yCJG0rc3k6LKfCkiTNNSbTkiRJkiT1ZDK9C/jk\nJz8JwMte9jKg+3a/TUjrWrl88580OalqEtkkp9lWpnSqX8vjfffdN7RuOzVVLel4lsmUVKlrznE8\n73nPA7q6cOgS4b322mtoG6nzjhxH1ElMjjmJdBLqefPmDZ13rl32BV26HOvWrRtaJueQ/W3cuHFi\n2aTX6T2QJPzggw8G4P3vfz+SNJfVPXVyv5uNGmynwpIkzVUm05IkSZIk9WQyvQvIt/ipt33+858P\nTB5VOikwdIn0nXfeCXTpah5Th5ykoE5320Qidc3tCNhJiOvkIv9u66tb8+fPB4ZHEa+Pv95PRiJP\nUp195Lok0YYuXU5CnudJqHOtksSvXbt2Yt3UpueapI78rrvuArrr3Z5jfZ4XXnjhyPOVpG1pVD3y\ndFPlUeu2r81mQi1J0lxhMi1JkiRJUk8m07uQfKt/1VVXAXDQQQcB3YjRqUeGLnlNypyENglqPXo3\nDKfCSaJTX5xkIttKUpy0t04bsk5S6+w3z7NOtlEn021Ndo4jz7Otdq7oJMb1eadmOql1m7ZnuTqJ\nSQLdjiaemvEk0W3KD9ZES5ob+qS/06llbmdViHZe66ybey10Y1M4erckaa4ymZYkSZIkqSeT6V1Y\n6qGXL18ODKfLSXGTDKxZswbo5p2uR6KG4dQhCUNqlpM0JHVOIpEEN/NS18eQ0a2TIidlTi1z9lfP\n/5xRu5csWQJ06XLOIdvKudXJcHvebYrS1v3lHOrlskxGR8/53XHHHUA3Mvd+++03tLwkjbPppNnt\nMnne3mvr5Ry9W5I015lMS5IkSZLUk8m0uOWWWwA46aSTJl5LbXKbrn7nO98BYNGiRUBXp5zn0M2V\nnPeSPOT5bbfdBsDxxx8PdPXPMLneep9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b973b9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A+TDdPVSgSWa+RHO9foxmsMLMClBfR4a6Vh/F4Ovi2jQf2B6i+YB0Hks/z/Rf6R4F+Kn+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\np2K6LEn96zW9q7QqMZmWJEmSJKlPJtM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b97237e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kSdp7TKYlSZIkSerJZHqGS8qaWluAww8/HBif+LbpZpLZPCb9rbebtLUdoTvb\nyDr1CN3ZT9ZJO+bPnw90NcurV68GhkeoznupDU79c2qGN23aNLSPOhFPEpx676S77QjYOba0eenS\npYP3su62bduGjq8dET211WlvfZzpEZAEOsumHelFUI9InnXb85ua7eynHQldkiRJ0t5hMi1JkiRJ\nUk8m0zNcUuXU9gIcf/zxQDd6d9LMpKrt/MtJSpOkQpdIJxFt51VOcprHpND1a6kDTmKc0bbTjqSu\n9YjcSaST4iaxTe3wvHnzgK4uPIk1jJ/POvtJApx2ZL9Z7qMf/ehgG6lBP+KII4CuJrw9d+0x1ucg\n7UhbM4p49ptt1DXVOb/ZT5tU5/2NGzciSZIkae8zmZYkSZIkqSeT6Rlu3bp1wHDt7oYNG4AuXU0C\n3T4m1c181PWo1lkmczUn3U5SmteTpNb11knL27mob7nlFgAWL14MdCnrCSecMFg3NdFJyevt1tvM\nPrI8dDXZSZOzbmqWU8ucxH7lypUAvOhFLxpsIyOf33zzzUPHl0S+TYwz+nfdpjbFnzNnztAxJZme\nPXv2YN3UaGd7Sc2TgCeZ37x5MwBvfvObAbjooouQJEmS9OAzmZYkSZIkqSeT6Rnu3//93wF42cte\nNnjtE5/4BADnn38+0NVEt/MeR96v63GTpqZ2uq07TnKaFDapLHRpbtZNqpq5mzNCdxLqeu7obDfL\nJmXPNtOutCepc71M2tjWJqet2faZZ54JdOk+dMlwOyJ32pxjirpWPG1J8p2Uux3NPPurR+bOuu1c\n2EnZs42057LLLkOSJEnS3mMyLUmSJElSTybT+4izzz578Pdrr70WgNtvvx2Ak046CejSzowqneQ0\nc1TXqWs7F3LmX846SXuTKidRrreT+uakrm39c0a5ruu9U1+dmuS5c+cCXY12Xk8SnuS2bkObjLd1\nyG3qW2