melanoma detection using InceptionV3.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os, random, re, math, time\nrandom.seed(a=42)\nimport numpy as np\n\nimport pandas as pd\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nimport PIL\nfrom kaggle_datasets import KaggleDatasets\nfrom tqdm import tqdm","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Reading in the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n## import the dataset\ndftrain = pd.read_csv('../input/siim-isic-melanoma-classification/train.csv')\ndftest = pd.read_csv('../input/siim-isic-melanoma-classification/train.csv')","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### check for missing values"},{"metadata":{"trusted":true},"cell_type":"code","source":"## Missing values in training data\ndftrain.isna().any()\ndftrain = dftrain.dropna()\n\n## Missing values in testing data\ndftest.isna().any()\ndftest = dftest.dropna()\n","execution_count":5,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### check unique values in each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Count of male and female \", dftrain.sex.value_counts())\nprint(\"uniques in anatom_site_general_challenge column \", dftrain.anatom_site_general_challenge.unique())\nprint(\"uniques in diagnosis column \", dftrain.diagnosis.unique())\nprint(\"uniques in benign_malignant column \", dftrain.benign_malignant.unique())","execution_count":6,"outputs":[{"output_type":"stream","text":"Count of male and female  male      16788\nfemale    15743\nName: sex, dtype: int64\nuniques in anatom_site_general_challenge column  ['head/neck' 'upper extremity' 'lower extremity' 'torso' 'palms/soles'\n 'oral/genital']\nuniques in diagnosis column  ['unknown' 'nevus' 'melanoma' 'seborrheic keratosis' 'lentigo NOS'\n 'lichenoid keratosis' 'solar lentigo' 'cafe-au-lait macule'\n 'atypical melanocytic proliferation']\nuniques in benign_malignant column  ['benign' 'malignant']\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### visualizations\n"},{"metadata":{},"cell_type":"markdown","source":"#### Below plot is the distribution of age of those affected"},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.kdeplot(dftrain[(dftrain['target'] == 1)].age_approx, shade = True)","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f680cbbe390>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#### Below plot is the distribution of sex of those affected"},{"metadata":{"trusted":true},"cell_type":"code","source":"dummy = dftrain[dftrain['target'] == 1]\n\nsns.barplot(x = \"sex\", y = \"target\", data = dummy, hue =\"sex\")\n\n","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f6829009c10>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#### Comparision of affected (0 - blue) to unaffected (1 - orange)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# viewing the distributions of data \n\nsns.barplot(x = \"target\", y = \"target\", data = dftrain, hue =\"target\")\n\n## hence the number of unaffected surpasses the affected, we need to normalise this data. But we need to understand \n# various other columns before normalising this data","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f680dc3c110>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### lets see the distributions of sex  in  unaffected before removing normalizing"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"no of unaffected males  : \", len(dftrain[(dftrain['target'] == 0) & (dftrain['sex'] == 'male')]))\nprint(\"no of unaffected females  : \", len(dftrain[(dftrain['target'] == 0) & (dftrain['sex'] == 'female')]))\n\n## not. much of a difference in. the ratios","execution_count":10,"outputs":[{"output_type":"stream","text":"no of unaffected males  :  16430\nno of unaffected females  :  15526\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Below chart is the distribution of anatom site challenge"},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.barplot(x = \"anatom_site_general_challenge\", y = \"target\", data = dftrain, hue =\"anatom_site_general_challenge\")\n","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f680dbc2650>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYkAAAEHCAYAAABbZ7oVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd3hUddbA8e8hBqmCi6gUheiClCSEQCgCMQiEoAg2VlgEIkpHLO9iWRUQsQG7smFRFKUpCoKC6IoiClKkRkIHKYaOIk2qtPP+cW/GSZhJJiRDIJzP8+TJrb97ZubOnFvPFVXFGGOM8aVAXgdgjDHm4mVJwhhjjF+WJIwxxvhlScIYY4xfliSMMcb4dUVeB5CbrrnmGq1YsWJeh2GMMZeM5OTk31S1tL/x+SpJVKxYkWXLluV1GMYYc8kQka2ZjbfDTcYYY/yyJGGMMcYvSxLGGGP8ylfnJEzuOnXqFDt27ODEiRN5HYoxABQqVIjy5csTGhqa16FcNixJGL927NhB8eLFqVixIiKS1+GYy5yqsm/fPnbs2EFYWFheh3PZsMNNxq8TJ05QqlQpSxDmoiAilCpVyvZsLzBLEiZTliDMxcTWxwvPkoQxxhi/7JyEMcbkQ0899RR79uzh+uuvZ/Dgwefdju1JmDwxZ84cfvjhhwu+3H79+jFr1iwAhg0bxrFjxy54DLltwIABDB06NFvzzJkzh5YtW2ZrnsTERKZMmQJAXFycVTe4yO3Zs4edO3eyZ8+eHLVjScLkibxKEgMHDqRp06bApZUkzpw5k9chmMuUJQmTbXfffTe1atWievXqvPPOOwAUK1aM5557jho1alCvXj1++eUXAD7//HPq1q1LzZo1adq0Kb/88gupqamMHDmSN954g6ioKObNm8fWrVtp0qQJkZGRNGnShG3btgHO1muPHj1o3LgxN910E99//z2dO3ematWqJCYm+o3xzJkzJCYmEh4eTkREBG+88YanvSlTppCUlMSuXbto3LgxjRs3BmDmzJnUr1+f6Oho2rRpw5EjR/y2/+WXX1KlShUaNmxInz59PFvlR48epXPnzsTExFCzZk0+++wzAMaOHcu9995LQkIClSpV4qmnnvK05W+5FStWZODAgTRs2JDJkyczatQoYmJiqFGjBvfdd1/ACW7Tpk00bdqUGjVqEB0dzebNmwE4cuQI999/P1WqVKF9+/akPcp44MCBxMTEEB4eTteuXcnqEceZxd+/f3+io6OJiIhg/fr1AOzdu5dmzZoRHR1Nt27dqFChAr/99hsAH3zwAXXq1CEqKopu3bpZcrwYqGq++atVq5aa3LN27Vqfw/ft26eqqseOHdPq1avrb7/9poBOnz5dVVX79u2rL730kqqq7t+/X8+ePauqqqNGjdInn3xSVVX79++vQ4YM8bTZsmVLHTt2rKqqvvfee9q6dWtVVe3UqZM+8MADevbsWZ02bZoWL15cV65cqWfOnNHo6Ghdvny5zxiXLVumTZs29fQfOHDA097kyZNVVbVChQq6d+9eVVXdu3evNmrUSI8cOaKqqq+99pq++OKLPts+fvy4li9fXrds2aKqqm3bttU777xTVVWfffZZff/99z3LrFSpkh45ckTHjBmjYWFhevDgQT1+/LjeeOONum3btkyXW6FCBX399dc9y/3tt9883c8995wmJSX5fC8zqlOnjn766aee2I8ePaqzZ8/Wq666Srdv365nzpzRevXq6bx581T1z89XVfXBBx/0fK7e791tt92mS5cuzTL+tBhHjBihDz/8sKqq9urVS1955RVVVZ0xY4YCunfvXl27dq22bNlST548qaqqPXr00HHjxp3zevytlya9Dh066O23364dOnTIdDpgmWbyu2onrk22JSUlMXXqVAC2b9/Oxo0bKViwoGdrulatWnzzzTeAc0PeAw88wO7duzl58qTfm6AWLlzIp59+CkCHDh3SbWnfddddiAgRERFcd911REREAFC9enVSU1OJioo6p72bbrqJLVu28Oijj3LnnXcSHx+f6WtatGgRa9eupUGDBgCcPHmS+vXr+5x2/fr13HTTTZ7X0q5dO88e1cyZM5k+fbrnHMGJEyc8e0VNmjShRIkSAFSrVo2tW7dy8ODBTJf7wAMPeLpXr17N888/z8GDBzly5AjNmzfP9DUBHD58mJ07d3LPPfcAzh3LaerUqUP58uUBiIqKIjU1lYYNGzJ79mwGDx7MsWPH2L9/P9WrV+euu+46r/ft3nvvBZx1Iu3znT9/vmf9SUhI4Oqrrwbg22+/JTk5mZiYGACOHz/Otddem+VrNMFlScJky5w5c5g1axYLFy6kSJEixMXFceLECUJDQz3XsIeEhHD69GkAHn30UZ588klatWrFnDlzGDBgQEDL8b4e/sorrwSgQIECnu60/rTlZHT11VezYsUKvv76a0aMGMHHH3/M6NGj/S5PVWnWrBkfffRRlrFpJodfVJVPPvmEW265Jd3wxYsXp4s97T3KarlFixb1dCcmJjJt2jRq1KjB2LFjmTNnTo5i9RXPiRMn6NmzJ8uWLeOGG25gwIABmd68llX8acvwXif8xaSqdOrUiVdffTXL12UuHDsnYbLl0KFDXH311RQpUoT169ezaNGiLKcvV64cAOPGjfMML168OIcPH/b033rrrUycOBGACRMm0LBhwxzF+dtvv3H27Fnuu+8+XnrpJX788cdzpvGOoV69eixYsIBNmzYBcOzYMX766SefbVepUoUtW7aQmpoKwKRJkzzjmjdvzvDhwz0/hMuXL880zuws9/Dhw5QpU4ZTp04xYcKETNtNc9VVV1G+fHmmTZsGwB9//JHpuYy0hHDNNddw5MgRz9VMuRF/moYNG/Lxxx8Dzp7XgQMHAGdPa8qUKfz6668A7N+/n61bM33UgbkALEmYbElISOD06dNERkbywgsvUK9evUynHzBgAG3atKFRo0Zcc801nuF33XUXU6dO9Zy4TkpKYsyYMURGRvL+++/zn//8J0dx7ty5k7i4OKKiokhMTPS5ddq1a1datGhB48aNKV26NGPHjqVdu3ZERkZSr149z4nWjAoXLsybb75JQkICDRs25LrrrvMcRnrhhRc4deoUkZGRhIeH88ILL2QaZ3aW+9JLL1G3bl2aNWtGlSpVAn4v3n//fZKSkoiMjOTWW2/N9JLIkiVL0qVLFyIiIrj77rs9h35yI/40/fv3Z+bMmURHRzNjxgzKlClD8eLFqVatGoMGDSI+Pp7IyEiaNWvG7t27A36dJjgks93RS03t2rXVrt3OPevWraNq1ap5HcZF6ciRIxQrVgxVpVevXlSqVIknnngir8O6JPzxxx+EhIRwxRVXsHDhQnr06EFKSkrA89t6GZiOHTuyc+dOypUrx/jx4/1OJyLJqlrb33g7J2HMeRg1ahTjxo3j5MmT1KxZk27duuV1SJeMbdu28be//Y2zZ89SsGBBRo0aldchmUxYkjCXvLp16/LHH3+kG/b+++97roLKiXvuuYeff/453bDXX3+dJ5544qLbc+jVqxcLFixIN+yxxx7joYceyqOIfKtUqVKW52rMxSOoSUJEEoD/ACHAu6r6Wobx4o6/AzgGJKrqj+64ksC7QDigQGdVXRjMeM2lafHixUFrO+1SzUvBiBEj8joEkw8F7cS1iIQAI4AWQDWgnYhUyzBZC6CS+9cVeMtr3H+Ar1S1ClADWBesWI0xxvgWzKub6gCbVHWLqp4EJgKtM0zTGhjv3vi3CCgpImVE5CogFngPQFVPqurBIMZqjDHGh2AmiXLAdq/+He6wQKa5CdgLjBGR5SLyrogUxQcR6Soiy0Rk2d69e3MvemOMMUE9J+HrEVIZr7f1N80VQDTwqKouFpH/AM8A51x0rqrvAO+AcwlsjiI2ea5WX/+X6p2P5CEds5wmNTWVli1bsnr16lxddsWKFVm2bJnn/pBu3brRsWNHTwmLnIqLi2Po0KHUru336kVjciyYexI7gBu8+ssDuwKcZgewQ1XTzkhOwUkaxlyyFi9enOXNh8ZcbIKZJJYClUQkTEQKAm2B6RmmmQ50FEc94JCq7lbVPcB2EUkrgNMEWBvEWM1l7syZM3Tp0oXq1asTHx/P8ePH2bx5MwkJCdSqVYtGjRp57iT2Vf4cYN++fcTHx3vum/C+UXXdunVUrlyZkJAQ4uLiePrpp6lTpw6VK1dm3rx5nhj69u1LTEwMkZGRvP322575Bw8eTEREBDVq1OCZZ55JF/vZs2fp1KkTzz//fLDfJnMZClqSUNXTQG/ga5wrkz5W1TUi0l1EuruTfQlsATYBo4CeXk08CkwQkZVAFPBKsGI1ZuPGjfTq1Ys1a9ZQsmRJPvnkE7p27crw4cNJTk5m6NCh9OzprJ4NGzZk0aJFLF++nLZt23oeDfniiy/SsGFDli9fTqtWrTzVXwFmzJhBQkKCp//06dMsWbKEYcOG8eKLLwLw3nvvUaJECZYuXcrSpUsZNWoUP//8MzNmzGDatGksXryYFStWpKuQe/r0adq3b0/lypUZNGjQhXirzGUmqPdJqOqXOInAe9hIr24FevmZNwWwg63mgggLC/OUHK9Vqxapqan88MMPtGnTxjNN2g17/sqfz50711MO+8477/SUwAb4+uuvGTNmjKffu4R2WqHAmTNnsnLlSk9RvUOHDrFx40ZmzZrFQw89RJEiRQD4y1/+4mmnW7du/O1vf+O5557L1ffDmDRW4M8Yzi2bvX//fkqWLElKSornb90651adRx99lN69e7Nq1SrefvvtdKW0vUucpzl27BgHDx6kbNmy5ywvYwnt4cOHe5b3888/Ex8fj6r6bBec6rmzZ8/OtJy3MTlhScIYH6666irCwsKYPHky4PyAr1ixAvBf/jw2NtZTwnvGjBmeEtizZ8/2PCI1M82bN+ett97i1KlTAPz0008cPXqU+Ph4Ro8e7SnxvX//fs88Dz/8MHfccQdt2rTx+2wNY3LCajeZi0ogl6xeKBMmTKBHjx4MGjSIU6dO0bZtW2rUqOEpf16uXDnq1avnqe3Uv39/2rVrR3R0NLfddhs33ngj4CSM+++/P8vlPfLII6SmphIdHY2qUrp0aaZNm0ZCQgIpKSnUrl2bggULcscdd/DKK3+eonvyySc5dOgQHTp0YMKECRQoYNt+JvdYqXDjl5Vkzh3R0dEsXryY0NDQvA4lX7D1MjBWKtyYS4Svp+IZc6mw/VJjjDF+WZIwxhjjlyUJY4wxflmSMMYY45clCWOMMX7Z1U3morJtYM6fS+3txn6rcrW9i8Err7zCP//5z6C0PX36dNauXcszzzzDtGnTqFy5MtWqZXygpLmc2J6EMReQqnL27NkcteF9I11ut92qVStPldlp06axdq0VX77cWZIwl7XU1FTCw8M9/UOHDmXAgAGA81Cfxx9/nFtvvZXw8HCWLFkCwIABA+jQoQO33347lSpVYtSoUZ75hwwZ4in13b9/f88yqlatSs+ePYmOjmb7du+HMUJycjK33XYbtWrVonnz5uzevZtDhw5xyy23sGHDBgDatWvHqFGjeOaZZzh+/DhRUVG0b9/eZ9v+YqhSpQqPPPII4eHhtG/fnlmzZtGgQQMqVarkeW1jx46ld+/e/PDDD0yfPp2+ffsSFRXF5s2biY7+85EuGzdupFatWrn8aZiLkSUJYzJx9OhRfvjhB9588006d+7sGb5y5Ur+97//sXDhQgYOHMiuXbuYOXMmGzduZMmSJaSkpJCcnMzcuXMB2LBhAx07dmT58uVUqFDB086pU6d49NFHmTJlCsnJyXTu3JnnnnuOEiVK8N///pfExEQmTpzIgQMH6NKlC6+99hqFCxcmJSXFUyfKu+0NGzb4jWHTpk089thjrFy5kvXr1/Phhx8yf/58hg4des7eya233kqrVq0YMmQIKSkp3HzzzZQoUYKUlBQAxowZQ2JiYjDfenORsHMSxmSiXbt2gFO87/fff+fgwYMAtG7dmsKFC1O4cGEaN27MkiVLmD9/PjNnzqRmzZoAHDlyhI0bN3LjjTdSoUIFn0+l27BhA6tXr6ZZs2aA8+ChMmXKANCsWTMmT55Mr169PMUFffFue+bMmX5jCAsLIyLCOedTvXp1mjRpgogQERHhKVeemUceeYQxY8bw73//m0mTJnn2Pkz+ZknCXNauuOKKdMfxM5bczliiO63f13BV5dlnn6Vbt27pxqWmplK0aFGfy1dVqlevzsKFC88Zd/bsWdatW0fhwoXZv38/5cuX99mGd9uZxeBdDr1AgQKe/gIFCgRUQfa+++7jxRdf5Pbbb6dWrVqUKlUqy3nMpc8ON5nL2nXXXcevv/7Kvn37+OOPP/jiiy/SjZ80aRIA8+fPp0SJEpQoUQKAzz77jBMnTrBv3z7mzJlDTEwMzZs3Z/To0Rw5cgSAnTt38uuvv2a6/FtuuYW9e/d6ksSpU6dYs2YNAG+88QZVq1blo48+onPnzp4S4qGhoZ7ujM4nBn+KFy/O4cOHPf2FChWiefPm9OjRg4ceeui82jSXHtuTMBeVC33JamhoKP369aNu3bqEhYVRpUqVdOOvvvpqbr31Vn7//XdGjx7tGV6nTh3uvPNOtm3bxgsvvEDZsmUpW7Ys69ato379+gAUK1aMDz74gJCQEL/LL1iwIFOmTKFPnz4cOnSI06dP8/jjjxMaGsq7777LkiVLKF68OLGxsQwaNIgXX3yRrl27EhkZSXR0NC+//HK69uLj47Mdgz9t27alS5cuJCUlMWXKFG6++Wbat2/Pp59+Snx8fLbbM5cmKxVu/LrcSzLHxcUxdOhQatdOX0V5wIABFCtWjH/84x95FFneGTp0KIcOHeKll17Ksxgu9/UyUFYq3BhzQd1zzz1s3ryZ7777Lq9DMReQJQlj/JgzZ47P4Wn3UVxupk6dmtchmDxgJ66NMcb4ZUnCGGOMX0FNEiKSICIbRGSTiDzjY7yISJI7fqWIRHuNSxWRVSKSIiJ2NtoYY/JA0M5JiEgIMAJoBuwAlorIdFX1rhjWAqjk/tUF3nL/p2msqr8FK0ZjjDGZC+aJ6zrAJlXdAiAiE4HWgHeSaA2MV+c63EUiUlJEyqjq7iDGZS5iDYY3yNX2Fjy6IMtpihUr5rn57GI2bNgwunbtSpEiRXK97WXLljF+/HiSkpKYM2cOBQsW5NZbb8315ZhLTzAPN5UDvMtd7nCHBTqNAjNFJFlEuvpbiIh0FZFlIrJs7969uRC2McFx5syZHM0/bNgwjh07FpS2a9euTVJSEuBc1fXDDz/kqD2TfwQzSYiPYRnv3MtsmgaqGo1zSKqXiMT6WoiqvqOqtVW1dunSpc8/WnPZU1X69u1LeHg4ERERnpIcPXv2ZPr06YBzr0BaNdj33nuP559/HoAPPviAOnXqEBUVRbdu3Tw/2sWKFfPc0Z2xPtPmzZtJSEigVq1aNGrUiPXr13P69GliYmI8l98+++yzPPfccyQlJbFr1y4aN25M48aNfbadWQxPP/00tWrVomnTpixZsoS4uDhuuukmz+uaM2cOLVu2JDU1lZEjR/LGG28QFRXFvHnzCAsL85QB+f3336lYsaLfsiAm/wlmktgB3ODVXx7YFeg0qpr2/1dgKs7hK2OC5tNPPyUlJYUVK1Ywa9Ys+vbty+7du4mNjWXevHmAUwsp7UE88+fPp1GjRqxbt45JkyaxYMECUlJSCAkJ8ZTxPnr0KOHh4SxevJiGDRumW17Xrl0ZPnw4ycnJDB06lJ49e3LFFVcwduxYevTowTfffMNXX31F//796dOnD2XLlmX27NnMnj37nLZLlSqVaQxxcXEkJydTvHhxnn/+eb755humTp1Kv3790sVUsWJFunfvzhNPPEFKSgqNGjUiLi6O//3vfwBMnDiR++67j9DQ0OB9EOaiEsxzEkuBSiISBuwE2gJ/zzDNdKC3e76iLnBIVXeLSFGggKoedrvjgYE5Deipp55iz549XH/99QwePDinzZl8Zv78+bRr146QkBCuu+46brvtNpYuXUqjRo0YNmwYa9eupVq1ahw4cIDdu3ezcOFCkpKSGDduHMnJycTExABw/Phxrr32WgBCQkK47777zlnWkSNH+OGHH2jTpo1n2B9//AE4Zbw7dOjAXXfdxcKFCylYsKDPeL3b/vbbb/3GULBgQRISEgCIiIjgyiuvJDQ0NFslwgcPHszdd9/NmDFj0j1kyeR/QUsSqnpaRHoDXwMhwGhVXSMi3d3xI4EvgTuATcAxIK205HXAVLcc8xXAh6r6VU5j2rNnDzt37sxpMyaf8lfHrFy5chw4cICvvvqK2NhY9u/fz8cff0yxYsUoXrw4qkqnTp149dVXz5m3UKFCPovrnT17lpIlS3oe4pPRqlWrKFmyJL/88ovfeL3bziyG0NBQT2nz8ykR3qBBA1JTU/n+++85c+ZMuif5mfwvqPdJqOqXqlpZVW9W1ZfdYSPdBIE6ernjI1R1mTt8i6rWcP+qp81rTDDFxsYyadIkzpw5w969e5k7dy516jhHOevXr8+wYcOIjY2lUaNGDB06lEaNGgHQpEkTpkyZ4inJvX//frZu3Zrpsq666irCwsKYPHky4PzIpz1Y6NNPP2Xfvn3MnTuXPn36eB50lLF0t7fzicEfX8vp2LEj7dq1sxLhlyGr3WQuKoFcshos99xzDwsXLqRGjRqICIMHD+b6668HoFGjRsycOZO//vWvVKhQgf3793uSRLVq1Rg0aBDx8fGcPXuW0NBQRowYke4xpb5MmDCBHj16MGjQIE6dOkXbtm0pV64czzzzDN9++y033HADvXv35rHHHmPcuHF07dqVFi1aUKZMGc95iTTnG4Mvd911F/fffz+fffYZw4cPp1GjRrRv357nn3/e86Q+c/m4rEqFB1o61zisJLNJM2XKFD777DPef//9vA7F1ssAWalwY8wF8eijjzJjxgy+/PLLvA7F5AFLEsaYTA0fPjyvQzB5yKrAGmOM8cuShDHGGL8sSRhjjPHLkoQxxhi/7MS1uah8H3tbrrZ329zvMx1/8OBBPvzwQ3r27JmryzUmv7A9CXNZO3jwIG+++WbA0+e0JLcxlxpLEuay9swzz7B582aioqLo27evz1Lhc+bMoXHjxvz9738nIiKCo0ePcuedd1KjRg3Cw8M903377bfUrFmTiIgIOnfu7CnYZ8ylzJKEuay99tpr3HzzzaSkpFCvXj2fpcIBlixZwssvv8zatWv56quvKFu2LCtWrGD16tUkJCRw4sQJEhMTmTRpEqtWreL06dO89dZbefzqjMk5SxLGuPyVCgeoU6cOYWFhgFNue9asWTz99NPMmzePEiVKsGHDBsLCwqhcuTIAnTp1Yu7cuXn2WozJLZYkjHFlVsesaNGinu7KlSuTnJxMREQEzz77LAMHDsx0XmMuZZYkzGXNuyx2ZqXCve3atYsiRYrw4IMP8o9//IMff/yRKlWqkJqayqZNmwB4//33ue223L1Sy5i8YJfAmotKVpes5rZSpUrRoEEDwsPDadGiBZGRkeeUCl+/fn26eVatWkXfvn0pUKAAoaGhvPXWWxQqVIgxY8bQpk0bz3Oqu3fvfkFfizHBYEnCXPY+/PDDdP1DhgxJ1x8XF0dcXJynv3nz5jRv3vycdpo0acLy5cuDEqMxecUONxljjPHLkoQxxhi/LEkYY4zxy5KEMcYYvyxJGGOM8cuShDHGGL+CegmsiCQA/wFCgHdV9bUM48UdfwdwDEhU1R+9xocAy4CdqtoymLGai8N//+/zXG2v97/uytX2ABITE2nZsiX3339/jtt69dVXufHGG2nfvn3A88yZM4ehQ4fyxRdf5Hj5xmQlaHsS7g/8CKAFUA1oJyLVMkzWAqjk/nUFMlZEewxYF6wYjclrM2fOJD4+Pq/DMMavYB5uqgNsUtUtqnoSmAi0zjBNa2C8OhYBJUWkDICIlAfuBN4NYozGkJqaSpUqVejUqRORkZHcf//9HDt2jIEDBxITE0N4eDhdu3b1WZ+pYsWK/POf/6R+/frUrl2bH3/8kebNm3PzzTczcuRIAHbv3k1sbCxRUVGEh4czb948AH7//XdOnjxJ6dKlmTx5MuHh4dSoUYPY2FgATpw4wUMPPURERAQ1a9Zk9uzZ5yz/6NGjdO7cmZiYGGrWrMlnn30GwJo1a6hTpw5RUVFERkaycePGYL19Jp8L5uGmcsB2r/4dQN0ApikH7AaGAU8BxXMSRK2+4z3dxX87TAiw7bfDnuHJQzrmpHmTT2zYsIH33nuPBg0a0LlzZ95880169+5Nv379AOjQoQNffPEFd9117uGrG264gYULF/LEE0+QmJjIggULOHHiBNWrV6d79+58+OGHNG/enOeee44zZ85w7NgxAGbNmkWTJk0AGDhwIF9//TXlypXj4MGDAIwYMQJwyoCsX7+e+Ph4fvrpp3TLfvnll7n99tsZPXo0Bw8epE6dOjRt2pSRI0fy2GOP0b59e06ePGkPSzLnLZh7EuJjWMZNMZ/TiEhL4FdVTc5yISJdRWSZiCzbu3fv+cRpDDfccAMNGjQA4MEHH2T+/PnMnj2bunXrEhERwXfffceaNWt8ztuqVSvAKSFet25dihcvTunSpSlUqBAHDx4kJiaGMWPGMGDAAFatWkXx4s52z1dffUWLFi0AaNCgAYmJiYwaNcrzgz5//nw6dOgAQJUqVahQocI5SWLmzJm89tprREVFERcXx4kTJ9i2bRv169fnlVde4fXXX2fr1q0ULlw49980c1kIZpLYAdzg1V8e2BXgNA2AViKSinOY6nYR+cDXQlT1HVWtraq1S5cunVuxm8uMcw1F+v6ePXsyZcoUVq1aRZcuXThx4oTPea+88koAChQo4OlO6z99+jSxsbHMnTuXcuXK0aFDB8aPd/ZilyxZ4qkyO3LkSAYNGsT27duJiopi3759AZUfV1U++eQTUlJSSElJYdu2bVStWpW///3vTJ8+ncKFC9O8eXO+++6783pfjAlmklgKVBKRMBEpCLQFpmeYZjrQURz1gEOqultVn1XV8qpa0Z3vO1V9MIixmsvctjvsEcMAACAASURBVG3bWLhwIQAfffQRDRs2BOCaa67hyJEjTJky5bzb3rp1K9deey1dunTh4Ycf5scff2TNmjVUqVKFkJAQADZv3kzdunUZOHAg11xzDdu3byc2NpYJEyYA8NNPP7Ft2zZuueWWdG03b96c4cOHexJKWoHBLVu2cNNNN9GnTx9atWrFypUrzzt+c3kL2jkJVT0tIr2Br3EugR2tqmtEpLs7fiTwJc7lr5twLoF9KFjxmEtDMC5ZDUTVqlUZN24c3bp1o1KlSvTo0YMDBw4QERFBxYoViYmJOe+258yZw5AhQwgNDaVYsWKMHz+eTz75hISEBM80ffv2ZePGjagqTZo0oUaNGlSpUoXu3bsTERHBFVdcwdixY9PtqQC88MILPP7440RGRqKqVKxYkS+++IJJkybxwQcfEBoayvXXX+85t2JMdkl+eqJW7dq1ddmyZemGpTtxvWYqIX/8zpkrr+Jw9XsAO3GdmXXr1lG1atW8DiPoUlNTadmyJatXr75gy2zWrBnjx4+nTJkyF2yZ+cXlsl7mVMeOHdm5cyflypXzHOL0RUSSVbW2v/H2PAlj8sA333yT1yEYE5Asz0mISFggw4y5VFWsWPGC7kUYcykJ5MT1Jz6Gnf9ZPGOMMZcMv4ebRKQKUB0oISL3eo26CigU7MCMMcbkvczOSdwCtARKAt6XnBwGugQzKGOMMRcHv0lCVT8DPhOR+qq68ALGZIwx5iIRyNVN+0TkW+A6VQ0XkUiglaoOCnJs5jL08oM5L7/t7bkPgnv6LC4ujqFDh1K7tnMF4fmU/s5Kv379iI2NpWnTpgwbNoyuXbtSpEiRbMVlzPkK5MT1KOBZ4BSAqq7EuQvamHxPVTl79mzA0wej9PfAgQNp2rQpAMOGDfMUCDTmQggkSRRR1SUZhp0ORjDG5IV///vfhIeHEx4ezrBhw0hNTaVq1ar07NmT6Ohotm/fTo8ePahduzbVq1enf//+PtvxLv29efNm6tWrR0xMDP369aNYsWKe6YYMGUJMTAyRkZGettKW2aVLF6pXr058fDzHjx8HnIccTZkyhaSkJHbt2kXjxo1p3LgxQEBxGZMTgSSJ30TkZtwKriJyP04pb2MuecnJyYwZM4bFixezaNEiRo0axYEDB9iwYQMdO3Zk+fLlVKhQgZdffplly5axcuVKvv/+e5+1kLxLfz/22GM89thjLF26lLJly3qmmTlzJhs3bmTJkiWkpKSQnJzM3LlzAdi4cSO9evVizZo1lCxZkk8+SX/1eZ8+fShbtiyzZ8/2PFsikLiMyYlAkkQv4G2giojsBB4HegQ1KmMukPnz53PPPfdQtGhRihUrxr333su8efOoUKEC9erV80z38ccfEx0dTc2aNVmzZg1r1649py3v0t8LFy6kTZs2APz973/3TDNz5kxmzpxJzZo1iY6OZv369Z4HAoWFhREVFQVArVq1SE1NzTL+QOIyJieyPHGtqluApiJSFCigqoeDH5YxF4a/2mVFixb1dP/8888MHTqUpUuXcvXVV5OYmOizbPiSJUt4662MT+A9d3nPPvss3bp1Szc8NTU1XfG+kJAQz+EmfwKNy5icCKQsx5Mi8iTQDeji9j8sIlHBD8+Y4IqNjWXatGkcO3aMo0ePMnXqVBo1apRumt9//52iRYtSokQJfvnlF2bMmHFOOxlLf9erV89zuGjixIme6Zo3b87o0aM5cuQIADt37uTXX38NON7ixYtz+PDhgOMyJqcCuQS2tvv3udt/J86zIrqLyGRVHRys4MzlJ9iXrGYUHR1NYmKi5+E/jzzyCFdffXW6aWrUqEHNmjWpXr06N910k+cJdt5mzJiRrvT3sGHDePDBB/nXv/7FnXfeSYkSJQCIj49n3bp11K9fH4BixYrxwQcfeJJLVrp27UqLFi0oU6YMs2fPzjIuY3Iqy1LhIvI1cJ+qHnH7i+HUbroHSFbVakGPMkBWKjx3WUnmwGUs/X3s2DEKFy6MiDBx4kQ++ugjPvvsszyOMn+w9TIwF7JU+I3ASa/+U0AFVT0uIn8EHLEx+VjG0t/Jycn07t0bVaVkyZKMHj06jyIzJmcCSRIfAotEJG0z6C7gI/dEtl1KYYwPjRo1YsWKFXkdhjE5lmmSEOfp8GNxHjPaEBCgu6qmHdPJvdoD5qKkqjirgTF5Lz89SfNSkWmSUFUVkWmqWgtIvkAxmYtEoUKF2LdvH6VKlbJEYfKcqrJv3z4KFbInFVxIgRxuWiQiMaq6NOjRmItK+fLl2bFjB3v37s3rUIwBnA2X8uXL53UYFy3vApn7fz3k/N+z2zP8fK4eDCRJNAa6ichW4CjOISdV1chsL81cUkJDQwkLsyfVGnM5CyRJtAh6FMYYYy5KgZTl2AogItdijy01xpjLSiBlOVqJyEbgZ+B7IBUI6P5/EUkQkQ0isklEnvExXkQkyR2/UkSi3eGFRGSJiKwQkTUi8mK2XpUxxphcEUgV2JeAesBPqhoGNAEWZDWTiIQAI3AOV1UD2olIxruzWwCV3L+uQFp1tD+A21W1BhAFJIhIPYwxxlxQgSSJU6q6DyggIgVUdTbOD3dW6gCbVHWLqp4EJgKtM0zTGhivjkVASREp4/YfcacJdf9yfIH02YJFOXPlVZwtWDTriY0xxgR04vqgW69pLjBBRH7FfZRpFsoB2736dwB1A5imHLDb3RNJBv4KjFDVxQEsM1NHK+XuYyWNMSa/C2RPYgVwDHgC+ArYDKwPYD5fd19l3BvwO42qnlHVKKA8UEdEwn0uRKSriCwTkWV2Pb8xxuSuQJJEY1U9q6qnVXWcqiYBMQHMtwO4wau/PLAru9Oo6kFgDpCAD6r6jqrWVtXapUuXDiAsY4wxgfKbJESkh4iswnls6Uqvv5+BQB6kuxSoJCJhIlIQaAtMzzDNdKCje5VTPeCQqu4WkdIiUtKNozDQlMD2XowxxuSizM5JfIhzqeurgPflq4dVdX9WDavqaRHpDXwNhACjVXWNiHR3x4/EKRx4B7AJ55DWQ+7sZYBx7nmJAsDHqvpFtl6ZMcaYHPObJFT1EHAIaHe+javqlziJwHvYSK9uBXr5mG8lUPN8l2uMMSZ3BHJOwhhjzGXKkoQxxhi/LEkYY4zxy5KEMcYYvwK549rkA0899RR79uzh+uuvZ/DgwXkdjjEXhK33OWdJ4jKxZ88edu7cmddhGHNB2Xqfc3a4yRhjjF+WJIwxxvhlScIYY4xfliSMMcb4ZUnCGGOMX5YkjDHG+GVJwhhjjF+WJIwxxvhlScIYY4xfliSMMcb4ZUnCGGOMX5YkjDHG+GVJwhhjjF9WBTYf2zYwwtN9ev9fgCs4vX+rZ/iN/VblUWTGmEuF7UkYY4zxy5KEMcYYvyxJGGOM8SuoSUJEEkRkg4hsEpFnfIwXEUlyx68UkWh3+A0iMltE1onIGhF5LJhxGmOM8S1oSUJEQoARQAugGtBORKplmKwFUMn96wq85Q4/DfyfqlYF6gG9fMxrjDEmyIK5J1EH2KSqW1T1JDARaJ1hmtbAeHUsAkqKSBlV3a2qPwKo6mFgHVAuiLEaY4zxIZhJohyw3at/B+f+0Gc5jYhUBGoCi3M9QmOMMZkKZpIQH8M0O9OISDHgE+BxVf3d50JEuorIMhFZtnfv3vMO1hhjzLmCmSR2ADd49ZcHdgU6jYiE4iSICar6qb+FqOo7qlpbVWuXLl06VwI3xhjjCGaSWApUEpEwESkItAWmZ5hmOtDRvcqpHnBIVXeLiADvAetU9d9BjNEYY0wmglaWQ1VPi0hv4GsgBBitqmtEpLs7fiTwJXAHsAk4Bjzkzt4A6ACsEpEUd9g/VfXLYMVrjDHmXEGt3eT+qH+ZYdhIr24FevmYbz6+z1cYY4y5gOyOa2OMMX5ZFdjLxDWFzgKn3f/GGBMYSxKXiX9EHszrEPLMU089xZ49e7j++usZPHhwXodjzCXFkoTJ9/bs2cPOnTvzOow8Y0nS5IQlCWPyucs9SZqcsRPXxhhj/LIkYYwxxi9LEsYYY/yyJGGMMcYvSxLGGGP8squbjMmHvo+9zdN9/IoQEOH4jh18H3sbt839Pg8jM5ca25Mwxhjjl+1JmHyrwfAGABQ8WJACFGD7we2eYQseXZCXoRlzybA9CWOMMX5ZkjDGGOOXJQljjDF+2TkJY0y+8t//+9zTffC3o57/acN7/+uuPInrUmVJwph8rqRquv/GZIclCWPyuQfP2IOmzPmzcxLGGGP8sj0Jk+9pEeUsZ9EidrjFmOyyJGHyvVMNTuV1CMZcsuxwkzHGGL8sSRhjjPErqElCRBJEZIOIbBKRZ3yMFxFJcsevFJFor3GjReRXEVkdzBiNMcb4F7QkISIhwAigBVANaCci1TJM1gKo5P51Bd7yGjcWSAhWfMYYY7IWzD2JOsAmVd2iqieBiUDrDNO0BsarYxFQUkTKAKjqXGB/EOMzxhiThWAmiXLAdq/+He6w7E6TKRHpKiLLRGTZ3r17zytQY4wxvgUzSYiPYRkvVA9kmkyp6juqWltVa5cuXTo7sxpjjMlCMJPEDuAGr/7ywK7zmMYYY0weCWaSWApUEpEwESkItAWmZ5hmOtDRvcqpHnBIVXcHMSZjjDHZELQkoaqngd7A18A64GNVXSMi3UWkuzvZl8AWYBMwCuiZNr+IfAQsBG4RkR0i8nCwYjXGGONbUMtyqOqXOInAe9hIr24FevmZt10wYzPGGJM1u+PaGGOMX1bgzxhj8qFCIQXS/T9fliSMMSYfqlmqeK60Y4ebjDHG+GVJwhhjjF+WJIwxxvhlScIYY4xfliSMMcb4ZUnCGGOMX5YkjDHG+GVJwhhjjF+WJIwxxvhlScIYY4xfliSMMcb4ZUnCGGOMX5YkjDHG+GVJwhhjjF+WJIwxxvhlz5MwxuRbRQtele6/yT5LEsaYfKvBzffmdQiXPDvcZIwxxi9LEsYYY/yyJGGMMcavoCYJEUkQkQ0isklEnvExXkQkyR2/UkSiA53XGGNM8AUtSYhICDACaAFUA9qJSLUMk7UAKrl/XYG3sjGvMcaYIAvmnkQdYJOqblHVk8BEoHWGaVoD49WxCCgpImUCnNcYY0yQBfMS2HLAdq/+HUDdAKYpF+C8AIhIV5y9EIAjIrIhO0HK0E7ZmdzbNcBv5zvzRaG/ZDb20n99mZA+kq9fH5l9fpLp536pOO/P79F/53IkuS9o6+bzE3x+9hUymyeYScJXNBrgNIHM6wxUfQd4J3uh5ZyILFPV2hd6uReKvb5Lm72+S9fF9tqCmSR2ADd49ZcHdgU4TcEA5jXGGBNkwTwnsRSoJCJhIlIQaAtMzzDNdKCje5VTPeCQqu4OcF5jjDFBFrQ9CVU9LSK9ga+BEGC0qq4Rke7u+JHAl8AdwCbgGPBQZvMGK9bzdMEPcV1g9voubfb6Ll0X1WsTVZ+H+o0xxhi749oYY4x/liSMMcb4lS+ThIhUFJHVQWg3VUSu8ep/W0Qa5GL7c0Tkorn0LSMR+ed5zHMkwOlapZVfEZG7L/Qd9iLyuIgUCVLbtUUkye2OE5Fbszl/SRHpGYzY8oKIjBWR+3OprWdFpH0254kTkS9yY/kBLi/d9/p8Yg5gGQNFpKnbHdC6HOjvTb5MEhdQXWBRXgcRCPcKspx+3j6TRG60rarTVfU1t/dunHIsAXNLueTE44DPL1ZO21bVZarax+2NA7KVJICSQMBJIhfei0tJPDAzLwM4j/U/12NW1X6qOsvt9bsun4/8nCRCRGSUiKwRkZkiUlhEbhaRr0QkWUTmiUgVABG5S0QWi8hyEZklIte5w0u58y4XkbfxuslPRKoCP6nqGTcjvy4iS0TkJxFp5E4TIiJDRGSpW8Cwm9f8r4rICRFZISKvicg/gIruuDkislZEtovIahGp4w4fICLvi8h3IrJRRLp4tdfXazkvusMqisg6EXkT+JH0954gIrVE5Hv3/fhaRMqISAlxCive4k7zkYh0EZHXgMIikiIiE3y17SsGpwlZLyLvisheETkoIlvcZWwUkSnuXkSiiGx2t/BaAW+JyB73M/vZfW9T3HiS3YaPuFtQi4H6GV7bOZ+1iFzhxhfn9Rm8LCJ9gLLAbBGZ7attEXnQK4a3036I3eled5czS0TquJ/fFhFp5U4TJyJfiEhFoDvwhNtOI/e1hbrTXSXO3mpohnX5NeBmd54h7t9qEVklIg94LWO2iHwIrBKRoiLyP3f9Wu01XRNx1udVIjJaRK70/fUJnLsurBeRce5nP0VEiohIP/f9Xi0i74ice6u3+3pfEZGFIrJMRKLddXGzuFdCuuvlXPf1r5Y/v19XAQVVda+ItHHHrRCRue74QiIyxn2ty0WksY/lF3Xfh6XuNK3d4dW9Pu+Vboyr3b/Hxff6/5b7GtbIn+t/xuV5x3yziCxylz1QvPa6JfPvc7rfNXfcWBG538+6nGVcmVLVfPeH82N7Gohy+z8GHgS+BSq5w+oC37ndV/PnlV6PAP9yu5OAfm73nTh3fV/j9j8JdHa753jNcwcwy+3uCjzvdl8JLAPCcAoXJgNr3HF/Af