Seg-NET Architecture/Seg-NET-implementation-TOP VIEW HEADS Dataset.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Seg-NET-implementation-TOP VIEW HEADS Dataset.ipynb","provenance":[],"authorship_tag":"ABX9TyOBK8ga0pcOdh6nIJnMbUui"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"UgP-sKTL1BDZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":122},"outputId":"1540c722-1380-469b-b019-c9eaa013b2ed","executionInfo":{"status":"ok","timestamp":1585144501437,"user_tz":-300,"elapsed":17409,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jtHzCIKD1RIa","colab_type":"code","colab":{}},"source":["import os\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","\n","from keras.models import *\n","from keras.layers import *\n","from keras.optimizers import *\n","from keras import backend as keras\n","from keras.preprocessing.image import ImageDataGenerator\n","from keras.callbacks import Callback\n","from keras.callbacks import ModelCheckpoint, CSVLogger"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NvsmNWe51wUK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":85},"outputId":"d4e25f5e-c308-455f-e50f-e5f8f367cc40","executionInfo":{"status":"ok","timestamp":1585144581963,"user_tz":-300,"elapsed":1749,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["os.chdir('/content/drive/My Drive/GitHub Repositories/State of Art Segmentation Network Architectures')\n","dataDir = ('./datasets/TOP-VIEW HEADS DATASET')\n","\n","images = np.load(os.path.join(dataDir, 'images(8 Bit).npy'))\n","\n","# Z-score Normalization\n","mean = np.mean(images)\n","std = np.std(images)\n","images -= mean\n","images /= std\n","\n","\n","masks = np.load(os.path.join(dataDir, 'masks.npy'))\n","# Normalize masks to [0, 1]\n","masks /= 255. \n","\n","print('Images: {} | Dtype: {} | Max-Val: {} | Min-Val: {}'.format(images.shape, images.dtype, np.amax(images), np.amin(images)))\n","print('Masks: {} | Dtype: {} | Max-Val: {} | Min-Val: {}'.format(masks.shape, masks.dtype, np.amax(masks), np.amin(masks)))\n","\n","images_train, images_test, masks_train, masks_test = train_test_split(images, masks, test_size=0.2, random_state=2020)\n","print('Train Images: {} | Train Masks: {}'.format(images_train.shape, masks_train.shape))\n","print('Validation Images: {} | Validation Masks: {}'.format(images_test.shape, masks_test.shape))"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Images: (1815, 96, 128, 1) | Dtype: float32 | Max-Val: 0.5567962527275085 | Min-Val: -3.5179946422576904\n","Masks: (1815, 96, 128, 1) | Dtype: float32 | Max-Val: 1.0 | Min-Val: 0.0\n","Train Images: (1452, 96, 128, 1) | Train Masks: (1452, 96, 128, 1)\n","Validation Images: (363, 96, 128, 1) | Validation Masks: (363, 96, 128, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1Oebjxy01w5A","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":287},"outputId":"4ecfab08-6168-4923-fa66-0b53958c61bb","executionInfo":{"status":"ok","timestamp":1585144583536,"user_tz":-300,"elapsed":2665,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["import matplotlib.pyplot as plt\n","\n","def plotMask(images, masks):\n","    sample = []\n","    for i in range(10):\n","        left = images[i, :, :, 0]\n","        right = masks[i, :, :, 0]\n","        combined = np.hstack((left,right))\n","        sample.append(combined)\n","        \n","    for i in range(0, 10, 5):\n","        plt.figure(figsize=(25,10))\n","        \n","        plt.subplot(2, 5, 1+i)\n","        plt.imshow(sample[i])\n","        \n","        plt.subplot(2, 5, 2+i)\n","        plt.imshow(sample[i+1])\n","        \n","        \n","        plt.subplot(2, 5, 3+i)\n","        plt.imshow(sample[i+2])\n","\n","        plt.subplot(2, 5, 4+i)\n","        plt.imshow(sample[i+2])\n","\n","        plt.subplot(2, 5, 5+i)\n","        plt.imshow(sample[i+2])\n","        \n","        plt.show()\n","\n","plotMask(images_test, masks_test)"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABZkAAAB9CAYAAADA3m1sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeZwkd3nY/8/z/VZ19/ScO3vqXkno\nAKEDCSGBQGBzGEwMGDs4DrEhXHYcJ1ZwHNsJeeEkxMbOLw52YgcTGzuOTQwYDJjDB2AOYQJIQjcr\nCUnLrlZ7X3N2d1V9v78/6ujqnt7Zmd2Z6Znt5/16zU53dXX1t3u3n6166qnnK957lFJKKaWUUkop\npZRSSqkzYfo9AKWUUkoppZRSSimllFIblyaZlVJKKaWUUkoppZRSSp0xTTIrpZRSSimllFJKKaWU\nOmOaZFZKKaWUUkoppZRSSil1xjTJrJRSSimllFJKKaWUUuqMaZJZKaWUUkoppZRSSiml1Bk7qySz\niLxSRB4Rke+KyC+t1KCUUmopNAYppfpJY5BSql80/iil+kljkFKqF/Hen9kTRSzwKPBy4CngW8CP\ne+8fXrnhKaVUbxqDlFL9pDFIKdUvGn+UUv2kMUgpdSpnU8n8POC73vsnvPct4M+A167MsJRS6rQ0\nBiml+kljkFKqXzT+KKX6SWOQUqqns0kyXwDsLd1/KlumlFJrQWOQUqqfNAYppfpF449Sqp80Biml\negpW+wVE5B3AOwAswU2XP9uu9kt2eLo1QeIEELwHD+Cl57oivnQbBA/ZbxEITUIgDiOecTO36Os6\n0tfYM7MZEsleOJNuFgDTguBIui03XiceXritjuGWbweOncNHFx3Hgm0BHiHG4ryQYNLf3uBZ+Lmc\nqplK9olixCN40j/Tz1idm6aenmX+RLP3l2cd64xB9qbaFVvSf+nZd9sj6Xd9mcrPy787Z7KdsyX5\n34iHBV9hf+rv8FK36buWL+U7nsdP74WkR7wVFo5LsoWTlVkmbLNYx5DHrfR5wSLnRue9Y96Hpx+g\n2pAO72syfSzeUDGoex9o+7OGqZqo+D903/wEMm25cNuRs44fvUJAP7gsNq7VWBzpvsz+I5OEB2Zp\nXjrEJcPH1jwez/sKTbfqu9Wqjw595/gR7/3Wfo9jOTQGrc3raQxSq20jHodp/Fmb19P4o9bCcvaB\nzuZfwj7gotL9C7NlHbz3HwA+ADBmNvv/+aktZ/GSZ+ZXHn8Nc1GaeIgTQ+JMmrTwUiSWi6SveIxx\nWOOxxmHFUw1iNlXnGA8bbKnOcGn1MFdX95/y9aZcjXd+88fwh6uc70DyDIkXpHyf9HfH/VJE8ib9\nQbJEs7Sz094Cm5v8+q0fZsw0lvV5tLxlzlc5FI8xk9QAaLj083EIDRfivBB5ixGPKyWJnBeGbASA\nFUdVYqw4jDhOxnVmkuqyxqI2jg+/8a/7PYRuZxCDJv11v/OTGEm/30Y8Bl/EAV/6tx779ARMYFx7\nmUsTnOVlkH4vjCz8z/xUy8tM105Ar7GUlyfeFGPuHkev1y9/f8sx75TjEV+Mufz8IPu8XNe4YmeK\n5+TPy9+TQ4rPLN92dzzJvXTbLt42/gAJo8AooRgi7yi/QwNssT3OxGUeas3zQPP8Rd+f2rje9fqH\n+j2EbqeNQR3xRyb9tck7+OlPfY4x06DhQwwOh8EwSkWS075gku0E2D6c0Fqv3nLnm7niTfdwOaT7\nSLuh+Tc7+ZXLP7Wm43ioeQGPzW9f09dUa+u/3/hn3+v3GEqWvw+kMWhVaAxSa2EjHodp/Fl9Gn/U\nWlnOPtDZJJm/BVwhIpeSBpR/BPzj0z2p4UNqEp3Fyy7fjuEpDsyOEeUJIpumLXxHsiP9HVqHZMmS\nPMFctTEVkxCYhMlgllG7eFL3l+//Yfyh6sIE8mK6TneV8zjdhYA+K/Hzx6q881tv4Gev/xI31PYs\n4UUWCiUh8pa6bWLxNHyAwRN5S0g72CfeFEllVxqsFUcoCUYcVhZPeCm1ws4oBi1VIA6XlfSWk8Xl\npHG+POjxbz/2ZtEEc3dyGbqvpmgnm8vL8+edattLSWwvZrHn5knh8jpBKVnfLZCEwCbFet1iZwiM\nYzhoMW7nSfBYBJN/7qSJZY0sap1aXgwS4c2f/jyQHijl+0KtU3zleh1M6YHV6QUXXkBYafZ7GEqt\ntuXvA2kMWhMag9SA0H2gdUjjj1oPzjjJ7L2PReRngb8GLPBB7/1py4y+MXs5tww/vqaJ5p/e8SU+\neOiFHJwbo2mCIlGSeOm4bFtoJ0JCm2DFMRI2qZiEico8k+Es28OTDJtTf3H3RJO0muHCBHOeKZaF\nl3P47kFIe7nPiwBLVcxlw9+s8ydfeBU3/NvfW/LnYcUREjNsmtSkReSDomo59DEYSsva6R3nTXE/\nT05bPKHEhJIwJ1rFrNbOmcYgYEE1b/mEk4hfUEVcTqp23+5OrjoEk7WS6ZWUNfiOEzXl8eQJ5bwq\nOHamqLjOK4KlR7L7VGM93fte6nPzuNhd2d1L/t4MnopNGLatrs8sfX7LBVRMzHNG9vDjY9/NH+0c\nD3RUNNdkbdstKbWYs4lBZRVJioOpMotfsDxB9CCry8j4PMHOi4l3pyfbH//NSd5/0Z/2eVRKra6V\nij+gMehsaQxSg0j3gdYHjT9qPTqrxine+88Cn13Oc+q2yWPNHVxVfXpJl0WslLdsu5P/c/gF7J8f\nI3ILExXFJeHisMZRMQnbh6aInWXIRmytTFO3TUZNg7r0TjLvjrbwGw++AnewdspxeMn6kQppeV4e\nO3v8LvJciySG4iFIasvr+mPx1CRiws7ScBWgBUDkLS1vsZJWMjdcWCSHKhJ3bCPxpki2h5IQSszJ\npL6scSh1ts4kBnUnfE+lOxGcVwh3VwrnSeXTvVbH8tL6eVJbxHckmMsJ6nLLiVy5TcWZCowrtrOY\nvOK4e73yZ7Hg80LYUpklMAvjfCgJW8IZtgTTvH7kCRbrotb0Wses1q8ziUGQXx3U/m6UD5rKB1G9\nDqb0IKvTb1/7Yd76c+/gGf/qzK7oUmqjOtP4AxqDVpLGIDWodB+o/zT+qPVozbtz102LUBIOJ2Ns\ntVNrmmj+ia1/z8eP38RTcxPsGJrmwPwoFZswHs5js4rEY606LWc5f+gkLxt/iO82dxBKQtVEDJsm\nE3a2Z2B7vLWN337k+2g8PVxMYAW0Z7jKkzNe2snjLNfdnd/pnOjPt7dTLGvfn78wYcvOY8v+LCye\nkARrmpjsYvQIS8NVCCXGYWgZ27F++bKVyFtCSaiZFok31CTisBlb9jiUWmvWOC4eOQ7A/rn2v9mJ\n6jxbKrM4hAPzo8xGVRxpT+ILhk+wf24caCeIrXFsqc3gvKHlLCebQx2Pd1usPUa5Orm7UrpXgrlX\nb+ReVdO9nldet9drLva8/Ll5RXPeJqRcvVysh7C5MkPdpCexEm8ITYzzhhHb4J+MPUqIBQIcjqTH\n5xN5j5XlnURTaj2LfEBNIhwGspZU5f9be1XzKKXUStEYpJTqF40/Sg2GNU8yb7Yzxe2GD7Hi1vRs\n1Gs33cOd4VU8d/gJvh5ewWQwy1W1pwlJqEjCA42LOBiN8Zz69xg2TS6rHkqTI9kEd73Gujvawu89\n8SKm95WSrNKZW24vX5hR7u653LHOIr2ZEY/PJig8E2mCv3wW0VHLJvVLvKFeej234DL29DWtpJMj\nKrUxCNdM7Oe1m+4h8Ya/nXp2kbC9emg/N9T2kCD8/dwV7G1M0nQhTRfwkoldfMVeifPt78Fw0OTm\nkScBOBiN88hce7KD/DsRSFIkaG32nelO6C6W4LU4Im871s23ky8PJd9JM1g6J+ebcxW+O72Vyeps\ncSLNiGff3DgVk3DV2MGO18q3A/Dk7GYaSUgrsR39qA2+6FVdCyK2VafZMzuZPZ631UgnJ9wUzDKa\nTUyaYKhIzPnBcSqS4LwnkgTbtUNpESKfxxfd2VTnlrDrqiCllFpLGoOUUv2i8UepwbDmSeYdwUkA\nZn0FWHi5xGqzeF48sguAl42mbYPKl11cW9vLtVm3iwRh1Myfdpv3zFzC4X0TIB6RLGksII7OBsy9\nckm9EkylnswLlnc/5lmxJG9HY/0Ffydr93ek1Gp67aZ7GJYWxjh+ZOKujr7j+Rn0l9QfoTEUcNQN\n03AVEoTbxx8lKU8WiiHxghXP9vAk548fx4grepfXJGLMNKhJVGw3lKTjErAWJutr3j6B1X1GP8oS\n292P58vSxHNnDIiySQdPuBp/Kc/hvMpJTDZJZ00i9g9NUDctvn/4OwDFOMpVBN+qXsqReLS4asHg\nOyb3NOKomxZXV5/mruplxeSgiTc4hMQbLgqPUcsqmS2eSTvDVWGc3W8n7MtVzIZ2+w7jPaEYnPck\n+CL5rNRGVZF0vocEWXDJZ/7dS7whgTU/Ca+UOvdpDFJK9YvGH6UGw5onmXPD0qLl7Zq2yziVUwWw\npQS23dEWvnX44o4EcEE6E8W9avI6HvflPswLtwXgTWeVs3jpeTm9Uqq3/HtdrkruVk70hhKT+LBI\nKBd8u4o/rSDOdobEFTtRocQYSa8DyF8vQajkVwL06Dm24La0102ySyRCWZhszVtVOKR4fFhavHzs\nQRo+zJLiaS/1m2rf62idUckqoENc0frixfXHaHpb7ASG4tqtQrLn5gnwV40+0DnGksgbEoSLbJNx\nU8nGZ0m8JxSbtsrwcdY6AxylyUZJK5utCIlPerbUUGojCSVOW1Vl8aL7+2Lxpz3xrgddp3byn9zK\nyy79dr+HodS6pTFodWkMUurUNP6sLo0/ar3oW5IZWBcJ5rP1VGuSg4fGs4rkLFBmCRiPIIaigrln\nSMyT03mCuSu53DH5XzkB7QED4XHD8eNb4aqVekdKndvKZ89Nj2Rtvk63coI58VLEr7y9RPdOT+IN\nkQ8IJSmqm4EiwZw/51T9x3r1KEuTzu3HLX7RyQstnm1Zi6K0KjqrSi5XP0vn2MvbC8UR0k4md181\nka9bl4RhU+rzTJognnUeJzBpDJvsCACRT9IkMw4rhrSm2WCyN+ZKr18Th8k+34ZPGDWVU75XpTaC\n7qqdpPhuLazYOdXkNjrpzUK33fowX/395/Kq6+/lRye/1e/hKLVuaQxaHRqDlDo9jT+rQ+OPWm/6\nmmTe6PbFm/jc089CjE9DndCRNAaPR7KKY3qWMndULne3veie7K/rt7eeaJNDxlor8n6UGgR5ywrT\ndRa9VzuKBEmrgLuqnhdrUdORsM3aZyQI3XXTSVZxHHLqFhAuq57Ox5m3tOh+HWhPvtf5Xkvb9gYr\neauKhZXT5UpoU/osuicY7Hiv2dhGjbDJDC14fNwkJN5TLyWHQ7HZc82CZel4Ol6htK1ax3pKbVQu\n658O6YFV5IOstVbnCSg9iFq6t2y7k7dsu7Pfw1BqQ9AYtPI0Bim1NBp/Vp7GH7XeaJL5LEwnNY6c\nTKvz8iSxN2kLC4S0lE98u0rZy8JEcvHc07yYZK0y8gR2NqugjEc847zDK/aelDrX2UWSur2kSeKs\n1UWW9E28EJp0xyhvlVFsX0qTYuKyZHLSUcEcigPfPZlmO8nbexyyoOq4vH7aM7m7mlqKx3u12CjW\nKyWWe42h/Ny8L3X+WmktsnQkjXMWc/rYtkSaYFbnAoPr6G0e+YBZV6VmWh2x6YSrU5dm6YoJrdxR\nSp09jUFKqX7R+KPUYNAk81kS8WlLDOOLojufTfgnpcvH08SwX1iRTFef5V4WPC5gPHYkxhhHaDd+\n2xGl1kpYatPTXcWc3y4m6iMhlKR0OVdnT+LyLMntbaXLaxIBUJOYWvaa+YR86XN7J30NviPJW96x\nct19obuUq7EXSzA3fDthG+K6Kp7bfZ0T35nY7U4wQ3ouTXslK7U0DpO2rSHBZSevhk2zY538e9zw\nYXEJaXsi0EBnZ1dKnTGNQUqpftH4o9Rg0CTzWZKsglmsx3tpVxkDXroqjzue2HW7Y0KxUm9nAazP\ntiFFBXMwEmGs47xNU7ztgq+uxltT6pxWvhSrO9ls8TRIJ8mrSMIJV6fVlXCtSFIsSyeqcO1k9CJt\nLRYdU1YlXZYnlherRIY0MZwnl/P7ufKkhUWi+FQTnpb6NYc4IjHtVh1dCe60J7RSaqmKmFFcCeCK\nSp1ZX8n6rqcnqfJ+hVaS4juYLPNKDKWUKtMYpJTqF40/Sg0GTTKfBSMeYxzGCFjB4fAYpJLgW6WE\n1KmSzN2T+uUJ6XIiRzxi0iT2+NgcUWLxXhDxbKrP8wuX/RXDoj2ZlVqqfBK+7t7LuY5K5mzHZ6ud\nAtLLuow4Im9p+JBQYiy+43ctu93CUOmuEmbhJHtWfDGxXvd6i1Utlxk8lew9Fe08xBXVzK5UDZ0m