+jTd7bbSUhznmpR/POcbTzXOe4U6OdUb7rVDujsSetzzpp6zXXXDO07rnnngvApZdeiiRJ\nkqQHn8m0JEmSJEk9eTMtSZIkSVJPdvOe4dId+SUvecngtd/+7d8GYPXq1QAsW7YM6Lowp6t2BsLK\noFZLly4dbCPdl9MVO9M4ZYCsvJ7u1ul+DXDfffcBXdfodJVOd+p2sK26i3i2k+7U6QpdD3AG4wcX\nq5epu41D12U9bY62K3ctg6a1U2Blv3mewcagO2fp3p32ZP+R4//ud787eO3www8f2n47NVbWSXvq\ngdckSZJSnfboAAAgAElEQVQkPfhMpiVJkiRJ6slkeob7tV/7NQCe9KQnDV5797vfDXQDgCX1zJRQ\nK1asAODGG28E4IwzzgBg27Ztg22sXbsWgEWLFgFdUrpp06ah/WcfdUK7fPlyoEvAM1hX0t4kwUmf\na0lzk8hm2UxVldQ3x1Kn2u0UYNn+5s2bgS6xTpszEFver89BtpsEOlNyJSlPgpzHuu3tYzvQWfZX\nD8iWc5Q2tvvP8V5//fUAPOUpT0GSJEnS3mMyLUmSJElSTybTM1zqk4844ojBa6mRTjK6YcMGAL74\nxS8CcOyxxwJwwQUXALBw4UJgOCnduHEj0KW9SaSTrrb1zkl5oUupM0VUpD2p1U4ddr1ctp9E9qij\njgK6qaMyVVZS75/+6Z8erJv2Z6qvpLw5hpyrWLNmDQBbt26llbYtWLAA6BLyNs2u66GTWqdWPPvL\nMvPnzwfgG9/4BjBc7522phY6z1PPngT8+OOPB7qfx8/93M8NtvHJT35y3HFIkiRJ2jNMpiVJkiRJ\n6slkeoZ6/etfD8BJJ50EwLXXXjt473GPexzQJcSpVT711FMBWLJkCdDVIScFTtoMXb1xUts21X3E\nIx4BdOnqkUceOXgvyXPS44xAnedJopNC17XT2U/alOQ7bU3tctSjfCeRTrqb99o66Mjr9TbSthxX\n1knqnTZnuXok8BxnEuo2mc/+8nOo181799xzD9D9LJJyJ6nO66n7PuywwwbbSEr/9a9/HUmSJEl7\nlsm0JEmSJEk9mUzPMK9+9asBOOaYYwD45je/CXSJJnQJaRLopJlJVzMydBLUpLD1XNHtvM5ZNvW/\n2Vae1yNip7436XXS3aTOGZE7qXdds5yRx5MAZ9nsL9vOfjMKNnSJeGrA21rpbDP1xjnezA9d/z3n\nJAlwaqiTricxrtPudr7q9rizjSxXjwSetmbu6ZyHtCP7y880PRFyjqGrr5YkSZK055lMS5IkSZLU\nk8n0DJGEMqNZ33HHHUA32nQ9R3TeS2p9yimnAPCtb30L6BLc1NsmQU0KXL9Wp7b160muk1jXy6VG\nO6luEtgskzQ5yXBdq53kN69lBOxbbrkF6JLZjKqdeZehqxXOMuecc87QuUlym6Q4tdV1upwEPDXb\nkdHN83NoU+j6eCL7yc+sTebr/abtOd627juP6YGQY8q2Ac4++2ygO1eSJEmS9hyTaUmSJEmSejKZ\nniGe+MQnAl26mxQ0CWrqoKFLnpMIp0Y4qevatWuBLv3NSNn16NNZNglsHpPMJiFNypp6ZOjqeDMC\ndRLZJNUZZTztq5PpJK6Pf/zjAbjpppuArqY46ybdzj6gqyvO8aR2OtvIOWvrnUelzEnXc77zmNdz\nTHXNcl23Xu8nNeHZf44l81xDl0inp0GOK70MMop5fi4Zubv+mWVU9PREqFN7SZIkSVPLZFqSJEmS\npJ68mZYkSZIkqSe7ec8Qz372s4Guu2873VO6UEPX3TddhdOdOMs85SlPAbpBvNJ1uZ6qKd2JM2hX\nBsRKN+90e047sny9n3RrTlfwdFnOshlUqx7sK8fTdo3OseQx3avrbs4ZeOxpT3saADfeeCPQDZIW\n2W+OIV3H6/2ni3a6bud52ppjrKcQawcLS/f1tDXnN+2pz3d+RosWLQK6gdhy3nPcbRf+DIwGcOKJ\nJw61UZIkSdKeYzItSZIkSVJPJtPTXJLgxz3ucUCXRCbdTTJcD6KVNLWdiikpc9LtpJtJf+t0uZ62\nqZbXs+2ksPW62W72m8HCkuYmTc469boZSC3pbbbRLpsBu773ve8N1s00UUnas40k9TnOtH3Usebc\nJd3N8WWZJNf5uYzaTvafAdCSKiepTo+AL37xi+OOO21tpxXLY9o1avC2Uf8WJEmSJO0ZJtOSJEmS\nJPVkMj3NnX766UPPt2zZMvS8rQeGLoFNre7VV18NdGln6pHzfh6TqEKXpiaZTdqZbScRzeOopDYp\nb7abKaGyrexjVDIcSWqzzsqVK4f2V+83yXTalKQ2aW62nePNNFypna6PL4l42pb9JFWva9Qjy6bN\nqXvOVF3ZX9q1YsWKwbove9nLhtaJ9CbItmfPng10P/e67UnNly9fDsDXv/71cW2UJEmSNDVMpiVJ\nkiRJ6slkepqbN28eAKtXrwaGR4CG8SNIQ1dXnET0qU99KgCf//znAbjuuusAOPLII4Gu/jh1u9Cl\nuEmE2xrttCOv1+3KOnlMMpzEOIl0RsiuZZ1sL8efkcCTcmfU61tuuWXcupGEuB2RO9seVWOcBDjJ\ndLaRZDpJcdLtjLYNXW143stxJzFOqtzWX0OX2idxTtvyM8zP9/bbbwfG153X2z3ttNMA+OAHP4gk\nSZKkPcNkWpIkSZKknkymp7mkm0k7169fD3Qpb1LlWup+k3yecsopQDdH8apVq4AuoT3iiCOA4VGu\n27rf7L8dITzPkzbXr2X/aU/S1aS5SXnrdeukF7okOq9nm0mTM8o5dDXJmTc6565NyNvUuU6Ik+62\ntdE5V3k/j3WNc0bpbuuuU9ecdH3x4sUA/Oqv/upg3bQ5+0vi3J7/9B7I/uua+ez35JNPRpIkSdKe\nZTItSZIkSVJPJtPT1Jlnngl0iWxqppP6Ju289dZbATj66KMH67ZzEieZPf744wH40pe+BMCCBQuG\ntlmPqt0msklKs628n1riuna3HZE72nQ5qXpd790mz0lzDz/8cKBLe3NsmzZtGrmvehs5hrS1fZ5t\nQXcucnxpW44piXUe63XTWyD7TfKdxDz7TW113augretOUp0kuh29PIl0Xe+dea2zriRJkqQ9x2Ra\nkiRJkqSeTKanqSSfp556KtClmXV9MXTJaOqhoRsBO2lxlkmanZQ3qXbmsk6SChOnuUlCU8ucOt26\n1jkjU2c+6+w/7cl+kgLX81unjW1dc9LdJLVJZusUPNtrRwlP29t5pttjrP/ejmKeY0hSneOuR0Bv\nz0nbriVLlgDdz7Keq7qtDZ8zZ87Q/tsa6aTgdTKeGvRs48orrwTg+c9/PpIkSZKmlsm0JEmSJEk9\neTMtSZIkSVJPdvOeptJ9N11207073Z3T7TddmuspmjL1VbobZ6CxbGPWrFlA16V55cqVABx55JGD\nbaQrdKS7cbpGpyt3tpEuxtB1kU5X9ayTbaYLd9Rd17Nulkm36mwj3aGzTj14WbpTp61tl/i2q3rO\nT90tO+c73ag3b94MdD+PdNF+7GMfO7SvervZT7pxp+t6Xs/UWGvXrh2s256TdOduz10es836+PP3\n/Cxy/iXpwVCXzOSalt9VbfnLROvW19SdrSNJ0t5mMi1JkiRJUk8m09PUCSecMPQ8SWm+5c/zJLV1\nkpxENK9lmaSchx12GNAlqUmuMw0VwOzZs4FuALAksm1ynDShThDalDXv1VNv1euOGgAs2uNNEp99\nZCqt+r2sk21NNL1VntdtzzpJqNspsvJ46KGHAt15gfHTh2X7Sfyz37SvPh/ZTj2gGHQ/szwmbU+b\n63OddZNq18m3JD2Y2ut7rou57uVa2v6+kCRpJjGZliRJkiSpJ5PpaaROZZOMtilm0s92mql6Wqt8\n05+62tT9Jm1OyplkoJ7WKtr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v0uTNmzcPtTHabSSZrn/u+TeR83zGGWcgSVNtXxjUcF84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\nCTufOgygMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b970a7b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vbr2WXgU5l0moTaYlSZKk8cFkWpIkSZKknkymHwcyr3Rqg5OuJolOGlon1lk2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\nktST3bwniEwzNX369Nbr6f48rKtyuhe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b96f3e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LX/oSMHzO4qS9WTcJcu2GG24ASjKbJDgJalLlpJuzZ8/urXvjjTcC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