4BpAL1gF+Ape64WGC12z0AWAEUxrl1f7u7QsTjXDYnOIn/C3e+isBZoJ6P9ygU+AEo7fY/gHOpMUAzYCHO/Slfec1zJMN77Gk7kxiOup/FE8A37uv+ENgGJOLcEzPE7f4FZ89sLPAd0ByoilOioLa7nGScel+4n8ff/KwD/j7r6sA69zUux/nC4r7313jN72nbjeFzINTtfxPo6DVdC7d7Ks4WYihQA0hxh8cBX3h9hv/wWs4Y4G6v9eVfftbntHXgPvd9DAGuc9/HMu4yjgJhXtON8mqjBFAIZ52p7A4bDzyeS983BRq4/aNx1ue/eE3zPnCX2z0WuN/rfe/hdr8BrASKA6WBX93h/wc853aHAMXd7nuBgW73KqCc213Sa74xbncV970qlOHzeAV4MG0+4CegKDAcaO+1/qx2hxcD1gA1yfDdSnu9boxzgEiv34faPmL+AmjndnfH/X6R+ff5nN81P+/pNdmJK7O//Pz40p9VNcXtTsZ5g28FJntt0KRtRZUHJolTXLAg8LM7PBbnQ0VV/yciB7zab457X4fr0wzLAufDjpQ/j7+WwKl42xSYjJO4UNX9XjG9jfNlf9odN9fdwizpjv9MVY8Dx90thTpAQ3dZy91pirnL2QZsVad4Yka3AOHAN+6yQ4Dd7jK/EZE2OJV4a/iYN4132/F+YgDn/bwR+AjnC/o1cAbnx7QI0Ag4AOzHSRSF3fh+ADq5sc0QkZ04h6HSdqvPAJ9kDEpEiuHns1bnXp33cX7066tTQNIX77abALWApW57hYFf3XEnga/c7lXAH6p6SkRW8ed6kJl3gaeAaTjrU5fMJ6ch8JGqngF+EZHvgRjgd2CJqqatu6uAoSLyOs4P4jwRqYHzvfjJnWYc0AsYFkCcWdmuqgvc7g+APsDPIvIUzmf8F5wf1899zJt2o+wqoJiqHgYOi7OnXRJnQ2K0u4c1zet7nYCTZAEWAGNF5GP+/C42xPmxR1XXi8hWoHKGZccDrcTZkwcnidyIs5H0nIiUx1mXP1XVowAi8inOOpvxu/U3cWrJXYGTuKvhJD1v3jHXxzm0Cs6G01CvmPx9n339rmUlkLj8ys9J4g+v7jM4W10HVTXKx7TDgX+r6nRxDkUM8Bp3zo0k4pwUKqmq3qVC0pZ3hj/fVwEeVdWvM8yf4E7nfbivkPv/B5wt+IyfjWb47z1cgFdV9e0My6mIk3B8EZw9mfrnjHCOr1YFjuN8uXf4acO7bX8xJOG8N2m/1mf5871S9+9qIAKn9Mo8oA1wQlUPu4coRuF8mfrjbN097c5/wv2xzKgA/j9r3GUdxFkn/PFuW4Bxqvqsj+lOqbtZ5v3aVPWsiGT5/VLVBe5hhNuAEFXN6oKLzKrzeT4PVf1JRGrh7Nm+KiIzCW7VAl/r5Zs4W6rbRWQAf67jGaWtD97rRlr/Fe6GUizO3vz7IjJEVcfjbCD1AFDV7iJS150mRUSiyPy9SiPAfaqasTDoOnEONd4J9MW58Tcjz/stImE4e08xqnpARMb6eb2emLOIyd/3OePvWuFMGwo8Lr/y8zmJjH7H2bJpA56TTWlbySWAnW63d1nYuUB7d/oWOD9mAI2B2QEs82ugh/x5zLmyiBTFOSRxL3CtOOc9rgdauvO8B+wD3hHnGHpDnHIlh9zxrd1jraVwtsqXusvp7G5BIyLlROTaLGLbAJQWkfruPKEiUt0d9wTOIZl2/LkFB3BKzj1e7v1aM4thLs4hLQGuwtlLW+GOW4iz5ZSWJKrh3IUPzmGje9z538LZO8i0aqWq+v2sReReoJS7/CSvPbTDOIc5fPkWuD/t9YjIX7KKIRO+ljMeZy9rzLmTnzPPXOABcc53lcZ5HUsyziAiZYFjqvoBzhZqNLAeqCgif3Un6wB8f56vI6Mb09YlnPVmvtv9m7tOnPfVTO57/auqjsL5fkS76+r6tEQuIjer6mJV7YdzePIG0n9/K+PsIWRMBl8Dj7obI4hITff/TcAWVU1yp2kqznmWojjr47wM7VyFkzQOiXNOs4WP15EuZpxDq/e53W0zxJTd77M37/Uly7iycjklCXBWmIdFZAXOrm/aMyoG4Pz4zCN9id4XgVgR+RHnR2ybO7wFfx5iyMy7wFrgR3EuyX0bZ8voK+AznOOLu3B+JNd7zbcD59jxbmAk8LDXuCXA/3BWsJdUdZeqzsTZXV3oHuaYgv8fPADcwyz3A6+770cKcKv7ZXoE+D9VnYfzRXvene0dYKWITPDRXlYxTMXZxW2Fs0fwFLDXHTcPZ108hHMS8Aqgmogsx9lyeh7nx7A08AzOLnNWzvmsxbl8+TXgYfeQy3+B/3i9thnuIbyMr22tG8NMEVmJc04gkBh8+Ry4R9wT1+6wCTgbIB/5mkFV9wEL3HWoPs77uALnvM1TqrrHx2wRwBIRSQGeAwap6gmcQ1qT3c/oLM76lRvWAZ3c9+cvOAl9FM4hpGk4GzPnKw5n72A5zo/qfzj3OzhEnBPUq3HW2RU4ezIh7mudBCSq6h+k9xLOYc+V7rwvucMfAFa7718pnEOvS4DFON9r70PPqOoKnMNDa3DOySzgXBljfhx4UkSW4KxPh9y2sv19zsCzLgcYV6asLMd5cJNGXVU9FaT25+Cc3FyWYfgAnJNbQ33Nl5+5x4xLqOoLeR1LbhPnnFVrVe2Q17GcD/cwyBeqGn4Bl/kNzsUDuy/UMnMqY8zuYevjqqoi0hbnJPZF93C1/HxOImhUNTrrqUxuEZGpwM3A7XkdS24TkeE4W5h35HUslxJVbZbXMWSXj5hrAf91D3UdBDpf+KiyZnsSxhhj/LrczkkYY4zJBksSxhhj/LIkYYwxxi9LEsYYY/yyJGHyhJxHyexcWm62Sypf7MQp/PiPrKdMN0+2y2WLV4lvucjL2pvcY0nC5JU4sl8yO8c0iCWVg0kur/Lf5iJiScJkm4hME6c09hpxCoellcx+WZxSzYvkz3Lr55RhF98lsyuIyLfilEb+VkRudOcfK06p49nilN++TZzSzuvEqUPjL8YQd960ktpPeLXnr6RyvDglq38UkclpZRH8tH+HOOWx54tIUtpWufgvPZ0oIp+KU758o4gM9mrL53LFKaPdT0TmA23EKdm+1H2PPwl0L0hE/uq+9yvcZdzsjiomTlnv9eKUf08rTZFlie8M7WcW/4vu8FXyZ2n+0iLyjTv8bRHZKu7DvMRPSXaTh7IqE2t/9pfxjz9LDxfGKaFcCqegW1op6MH8WSLdXxn2AaQvmf050Mnt7oxT7ROcEsgTcWo+tcapwRWBs4GTjFs22UeMtYBvvPpLerV3TkllnNLrc4Gibv/TuGXifbSdVnI7rSz3R2RdejoR2MKfJbu34tQX8rtcN76nvJZbyqt7EE7xyHPeSx/xLgbu8Yq9CM6e3CGcCsgFcErDNPT+fN1ufyW+5wC1A4g/LcaewLtu93+BZ93uBNwS/GRSkt3+8u7P7rg256OPiNzjdt+AU8b4JE7de3B+vNPuLvVXhj2j+rhl2XF+mAZ7jftcVVWcOja/qOoqABFZg1MqOYVzbQFuEueO5v+R9ZPA6uEUFlzgbjgXxPnh9KUKTvG3tNfyEc6zIMB/6WmAb9Ut1Cgia4EKOIkks+VO8uoOF5FB7jzFcArBZUpEiuM8Z2EqgDr1m3CXtURVd7j9KTjv5XygsQRW4huyft+8S+infb4NcYrkoapfyZ8l+DMryW7yiCUJky3ilFJvivMshmPi1JkqRPqS2d7l0jMrw54Z71IAmZaS9jmzUxa5Bs5zP3oBfyPzsgeCs+fRLoDYMjv84rP0tDhlrDOWeb4igOV6l2Mfi/OAohUikoizN5CTWM+JR0QKEXiJ77T2M4vfXwl9f235K8lu8oidkzDZVQI44CaIKjhbkllN76sMe8aS2WnP0QCngut8csA9xl1AVT8BXsAplZ2RdwyLgAbiltEWpyx0xgfUpFmPs5dS0e1/wGucz9LTmcjOcosDu8Up194+i3YBT9n0HSJyt9v+lVmcy0hLCIGW+M5O/Gnm4yRtRCSeP0vw52ZJdpNLLEmY7PoKZ4tzJU5ZZV9PvfM2AN9l2DOWzO4DPOS22wF4LIdxlgPmuIdRxgK+tk69SyrvxTlv8JEbwyKcw0rnUOfJgD2Br9yTyr/glnnGf+lpn7KzXJxktxinVPl6P9P40gHnEOFKnGR8fSbxHCQbJb6zGX+aF4F4caopt8ApiX9Yc7cku8klVuDPmPMgIsVU9Yi7xzAC2Kiqb+R1XJcCEbkSOKOqp8V5UNFb6v8pgiaP2TkJY85PFxHphHOidjnOA6VMYG4EPhbnMbknyfq53iYP2Z6EueSJ8yziKzMM7pB2FVQO254KhGUYAdVrggAAAD5JREFU/LRmeG75xUBERgANMgz+j6r6eyyqMVmyJGGMMcYvO3FtjDHGL0sSxhhj/LIkYYwxxi9LEsYYY/z6f+C3j/aXmaudAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## looking at the unique values of anatom_site_general_challenge column\n\ndftrain.anatom_site_general_challenge.unique()","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"array(['head/neck', 'upper extremity', 'lower extremity', 'torso',\n       'palms/soles', 'oral/genital'], dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# looking at the number of unique values of anatom_site_general_challenge column\n\ndf = dftrain.copy()\nprint(\"no of head/neck  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'head/neck')]))\nprint(\"no of upper extremity  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'upper extremity')]))\nprint(\"no of lower extremity  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'lower extremity')]))\nprint(\"no of torso  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'torso')]))\nprint(\"no of palms/soles'  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'palms/soles')]))\nprint(\"no of oral/genital'  : \", len(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'oral/genital')]))","execution_count":13,"outputs":[{"output_type":"stream","text":"no of head/neck  :  1771\nno of upper extremity  :  4852\nno of lower extremity  :  8275\nno of torso  :  16568\nno of palms/soles'  :  370\nno of oral/genital'  :  120\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## number of affected in the whole dataset\n\nprint(\"no of affected : \", len(dftrain[dftrain['target'] == 1]))","execution_count":14,"outputs":[{"output_type":"stream","text":"no of affected :  575\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"temp = dftrain[dftrain['target'] == 1]\n\n## for training  (500 samples)\nfinal_df = temp[:500]\n\n## for testing (75 samples)\naffected_validationdata = temp[500:]","execution_count":15,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### selecting few samples randomly from the unaffected to normalize  the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"def random_data_selector(data, n):\n    data =  data.sample(n = n)\n    return data\n\n\ndf = dftrain.copy()\n\n## selecting samples for testing (100 samples)\ntemp  = df[df['target'] == 0]\nunaffected_validationdata = temp[:100]\n\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'head/neck')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'upper extremity')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'lower extremity')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'torso')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'palms/soles')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\ndata = random_data_selector(df[(df['target'] == 0) & (df['anatom_site_general_challenge'] == 'oral/genital')], 100)\nfinal_df = final_df.append(data, ignore_index = True)\n\n","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_df.head()","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"     image_name  patient_id     sex  age_approx anatom_site_general_challenge  \\\n0  ISIC_0149568  IP_0962375  female        55.0               upper extremity   \n1  ISIC_0188432  IP_0135517  female        50.0               upper extremity   \n2  ISIC_0207268  IP_7735373    male        55.0                         torso   \n3  ISIC_0232101  IP_8349964    male        65.0                         torso   \n4  ISIC_0247330  IP_3232631  female        65.0               lower extremity   \n\n  diagnosis benign_malignant  target  \n0  melanoma        malignant       1  \n1  melanoma        malignant       1  \n2  melanoma        malignant       1  \n3  melanoma        malignant       1  \n4  melanoma        malignant       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_name</th>\n      <th>patient_id</th>\n      <th>sex</th>\n      <th>age_approx</th>\n      <th>anatom_site_general_challenge</th>\n      <th>diagnosis</th>\n      <th>benign_malignant</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ISIC_0149568</td>\n      <td>IP_0962375</td>\n      <td>female</td>\n      <td>55.0</td>\n      <td>upper extremity</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ISIC_0188432</td>\n      <td>IP_0135517</td>\n      <td>female</td>\n      <td>50.0</td>\n      <td>upper extremity</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ISIC_0207268</td>\n      <td>IP_7735373</td>\n      <td>male</td>\n      <td>55.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ISIC_0232101</td>\n      <td>IP_8349964</td>\n      <td>male</td>\n      <td>65.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ISIC_0247330</td>\n      <td>IP_3232631</td>\n      <td>female</td>\n      <td>65.0</td>\n      <td>lower extremity</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## finding the number of infected\nlen(final_df[final_df['target'] == 1])","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"500"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## finding the number of infected\nlen(final_df[final_df['target'] == 0])","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"600"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## shuffle the dataset\n\nimport sklearn\n\nfinal_df  = sklearn.utils.shuffle(final_df)","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2\nimport pathlib\nimport imageio\nfrom skimage.transform import resize\nimport numpy as np\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\n\n\n\n#converting normal images into numpy array\n\n\ntraining_paths = pathlib.Path('../input/siim-isic-melanoma-classification/jpeg').glob('train/*.jpg')\ntraining_sorted = sorted([x for x in training_paths])\ndirectory_path = '../input/siim-isic-melanoma-classification/jpeg/train/'\n\n\n\nfor index in range(len(training_sorted)) : training_sorted[index] = str(training_sorted[index])    \n    \ntraining_images = np.zeros(150528)\ntraining_images = training_images.reshape(1,224,224,3)\n\nfor index in range(len(final_df)):\n    img_name = final_df.loc[index].image_name\n    img_name = str(directory_path +  img_name + '.jpg')\n    position_in_list = training_sorted.index(img_name)\n    img_path = training_sorted[position_in_list]\n    \n    img = image.load_img(img_path, target_size = (224,224))\n    x = image.img_to_array(img)\n    x = np.expand_dims(x, axis = 0)\n    x = preprocess_input(x)    \n    training_images = np.vstack((training_images, x))\n        \n    print(index)\n    ","execution_count":21,"outputs":[{"output_type":"stream","text":"0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n175\n176\n177\n178\n179\n180\n181\n182\n183\n184\n185\n186\n187\n188\n189\n190\n191\n192\n193\n194\n195\n196\n197\n198\n199\n200\n201\n202\n203\n204\n205\n206\n207\n208\n209\n210\n211\n212\n213\n214\n215\n216\n217\n218\n219\n220\n221\n222\n223\n224\n225\n226\n227\n228\n229\n230\n231\n232\n233\n234\n235\n236\n237\n238\n239\n240\n241\n242\n243\n244\n245\n246\n247\n248\n249\n250\n251\n252\n253\n254\n255\n256\n257\n258\n259\n260\n261\n262\n263\n264\n265\n266\n267\n268\n269\n270\n271\n272\n273\n274\n275\n276\n277\n278\n279\n280\n281\n282\n283\n284\n285\n286\n287\n288\n289\n290\n291\n292\n293\n294\n295\n296\n297\n298\n299\n300\n301\n302\n303\n304\n305\n306\n307\n308\n309\n310\n311\n312\n313\n314\n315\n316\n317\n318\n319\n320\n321\n322\n323\n324\n325\n326\n327\n328\n329\n330\n331\n332\n333\n334\n335\n336\n337\n338\n339\n340\n341\n342\n343\n344\n345\n346\n347\n348\n349\n350\n351\n352\n353\n354\n355\n356\n357\n358\n359\n360\n361\n362\n363\n364\n365\n366\n367\n368\n369\n370\n371\n372\n373\n374\n375\n376\n377\n378\n379\n380\n381\n382\n383\n384\n385\n386\n387\n388\n389\n390\n391\n392\n393\n394\n395\n396\n397\n398\n399\n400\n401\n402\n403\n404\n405\n406\n407\n408\n409\n410\n411\n412\n413\n414\n415\n416\n417\n418\n419\n420\n421\n422\n423\n424\n425\n426\n427\n428\n429\n430\n431\n432\n433\n434\n435\n436\n437\n438\n439\n440\n441\n442\n443\n444\n445\n446\n447\n448\n449\n450\n451\n452\n453\n454\n455\n456\n457\n458\n459\n460\n461\n462\n463\n464\n465\n466\n467\n468\n469\n470\n471\n472\n473\n474\n475\n476\n477\n478\n479\n480\n481\n482\n483\n484\n485\n486\n487\n488\n489\n490\n491\n492\n493\n494\n495\n496\n497\n498\n499\n500\n501\n502\n503\n504\n505\n506\n507\n508\n509\n510\n511\n512\n513\n514\n515\n516\n517\n518\n519\n520\n521\n522\n523\n524\n525\n526\n527\n528\n529\n530\n531\n532\n533\n534\n535\n536\n537\n538\n539\n540\n541\n542\n543\n544\n545\n546\n547\n548\n549\n550\n551\n552\n553\n554\n555\n556\n557\n558\n559\n560\n561\n562\n563\n564\n565\n566\n567\n568\n569\n570\n571\n572\n573\n574\n575\n576\n577\n578\n579\n580\n581\n582\n583\n584\n585\n586\n587\n588\n589\n590\n591\n592\n593\n594\n595\n596\n597\n598\n599\n600\n601\n602\n603\n604\n605\n606\n607\n608\n609\n610\n611\n612\n613\n614\n615\n616\n617\n618\n619\n620\n621\n622\n623\n624\n625\n626\n627\n628\n629\n630\n631\n632\n633\n634\n635\n636\n637\n638\n639\n640\n641\n642\n643\n644\n645\n646\n647\n648\n649\n650\n651\n652\n653\n654\n655\n656\n657\n658\n659\n660\n661\n662\n663\n664\n665\n666\n667\n668\n669\n670\n671\n672\n673\n674\n675\n676\n677\n678\n679\n680\n681\n682\n683\n684\n685\n686\n687\n688\n689\n690\n691\n692\n693\n694\n695\n696\n697\n698\n699\n700\n701\n702\n703\n704\n705\n706\n707\n708\n709\n710\n711\n712\n713\n714\n715\n716\n717\n718\n719\n720\n721\n722\n723\n724\n725\n726\n727\n728\n729\n730\n731\n732\n733\n734\n735\n736\n737\n738\n739\n740\n741\n742\n743\n744\n745\n746\n747\n748\n749\n750\n751\n752\n753\n754\n755\n756\n757\n758\n759\n760\n761\n762\n763\n764\n765\n766\n767\n768\n769\n770\n771\n772\n773\n774\n775\n776\n777\n778\n779\n780\n781\n782\n783\n784\n785\n786\n787\n788\n789\n790\n791\n792\n793\n794\n795\n796\n797\n798\n799\n800\n801\n802\n803\n804\n805\n806\n807\n808\n809\n810\n811\n812\n813\n814\n815\n816\n817\n818\n819\n820\n821\n822\n823\n824\n825\n826\n827\n828\n829\n830\n831\n832\n833\n834\n835\n836\n837\n838\n839\n840\n841\n842\n843\n844\n845\n846\n847\n848\n849\n850\n851\n852\n853\n854\n855\n856\n857\n858\n859\n860\n861\n862\n863\n864\n865\n866\n867\n868\n869\n870\n871\n872\n873\n874\n875\n876\n877\n878\n879\n880