\nkU9/9UOemHZ+8cprpdTy1CTCYYqrHXpdUWGyGh/bFausfg+VUmdJY5BSql80/ih17tMk81mwOCpB\nQpKYtEeGCN5kSeHQkbQs3gkdJ93KfZnzvFKWSC4eB8S6jmrnC7aeoJVYrPEkThgfavDLl362OPun\nlFqaFpZa9qVMkEXXjbIz7qEkRN5SN00qOFpiGKXR8zlpstmlbS+y73M7aX3qnaPyBH9lZ9KHrDs5\nvdQEceRNx7oNb5f02rrLp9TSNXzIsGnS8haHIZSYxJuOXoX5ZaR4PahSSq0sjUFKqX7R+KPUuU+T\nzGfh8sohfuLyb/KHj91KLCBOwKZ9miuVmOpYmgA+eiybHNCDT0zRXkNKvZbzthuQVkjbwBGGMWO1\nJtvq08Te0owD5uOQoSDil3d+dq3frlLnlChLoJYn7SvaQuCJSCeeyK8UyKuaWxgOJ6M0XIVRM8+w\naRbbySuXQ3EYLzjpPDsfiqPhLSGuqGB2vj3pXl6JbPEdPZXLykngcgV03iqjnGDubH3hibL3Z4AI\nSXtElyqm8+pnK9ky74r+zMU2S89PXwPClZrdT6kBUfQV9OntwiIHVKc7KaaUUkulMUgp1S8af5Q6\nt51zSebuALTaM5HeOLSb6ctqfOzJ6/FecE4QgWqQENiExBlMkKaKnBPEZJXHAtZmQVQ8Uhq2MZ4g\nSAhtQjWIecf5X6EmEb+976WaXIaO2WcT7Qirlun2GkBYWmJPsSZQVCsbKP1ba/ppPj27mVfUo9Ly\nxbaT+k5rjusqtY5lc67F9+KYyBu2W8cWO9zzufe3GoSkSeCaOC4NRzq2sT9psdUKETAiIcdci2mX\njq0iLp2gzwsN2n2kI2+oSlIkq0NxHYnpBrajv3TLmyLGDpv0e1gTYZOtn/a9K6Vg2g0xauaBbP9E\nHK5Huxyd2EYptRo0Biml+kXjj1KD4ZxIMicIs64KwM996Y1M3BemVcMG3vrTn+Ha2t5Vff0Xj+zi\nxdfuKu7/+bGbeWRqG804wIhncnyWmfkqSWLwTtKeysYRBA5rXdrbWTyS/RiBwCZUbcLPXfL5omfR\nv7zgC6v6PtazvIo0lIRf3X5/sfzxaIbfPXK7JpvVmqpKyI+MTC37ec+sLEzG1k2FZ1Yqp31ud3K6\nexuXm8qCZUqpdcR7PvTSW/mFL3+uWJT2Xo+WvIkzaZ+jlFKAxiClVP9o/FFqYJwTSeZfffLVHPv4\nhQBMFJPnpd0n/uD9r+bt/+wvmbBzWBw7gpOrPp4fnfwW/zu5jd3Tk+AM2ISRoSaNKCCOLc4Jxnis\ndYQ2wRqPNQ7JLlE34gltwnCoE/rl3rv9bqwsTCRfHo7wls1f40+P3wKkLRDyPrpKqcGReKeX0qn1\nTaTj4EoppdaUxiClVL9o/FFqYJy2/FNELhKRvxORh0XkIRH5uWz5r4jIPhG5N/v5wdUfbqeGD3mo\neQEn5k9R4edAHPz+7/wQ/+V//Bi/9v4fZ1fzvDUZ22Q4y1AQpUlk8VRswnC1xXCtRRgmRYK5EqQt\nMYbCiJFKi7Fqg8naHBcMn+At59/JmOk9udigGA/mGQ/mF13nmsoQv7r9fn51+/28ZOw7RdWzOjes\n5xik1oen4hm+1Ah5ItrS76Goc8x6iz9awaPUYNEYpJTqF40/SqkzsZRK5hj4ee/9PSIyCtwtIn+b\nPfbfvPf/3+oN79QaPuR9e17GoY9enC7oLmDzpd9ZVTMJ/M4HX8tPvvmvuXnoyVUd3+ZwlvOGpjjc\nHKEpAS1nCbwQGofzYISiPUZoEyom7cE8EjYZti1+YPIBJszcqo5xvdsSznDH5L2MmBpLOB8CwKvr\nDSL/GF+bvkIrms8d6zIGqf5q+vTyumNJk/taaXI56dHXTamzpPFHKdVPGoOUUv2i8UcptWynTTJ7\n7/cD+7Pb0yLyHeCC1R7Y6dw3fzEH//wSBA8exHsQ8EiacM6TzFny2Uv6YyL44Ed+gJvf9P5VHd8L\nhh8rkpzTtkrsDLG3OC/UszYYkbNUbcz2oWkMnsAkPG/0SS6vHFrVsW0Ub524ixEzcvoVu7xueIZ9\n0REem9++CqNSa229xiC19iKf4HBEPuG7URrcZ30di6fhQ22XoVacxh+lVD9pDFJK9YvGH6XUmVhW\nT2YR2Qk8B/gGcBvwsyLyk8BdpGe5jq/0AHuZ9RU+te/apT9BOm+bGD5y7Hm8YfKbKz62sheP7OJv\n3LOp2jqJF+aTEOcNQzYiNAnDtsmmYI4XDD+2quPYiHbWjlDr0YN5yc+vHGZPczNNd060HVeZ9RKD\n1NpJvAMgJuFw0uSkS0/etbA4b0gwGByhxCTe4Po5WHVOG+T480DjouIkTigJ11T39XlE/aMTD6l+\n0RikMQg0Bqn+0Pij8Qc0/qilWXIGTkRGgI8Bd3jvp0TkfwL/ibRm+D8B/xV4S4/nvQN4B0CN+kqM\nmQPRBHOf2lF6kfSXl/xG5/JitbTomWAevvzxG/ncFc9m6/aT/MbVH1uRcfWyrTLFtsoUzhuOxcMA\nbA+nmAxmuCA4Z2PxWfuhkQfZZJdfxZx7db3Bw41p9jU3reCoVD+tpxik1k5MQuQTjrmYJIvtkTdY\nPA5w3lCRBDxE4rRNjloVgxp/vjF3OQ0X8pmnrimWVWzC6y8MuXFod/8G1keJN1id+0GtMY1BGoNy\nGoPUWtP4o/Enp/FHLcWSkswiEpIGlj/13n8cwHt/sPT4/wI+3eu53vsPAB8AGDObV+60R9YSw4uk\nrTLKD3mfJpx9e728g4YA3kPlJFzw+xH7X7AVrl6xUS2ws3KEE0mdlg8YsekkfjVpMWyaq/eiCoCb\nh57kiupB7c98DliXMUitusQ7Eu9p+M6dGZOdMbQSA2lVcwWY079dtQoGNf7cOXsln9p7LVGctvqC\n9LsXJ4ZP7LuexnnhwF2JlSA4DJB0LFNqNWkM0hiU0xik1prGH40/OY0/aqlO249ARAT4A+A73vvf\nLC0/r7TaDwMPrvzwetsczDBz21w2oV87Xon3xf3idmkCQPEU910V9r9gmPkdjvc99XL+buaZqzLW\nCTPHpJ1hzMwzZuaZsLNM2DnqoknmxXxtficzrnFW23jJkON1wzOEerZtQ1uPMUitHZc1wAiBUKAm\nCTVJCMURiqNuIirod1ytjkGOP18+fAVxYki84LzggcQLiTPMNCv81f5ncefslf0e5pqyeAyOBKHl\nLS1viXxAw6ft0JRaaRqDNAaVaQxSa0njj8afMo0/aqmWUsl8G/ATwAMicm+27N8CPy4iN5CmbXcD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zzK1xT3ceJpF5Mxgnw7blLmAxm2VY/u8tGW94S+YBZV2XOVTiajHBReJQx0zir\n7Q6iUBKuqe/j/OD4goMrgNcMzwFzQI23jh8A4I9Mk3tnL17bgarBoDFo4GgMUuuGxp+Bo/Fn9fUt\nyXwgHud/PPl9C5Yb8ey/dweVEwIC77l9gpFKk1+45K+weP7k8Av45r6LmZ+ugUkTuM4IBkdCWvnr\nbR4MApxNkxNVYqZaNSJncF5InGG2UcE5QxxZksjgW2kwkDmbVjCX2mIUSi0zvHQuFwckkq4vaXLX\n4TAViplJ88bxjVbIJ/bdwA2X7+GjR27mm/sv5tbzv8e9hy9grlEt1itPHuhdOvlgGghJ+wSVxuaz\npLMHZDYLiuIxW5oI6Xg+fvwmqibm1eP3ndHf23LcMLyH0R6BL5Q4+/LCkWSWD8vVa3p2KJSEW4Yf\nX7PXWw2hJNw6kr6Hw/GoVlirc5IVQ1xKMPeST+4ayOLrKaUWSny23+TJSnnSHRsza5FIFu7rkK2T\nnQAX0uqfxp5R/nrLNVxz4T62BlPUJOJIPErkLaFPuKRyZHnjQtKDNCyJNzR8SMOFONIDt7mkSuQt\nM0mNS6uHAHiqtZlLKkfYbGdOs3U1Ypu8eezQsp7z5rFD3KEHWGqFaQwaTBqD1Hqg8WcwafxZfX1J\nMp9wdf793a+l/o16R6Vw/iWuG0gq4A0c++oOjhlI3mT4rX0v5YFdF6WJXuvBZQ3axePEgPGYwBVV\nvolPSFzaNgMCGnFAlKTBJHGGJDG0GiG+adPrH7xg5g3Vo6ZdxZz/QPq6pdvl3sw4ECfZ2a60jw8e\nPAbnHQZweX/lbPK+vYc28VPf+ik2PewJxg27f2Samflq0Xwe2hP9xZHFxYJPzMLkd8sQzNiOsVZO\nSpooF5hv1fDnN/CJ8LlHr8EGCc3LAl6/6e4V+fvs5eaRJ3nt8D5GTG3R9bbYYf7h6C4+jluTRKnF\n8crx+3np0MZKSL18/MGO3ks1iXjJUFrReSTZz8fF8/Dc+f0anlKrKsqqlxPf/g4YwOCL78WQjbDa\nMkOpZUsvE80reYCmxcTtfaB8X8eb0m0p/kiXCTxyYBu/4V7J63bcm102Os9Trc0kCHujyWX1JZx1\nVU4kdRq+gvNC5AMSpJgU2SHMuQrH4zqHWqM0XchTcxPsHJnkRaOP6kHWIkJJePHIrjN67ivHH6Dl\nLV+cetYKj0oNMo1Bg0VjkFpPNP4MFo0/a6MvSeaj8Ug7wVxKMucnirwDE6dfZhek6/yr+97A/IGR\n0jqdl0Xn95LA4asJPshmBrWCRJ6TUYj37cpg54TWfIhv2PYZKQ/htJy6TUY52ZwvkvZj4rJ1nJTe\nDLiWpRWnCWbJWmaIeFzDsu0uR/0vvsHxNz2fo7P1dosML8W6rUaAi2xaJZ0lw4MpWwQ+2xCC2fb4\nOsbuob7fMB8PFWNqTsZ8+sHrePTibVw0fII3bvn6Kf+uztStte8xYkaWtO42O8xrRx4BWPVEsxG/\nIRLMLxh9rOM/o9sXydVvscO8buQxrq4+DcA35i5nX3PTag9RqTURYKln5+cS7zniWh3TL9QkombS\nH6XU8jhKvb/EIwg0TXrSvOske7GP07GL0z7jHj9d59DnL+Gj/8jy73Z+hm12GlvxRN5ilnEC6Ggy\nwoF4nJmkllXuCJGzJBgsjqqJibyl6QIONUaZiys0knR39tGpbcwnIa+YeEgPsk4hlKQ4Sb1cr6w3\n+cjM+AqPSA0yjUGDR2OQWi80/gwejT9rY82TzLO+wnu+/BrG86RsxpeqhAUgBjHpHWfBf3sc2eKK\ndSXJVsx/AC8eIoNLBGc9kYQgYMIkrQDOZgatDbXSSfWaWUsJD1JqsHyqPszl5HIeZDqWkVUzSzaW\nbHu+adKyO+vBeLAOjBAORzz1g8KF3ELQ8Jycr+Kzify8BysJrWaAawRpghmKiulwOk04m6Qd9MpJ\n++73UD8oRQyN5kKak45Hdl3Ao8PbeWTHNm7cspfXTdxzyr+35XjR6KNst8v7p3VeMMLOyhFt+5C5\nofo0V4bDS15/mx1mW9rthYvsfXzEP4f9LQ2CauOzYrB5WllgS+mxOR/R9DFb7RSHg1Gc19l/lVou\nf6p9nkWfRLYP1H7ypoeF7Z9+ku+9anOxbLkHOUeTEfZGkxyPh4mcZSap4rwh8oaWCzB4ApMwZCOe\nnh9nJqrSTAISl373nRceibbzvNEn9QBrFfzx1Bbumd3Z72Goc4zGILVUGoPUStP4o5ZK48/SrXmS\nueEqjD8UdFYxl1pPlE4m4R1InmzObudnj7xkyVxD8WQx2ZMTgZgiALhWmmDGeLzAvKviYwOJpElh\n0nXtnME2S4NdpFVGeZ08ryEJSCx449tjIQternQ7bZ6BF6E63uCpV1eR2QCbGJLYpGPLKqBpmaxK\nup1kLhLjLkswu9IlHa49rg6ls27hNIgzNCfBRxX2zm7l6aPj2KscPzRxb6+/tmW5onLotG0y+uXt\nm+8Elp683YguDUd44/i3+aMTz+VItLRqcqU2irqptG9ToSYNLg2nqEjCrqaepFJqqfZEk3z6qWd3\nLswmKvYi3efQF8r7F4oHD/Nbhad/5DJGPuZ415++nZf8+7/nZaMPLWksCcKJZLio3jkZD9FyATNx\nhUYS4rwQO0vs03k1PXNXMwAAIABJREFUjHjm45DEGZqJJXGmmPjYiCdBTzithptre7iuuo8/OnZb\nv4eizgEag9RyaQxSK0Xjj1oujT9Lt7b/Ar3nl/7ijdnt8vL2/QUtNFzpcS+lyuMsyZoldtN+yKQ/\niUAs6e8EJL+f9Uz2kYFW6VKIvBI47qyuXlC53Ot2nvutgguzbeRtLZJ0DJIns7P73mU/2SbCkRZ2\nayNtPJ+PNTLp5RqRIHH6Por3AEQjvv3ZZGMqJ5rFd/50vE8PtpGuZ2IwTUNyssK+xsRS/hYX9f1j\nD/OMYP0GtuVUB3d73/GdKzeQ0/jzkzdyJJk94+dfGIxQN60VHJFS69OIqTFuhnhetcENtT39Ho5S\nG8a0G+LYyVP/n7hgshvo3C/qery52TF1mWNi1zTDH/sG+xtLv5om8gEnkjpNFxJKQuwsU1GNRhLS\nSgLm4grzcVj8TDVrNOJ0ro1WbIkTU/wkzvCpg9dzNNGTrCvtmZU6z6ys3308tbFoDFLLpTFIrRSN\nP2q5NP4s3Wk/JRG5SkTuLf1MicgdIvIrIrKvtPwHl/KCw/tK7Sg8HQnmBdW3+SCzHsmVE+lzJUvc\n4qSzmjfOErpJlnzOJ+OLO5O8dCVd8598BtGeQaXjQ6EjsLgQ4iHfrsLOXrdI8JYT4PltLwzXm/zQ\nMx5M37oXXGQgNsU6EmXvsUhUt6uXfeiJxrIK7uJ1WPjZ0n5veWKeLLlcPZq+lsSCnTN8475n8H+P\n3nKaN7+4zXamo9JwOW6vTXPj8O6zev3V8l+OXc7uxpbTr7hCDkWj/M6xmznp5s94G/90/EHGgzN/\n/nqx0jFInZvqpsKonlhRK2wQ4k86/8MpdsCk9Dv78Vkrs/K+kjfZ/WzZd//kOfzQ5qVfGZUgzLkq\n+1vj7G1MMhVXmYvDotdg5CwtZ9sHVYklyqp3XFbB47xkEzsL+6dHafhw+R+GUuuMxqDSb41BSq0p\njT+l3xp/lFqy0yaZvfePeO9v8N7fANwEzAF/kT383/7/9s49SrK7qvef/Tt1qqqrX/OeSSaEmQwJ\nYUgwbyHGGIRwgajAusCNXg1ewMi6eO/Fi8hLRFeUiCwQHzwWYIIiAqJBRSGXh/EBmgQICXknTjLJ\nZJh3z/Sruh7n/H73j/OoU9XVPd2d6qqumv1Zq1ZVnec+v67z7d9vn/3bO1nnnPvKks7Y6ljOLm/5\nntzvybsJIucxWYdq7BXOOmBbHapAw6EMkQDkXHqe6D2jFIs8pWrOAR3vZprTdiRR0VnnNVaivM+Z\n6/SM5eqx+3jJrodxjiiKOXGYx9HPzQ7zRvQ2DpzHyWnj0E8c0l4V8pMNx7036/H1//gRXn/rL/Gt\n2XOWcPDOUjL5NVu863BtrOvnPB6UsCtKFBWx3ivxto3f6/uI5o5rkKIoyhIZeP1JO1o0+jtjAc53\nTRXVofkhfNplEjevn5T74AR/8KNfYJ0ptz1liBBmdgoRKjafLktyD9p4WmjdegQ2is6xTtL3ZDCV\n4IjyEUL04D48acTAqUnZ5vnQxFm9NkNZIqpBqkGDhmpQ/6D6o/ozaKj+dIflxnu/CNjjnHtipSeM\nnL8OcY1XStYJmvmOixyrwbBrOFohLoLX7Gxu58RO1qfF/YQoR3N63mh5mI+ikptoTY2RiEvsWLYF\nCEqueZuM47vJhgy5Yp0bdv8deQkZ8mqRgzm2RaykKTckez2ZtCFpepB2OOYJ3kLbSaZwIIBXFbxZ\nw83fuIo3fPl6nqxvWMKBOsM35zzumN61quf4jcPnL3uf3zl6LvUlefTXHiOmiFmwkmVf8rQ1SFEU\nZYUMnP5I9vm6cZicw/gW2VzFnTGHzS3j/4eBSy97hP99xjcYM5V5q0OEivOpuxx1l6MW/18NnYmX\nR9XSj9dKnKgOMRf4VMNcOqh66uB6Dh0ZxzlJp4Um0Ts2M5gScXjG4Q3W/76O8mR1A28/dAGfmDx9\nWfuFzvKbhy9dFZuuGnuI92/7Dy4Y1rRHi6AatBiqQX2DalBfovqzGKo/fYPqz+qz3MJ/1wKfy3z/\nFRG5Dvgu8Fbn3PGTHqE1t7GAuCjB+kn3cwLORX5cC2Liw7mGb7c5Itkludibw6IdUDUZR6xr7Jc8\npZIWx3CLgzl5D4ZabuDW63OZYyaH8i1/csnnyEuY2VbmOZMl41TOkj5Z86A25iicaGk7abFjEbI5\nsFNzBEwgvPFl3+BMf2JpB+oAdZdb9UT1Vbu8n/z7jj67p8XzfufwFdyw9fYVpyAZQJ6+BimKoqyM\njutPxfmcCEsAGLGMmQpF6f6MnqYumET9LONZ2F4mfKqEs4v30bbtPsx7nvWP+BLgnaQDUnNeU/+n\njpc+yA2cRy3MRYOouLO196k4VVXcFzp2bITxdeVGxE6bc6x/3xDf+NBuXrf524vacipTtTkeKJ/O\nn0uN68aOnnT7tx28EKDjD913l37IG8aewhMD+PzC6EFmbYFH57Z29DwDgmrQAqgG9R+qQX2H6s8C\nqP70H6o/q8uSPW4ikgd+BnhnvOhjwA1Ev+0bgA8Cr2+z3/XA9QBFSs1RxkJUvTOJZs5OVWg9jqPJ\n6ysOXItDV0yLP9Y1HMIAThzkHDdfdRMPVU/j/f9yTRoRbOpROo5wyBEMgT8tmJAmZ3M2144TqI+6\nhoM2Xth8/jbe3pzjpis+3bTomvF7OHDOON++/+zYnqzDWeZFQWejvV0O6iPgzzQ7jJdFy0wPB7zm\nJd/mR0t7VnCwtU3debzlwCWsz5V57+YHFt32AxO7OFwf7ZJl7QkxvOvQ5Xxg2x340p/R1J2iYxqk\nKIqyTDqpP1O22LSNEYuHw5cgjXSZtQV8CQDaRsR0Ag+LxIOppFPh4k5Mkp9QxOGfOUsuFzK3p5E2\nKlwX8KkX3pw51sk7Hx4OT+pM2SIV55OXkLyUm/bd4M9y2IwQuAL7jq4jqPhp0ebIQHB1jxMH4//N\nBkwxoDRSTe197Y7vc8lnHksjhZoe6CvzuGt2B3fN7uCl4/fy0lI1Xf6BiV3sr65ftfNu8ad516aH\n42+Nzronhjev2wfr9qXL3nbwwr6dUdYpVINUgwYV1aC1j+qP6s+govqzOiwnrPNlwF3OuUMAyTuA\niHwS+Id2OznnPgF8AmBMNrisQzmJXl4wilkyL0gTrTcOHj15WvC2lpbPmf3PLRzgf/74N/not16E\n0Lhxk4/1UZfulj/eHOVcH3VgMgHDmYjl1mDqRsJ4h/Mc77ziH9ua6psQjMMZh9DesZxec/Z7sji5\nvsQBvUg0c9ZZnhY8pLHPVT/5A14ydl/7nQeE40GJtxy4BIDzS0/xi2M/TNd9Znobd8+e2SvT2mKx\nhE7ip1xLJ3S2aQpNn/P0Nchs1LlDiqKshI7oz+FwtClKJy8hwybq1CYDjRChaCT9ngzIOj3QOrdw\ngOvOvZOb73tBuqy1+I0IeF40EBs9+wR/cN5fPe3zVqyPJ46K9ak5jy3eNOu8MhPBCJ7YpqmfLvvg\nHZoGWghgHc5GuQmNsRhx6YCVeIAaIksaAJ7q3Dp5PrdO9tqK9pi0o3pKoxqkGjTQqAataVR/VH8G\nGtWfzrIcj9XPkpkiISKnZda9CliaVzLTQGleZpvJzdwU5ZzZbSE/WbJ965VkErk7EzmFb776U9z8\nEzelm1w69Dg3X/0pXnrZPYTDlmDYpfds1g9bW++orXPUxl3qfJ6HzLc5WW7zFpd3vOnHbqNqfR6p\nbUtX15xHiPC6zd/m5qtuYuszJ9peb1rMsM3127yjtt4RFqN2SBPTt0aGS+ZdonQbwXDW6R858o0s\nlOx5MLm3fAZvPXhZ+lprDmaAtx98AW89eNmy97vx2G7KdmBSbXRGgxRFUZZPR/SnKHXyEjJmKum0\nUA/XNADwcAxLjaLUKUo9HViFCMfCkaaCMU8X3wTk/HDByurJ8qIfdGRwBaRpuMq2wJFgjP1BFCni\nS8jDM1s5OjdCaA2b1800noJbgUxx5GhZtNyVc5SnC1hrolyEcVtm2zXbZlpxfW1wuD7Kx09sX9K2\n7996NwUTrLJFax7VINUgpYOoBi0L1R/VH6WDDLr+LCmSWUSGgauBX84s/n0RuYDoZ763Zd1JDtgc\nvdxU/O9kZKKN53mDGwdspMlIHKr5hR2nr91wJ6+9+k