\naNmyZQDMmDEDgGnTpgElqY1M/5T91/t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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b96d40fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tujxq33aysvS5HjUAwyMDJEmSJB1cJtOSJEmSJPVkMj3Npba5Tj1vu+02ANas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\nWaWkyHlsJ+upE5EktEnIkxRnGatMZjZ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73L7y/VHS/pjJ748m05L2h8m0JEmSJEkHwnT4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\ntY5gUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b96c2bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1x1/Xe0f2l8e0px41vW17znvakePL9nMuc0z16OmRHgFpW/af8/2qV70KMJmW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\n71nalv0mec+5Suq9fPlyoJz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b96a43890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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kSZJGMpk+DiR52bRpEwDr1q0DhpGi61zKMHck7DoXdZLpvN/XDmeZ1F2nhjl1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\nOlk2+hrlvl192+pxTZsnESa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b968d3250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ioBv4qu5mXC+Hrnt3umynW/ftt98+8P6cOXOArmtzvd9MPZVBw4466ihg7BRW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b967b1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range (0,19):\n",
    "    data = np.zeros((1, 1, 128, PATCH_SIZE, PATCH_SIZE),dtype=np.float32)\n",
    "    for j in range(0,128):\n",
    "        data[0][0][j] = data_valid[i][0][j]\n",
    "\n",
    "    res = get_segmentation(data)\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    ax = plt.subplot(1, 3, 1)\n",
    "    ax.set_title(\"Original Image\")\n",
    "    ax.imshow(data[0][0][50], cmap = cm.Greys_r, interpolation='none')\n",
    "    ax.axis('off')\n",
    "    ax = plt.subplot(1, 3, 2)\n",
    "    ax.set_title(\"Ground Truth\")\n",
    "    ax.imshow(target_valid[i][0][50], cmap = cm.Greys_r, interpolation='none')\n",
    "    ax.axis('off')\n",
    "    ax = plt.subplot(1, 3, 3)\n",
    "    ax.set_title(\"Deep Net Output\")\n",
    "    ax.imshow(res.argmax(1)[0][50], cmap = cm.Greys_r, interpolation='none')            \n",
    "    ax.axis('off')\n",
    "\n"
   ]
  },
  {
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