\n881\n882\n883\n884\n885\n886\n887\n888\n889\n890\n891\n892\n893\n894\n895\n896\n897\n898\n899\n900\n901\n902\n903\n904\n905\n906\n907\n908\n909\n910\n911\n912\n913\n914\n915\n916\n917\n918\n919\n920\n921\n922\n923\n924\n925\n926\n927\n928\n929\n930\n931\n932\n933\n934\n935\n936\n937\n938\n939\n940\n941\n942\n943\n944\n945\n946\n947\n948\n949\n950\n951\n952\n953\n954\n955\n956\n957\n958\n959\n960\n961\n962\n963\n964\n965\n966\n967\n968\n969\n970\n971\n972\n973\n974\n975\n976\n977\n978\n979\n980\n981\n982\n983\n984\n985\n986\n987\n988\n989\n990\n991\n992\n993\n994\n995\n996\n997\n998\n999\n1000\n1001\n1002\n1003\n1004\n1005\n1006\n1007\n1008\n1009\n1010\n1011\n1012\n1013\n1014\n1015\n1016\n1017\n1018\n1019\n1020\n1021\n1022\n1023\n1024\n1025\n1026\n1027\n1028\n1029\n1030\n1031\n1032\n1033\n1034\n1035\n1036\n1037\n1038\n1039\n1040\n1041\n1042\n1043\n1044\n1045\n1046\n1047\n1048\n1049\n1050\n1051\n1052\n1053\n1054\n1055\n1056\n1057\n1058\n1059\n1060\n1061\n1062\n1063\n1064\n1065\n1066\n1067\n1068\n1069\n1070\n1071\n1072\n1073\n1074\n1075\n1076\n1077\n1078\n1079\n1080\n1081\n1082\n1083\n1084\n1085\n1086\n1087\n1088\n1089\n1090\n1091\n1092\n1093\n1094\n1095\n1096\n1097\n1098\n1099\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"training_images","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n\n        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n\n        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n\n        ...,\n\n        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n\n        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n\n        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         ...,\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],\n\n\n       [[[ 1.20609970e+01, -3.77899933e+00,  4.43199997e+01],\n         [ 1.10609970e+01, -2.77899933e+00,  4.03199997e+01],\n         [ 1.10609970e+01, -1.77899933e+00,  4.53199997e+01],\n         ...,\n         [ 2.70609970e+01,  1.02210007e+01,  5.23199997e+01],\n         [ 3.06099701e+00, -5.77899933e+00,  4.03199997e+01],\n         [ 1.10609970e+01,  2.22100067e+00,  4.33199997e+01]],\n\n        [[ 1.20609970e+01, -5.77899933e+00,  4.23199997e+01],\n         [ 1.40609970e+01, -3.77899933e+00,  4.43199997e+01],\n         [ 1.20609970e+01,  2.21000671e-01,  4.43199997e+01],\n         ...,\n         [ 2.00609970e+01,  1.22100067e+00,  4.93199997e+01],\n         [ 5.06099701e+00, -6.77899933e+00,  4.23199997e+01],\n         [ 1.80609970e+01,  5.22100067e+00,  4.63199997e+01]],\n\n        [[ 8.06099701e+00, -8.77899933e+00,  3.63199997e+01],\n         [ 2.10609970e+01,  2.22100067e+00,  4.83199997e+01],\n         [ 1.10609970e+01, -2.77899933e+00,  4.63199997e+01],\n         ...,\n         [ 2.10609970e+01,  4.22100067e+00,  4.93199997e+01],\n         [ 2.00609970e+01,  5.22100067e+00,  5.03199997e+01],\n         [ 1.00609970e+01,  2.21000671e-01,  4.43199997e+01]],\n\n        ...,\n\n        [[-2.59390030e+01, -4.77789993e+01, -4.68000031e+00],\n         [-1.29390030e+01, -2.97789993e+01,  1.23199997e+01],\n         [-1.59390030e+01, -2.67789993e+01,  1.43199997e+01],\n         ...,\n         [-2.09390030e+01, -1.87789993e+01,  3.03199997e+01],\n         [-2.09390030e+01, -1.57789993e+01,  3.03199997e+01],\n         [-2.09390030e+01, -1.47789993e+01,  2.93199997e+01]],\n\n        [[-1.89390030e+01, -3.37789993e+01,  1.31999969e+00],\n         [-2.19390030e+01, -3.97789993e+01,  4.31999969e+00],\n         [-1.29390030e+01, -2.87789993e+01,  1.03199997e+01],\n         ...,\n         [-2.09390030e+01, -1.97789993e+01,  2.63199997e+01],\n         [-2.39390030e+01, -2.17789993e+01,  2.73199997e+01],\n         [-1.99390030e+01, -1.57789993e+01,  2.83199997e+01]],\n\n        [[-1.99390030e+01, -4.37789993e+01, -6.80000305e-01],\n         [-2.19390030e+01, -3.87789993e+01,  6.31999969e+00],\n         [-1.49390030e+01, -3.37789993e+01,  8.31999969e+00],\n         ...,\n         [-2.09390030e+01, -2.57789993e+01,  2.73199997e+01],\n         [-2.19390030e+01, -2.27789993e+01,  2.43199997e+01],\n         [-1.89390030e+01, -1.67789993e+01,  2.53199997e+01]]],\n\n\n       [[[-8.69390030e+01, -9.77789993e+01, -9.46800003e+01],\n         [-8.09390030e+01, -9.27789993e+01, -8.96800003e+01],\n         [-7.49390030e+01, -8.87789993e+01, -8.56800003e+01],\n         ...,\n         [-6.09390030e+01, -7.37789993e+01, -6.86800003e+01],\n         [-6.39390030e+01, -7.57789993e+01, -7.26800003e+01],\n         [-7.39390030e+01, -8.07789993e+01, -7.66800003e+01]],\n\n        [[-7.79390030e+01, -9.27789993e+01, -8.76800003e+01],\n         [-7.99390030e+01, -9.27789993e+01, -8.76800003e+01],\n         [-7.79390030e+01, -9.27789993e+01, -8.76800003e+01],\n         ...,\n         [-6.29390030e+01, -7.47789993e+01, -7.16800003e+01],\n         [-6.89390030e+01, -8.07789993e+01, -7.76800003e+01],\n         [-6.99390030e+01, -8.07789993e+01, -7.66800003e+01]],\n\n        [[-7.39390030e+01, -8.87789993e+01, -8.36800003e+01],\n         [-7.29390030e+01, -8.57789993e+01, -8.06800003e+01],\n         [-7.39390030e+01, -8.87789993e+01, -8.36800003e+01],\n         ...,\n         [-7.39390030e+01, -8.57789993e+01, -8.26800003e+01],\n         [-6.69390030e+01, -7.87789993e+01, -7.56800003e+01],\n         [-6.99390030e+01, -8.07789993e+01, -7.66800003e+01]],\n\n        ...,\n\n        [[-8.29390030e+01, -9.17789993e+01, -8.76800003e+01],\n         [-8.29390030e+01, -9.77789993e+01, -9.26800003e+01],\n         [-7.89390030e+01, -9.27789993e+01, -8.96800003e+01],\n         ...,\n         [-7.09390030e+01, -6.27789993e+01, -3.86800003e+01],\n         [-7.49390030e+01, -6.77789993e+01, -4.36800003e+01],\n         [-7.19390030e+01, -6.77789993e+01, -4.76800003e+01]],\n\n        [[-6.89390030e+01, -7.87789993e+01, -7.06800003e+01],\n         [-8.59390030e+01, -1.00778999e+02, -9.56800003e+01],\n         [-8.29390030e+01, -9.67789993e+01, -9.36800003e+01],\n         ...,\n         [-6.39390030e+01, -5.57789993e+01, -3.16800003e+01],\n         [-7.19390030e+01, -6.47789993e+01, -4.06800003e+01],\n         [-7.29390030e+01, -7.07789993e+01, -4.56800003e+01]],\n\n        [[-7.39390030e+01, -7.77789993e+01, -7.06800003e+01],\n         [-8.49390030e+01, -9.77789993e+01, -9.26800003e+01],\n         [-8.29390030e+01, -9.67789993e+01, -9.36800003e+01],\n         ...,\n         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...,\n         [-5.79390030e+01, -5.87789993e+01, -2.36800003e+01],\n         [-6.59390030e+01, -6.27789993e+01, -2.36800003e+01],\n         [-7.59390030e+01, -6.77789993e+01, -2.36800003e+01]],\n\n        [[-6.09390030e+01, -6.87789993e+01, -7.76800003e+01],\n         [-7.59390030e+01, -7.17789993e+01, -5.76800003e+01],\n         [-7.49390030e+01, -7.47789993e+01, -4.96800003e+01],\n         ...,\n         [-9.59390030e+01, -1.08778999e+02, -8.56800003e+01],\n         [-1.01939003e+02, -1.10778999e+02, -8.26800003e+01],\n         [-1.03939003e+02, -1.13778999e+02, -8.06800003e+01]]],\n\n\n       [[[-3.19390030e+01, -3.47789993e+01, -2.46800003e+01],\n         [-3.49390030e+01, -3.17789993e+01, -9.68000031e+00],\n         [-5.19390030e+01, -3.57789993e+01, -3.68000031e+00],\n         ...,\n         [-2.19390030e+01, -1.37789993e+01,  1.73199997e+01],\n         [-2.49390030e+01, -1.67789993e+01,  1.43199997e+01],\n         [-2.49390030e+01, -1.67789993e+01,  1.43199997e+01]],\n\n        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...,\n         [ 6.09970093e-02,  3.22100067e+00,  1.93199997e+01],\n         [-1.39390030e+01, -9.77899933e+00,  9.31999969e+00],\n         [-1.99390030e+01, -1.57789993e+01,  3.31999969e+00]],\n\n        [[-2.69390030e+01, -3.07789993e+01, -2.46800003e+01],\n         [-2.99390030e+01, -2.77789993e+01, -1.56800003e+01],\n         [-3.99390030e+01, -3.07789993e+01, -9.68000031e+00],\n         ...,\n         [-7.93900299e+00, -6.77899933e+00,  8.31999969e+00],\n         [-1.29390030e+01, -9.77899933e+00,  6.31999969e+00],\n         [-1.49390030e+01, -1.07789993e+01,  8.31999969e+00]],\n\n        [[-3.09390030e+01, -3.47789993e+01, -2.86800003e+01],\n         [-3.29390030e+01, -3.07789993e+01, -1.86800003e+01],\n         [-3.89390030e+01, -2.97789993e+01, -8.68000031e+00],\n         ...,\n         [-7.93900299e+00, -6.77899933e+00,  8.31999969e+00],\n         [-1.39390030e+01, -1.07789993e+01,  5.31999969e+00],\n         [-1.89390030e+01, -1.57789993e+01,  3.19999695e-01]]],\n\n\n       [[[-8.93900299e+00, -1.67789993e+01, -8.68000031e+00],\n         [-3.93900299e+00, -1.17789993e+01, -3.68000031e+00],\n         [-1.93900299e+00, -7.77899933e+00,  3.19999695e-01],\n         ...,\n         [ 4.70609970e+01,  4.72210007e+01,  6.23199997e+01],\n         [ 4.80609970e+01,  4.02210007e+01,  4.83199997e+01],\n         [ 5.06099701e+00, -1.27789993e+01, -1.06800003e+01]],\n\n        [[-5.69390030e+01, -5.27789993e+01, -3.26800003e+01],\n         [-5.59390030e+01, -5.17789993e+01, -3.16800003e+01],\n         [-5.69390030e+01, -5.07789993e+01, -3.06800003e+01],\n         ...,\n         [-2.09390030e+01, -4.77899933e+00,  9.31999969e+00],\n         [-1.09390030e+01, -4.77899933e+00,  3.31999969e+00],\n         [-4.29390030e+01, -4.57789993e+01, -4.26800003e+01]],\n\n        [[-6.99390030e+01, -5.47789993e+01, -2.66800003e+01],\n         [-6.79390030e+01, -5.27789993e+01, -2.46800003e+01],\n         [-6.79390030e+01, -5.07789993e+01, -2.26800003e+01],\n         ...,\n         [-1.69390030e+01,  1.12210007e+01,  2.93199997e+01],\n         [-1.99390030e+01, -7.78999329e-01,  1.13199997e+01],\n         [-5.59390030e+01, -4.67789993e+01, -4.16800003e+01]],\n\n        ...,\n\n        [[-3.19390030e+01, -1.27789993e+01,  9.31999969e+00],\n         [-3.29390030e+01, -1.47789993e+01,  9.31999969e+00],\n         [-2.79390030e+01, -6.77899933e+00,  1.63199997e+01],\n         ...,\n         [-2.59390030e+01,  1.22100067e+00,  2.93199997e+01],\n         [-1.79390030e+01, -7.78999329e-01,  1.93199997e+01],\n         [-6.19390030e+01, -5.37789993e+01, -3.96800003e+01]],\n\n        [[-2.59390030e+01, -1.77789993e+01, -3.68000031e+00],\n         [-2.99390030e+01, -2.17789993e+01, -6.68000031e+00],\n         [-2.59390030e+01, -1.67789993e+01,  1.31999969e+00],\n         ...,\n         [-2.99390030e+01, -1.27789993e+01,  5.31999969e+00],\n         [-5.93900299e+00,  3.22100067e+00,  1.43199997e+01],\n         [-4.59390030e+01, -4.77789993e+01, -4.06800003e+01]],\n\n        [[ 3.10609970e+01,  2.72210007e+01,  3.03199997e+01],\n         [ 3.10609970e+01,  2.72210007e+01,  3.03199997e+01],\n         [ 3.30609970e+01,  2.92210007e+01,  3.53199997e+01],\n         ...,\n         [-1.03939003e+02, -1.15778999e+02, -1.11680000e+02],\n         [-1.03939003e+02, -1.16778999e+02, -1.16680000e+02],\n         [-9.79390030e+01, -1.11778999e+02, -1.14680000e+02]]]])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## making the xtrain data ready\n\nxtraindf = training_images.copy()\nxtraindf = xtraindf[1:]\nxtraindf.shape\nxtraindf","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"array([[[[ 1.20609970e+01, -3.77899933e+00,  4.43199997e+01],\n         [ 1.10609970e+01, -2.77899933e+00,  4.03199997e+01],\n         [ 1.10609970e+01, -1.77899933e+00,  4.53199997e+01],\n         ...,\n         [ 2.70609970e+01,  1.02210007e+01,  5.23199997e+01],\n         [ 3.06099701e+00, 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4.40609970e+01,  2.02210007e+01,  5.93199997e+01],\n         [ 4.50609970e+01,  2.32210007e+01,  5.93199997e+01],\n         ...,\n         [ 5.90609970e+01,  3.82210007e+01,  6.83199997e+01],\n         [ 6.40609970e+01,  4.12210007e+01,  7.03199997e+01],\n         [ 6.40609970e+01,  3.82210007e+01,  6.93199997e+01]],\n\n        [[ 4.50609970e+01,  2.22210007e+01,  5.73199997e+01],\n         [ 4.60609970e+01,  2.32210007e+01,  6.33199997e+01],\n         [ 4.70609970e+01,  2.42210007e+01,  6.03199997e+01],\n         ...,\n         [ 6.40609970e+01,  4.02210007e+01,  7.13199997e+01],\n         [ 6.40609970e+01,  4.02210007e+01,  7.13199997e+01],\n         [ 6.20609970e+01,  3.62210007e+01,  6.73199997e+01]],\n\n        ...,\n\n        [[ 2.80609970e+01,  9.22100067e+00,  4.33199997e+01],\n         [ 2.20609970e+01,  2.21000671e-01,  3.63199997e+01],\n         [ 3.40609970e+01,  7.22100067e+00,  4.83199997e+01],\n         ...,\n         [ 5.40609970e+01,  2.42210007e+01,  5.43199997e+01],\n         [ 4.30609970e+01,  1.72210007e+01,  4.83199997e+01],\n         [ 4.70609970e+01,  2.22210007e+01,  5.13199997e+01]],\n\n        [[ 2.50609970e+01,  6.22100067e+00,  4.03199997e+01],\n         [ 2.10609970e+01,  4.22100067e+00,  3.93199997e+01],\n         [ 2.90609970e+01,  6.22100067e+00,  4.13199997e+01],\n         ...,\n         [ 4.70609970e+01,  1.72210007e+01,  4.73199997e+01],\n         [ 3.90609970e+01,  7.22100067e+00,  4.33199997e+01],\n         [ 4.40609970e+01,  1.72210007e+01,  5.13199997e+01]],\n\n        [[ 1.70609970e+01, -3.77899933e+00,  3.63199997e+01],\n         [ 3.10609970e+01,  1.12210007e+01,  4.13199997e+01],\n         [ 3.30609970e+01,  1.22210007e+01,  4.53199997e+01],\n         ...,\n         [ 4.10609970e+01,  1.32210007e+01,  5.03199997e+01],\n         [ 3.70609970e+01,  5.22100067e+00,  4.13199997e+01],\n         [ 4.30609970e+01,  1.62210007e+01,  5.03199997e+01]]],\n\n\n       ...,\n\n\n       [[[-7.49390030e+01, 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-3.07789993e+01, -2.46800003e+01],\n         [-2.99390030e+01, -2.77789993e+01, -1.56800003e+01],\n         [-3.99390030e+01, -3.07789993e+01, -9.68000031e+00],\n         ...,\n         [-7.93900299e+00, -6.77899933e+00,  8.31999969e+00],\n         [-1.29390030e+01, -9.77899933e+00,  6.31999969e+00],\n         [-1.49390030e+01, -1.07789993e+01,  8.31999969e+00]],\n\n        [[-3.09390030e+01, -3.47789993e+01, -2.86800003e+01],\n         [-3.29390030e+01, -3.07789993e+01, -1.86800003e+01],\n         [-3.89390030e+01, -2.97789993e+01, -8.68000031e+00],\n         ...,\n         [-7.93900299e+00, -6.77899933e+00,  8.31999969e+00],\n         [-1.39390030e+01, -1.07789993e+01,  5.31999969e+00],\n         [-1.89390030e+01, -1.57789993e+01,  3.19999695e-01]]],\n\n\n       [[[-8.93900299e+00, -1.67789993e+01, -8.68000031e+00],\n         [-3.93900299e+00, -1.17789993e+01, -3.68000031e+00],\n         [-1.93900299e+00, -7.77899933e+00,  3.19999695e-01],\n         ...,\n         [ 4.70609970e+01,  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-1.27789993e+01,  