4AfuWen6X2yFjTekdrrp6Mr7zVES4u9RDb\nosX5jp++5PsMeXWmwyIV67MhN8ue2hY8sdx67DyeWZrgRaP3k5eQYi4AzzWeVLW57IVwHlgv9re7\nJn99xr5oO6CpWmqSqtjmHSNeY6rAUjkcjnKgto4Lik8se1+AugvTBPjKwpRtDWDRHM1VVyeM76nQ\nLS/yea3ScQ1SFEVZIp3Un2FTXVFESTaCp+5yeB3KWXjp0OPMnFPki49eSFBv/A+WuDhyMV/HM44b\nz70F6FxEzGZvillboG4Nh+rjhAglU2PIq2PEYZ1w4OB6CExTmjVxrX2jeIprOceccfzMefdy0dDe\npm2yg6xkSm7NeVhMmj8xRAidwWIwWGwmcqEX+SFPBU7LT/KmdfuXvP37t97Nbxw+n5mwsIpWrU1U\ng1SDlM6jGrQ0VH9Uf5TOM+j6syQns3NuFtjYsuwXVnJCZ+a7Sp2RJsexa5NbubbepZ7WbJRu9jZ3\nWeczsTNVHG4k4FNXfnpJ9o0OVTiSG0UCSQ+VZp7IOGijlZnQ4qwXOI46Pve8fWwvTVK1Oao2hyeO\nnITUnceoV+GO4zsw4nh0ejPwXF44+iC/tevveWf4Kg7t2QROkDC6LjHgklQYCxAWo2jm3IxgMnqQ\n3ccJVDa5RsSz53C+wxUsOHjxBQ/w0+vubnv8W45fjBHHK9fdNW/dO269luGnPF72pnsWa94FubMq\n/NPU7hXteyrxrkOXUzAB79j07wtu88WZZ/FAeXnVUtc6ndQgRVGU5dBJ/Xm6g5NRM9fU+U8GBl4y\nPXIFvHDkQcpn5fnq3t3U40GWiGN4qMp7zv0K60y5Y/Yn5CVkuz/BfXPPoO48QmeoWp+5sBFhIzmL\nq2cLazDvc5LuzOGw0z4HquPzzpUdFCbRPHkJCbHpsmRwBcxrX2V1qDuPGVthxBRPvnHMb2++Z0Wz\nuvod1SDVIKXzqAYtDdUf1R+l8wy6/iwnJ/PqkHUut0k1kS20R6tzuXXf2OnrTBylu76GyVn+8LLP\nL1kUbjznS7wtfDVHn1qHN+2R5EoX5jt5XbvEx/HXsGgJrWE2yJMzIV68Y048fAmp2hxFL2CqXsQ6\n4f7J0/Al5IqRR7jxnC/xy1M/T/1gKYrydtG5EhugObK6tb5gU0qPVmd83D7hphoEBqkYdj7nAO/Z\n2TaVUkqI8OBbnovNe7zykw0nsyaU7z5Vm+O3D1/ZazMURVGUZTLr8ukMk7yEGNrPsrIYQgSfMP1u\nsOk0x6Tjn/ZtHISAl0l3tZzB0DXj91B/psd3jpyJdYIRx//aeVvT4KrTRBE1IZPBECEG64Ra6KX5\nCLdsnOLQkXHcXMssJ5d5jx/wC4ITxz2HT+ehsdM4t3AAWNoAKRlctbZpiDAstQ5drdLK0foIHzvx\nXN44fi/rPS3K2y1UgxqoBp3aqAZ1H9WfBqo/pzaDrj/ddzK3Rv62OpezkcrJpsL89BdN+8RTBQxR\nroiNVfx8yLt/5Ctp3pvl8IHn/DVf3f48vnjvRcjBAon+CYCN722TpMmRhg0uMtYWLKNnTlENc0xU\nS4z41ShhN6Tvm/PTXLPpB3xm//PT89594gw2+dNcUHyS0zdMsvd4EYtBLIh1kYM58S63EZhMEzZt\nkjrqDWCiKqmfuuizPFQ9jS/svyR1MCdTJwyWvIRN3yfCEd766c9Rcx5TtkheQmZtgRNhiV35w+S3\nlZl1w8tua0VRFEU5VajYfDQ9MR4IJR37dnnzouicxiCpKbok7hR50jwAy9Ia4XOyaZ6vXHdX25lK\nq0VR6jwzf5QDtXFmwwJzoU85yGOdEDohtIZ162Y5Pjc2v2PTGtETr1/3RyN8+Pqf5CMX/2W6Opuf\nsLUNknbMtlXTOg3iWVX2V9dzy8xZvGH8YK9NOWVQDWqgGqSoBnUX1Z8Gqj/KIOtP75zM2Qjl1u9t\nIpjNzllEHMa0FwcRh0j0/j/Ovn1ePprlUrU5nr/rcW53O3FBdJPljmQSpSdPjlwSSSxR2gnfkttQ\nwYijXPcJrElv2rwXkpOQOfGjSGaps2VomgPlcZxzWHHY+GZ/71lf5gb5KSZmS4ROsNbgHFQfH41P\nnzi356fQCLZWKZTq1PYPY2pRGLO3cwZjHJ5n+ePnfR6Iqqq+96wvA5G4VJxP2RbS6OQTYYkQQ1Hq\n1J3HsXCEss1jnaFkatRdFJV9MBjnx858nL0bNjytNlcUZfWpujo/ee9/A2Dn2AR/seOfAfj01BY+\nufcKAH737L/lz478GL952lfZ6Y+k+4bOcuW9r2bEr/H/nrP47AdFUebjLVBYt2mQlQyKxM6LLPHE\nNuXNy2KJcvdlz5EsQ+y886wFfAkoeTUOVMap2WjKqANCa3BxB9GUAuzsyQvViBOO/kiBszYfSAdT\n0D6SJ2mHejxo9XDpYHWhfZTVYTIs8VQwwxm5kZNvrDxtVIOaUQ1SVIO6h+pPM6o/yqDqT3edzAIn\nLqox/v18+j2hKXo5jlROnKezF8zxiUs+21VRmAt9KmGOS3Y9AUQRyHd6O3BW8I42iq45z+HyDgkE\nO17HLwaIcVRqkYM58AyhE3xjca6O9QQTOmbCAhu9GS4f38Pnpy+NHOjimm7qdiksfr34XyP7aj7l\nx8cQB8FYyIZtk+k21+78HpcOPc7vbriGifIQQejxofP/atH283CRY9l5TNshJoMSVRf9PHwJ0ydd\ndRdN45gkmtrhYXmyuoFvff188tPCntdvYbf/5LLC/o+HZfbUzlzy9oqirJwjYZWRlz4GwMTuczj7\nN34RgPHbhtj4yf8A4M3vehNnfuUEL77u1zDbKnz3yo9x6bd+GRt67Prv38dbv56L//y1fO/izlRa\nVpRTheghLnhYfIkrRbtGV8zDQSbqpm1kSfxu2gzW0gFVyzKP6Jg156XHXwuMmQrPKf6Qg9Uxjs5s\noB5PFXVOsLGJ4+NlJilhZ32cJMnDYjIRPLmNc1z743dw+fCjSz5/MkhtLXYzKEVz+4Enqxv46+nz\nePXofScdZN2+/LrUSguqQc2oBimqQd1D9acZ1R9lUPWny5HMwp9c+Rf82uhrqM75jH6vmF01DydQ\nuWyGj1zyha6LQTJlIe+FFL06gTXsfuYBZmoFnnCboBbny8g5xLfYmkE8R1D3EOMQ47BWcH4kFGEu\nbEqZMRf67KltSXPw4IQQ0kjmhfj9c/8GgGPhCB8dvQqAZ48f5rUb7py37bt3/CNfmzqPqWCIv5m4\ntO02WYpSZ2NuBi+M7NxfXc9MWMA6Q9XmMGLxxFG3HoEz5MQy5NWwzhDummO25nHX7A4AtvvHeUFh\njpLJL3LGyMH8d7M7uHtWncyK0m3CBx7hrJ+bv/yM9/07FnhWXAP04g/+Ks96+3dxQdQhDI8fZ9v1\nBXb/8c/zwOV/0T2DFaXPsc6QFHtI+jXZut2tUTYLRf14kgwG5tdEaJdbDxopsTzsmorm2Zab5AVj\ne9joz3Lf1Okcno062c41Oobj42WmvCHCyfy8FGFINLi6bvedSxpcZaN7kqgoaEzFzVZdV7rD3som\nvsRzedXo/QsOsr455/HlE5d02bLBQzVoPqpBimpQd1D9mY/qjzKI+tP1dBnDUuNjF32Wg8E4H9r4\n4qZ1xx7diB0J2Hr6CQCm/n0L77rgVopSb3eoVaVmc1TDXNMNbp0wUR5iaKxCreoj4nBOcBbIOVwY\nOYtd7Ey2nsGGhtA3BNbgGYt1gmcsldDnobnT2FE8GqXDcNKUd+hkbPRmTlqsD+C2g2czOTuEc8JU\nPXLqv3Hrvy64/bDUwIPQCQUzyqHqWBrVnTjJo4T0hmKunlZBff6OvQx50d/prtkd3MUODo48zrBp\nPHIxWK4pzfDl8hgA60yZ03NOHcyKssbZ9dbb53XFggMHOe2j2+HynpikKH1JEnkTOkMND09sGj2S\nRO0k0zqz0x3nHSezT7Jtltbon7rLEU0wtZn1a6do7w7/KDv8oxypjaYDLGnpE42PlpkWxzmnHeaB\n+8/EicOM17l4x5M8b2w/Vww/0vbYs7bAsKnOa89spE466MpMw10rA9BThT2VzXyJ5/KM/DGuKB5i\nkxfV+bi1XKDifP556tweWzgYqAa1RzVIUQ1afVR/2qP6owya/nTdyVxxPkWpsy03yY3n3kLoDPn4\nJv/a6eexJT/FBcUnAfj4+FVszk1120S+NnUeB2fHqIYezhNq1mOu7hNaw8yJUlSAL8nn4aRRfK8l\nwbQLHUEoOMBaoSyOfC7EM5a8CTm/tI/JcJhaEP8ZvHDZ0xNqzqPifGycO7nVIR9agzEOa+E7+yJn\nbuCu4k3b/rnt8RKRsRjGc2XW+UWGPJ+9sxsIMvmBLEIljOx2seN8Q6HMcK7hVP7OzM55xz8R7k2j\nnUumxvbC8WVdr6IoT4/2MQEro/jwAXb+wy/x+E99soNHVZTBJpkuajFNs5eyOfGyA4F2Hf2okI1d\ndBCW0Fgf9ROSKJVwDQ4inj+2h51DR7ntyDmcmBtKU4m9bPsD6YDxwtJe/nboYoxYthWmeOHIg22P\nlY3OSfqesPjA1RObTrVNalX0ItDhVGVPZTN7KpvZX1/POq8MwN2zZ1J3Xo8tGyxUgxZGNejURjVo\n9VH9WRjVn1ObQdKfrjuZT4QliqaOLwGhi370Y6ZCUeq8ZOy+pm0XcoSuNg9Nb2W6GqV5MOLwxHH4\nyFgUpVyNxTBxKIuLfMpNTuYEAesInSDFgHoQ/UAqXo5h33CmP8Hd4QhB2BBYu8xE62VXYCospk+d\ntuSmm8QgtEKYOb6zwt37t/Ph4GrecsbXG9vFQpI8wUqEaSRXJWctY36FY5VhqmEujmSWVJYlbqcR\n/+SJYhIHM0DZ5nl0buuyrldRlJVTtjUq83Rq5QT7f8juGww7UUezoiyX1uma2Xx4CR6OivPTdUkU\nzryonUUGZNkq7LWWjupCUUC9Ylf+MLvyh9nkTzMTFqM0XTjOL+5LtwkRrt10O7C43dncgoaogHO7\nabjZHI/Z/bLFgpTuon3D7qAaNB/VIAVUg7qB6s98VH8UGAz96bqTeV99AyVTw8NSNFHkbYismdw4\ntxy/mCcm1xOEHkYclXqOEyeGcXNxUyVRyzHiTJoeQxLnTSZXjrOAFYLQT6c9VE2Oaj7H/mA9tzx1\nYeSEBoLQLOtJRc15TIVFpu1Q+iSw4vJs9yeitBeAtSbKDZ2x2YaGJ06shzMay0JnqNg807ZIzeWo\nWj+1xTphXX6OnAmZqA5TDz2OlkvRseNr9oylVuz6z0lRlGVQkBwTYWefhgb7nmL3DXBW/g089pI/\n7eixFWUQ8XDpVMRsdfRsBMlCc5qy+fNg4QGSlylsM6/SesaO7DF6Tc151PHwCdmVP7RgnzCJwmk3\nyGy9JoNl2FQbU2Rb2iuJaIKonSo2T915FE30sH7WFhj2ah2+UkXpLapB7VENUpTVR/WnPao/yiDR\n9dKRM2GRo/VRJsMSNbf2nJKPzWxicrpEuVzgxLERJg6NYWd8sCChIFYQF7+sNJaH0eeoel/jJVaQ\nQKAeVwq1QmANE+Uh/uSxq5iYLREEHqGN8jYvJ11GxflUXCQGZZunbPNMhkPUM+36q8/+BrlcGKf1\nkKYc01kSAa5Yn7LNU3E56rb57+OLpZSrUcgFrBuqpBHNoTVU6zlqtv9C+RXlVMITQ1EC7Def0dHj\nBvueorC30NFjKsogYl3jYbLBtq2a7oltepWkmr6vpOK3h2s7WGnKzZcpBNNtkkCDLHWXo+L8FR/T\nw+FLgCdRBft8S+7F7DmTPJDJ4KoWv+ouxzpTXrENirIWUQ2aj2qQonQH1Z/5qP4og0jXvbwzQYGC\nCfAlxDqh5rwFb/5uc9PhK3jwqW3YqgeBgG04krPpMIpHTPTdQDDkCEuuKbq5VZ+cRLMNwuMFZOMc\nzgm1IMfEkTEkZymWalgryDJ0LUQo20LkXA4L1J2Xptp4YG475w3tY8xUONOfaOtYti3LEtE2El3L\nZFDiRL2ERTBEKT8sghGHwTHs1zCjjplanum5IkHgUQ3X3kODlfCa9XdyXr6R+uP9R36Uss330CJF\n6RzHbIkXb3mIf2K4o8fd+ccPcdZZr+exF9/U0eMqyqCRFL5J8hEmESbZKYrZgVcS3ZO8ZwcLC/Wd\nkuI57dZnI4fWAsmDcV8CPGzTg/LFmFeFfpF+5GL5B60z1IkGviGGD//hayi94hC/vuvWeQMzRRkE\nVIOaUQ1SlO6h+tOM6o8yiHQ9knkudtZFT1ZCPOm9c/mrU8/jjXdcx789dDZ2Lgc1gwQmcjC3FPUT\nByYAE4IEkCsLuWlBLOkrjWIOG+8SCFIX6hNFwtAwc6wEgeCqHnOTRapzflP6iaUQPWXyqLpcFMUc\nDDEZDHG4Psq/TT+bKVtsu99C0cxZAUrsqIY5qjaXpuMIbOPdE0vBCxnKD1ZC+GGpM26G0td7t3wP\nX0VWGRBm44dTnSY8NsGz3/wo59/xcx0/tqIMEu1yECYV1lsjexIW6+gvNGBqjfjJDuTSXHyZiuLJ\nQ//VYLHoIF8CDNEU2GRwldjamj8xYbl2thu0JecLEQyWkqkyauYonHCMXXuMj19xBf8y01/VvBVl\nKagGNaMapCjdQ/WnGdUfZRDpupM5dNKUd9iLBaVXfGv2HL74g4sIE+dy1SA1g9QFgijVRZQiA4b3\nGYb3GUwNTA28GnjV2NE823BIp87mzOfE+WyqhsoPh5GKB6FEr0BwoSEMhbDlT7Lgkyccw6ZG3eao\nWJ+50Kdmc8yF0eepoJg+EfzwBV8g50finOSFnpvL885HXjXvmFEKDBNFLItlOigwHRQ4Xhtiul6g\nFqfQyJnG3ywcZcOHAAAIsUlEQVTnhYyUKgz7/Z2z5+rx+/jAtju4oNA87b8gPu/b+t0eWaUonWUi\nHOFYbWRVjm2np9l+7R5e9MDPrMrxFaXfSR4OJ1gMvgRAHFESVwFPCvEmJH2BZKCVTGdMjpHQ2p9a\naLvsYCY7WFmNqaJLOWYyvMziS8C0HWLKFqP2aDOl9GTUXS5tx7rLMWsL6SuqLi/M2gJ1l8OTKILo\nde/5Mtfefj/2GVv6sqK3oiyGalB7VIMUZfVR/WmP6o8yaHTdyZwQRTHb9H01+L1X/yw3nn0hN559\nIbOufeRe3Xm4uokcynUTO5VpikTGwvCTJopetlE0s2QcyiaJaC7H6TVc+1fqcA5bcjmnBQPnC8di\nOXmKphZFMtscgfOoW4+azaWvhLyEfPTCv4yNjk8V51JOSEQ9jAUHIHAeRa+OwVGzOcpBntl6nrnA\nTyOaIUqxkc+F5Po42vfy0Ue5plTBl/Zi6ovHB7fd2WWrFKXzVKzPWUNH8G47fVWO76pV6h0uLqgo\ng0Ld5dIBT815hAh1l0tfaXEWFw04sgOhLHkJ08FWduDROkhJtm0lO7ALkTT34WqwWEq0/3v/a3j7\n712f2pFE1Fii6/7Tl7+IJ2qb0zZLom6Wkj8x29bQHBGUXVeUOkVTS4+3ITfDttwky8phpih9gmpQ\nM6pBitI9VH+aUf1RBhVxrnvpKkRkGni4ayfsLJuAo702YoWo7b1h0Gx/pnNucy+M6RSqQT1Dbe8N\ng2Z7X2uQiBwBZhmsv0m/oLb3hkGzvd81SPtAvUFt7w2DZnu/64/2gXqH2t4bBs32JWtQtyu1Peyc\nu6TL5+wIIvJdtb37qO29oZ9tPwmqQT1Abe8Navvawjm3uZ+vS23vDWp7b+hn2xdB+0A9QG3vDWr7\n2kL7QL1Dbe8Np7LtPUuXoSiKoiiKoiiKoiiKoiiKovQ/6mRWFEVRFEVRFEVRFEVRFEVRVky3ncyf\n6PL5Oona3hvU9t7Qz7YvRj9fl9reG9T23tDPti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UgMywWfyz+WPwxy7XsfxYiMg78CfCTqnpBRN4N/AL5v+dfAH4VePOA494CvAWgweha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\nU5FZRERERERERERERA7s/wA2UnhkCtnOmgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1800x720 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"5q8RDeHL1172","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"80035727-3949-4c1c-9031-85264a5ed767","executionInfo":{"status":"ok","timestamp":1585144593095,"user_tz":-300,"elapsed":10943,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["img_rows = 96\n","img_cols = 128\n","smooth = 1\n","\n","def dice_coef(y_true, y_pred):\n","    y_true_f = K.flatten(y_true)\n","    y_pred_f = K.flatten(y_pred)\n","    intersection = K.sum(y_true_f * y_pred_f)\n","    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n","\n","def dice_coef_loss(y_true, y_pred):\n","    return -dice_coef(y_true, y_pred)\n","\n","def precision(y_true, y_pred):\n","    \"\"\" Precision metric. Only computes a batch-wise average of precision. Computes the precision, a metric for multi-label classification of\n","    how many selected items are relevant.\"\"\"\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","    precision = true_positives / (predicted_positives + K.epsilon())\n","    return precision\n","\n","def recall(y_true, y_pred):\n","    \"\"\" Recall metric. Only computes a batch-wise average of recall. Computes the recall, a metric for multi-label classification of\n","    how many relevant items are selected. \"\"\"\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","    recall = true_positives / (possible_positives + K.epsilon())\n","    return recall\n","\n","def f1score(y_true, y_pred):\n","    def recall(y_true, y_pred):\n","        \"\"\" Recall metric. Only computes a batch-wise average of recall. Computes the recall, a metric for multi-label classification of\n","        how many relevant items are selected. \"\"\"\n","        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","        recall = true_positives / (possible_positives + K.epsilon())\n","        return recall\n","\n","    def precision(y_true, y_pred):\n","        \"\"\"Precision metric. Only computes a batch-wise average of precision. Computes the precision, a metric for multi-label classification of\n","        how many selected items are relevant. \"\"\"\n","        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","        precision = true_positives / (predicted_positives + K.epsilon())\n","        return precision\n","\n","    precision = precision(y_true, y_pred)\n","    recall = recall(y_true, y_pred)\n","    f1score =  2 * ((precision * recall) / (precision + recall))\n","    return f1score\n","\n","def SegNet_Architecture():\n","  inputs = Input((img_rows, img_cols, 1))\n","  # Encoder \n","  outputs = Conv2D(32, (3, 3), padding='same') (inputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(32, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = MaxPooling2D()(outputs)\n","\n","  outputs = Conv2D(64, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(64, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = MaxPooling2D()(outputs)\n","\n","  outputs = Conv2D(128, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)  \n","  outputs = Conv2D(128, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(128, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = MaxPooling2D()(outputs)\n","\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)  \n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = MaxPooling2D()(outputs)\n","\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)  \n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = MaxPooling2D()(outputs)\n","\n","\n","  outputs = UpSampling2D(size=(2, 2))(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","\n","  outputs = UpSampling2D(size=(2, 2))(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(256, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","\n","  outputs = UpSampling2D(size=(2, 2))(outputs)\n","  outputs = Conv2D(128, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(128, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(64, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","\n","  outputs = UpSampling2D(size=(2, 2))(outputs)\n","  outputs = Conv2D(64, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs = Conv2D(32, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","\n","  outputs = UpSampling2D(size=(2, 2))(outputs)\n","  outputs = Conv2D(32, (3, 3), padding='same')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('relu')(outputs)\n","  outputs= Conv2D(1, (1, 1), padding='valid')(outputs)\n","  outputs = BatchNormalization(axis=3)(outputs)\n","  outputs = Activation('sigmoid')(outputs)\n","\n","  model = Model(inputs=[inputs], outputs=[outputs])\n","  return model\n","\n","model = SegNet_Architecture()\n","model.compile(loss=dice_coef_loss, optimizer=Adam(lr=1e-3), metrics=[dice_coef, 'accuracy', precision, recall, f1score])\n","model.summary()"],"execution_count":10,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2239: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","Model: \"model_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         (None, 96, 128, 1)        0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 96, 128, 32)       320       \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 96, 128, 32)       128       \n","_________________________________________________________________\n","activation_1 (Activation)    (None, 96, 128, 32)       0         \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 96, 128, 32)       9248      \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 96, 128, 32)       128       \n","_________________________________________________________________\n","activation_2 (Activation)    (None, 96, 128, 32)       0         \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 48, 64, 32)        0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 48, 64, 64)        18496     \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 48, 64, 64)        256       \n","_________________________________________________________________\n","activation_3 (Activation)    (None, 48, 64, 64)        0         \n","_________________________________________________________________\n","conv2d_4 (Conv2D)            (None, 48, 64, 64)        36928     \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 48, 64, 64)        256       \n","_________________________________________________________________\n","activation_4 (Activation)    (None, 48, 64, 64)        0         \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 24, 32, 64)        0         \n","_________________________________________________________________\n","conv2d_5 (Conv2D)            (None, 24, 32, 128)       73856     \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 24, 32, 128)       512       \n","_________________________________________________________________\n","activation_5 (Activation)    (None, 24, 32, 128)       0         \n","_________________________________________________________________\n","conv2d_6 (Conv2D)            (None, 24, 32, 128)       147584    \n","_________________________________________________________________\n","batch_normalization_6 (Batch (None, 24, 32, 128)       512       \n","_________________________________________________________________\n","activation_6 (Activation)    (None, 24, 32, 128)       0         \n","_________________________________________________________________\n","conv2d_7 (Conv2D)            (None, 24, 32, 128)       147584    \n","_________________________________________________________________\n","batch_normalization_7 (Batch (None, 24, 32, 128)       512       \n","_________________________________________________________________\n","activation_7 (Activation)    (None, 24, 32, 128)       0         \n","_________________________________________________________________\n","max_pooling2d_3 (MaxPooling2 (None, 12, 16, 128)       0         \n","_________________________________________________________________\n","conv2d_8 (Conv2D)            (None, 12, 16, 256)       295168    \n","_________________________________________________________________\n","batch_normalization_8 (Batch (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_8 (Activation)    (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","conv2d_9 (Conv2D)            (None, 12, 16, 256)       590080    \n","_________________________________________________________________\n","batch_normalization_9 (Batch (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_9 (Activation)    (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","conv2d_10 (Conv2D)           (None, 12, 16, 256)       590080    \n","_________________________________________________________________\n","batch_normalization_10 (Batc (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_10 (Activation)   (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","max_pooling2d_4 (MaxPooling2 (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_11 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_11 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_11 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_12 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_12 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_12 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_13 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_13 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_13 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","max_pooling2d_5 (MaxPooling2 (None, 3, 4, 256)         0         \n","_________________________________________________________________\n","up_sampling2d_1 (UpSampling2 (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_14 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_14 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_14 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_15 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_15 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_15 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","conv2d_16 (Conv2D)           (None, 6, 8, 256)         590080    \n","_________________________________________________________________\n","batch_normalization_16 (Batc (None, 6, 8, 256)         1024      \n","_________________________________________________________________\n","activation_16 (Activation)   (None, 6, 8, 256)         0         \n","_________________________________________________________________\n","up_sampling2d_2 (UpSampling2 (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","conv2d_17 (Conv2D)           (None, 12, 16, 256)       590080    \n","_________________________________________________________________\n","batch_normalization_17 (Batc (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_17 (Activation)   (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","conv2d_18 (Conv2D)           (None, 12, 16, 256)       590080    \n","_________________________________________________________________\n","batch_normalization_18 (Batc (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_18 (Activation)   (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","conv2d_19 (Conv2D)           (None, 12, 16, 256)       590080    \n","_________________________________________________________________\n","batch_normalization_19 (Batc (None, 12, 16, 256)       1024      \n","_________________________________________________________________\n","activation_19 (Activation)   (None, 12, 16, 256)       0         \n","_________________________________________________________________\n","up_sampling2d_3 (UpSampling2 (None, 24, 32, 256)       0         \n","_________________________________________________________________\n","conv2d_20 (Conv2D)           (None, 24, 32, 128)       295040    \n","_________________________________________________________________\n","batch_normalization_20 (Batc (None, 24, 32, 128)       512       \n","_________________________________________________________________\n","activation_20 (Activation)   (None, 24, 32, 128)       0         \n","_________________________________________________________________\n","conv2d_21 (Conv2D)           (None, 24, 32, 128)       147584    \n","_________________________________________________________________\n","batch_normalization_21 (Batc (None, 24, 32, 128)       512       \n","_________________________________________________________________\n","activation_21 (Activation)   (None, 24, 32, 128)       0         \n","_________________________________________________________________\n","conv2d_22 (Conv2D)           (None, 24, 32, 64)        73792     \n","_________________________________________________________________\n","batch_normalization_22 (Batc (None, 24, 32, 64)        256       \n","_________________________________________________________________\n","activation_22 (Activation)   (None, 24, 32, 64)        0         \n","_________________________________________________________________\n","up_sampling2d_4 (UpSampling2 (None, 48, 64, 64)        0         \n","_________________________________________________________________\n","conv2d_23 (Conv2D)           (None, 48, 64, 64)        36928     \n","_________________________________________________________________\n","batch_normalization_23 (Batc (None, 48, 64, 64)        256       \n","_________________________________________________________________\n","activation_23 (Activation)   (None, 48, 64, 64)        0         \n","_________________________________________________________________\n","conv2d_24 (Conv2D)           (None, 48, 64, 32)        18464     \n","_________________________________________________________________\n","batch_normalization_24 (Batc (None, 48, 64, 32)        128       \n","_________________________________________________________________\n","activation_24 (Activation)   (None, 48, 64, 32)        0         \n","_________________________________________________________________\n","up_sampling2d_5 (UpSampling2 (None, 96, 128, 32)       0         \n","_________________________________________________________________\n","conv2d_25 (Conv2D)           (None, 96, 128, 32)       9248      \n","_________________________________________________________________\n","batch_normalization_25 (Batc (None, 96, 128, 32)       128       \n","_________________________________________________________________\n","activation_25 (Activation)   (None, 96, 128, 32)       0         \n","_________________________________________________________________\n","conv2d_26 (Conv2D)           (None, 96, 128, 1)        33        \n","_________________________________________________________________\n","batch_normalization_26 (Batc (None, 96, 128, 1)        4         \n","_________________________________________________________________\n","activation_26 (Activation)   (None, 96, 128, 1)        0         \n","=================================================================\n","Total params: 7,817,541\n","Trainable params: 7,809,347\n","Non-trainable params: 8,194\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7d9qe4469Wzo","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"80424e66-e5d6-4436-d7e6-537feacf61b7","executionInfo":{"status":"ok","timestamp":1585144973492,"user_tz":-300,"elapsed":379253,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["modelDir = './saved Models/SegNet Models/TOP VIEW HEADS'\n","if not os.path.exists(modelDir):\n","  os.makedirs(modelDir)\n","  print('Directory Created')\n","  \n","csv_logger = CSVLogger('./saved Models/SegNet Models/TOP VIEW HEADS/Training-Logs.csv')\n","checkpoint = ModelCheckpoint('./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5', verbose=1, monitor='val_acc',\n","                             save_best_only=True, mode='auto')  \n","\n","model.fit(images_train, masks_train, batch_size=64, epochs=100, verbose=1, shuffle=True, validation_data = (images_test, masks_test),\n","          callbacks=[csv_logger, checkpoint])"],"execution_count":11,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","Train on 1452 samples, validate on 363 samples\n","Epoch 1/100\n","1452/1452 [==============================] - 17s 12ms/step - loss: -0.1196 - dice_coef: 0.1196 - acc: 0.7301 - precision: 0.1204 - recall: 0.8753 - f1score: 0.2110 - val_loss: -0.0987 - val_dice_coef: 0.0987 - val_acc: 0.3701 - val_precision: 0.0561 - val_recall: 0.9086 - val_f1score: 0.1057\n","\n","Epoch 00001: val_acc improved from -inf to 0.37013, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 2/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1429 - dice_coef: 0.1429 - acc: 0.8353 - precision: 0.1941 - recall: 0.9808 - f1score: 0.3232 - val_loss: -0.1593 - val_dice_coef: 0.1593 - val_acc: 0.8407 - val_precision: 0.1784 - val_recall: 0.8022 - val_f1score: 0.2917\n","\n","Epoch 00002: val_acc improved from 0.37013 to 0.84065, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 3/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1519 - dice_coef: 0.1519 - acc: 0.8898 - precision: 0.2666 - recall: 0.9900 - f1score: 0.4191 - val_loss: -0.1575 - val_dice_coef: 0.1575 - val_acc: 0.9325 - val_precision: 0.3718 - val_recall: 0.9128 - val_f1score: 0.5280\n","\n","Epoch 00003: val_acc improved from 0.84065 to 0.93250, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 4/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1571 - dice_coef: 0.1571 - acc: 0.9160 - precision: 0.3210 - recall: 0.9927 - f1score: 0.4843 - val_loss: -0.1618 - val_dice_coef: 0.1618 - val_acc: 0.9430 - val_precision: 0.4184 - val_recall: 0.9571 - val_f1score: 0.5819\n","\n","Epoch 00004: val_acc improved from 0.93250 to 0.94304, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 5/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1614 - dice_coef: 0.1614 - acc: 0.9329 - precision: 0.3738 - recall: 0.9942 - f1score: 0.5424 - val_loss: -0.1553 - val_dice_coef: 0.1553 - val_acc: 0.9500 - val_precision: 0.4475 - val_recall: 0.8864 - val_f1score: 0.5944\n","\n","Epoch 00005: val_acc improved from 0.94304 to 0.95002, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 6/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1627 - dice_coef: 0.1627 - acc: 0.9194 - precision: 0.3325 - recall: 0.9939 - f1score: 0.4974 - val_loss: -0.1701 - val_dice_coef: 0.1701 - val_acc: 0.9499 - val_precision: 0.4503 - val_recall: 0.9610 - val_f1score: 0.6129\n","\n","Epoch 00006: val_acc did not improve from 0.95002\n","Epoch 7/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1678 - dice_coef: 0.1678 - acc: 0.9441 - precision: 0.4154 - recall: 0.9963 - f1score: 0.5856 - val_loss: -0.1734 - val_dice_coef: 0.1734 - val_acc: 0.9540 - val_precision: 0.4727 - val_recall: 0.9861 - val_f1score: 0.6389\n","\n","Epoch 00007: val_acc improved from 0.95002 to 0.95403, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 8/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1709 - dice_coef: 0.1709 - acc: 0.9494 - precision: 0.4394 - recall: 0.9965 - f1score: 0.6089 - val_loss: -0.1736 - val_dice_coef: 0.1736 - val_acc: 0.9455 - val_precision: 0.4300 - val_recall: 0.9931 - val_f1score: 0.6000\n","\n","Epoch 00008: val_acc did not improve from 0.95403\n","Epoch 9/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1740 - dice_coef: 0.1740 - acc: 0.9501 - precision: 0.4442 - recall: 0.9968 - f1score: 0.6138 - val_loss: -0.1751 - val_dice_coef: 0.1751 - val_acc: 0.9638 - val_precision: 0.5340 - val_recall: 0.9667 - val_f1score: 0.6877\n","\n","Epoch 00009: val_acc improved from 0.95403 to 0.96379, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 10/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1772 - dice_coef: 0.1772 - acc: 0.9545 - precision: 0.4664 - recall: 0.9967 - f1score: 0.6348 - val_loss: -0.1785 - val_dice_coef: 0.1785 - val_acc: 0.9421 - val_precision: 0.4153 - val_recall: 0.9958 - val_f1score: 0.5859\n","\n","Epoch 00010: val_acc did not improve from 0.96379\n","Epoch 11/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1803 - dice_coef: 0.1803 - acc: 0.9562 - precision: 0.4761 - recall: 0.9970 - f1score: 0.6435 - val_loss: -0.1834 - val_dice_coef: 0.1834 - val_acc: 0.9481 - val_precision: 0.4418 - val_recall: 0.9929 - val_f1score: 0.6113\n","\n","Epoch 00011: val_acc did not improve from 0.96379\n","Epoch 12/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1831 - dice_coef: 0.1831 - acc: 0.9545 - precision: 0.4681 - recall: 0.9961 - f1score: 0.6360 - val_loss: -0.1788 - val_dice_coef: 0.1788 - val_acc: 0.9148 - val_precision: 0.3243 - val_recall: 0.9937 - val_f1score: 0.4889\n","\n","Epoch 00012: val_acc did not improve from 0.96379\n","Epoch 13/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1856 - dice_coef: 0.1856 - acc: 0.9535 - precision: 0.4599 - recall: 0.9955 - f1score: 0.6280 - val_loss: -0.1855 - val_dice_coef: 0.1855 - val_acc: 0.9314 - val_precision: 0.3744 - val_recall: 0.9966 - val_f1score: 0.5442\n","\n","Epoch 00013: val_acc did not improve from 0.96379\n","Epoch 14/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1879 - dice_coef: 0.1879 - acc: 0.9473 - precision: 0.4326 - recall: 0.9948 - f1score: 0.6016 - val_loss: -0.1870 - val_dice_coef: 0.1870 - val_acc: 0.9402 - val_precision: 0.4063 - val_recall: 0.9803 - val_f1score: 0.5742\n","\n","Epoch 00014: val_acc did not improve from 0.96379\n","Epoch 15/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1930 - dice_coef: 0.1930 - acc: 0.9595 - precision: 0.4964 - recall: 0.9964 - f1score: 0.6619 - val_loss: -0.1933 - val_dice_coef: 0.1933 - val_acc: 0.9472 - val_precision: 0.4372 - val_recall: 0.9900 - val_f1score: 0.6064\n","\n","Epoch 00015: val_acc did not improve from 0.96379\n","Epoch 16/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.1973 - dice_coef: 0.1973 - acc: 0.9653 - precision: 0.5355 - recall: 0.9976 - f1score: 0.6964 - val_loss: -0.1999 - val_dice_coef: 0.1999 - val_acc: 0.9528 - val_precision: 0.4660 - val_recall: 0.9936 - val_f1score: 0.6342\n","\n","Epoch 00016: val_acc did not improve from 0.96379\n","Epoch 17/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2007 - dice_coef: 0.2007 - acc: 0.9665 - precision: 0.5433 - recall: 0.9976 - f1score: 0.7027 - val_loss: -0.2002 - val_dice_coef: 0.2002 - val_acc: 0.9424 - val_precision: 0.4157 - val_recall: 0.9928 - val_f1score: 0.5860\n","\n","Epoch 00017: val_acc did not improve from 0.96379\n","Epoch 18/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2048 - dice_coef: 0.2048 - acc: 0.9695 - precision: 0.5679 - recall: 0.9977 - f1score: 0.7235 - val_loss: -0.2049 - val_dice_coef: 0.2049 - val_acc: 0.9453 - val_precision: 0.4291 - val_recall: 0.9959 - val_f1score: 0.5996\n","\n","Epoch 00018: val_acc did not improve from 0.96379\n","Epoch 19/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2081 - dice_coef: 0.2081 - acc: 0.9681 - precision: 0.5553 - recall: 0.9971 - f1score: 0.7129 - val_loss: -0.2106 - val_dice_coef: 0.2106 - val_acc: 0.9615 - val_precision: 0.5173 - val_recall: 0.9831 - val_f1score: 0.6777\n","\n","Epoch 00019: val_acc did not improve from 0.96379\n","Epoch 20/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2097 - dice_coef: 0.2097 - acc: 0.9572 - precision: 0.4856 - recall: 0.9939 - f1score: 0.6507 - val_loss: -0.1902 - val_dice_coef: 0.1902 - val_acc: 0.8850 - val_precision: 0.2624 - val_recall: 0.9977 - val_f1score: 0.4153\n","\n","Epoch 00020: val_acc did not improve from 0.96379\n","Epoch 21/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2131 - dice_coef: 0.2131 - acc: 0.9560 - precision: 0.4776 - recall: 0.9937 - f1score: 0.6436 - val_loss: -0.2177 - val_dice_coef: 0.2177 - val_acc: 0.9619 - val_precision: 0.5206 - val_recall: 0.9754 - val_f1score: 0.6784\n","\n","Epoch 00021: val_acc did not improve from 0.96379\n","Epoch 22/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2177 - dice_coef: 0.2177 - acc: 0.9626 - precision: 0.5146 - recall: 0.9937 - f1score: 0.6764 - val_loss: -0.2149 - val_dice_coef: 0.2149 - val_acc: 0.9380 - val_precision: 0.3978 - val_recall: 0.9900 - val_f1score: 0.5673\n","\n","Epoch 00022: val_acc did not improve from 0.96379\n","Epoch 23/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2221 - dice_coef: 0.2221 - acc: 0.9641 - precision: 0.5251 - recall: 0.9943 - f1score: 0.6863 - val_loss: -0.2236 - val_dice_coef: 0.2236 - val_acc: 0.9545 - val_precision: 0.4744 - val_recall: 0.9839 - val_f1score: 0.6400\n","\n","Epoch 00023: val_acc did not improve from 0.96379\n","Epoch 24/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2261 - dice_coef: 0.2261 - acc: 0.9655 - precision: 0.5346 - recall: 0.9944 - f1score: 0.6939 - val_loss: -0.2253 - val_dice_coef: 0.2253 - val_acc: 0.9488 - val_precision: 0.4451 - val_recall: 0.9923 - val_f1score: 0.6144\n","\n","Epoch 00024: val_acc did not improve from 0.96379\n","Epoch 25/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2313 - dice_coef: 0.2313 - acc: 0.9670 - precision: 0.5481 - recall: 0.9948 - f1score: 0.7066 - val_loss: -0.2299 - val_dice_coef: 0.2299 - val_acc: 0.9520 - val_precision: 0.4613 - val_recall: 0.9953 - val_f1score: 0.6302\n","\n","Epoch 00025: val_acc did not improve from 0.96379\n","Epoch 26/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2363 - dice_coef: 0.2363 - acc: 0.9717 - precision: 0.5852 - recall: 0.9957 - f1score: 0.7363 - val_loss: -0.2337 - val_dice_coef: 0.2337 - val_acc: 0.9497 - val_precision: 0.4493 - val_recall: 0.9955 - val_f1score: 0.6190\n","\n","Epoch 00026: val_acc did not improve from 0.96379\n","Epoch 27/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2406 - dice_coef: 0.2406 - acc: 0.9710 - precision: 0.5795 - recall: 0.9959 - f1score: 0.7314 - val_loss: -0.2312 - val_dice_coef: 0.2312 - val_acc: 0.9359 - val_precision: 0.3899 - val_recall: 0.9941 - val_f1score: 0.5600\n","\n","Epoch 00027: val_acc did not improve from 0.96379\n","Epoch 28/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2426 - dice_coef: 0.2426 - acc: 0.9621 - precision: 0.5178 - recall: 0.9912 - f1score: 0.6777 - val_loss: -0.2187 - val_dice_coef: 0.2187 - val_acc: 0.9393 - val_precision: 0.3980 - val_recall: 0.9251 - val_f1score: 0.5562\n","\n","Epoch 00028: val_acc did not improve from 0.96379\n","Epoch 29/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2484 - dice_coef: 0.2484 - acc: 0.9675 - precision: 0.5519 - recall: 0.9923 - f1score: 0.7086 - val_loss: -0.2541 - val_dice_coef: 0.2541 - val_acc: 0.9610 - val_precision: 0.5144 - val_recall: 0.9833 - val_f1score: 0.6753\n","\n","Epoch 00029: val_acc did not improve from 0.96379\n","Epoch 30/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2540 - dice_coef: 0.2540 - acc: 0.9712 - precision: 0.5808 - recall: 0.9939 - f1score: 0.7326 - val_loss: -0.2448 - val_dice_coef: 0.2448 - val_acc: 0.9491 - val_precision: 0.4458 - val_recall: 0.9789 - val_f1score: 0.6125\n","\n","Epoch 00030: val_acc did not improve from 0.96379\n","Epoch 31/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2589 - dice_coef: 0.2589 - acc: 0.9718 - precision: 0.5864 - recall: 0.9935 - f1score: 0.7364 - val_loss: -0.2489 - val_dice_coef: 0.2489 - val_acc: 0.9471 - val_precision: 0.4369 - val_recall: 0.9903 - val_f1score: 0.6062\n","\n","Epoch 00031: val_acc did not improve from 0.96379\n","Epoch 32/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2634 - dice_coef: 0.2634 - acc: 0.9708 - precision: 0.5778 - recall: 0.9931 - f1score: 0.7296 - val_loss: -0.2543 - val_dice_coef: 0.2543 - val_acc: 0.9526 - val_precision: 0.4642 - val_recall: 0.9913 - val_f1score: 0.6322\n","\n","Epoch 00032: val_acc did not improve from 0.96379\n","Epoch 33/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2699 - dice_coef: 0.2699 - acc: 0.9755 - precision: 0.6204 - recall: 0.9952 - f1score: 0.7638 - val_loss: -0.2544 - val_dice_coef: 0.2544 - val_acc: 0.9624 - val_precision: 0.5225 - val_recall: 0.9945 - val_f1score: 0.6849\n","\n","Epoch 00033: val_acc did not improve from 0.96379\n","Epoch 34/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2756 - dice_coef: 0.2756 - acc: 0.9778 - precision: 0.6434 - recall: 0.9951 - f1score: 0.7808 - val_loss: -0.2597 - val_dice_coef: 0.2597 - val_acc: 0.9500 - val_precision: 0.4510 - val_recall: 0.9974 - val_f1score: 0.6210\n","\n","Epoch 00034: val_acc did not improve from 0.96379\n","Epoch 35/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2805 - dice_coef: 0.2805 - acc: 0.9766 - precision: 0.6304 - recall: 0.9943 - f1score: 0.7709 - val_loss: -0.2718 - val_dice_coef: 0.2718 - val_acc: 0.9618 - val_precision: 0.5188 - val_recall: 0.9805 - val_f1score: 0.6784\n","\n","Epoch 00035: val_acc did not improve from 0.96379\n","Epoch 36/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2834 - dice_coef: 0.2834 - acc: 0.9716 - precision: 0.5842 - recall: 0.9907 - f1score: 0.7343 - val_loss: -0.2679 - val_dice_coef: 0.2679 - val_acc: 0.9434 - val_precision: 0.4201 - val_recall: 0.9929 - val_f1score: 0.5902\n","\n","Epoch 00036: val_acc did not improve from 0.96379\n","Epoch 37/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2873 - dice_coef: 0.2873 - acc: 0.9690 - precision: 0.5639 - recall: 0.9871 - f1score: 0.7169 - val_loss: -0.2506 - val_dice_coef: 0.2506 - val_acc: 0.9088 - val_precision: 0.3099 - val_recall: 0.9934 - val_f1score: 0.4717\n","\n","Epoch 00037: val_acc did not improve from 0.96379\n","Epoch 38/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.2950 - dice_coef: 0.2950 - acc: 0.9752 - precision: 0.6167 - recall: 0.9908 - f1score: 0.7594 - val_loss: -0.2747 - val_dice_coef: 0.2747 - val_acc: 0.9400 - val_precision: 0.4057 - val_recall: 0.9956 - val_f1score: 0.5763\n","\n","Epoch 00038: val_acc did not improve from 0.96379\n","Epoch 39/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3010 - dice_coef: 0.3010 - acc: 0.9748 - precision: 0.6148 - recall: 0.9910 - f1score: 0.7576 - val_loss: -0.2938 - val_dice_coef: 0.2938 - val_acc: 0.9652 - val_precision: 0.5425 - val_recall: 0.9782 - val_f1score: 0.6978\n","\n","Epoch 00039: val_acc improved from 0.96379 to 0.96516, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 40/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3089 - dice_coef: 0.3089 - acc: 0.9791 - precision: 0.6585 - recall: 0.9931 - f1score: 0.7913 - val_loss: -0.2965 - val_dice_coef: 0.2965 - val_acc: 0.9556 - val_precision: 0.4795 - val_recall: 0.9873 - val_f1score: 0.6451\n","\n","Epoch 00040: val_acc did not improve from 0.96516\n","Epoch 41/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3145 - dice_coef: 0.3145 - acc: 0.9805 - precision: 0.6726 - recall: 0.9924 - f1score: 0.8008 - val_loss: -0.3024 - val_dice_coef: 0.3024 - val_acc: 0.9576 - val_precision: 0.4924 - val_recall: 0.9942 - val_f1score: 0.6585\n","\n","Epoch 00041: val_acc did not improve from 0.96516\n","Epoch 42/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3209 - dice_coef: 0.3209 - acc: 0.9809 - precision: 0.6787 - recall: 0.9925 - f1score: 0.8056 - val_loss: -0.3062 - val_dice_coef: 0.3062 - val_acc: 0.9580 - val_precision: 0.4943 - val_recall: 0.9940 - val_f1score: 0.6600\n","\n","Epoch 00042: val_acc did not improve from 0.96516\n","Epoch 43/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3274 - dice_coef: 0.3274 - acc: 0.9809 - precision: 0.6796 - recall: 0.9924 - f1score: 0.8062 - val_loss: -0.3235 - val_dice_coef: 0.3235 - val_acc: 0.9712 - val_precision: 0.5891 - val_recall: 0.9890 - val_f1score: 0.7383\n","\n","Epoch 00043: val_acc improved from 0.96516 to 0.97119, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 44/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3332 - dice_coef: 0.3332 - acc: 0.9810 - precision: 0.6796 - recall: 0.9919 - f1score: 0.8056 - val_loss: -0.3358 - val_dice_coef: 0.3358 - val_acc: 0.9790 - val_precision: 0.6685 - val_recall: 0.9748 - val_f1score: 0.7930\n","\n","Epoch 00044: val_acc improved from 0.97119 to 0.97901, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 45/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3401 - dice_coef: 0.3401 - acc: 0.9822 - precision: 0.6945 - recall: 0.9914 - f1score: 0.8163 - val_loss: -0.3151 - val_dice_coef: 0.3151 - val_acc: 0.9596 - val_precision: 0.5038 - val_recall: 0.9856 - val_f1score: 0.6665\n","\n","Epoch 00045: val_acc did not improve from 0.97901\n","Epoch 46/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3457 - dice_coef: 0.3457 - acc: 0.9827 - precision: 0.7006 - recall: 0.9899 - f1score: 0.8195 - val_loss: -0.3309 - val_dice_coef: 0.3309 - val_acc: 0.9652 - val_precision: 0.5428 - val_recall: 0.9744 - val_f1score: 0.6971\n","\n","Epoch 00046: val_acc did not improve from 0.97901\n","Epoch 47/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3435 - dice_coef: 0.3435 - acc: 0.9712 - precision: 0.5889 - recall: 0.9818 - f1score: 0.7338 - val_loss: -0.3065 - val_dice_coef: 0.3065 - val_acc: 0.9317 - val_precision: 0.3741 - val_recall: 0.9890 - val_f1score: 0.5427\n","\n","Epoch 00047: val_acc did not improve from 0.97901\n","Epoch 48/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3554 - dice_coef: 0.3554 - acc: 0.9790 - precision: 0.6584 - recall: 0.9866 - f1score: 0.7890 - val_loss: -0.3202 - val_dice_coef: 0.3202 - val_acc: 0.9443 - val_precision: 0.4238 - val_recall: 0.9859 - val_f1score: 0.5924\n","\n","Epoch 00048: val_acc did not improve from 0.97901\n","Epoch 49/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3630 - dice_coef: 0.3630 - acc: 0.9810 - precision: 0.6815 - recall: 0.9874 - f1score: 0.8059 - val_loss: -0.3450 - val_dice_coef: 0.3450 - val_acc: 0.9622 - val_precision: 0.5214 - val_recall: 0.9783 - val_f1score: 0.6799\n","\n","Epoch 00049: val_acc did not improve from 0.97901\n","Epoch 50/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3694 - dice_coef: 0.3694 - acc: 0.9811 - precision: 0.6827 - recall: 0.9865 - f1score: 0.8062 - val_loss: -0.3645 - val_dice_coef: 0.3645 - val_acc: 0.9722 - val_precision: 0.5990 - val_recall: 0.9845 - val_f1score: 0.7447\n","\n","Epoch 00050: val_acc did not improve from 0.97901\n","Epoch 51/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3730 - dice_coef: 0.3730 - acc: 0.9790 - precision: 0.6577 - recall: 0.9849 - f1score: 0.7880 - val_loss: -0.3523 - val_dice_coef: 0.3523 - val_acc: 0.9605 - val_precision: 0.5106 - val_recall: 0.9901 - val_f1score: 0.6736\n","\n","Epoch 00051: val_acc did not improve from 0.97901\n","Epoch 52/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3833 - dice_coef: 0.3833 - acc: 0.9825 - precision: 0.6992 - recall: 0.9860 - f1score: 0.8178 - val_loss: -0.3650 - val_dice_coef: 0.3650 - val_acc: 0.9689 - val_precision: 0.5719 - val_recall: 0.9718 - val_f1score: 0.7198\n","\n","Epoch 00052: val_acc did not improve from 0.97901\n","Epoch 53/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3922 - dice_coef: 0.3922 - acc: 0.9847 - precision: 0.7287 - recall: 0.9871 - f1score: 0.8377 - val_loss: -0.3643 - val_dice_coef: 0.3643 - val_acc: 0.9639 - val_precision: 0.5331 - val_recall: 0.9831 - val_f1score: 0.6912\n","\n","Epoch 00053: val_acc did not improve from 0.97901\n","Epoch 54/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.3989 - dice_coef: 0.3989 - acc: 0.9856 - precision: 0.7397 - recall: 0.9874 - f1score: 0.8451 - val_loss: -0.3791 - val_dice_coef: 0.3791 - val_acc: 0.9689 - val_precision: 0.5707 - val_recall: 0.9868 - val_f1score: 0.7230\n","\n","Epoch 00054: val_acc did not improve from 0.97901\n","Epoch 55/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4060 - dice_coef: 0.4060 - acc: 0.9855 - precision: 0.7392 - recall: 0.9874 - f1score: 0.8451 - val_loss: -0.3899 - val_dice_coef: 0.3899 - val_acc: 0.9694 - val_precision: 0.5743 - val_recall: 0.9936 - val_f1score: 0.7278\n","\n","Epoch 00055: val_acc did not improve from 0.97901\n","Epoch 56/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4132 - dice_coef: 0.4132 - acc: 0.9861 - precision: 0.7465 - recall: 0.9866 - f1score: 0.8495 - val_loss: -0.3970 - val_dice_coef: 0.3970 - val_acc: 0.9723 - val_precision: 0.5986 - val_recall: 0.9904 - val_f1score: 0.7462\n","\n","Epoch 00056: val_acc did not improve from 0.97901\n","Epoch 57/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4197 - dice_coef: 0.4197 - acc: 0.9855 - precision: 0.7399 - recall: 0.9865 - f1score: 0.8450 - val_loss: -0.4105 - val_dice_coef: 0.4105 - val_acc: 0.9760 - val_precision: 0.6344 - val_recall: 0.9771 - val_f1score: 0.7691\n","\n","Epoch 00057: val_acc did not improve from 0.97901\n","Epoch 58/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4268 - dice_coef: 0.4268 - acc: 0.9862 - precision: 0.7485 - recall: 0.9853 - f1score: 0.8498 - val_loss: -0.4009 - val_dice_coef: 0.4009 - val_acc: 0.9677 - val_precision: 0.5613 - val_recall: 0.9860 - val_f1score: 0.7152\n","\n","Epoch 00058: val_acc did not improve from 0.97901\n","Epoch 59/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4308 - dice_coef: 0.4308 - acc: 0.9841 - precision: 0.7229 - recall: 0.9831 - f1score: 0.8324 - val_loss: -0.4193 - val_dice_coef: 0.4193 - val_acc: 0.9764 - val_precision: 0.6405 - val_recall: 0.9727 - val_f1score: 0.7724\n","\n","Epoch 00059: val_acc did not improve from 0.97901\n","Epoch 60/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4410 - dice_coef: 0.4410 - acc: 0.9873 - precision: 0.7650 - recall: 0.9843 - f1score: 0.8605 - val_loss: -0.4224 - val_dice_coef: 0.4224 - val_acc: 0.9748 - val_precision: 0.6226 - val_recall: 0.9886 - val_f1score: 0.7639\n","\n","Epoch 00060: val_acc did not improve from 0.97901\n","Epoch 61/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4491 - dice_coef: 0.4491 - acc: 0.9871 - precision: 0.7635 - recall: 0.9847 - f1score: 0.8596 - val_loss: -0.4313 - val_dice_coef: 0.4313 - val_acc: 0.9780 - val_precision: 0.6572 - val_recall: 0.9764 - val_f1score: 0.7855\n","\n","Epoch 00061: val_acc did not improve from 0.97901\n","Epoch 62/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4541 - dice_coef: 0.4541 - acc: 0.9868 - precision: 0.7587 - recall: 0.9829 - f1score: 0.8558 - val_loss: -0.4369 - val_dice_coef: 0.4369 - val_acc: 0.9773 - val_precision: 0.6486 - val_recall: 0.9813 - val_f1score: 0.7809\n","\n","Epoch 00062: val_acc did not improve from 0.97901\n","Epoch 63/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4606 - dice_coef: 0.4606 - acc: 0.9871 - precision: 0.7627 - recall: 0.9833 - f1score: 0.8585 - val_loss: -0.4104 - val_dice_coef: 0.4104 - val_acc: 0.9642 - val_precision: 0.5354 - val_recall: 0.9815 - val_f1score: 0.6928\n","\n","Epoch 00063: val_acc did not improve from 0.97901\n","Epoch 64/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4679 - dice_coef: 0.4679 - acc: 0.9871 - precision: 0.7639 - recall: 0.9820 - f1score: 0.8588 - val_loss: -0.4610 - val_dice_coef: 0.4610 - val_acc: 0.9821 - val_precision: 0.7030 - val_recall: 0.9812 - val_f1score: 0.8190\n","\n","Epoch 00064: val_acc improved from 0.97901 to 0.98208, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 65/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4769 - dice_coef: 0.4769 - acc: 0.9884 - precision: 0.7835 - recall: 0.9831 - f1score: 0.8717 - val_loss: -0.4388 - val_dice_coef: 0.4388 - val_acc: 0.9707 - val_precision: 0.5856 - val_recall: 0.9871 - val_f1score: 0.7350\n","\n","Epoch 00065: val_acc did not improve from 0.98208\n","Epoch 66/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4839 - dice_coef: 0.4839 - acc: 0.9888 - precision: 0.7904 - recall: 0.9822 - f1score: 0.8753 - val_loss: -0.4548 - val_dice_coef: 0.4548 - val_acc: 0.9733 - val_precision: 0.6088 - val_recall: 0.9818 - val_f1score: 0.7513\n","\n","Epoch 00066: val_acc did not improve from 0.98208\n","Epoch 67/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4885 - dice_coef: 0.4885 - acc: 0.9877 - precision: 0.7760 - recall: 0.9800 - f1score: 0.8648 - val_loss: -0.4707 - val_dice_coef: 0.4707 - val_acc: 0.9794 - val_precision: 0.6750 - val_recall: 0.9664 - val_f1score: 0.7947\n","\n","Epoch 00067: val_acc did not improve from 0.98208\n","Epoch 68/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.4961 - dice_coef: 0.4961 - acc: 0.9880 - precision: 0.7785 - recall: 0.9802 - f1score: 0.8675 - val_loss: -0.4825 - val_dice_coef: 0.4825 - val_acc: 0.9812 - val_precision: 0.6934 - val_recall: 0.9745 - val_f1score: 0.8102\n","\n","Epoch 00068: val_acc did not improve from 0.98208\n","Epoch 69/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5021 - dice_coef: 0.5021 - acc: 0.9882 - precision: 0.7816 - recall: 0.9794 - f1score: 0.8690 - val_loss: -0.4739 - val_dice_coef: 0.4739 - val_acc: 0.9773 - val_precision: 0.6524 - val_recall: 0.9646 - val_f1score: 0.7783\n","\n","Epoch 00069: val_acc did not improve from 0.98208\n","Epoch 70/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5092 - dice_coef: 0.5092 - acc: 0.9881 - precision: 0.7829 - recall: 0.9770 - f1score: 0.8689 - val_loss: -0.4837 - val_dice_coef: 0.4837 - val_acc: 0.9777 - val_precision: 0.6523 - val_recall: 0.9810 - val_f1score: 0.7835\n","\n","Epoch 00070: val_acc did not improve from 0.98208\n","Epoch 71/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5170 - dice_coef: 0.5170 - acc: 0.9891 - precision: 0.7968 - recall: 0.9801 - f1score: 0.8783 - val_loss: -0.4840 - val_dice_coef: 0.4840 - val_acc: 0.9759 - val_precision: 0.6330 - val_recall: 0.9880 - val_f1score: 0.7715\n","\n","Epoch 00071: val_acc did not improve from 0.98208\n","Epoch 72/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5224 - dice_coef: 0.5224 - acc: 0.9889 - precision: 0.7939 - recall: 0.9777 - f1score: 0.8756 - val_loss: -0.5005 - val_dice_coef: 0.5005 - val_acc: 0.9785 - val_precision: 0.6604 - val_recall: 0.9857 - val_f1score: 0.7908\n","\n","Epoch 00072: val_acc did not improve from 0.98208\n","Epoch 73/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5287 - dice_coef: 0.5287 - acc: 0.9891 - precision: 0.7959 - recall: 0.9784 - f1score: 0.8774 - val_loss: -0.4361 - val_dice_coef: 0.4361 - val_acc: 0.9529 - val_precision: 0.4647 - val_recall: 0.9844 - val_f1score: 0.6307\n","\n","Epoch 00073: val_acc did not improve from 0.98208\n","Epoch 74/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5365 - dice_coef: 0.5365 - acc: 0.9900 - precision: 0.8120 - recall: 0.9773 - f1score: 0.8863 - val_loss: -0.4871 - val_dice_coef: 0.4871 - val_acc: 0.9741 - val_precision: 0.6162 - val_recall: 0.9835 - val_f1score: 0.7576\n","\n","Epoch 00074: val_acc did not improve from 0.98208\n","Epoch 75/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5424 - dice_coef: 0.5424 - acc: 0.9897 - precision: 0.8067 - recall: 0.9774 - f1score: 0.8834 - val_loss: -0.5350 - val_dice_coef: 0.5350 - val_acc: 0.9842 - val_precision: 0.7322 - val_recall: 0.9756 - val_f1score: 0.8365\n","\n","Epoch 00075: val_acc improved from 0.98208 to 0.98424, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 76/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5525 - dice_coef: 0.5525 - acc: 0.9906 - precision: 0.8211 - recall: 0.9787 - f1score: 0.8926 - val_loss: -0.4797 - val_dice_coef: 0.4797 - val_acc: 0.9647 - val_precision: 0.5376 - val_recall: 0.9884 - val_f1score: 0.6962\n","\n","Epoch 00076: val_acc did not improve from 0.98424\n","Epoch 77/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5613 - dice_coef: 0.5613 - acc: 0.9913 - precision: 0.8357 - recall: 0.9779 - f1score: 0.9010 - val_loss: -0.5143 - val_dice_coef: 0.5143 - val_acc: 0.9768 - val_precision: 0.6418 - val_recall: 0.9868 - val_f1score: 0.7777\n","\n","Epoch 00077: val_acc did not improve from 0.98424\n","Epoch 78/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5630 - dice_coef: 0.5630 - acc: 0.9901 - precision: 0.8164 - recall: 0.9783 - f1score: 0.8889 - val_loss: -0.5437 - val_dice_coef: 0.5437 - val_acc: 0.9812 - val_precision: 0.6940 - val_recall: 0.9708 - val_f1score: 0.8093\n","\n","Epoch 00078: val_acc did not improve from 0.98424\n","Epoch 79/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5691 - dice_coef: 0.5691 - acc: 0.9904 - precision: 0.8221 - recall: 0.9743 - f1score: 0.8910 - val_loss: -0.5532 - val_dice_coef: 0.5532 - val_acc: 0.9822 - val_precision: 0.7042 - val_recall: 0.9798 - val_f1score: 0.8194\n","\n","Epoch 00079: val_acc did not improve from 0.98424\n","Epoch 80/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5774 - dice_coef: 0.5774 - acc: 0.9912 - precision: 0.8341 - recall: 0.9757 - f1score: 0.8990 - val_loss: -0.5529 - val_dice_coef: 0.5529 - val_acc: 0.9841 - val_precision: 0.7285 - val_recall: 0.9812 - val_f1score: 0.8361\n","\n","Epoch 00080: val_acc did not improve from 0.98424\n","Epoch 81/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5871 - dice_coef: 0.5871 - acc: 0.9920 - precision: 0.8483 - recall: 0.9773 - f1score: 0.9079 - val_loss: -0.5436 - val_dice_coef: 0.5436 - val_acc: 0.9811 - val_precision: 0.6894 - val_recall: 0.9872 - val_f1score: 0.8118\n","\n","Epoch 00081: val_acc did not improve from 0.98424\n","Epoch 82/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5920 - dice_coef: 0.5920 - acc: 0.9918 - precision: 0.8441 - recall: 0.9776 - f1score: 0.9055 - val_loss: -0.5609 - val_dice_coef: 0.5609 - val_acc: 0.9807 - val_precision: 0.6867 - val_recall: 0.9782 - val_f1score: 0.8068\n","\n","Epoch 00082: val_acc did not improve from 0.98424\n","Epoch 83/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.5992 - dice_coef: 0.5992 - acc: 0.9922 - precision: 0.8507 - recall: 0.9774 - f1score: 0.9094 - val_loss: -0.5750 - val_dice_coef: 0.5750 - val_acc: 0.9860 - val_precision: 0.7577 - val_recall: 0.9728 - val_f1score: 0.8518\n","\n","Epoch 00083: val_acc improved from 0.98424 to 0.98601, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 84/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6049 - dice_coef: 0.6049 - acc: 0.9922 - precision: 0.8527 - recall: 0.9761 - f1score: 0.9099 - val_loss: -0.5822 - val_dice_coef: 0.5822 - val_acc: 0.9871 - val_precision: 0.7762 - val_recall: 0.9684 - val_f1score: 0.8617\n","\n","Epoch 00084: val_acc improved from 0.98601 to 0.98713, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 85/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6102 - dice_coef: 0.6102 - acc: 0.9922 - precision: 0.8537 - recall: 0.9743 - f1score: 0.9097 - val_loss: -0.5877 - val_dice_coef: 0.5877 - val_acc: 0.9876 - val_precision: 0.7850 - val_recall: 0.9647 - val_f1score: 0.8656\n","\n","Epoch 00085: val_acc improved from 0.98713 to 0.98758, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 86/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6141 - dice_coef: 0.6141 - acc: 0.9920 - precision: 0.8492 - recall: 0.9742 - f1score: 0.9071 - val_loss: -0.5927 - val_dice_coef: 0.5927 - val_acc: 0.9868 - val_precision: 0.7690 - val_recall: 0.9739 - val_f1score: 0.8593\n","\n","Epoch 00086: val_acc did not improve from 0.98758\n","Epoch 87/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6211 - dice_coef: 0.6211 - acc: 0.9922 - precision: 0.8533 - recall: 0.9741 - f1score: 0.9093 - val_loss: -0.6076 - val_dice_coef: 0.6076 - val_acc: 0.9890 - val_precision: 0.8157 - val_recall: 0.9475 - val_f1score: 0.8766\n","\n","Epoch 00087: val_acc improved from 0.98758 to 0.98896, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 88/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6244 - dice_coef: 0.6244 - acc: 0.9922 - precision: 0.8546 - recall: 0.9741 - f1score: 0.9100 - val_loss: -0.5918 - val_dice_coef: 0.5918 - val_acc: 0.9845 - val_precision: 0.7344 - val_recall: 0.9807 - val_f1score: 0.8398\n","\n","Epoch 00088: val_acc did not improve from 0.98896\n","Epoch 89/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6322 - dice_coef: 0.6322 - acc: 0.9924 - precision: 0.8590 - recall: 0.9724 - f1score: 0.9120 - val_loss: -0.6167 - val_dice_coef: 0.6167 - val_acc: 0.9903 - val_precision: 0.8401 - val_recall: 0.9489 - val_f1score: 0.8912\n","\n","Epoch 00089: val_acc improved from 0.98896 to 0.99035, saving model to ./saved Models/SegNet Models/TOP VIEW HEADS/best-model.h5\n","Epoch 90/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6366 - dice_coef: 0.6366 - acc: 0.9924 - precision: 0.8587 - recall: 0.9725 - f1score: 0.9117 - val_loss: -0.5881 - val_dice_coef: 0.5881 - val_acc: 0.9813 - val_precision: 0.6926 - val_recall: 0.9813 - val_f1score: 0.8120\n","\n","Epoch 00090: val_acc did not improve from 0.99035\n","Epoch 91/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6441 - dice_coef: 0.6441 - acc: 0.9926 - precision: 0.8637 - recall: 0.9710 - f1score: 0.9141 - val_loss: -0.6225 - val_dice_coef: 0.6225 - val_acc: 0.9876 - val_precision: 0.7816 - val_recall: 0.9738 - val_f1score: 0.8670\n","\n","Epoch 00091: val_acc did not improve from 0.99035\n","Epoch 92/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6475 - dice_coef: 0.6475 - acc: 0.9923 - precision: 0.8570 - recall: 0.9722 - f1score: 0.9108 - val_loss: -0.6197 - val_dice_coef: 0.6197 - val_acc: 0.9859 - val_precision: 0.7574 - val_recall: 0.9703 - val_f1score: 0.8507\n","\n","Epoch 00092: val_acc did not improve from 0.99035\n","Epoch 93/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6489 - dice_coef: 0.6489 - acc: 0.9919 - precision: 0.8510 - recall: 0.9681 - f1score: 0.9052 - val_loss: -0.6206 - val_dice_coef: 0.6206 - val_acc: 0.9838 - val_precision: 0.7228 - val_recall: 0.9854 - val_f1score: 0.8338\n","\n","Epoch 00093: val_acc did not improve from 0.99035\n","Epoch 94/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6580 - dice_coef: 0.6580 - acc: 0.9927 - precision: 0.8669 - recall: 0.9694 - f1score: 0.9149 - val_loss: -0.6287 - val_dice_coef: 0.6287 - val_acc: 0.9847 - val_precision: 0.7365 - val_recall: 0.9816 - val_f1score: 0.8415\n","\n","Epoch 00094: val_acc did not improve from 0.99035\n","Epoch 95/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6649 - dice_coef: 0.6649 - acc: 0.9927 - precision: 0.8660 - recall: 0.9726 - f1score: 0.9159 - val_loss: -0.6455 - val_dice_coef: 0.6455 - val_acc: 0.9879 - val_precision: 0.7851 - val_recall: 0.9738 - val_f1score: 0.8692\n","\n","Epoch 00095: val_acc did not improve from 0.99035\n","Epoch 96/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6685 - dice_coef: 0.6685 - acc: 0.9931 - precision: 0.8716 - recall: 0.9714 - f1score: 0.9184 - val_loss: -0.6353 - val_dice_coef: 0.6353 - val_acc: 0.9860 - val_precision: 0.7574 - val_recall: 0.9726 - val_f1score: 0.8516\n","\n","Epoch 00096: val_acc did not improve from 0.99035\n","Epoch 97/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6761 - dice_coef: 0.6761 - acc: 0.9933 - precision: 0.8780 - recall: 0.9709 - f1score: 0.9219 - val_loss: -0.6485 - val_dice_coef: 0.6485 - val_acc: 0.9893 - val_precision: 0.8120 - val_recall: 0.9662 - val_f1score: 0.8824\n","\n","Epoch 00097: val_acc did not improve from 0.99035\n","Epoch 98/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6797 - dice_coef: 0.6797 - acc: 0.9932 - precision: 0.8751 - recall: 0.9708 - f1score: 0.9201 - val_loss: -0.6380 - val_dice_coef: 0.6380 - val_acc: 0.9851 - val_precision: 0.7418 - val_recall: 0.9813 - val_f1score: 0.8448\n","\n","Epoch 00098: val_acc did not improve from 0.99035\n","Epoch 99/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6843 - dice_coef: 0.6843 - acc: 0.9934 - precision: 0.8806 - recall: 0.9694 - f1score: 0.9226 - val_loss: -0.6632 - val_dice_coef: 0.6632 - val_acc: 0.9888 - val_precision: 0.8014 - val_recall: 0.9715 - val_f1score: 0.8783\n","\n","Epoch 00099: val_acc did not improve from 0.99035\n","Epoch 100/100\n","1452/1452 [==============================] - 3s 2ms/step - loss: -0.6862 - dice_coef: 0.6862 - acc: 0.9931 - precision: 0.8753 - recall: 0.9684 - f1score: 0.9189 - val_loss: -0.6553 - val_dice_coef: 0.6553 - val_acc: 0.9888 - val_precision: 0.8053 - val_recall: 0.9625 - val_f1score: 0.8769\n","\n","Epoch 00100: val_acc did not improve from 0.99035\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fc9c5d0a358>"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"BQih4Rlp9uwE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":413},"outputId":"da5ed0e6-f7b2-4df9-8c28-b915c0a53bf2","executionInfo":{"status":"ok","timestamp":1585145651873,"user_tz":-300,"elapsed":4279,"user":{"displayName":"ZEESHAN NISAR","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhJ1mFA3pQtq_kbc72-pLy5yrV90Q5vLSpZX076=s64","userId":"05353617243036990298"}}},"source":["def plot_images(images_test, pred, masks_test):\n","  plt.figure(1, figsize = (20, 20))\n","  for i in range(7):\n","    plt.subplot(1, 7, i+1).set_title('Image# {}' .format(i+1))\n","    plt.imshow(images_test[i, :, :, 0], cmap='gray')\n","    plt.axis('off')\n","  plt.figure(2, figsize = (20, 20))\n","  for i in range(7):\n","    plt.subplot(1, 7, i+1).set_title('Prediction# {}' .format(i+1))\n","    plt.imshow(pred[i, :, :, 0], cmap='gray')\n","    plt.axis('off')\n","\n","  plt.figure(3, figsize = (20, 20))\n","  for i in range(7):\n","    plt.subplot(1, 7, i+1).set_title('Ground-Truth# {}' .format(i+1))\n","    plt.imshow(masks_test[i, :, :, 0], cmap='gray')\n","    plt.axis('off')\n","\n","  plt.show()\n","  plt.close()\n","\n","pred = model.predict(images_test)\n","plot_images(images_test, pred, masks_test)"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABGoAAACECAYAAADIts3nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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ubmjPA3nTFPNdgMDw87+ha6kGv1OJ2dnZgzZ86Met4Gw3Sj4G9vZ2cnfvnLX8r/agO+efNm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size 1440x1440 with 7 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+4UUZ5nbi7Ee2H3g3DsOpN/TedTiORSMDv91tWNaZpIhAIZIgo3L3LqU6Ul6cl8SUY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\nPeigg/DQQw/hpJNO8rhWgiAUAxEQio+0sSAIQuWSVa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N//vnn+vzzzzVixAgVFxeHNA9XfUJQOIM/vIQz+MMrqMXQa9KkiV/fdG3YsEHDhw/X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\nagAAAAAAADyCRg0AAAAAAIBH/H84SggQrYcWWgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1440x1440 with 7 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"fEjgST_t-wzG","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}