9.31999969e+00],\n         [-3.29390030e+01, -1.47789993e+01,  9.31999969e+00],\n         [-2.79390030e+01, -6.77899933e+00,  1.63199997e+01],\n         ...,\n         [-2.59390030e+01,  1.22100067e+00,  2.93199997e+01],\n         [-1.79390030e+01, -7.78999329e-01,  1.93199997e+01],\n         [-6.19390030e+01, -5.37789993e+01, -3.96800003e+01]],\n\n        [[-2.59390030e+01, -1.77789993e+01, -3.68000031e+00],\n         [-2.99390030e+01, -2.17789993e+01, -6.68000031e+00],\n         [-2.59390030e+01, -1.67789993e+01,  1.31999969e+00],\n         ...,\n         [-2.99390030e+01, -1.27789993e+01,  5.31999969e+00],\n         [-5.93900299e+00,  3.22100067e+00,  1.43199997e+01],\n         [-4.59390030e+01, -4.77789993e+01, -4.06800003e+01]],\n\n        [[ 3.10609970e+01,  2.72210007e+01,  3.03199997e+01],\n         [ 3.10609970e+01,  2.72210007e+01,  3.03199997e+01],\n         [ 3.30609970e+01,  2.92210007e+01,  3.53199997e+01],\n         ...,\n         [-1.03939003e+02, -1.15778999e+02, -1.11680000e+02],\n         [-1.03939003e+02, -1.16778999e+02, -1.16680000e+02],\n         [-9.79390030e+01, -1.11778999e+02, -1.14680000e+02]]]])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nfig, axes = plt.subplots(5,5, figsize=(8,8))\n\nfor i,ax in enumerate(axes.flat):\n    ax.imshow(xtraindf[i])","execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x576 with 25 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## making the ytrain data ready\n\nytraindf = final_df.target\nytraindf.head()","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"300    1\n142    1\n278    1\n152    1\n958    0\nName: target, dtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Test data analysis"},{"metadata":{"trusted":true},"cell_type":"code","source":"affected_validationdata = affected_validationdata.append(unaffected_validationdata)\ntestdata_copy = affected_validationdata.copy()","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"testdata_copy.head()","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"         image_name  patient_id     sex  age_approx  \\\n28289  ISIC_8564474  IP_6649505    male        75.0   \n28296  ISIC_8566006  IP_6624700    male        65.0   \n28446  ISIC_8609756  IP_1264754  female        65.0   \n28463  ISIC_8615491  IP_5278162    male        60.0   \n28467  ISIC_8617277  IP_3281766  female        40.0   \n\n      anatom_site_general_challenge diagnosis benign_malignant  target  \n28289                         torso  melanoma        malignant       1  \n28296                         torso  melanoma        malignant       1  \n28446               lower extremity  melanoma        malignant       1  \n28463                     head/neck  melanoma        malignant       1  \n28467                         torso  melanoma        malignant       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_name</th>\n      <th>patient_id</th>\n      <th>sex</th>\n      <th>age_approx</th>\n      <th>anatom_site_general_challenge</th>\n      <th>diagnosis</th>\n      <th>benign_malignant</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28289</th>\n      <td>ISIC_8564474</td>\n      <td>IP_6649505</td>\n      <td>male</td>\n      <td>75.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28296</th>\n      <td>ISIC_8566006</td>\n      <td>IP_6624700</td>\n      <td>male</td>\n      <td>65.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28446</th>\n      <td>ISIC_8609756</td>\n      <td>IP_1264754</td>\n      <td>female</td>\n      <td>65.0</td>\n      <td>lower extremity</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28463</th>\n      <td>ISIC_8615491</td>\n      <td>IP_5278162</td>\n      <td>male</td>\n      <td>60.0</td>\n      <td>head/neck</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28467</th>\n      <td>ISIC_8617277</td>\n      <td>IP_3281766</td>\n      <td>female</td>\n      <td>40.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"affected : \", len(testdata_copy[testdata_copy['target'] == 1]))\nprint(\"unaffected : \", len(testdata_copy[testdata_copy['target'] == 0]))","execution_count":28,"outputs":[{"output_type":"stream","text":"affected :  75\nunaffected :  100\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## shuffle the df \nimport sklearn\n\ntestdata_copy  = sklearn.utils.shuffle(testdata_copy).reset_index(drop=True)","execution_count":29,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"testdata_copy.tail()","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"       image_name  patient_id     sex  age_approx  \\\n170  ISIC_0148751  IP_5629228  female        40.0   \n171  ISIC_0084270  IP_2961528    male        40.0   \n172  ISIC_8564474  IP_6649505    male        75.0   \n173  ISIC_0077472  IP_3691360  female        40.0   \n174  ISIC_8922893  IP_9455054    male        50.0   \n\n    anatom_site_general_challenge diagnosis benign_malignant  target  \n170               upper extremity   unknown           benign       0  \n171               lower extremity     nevus           benign       0  \n172                         torso  melanoma        malignant       1  \n173                         torso   unknown           benign       0  \n174               upper extremity  melanoma        malignant       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_name</th>\n      <th>patient_id</th>\n      <th>sex</th>\n      <th>age_approx</th>\n      <th>anatom_site_general_challenge</th>\n      <th>diagnosis</th>\n      <th>benign_malignant</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>170</th>\n      <td>ISIC_0148751</td>\n      <td>IP_5629228</td>\n      <td>female</td>\n      <td>40.0</td>\n      <td>upper extremity</td>\n      <td>unknown</td>\n      <td>benign</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>ISIC_0084270</td>\n      <td>IP_2961528</td>\n      <td>male</td>\n      <td>40.0</td>\n      <td>lower extremity</td>\n      <td>nevus</td>\n      <td>benign</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>ISIC_8564474</td>\n      <td>IP_6649505</td>\n      <td>male</td>\n      <td>75.0</td>\n      <td>torso</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>ISIC_0077472</td>\n      <td>IP_3691360</td>\n      <td>female</td>\n      <td>40.0</td>\n      <td>torso</td>\n      <td>unknown</td>\n      <td>benign</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>ISIC_8922893</td>\n      <td>IP_9455054</td>\n      <td>male</td>\n      <td>50.0</td>\n      <td>upper extremity</td>\n      <td>melanoma</td>\n      <td>malignant</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### convert test images to numpy arrays"},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\n#converting normal images into numpy array\n\n\ntest_paths = pathlib.Path('../input/siim-isic-melanoma-classification/jpeg/').glob('train/*.jpg')\ntest_sorted = sorted([x for x in test_paths])\ndirectory_path = '../input/siim-isic-melanoma-classification/jpeg/train/'\n\n\n\nfor index in range(len(test_sorted)) : test_sorted[index] = str(test_sorted[index])    \n    \ntest_images = np.zeros(150528)\ntest_images = test_images.reshape(1,224,224,3)\n\nfor index in range(len(testdata_copy)):\n    img_name = testdata_copy.loc[index].image_name\n    img_name = str(directory_path +  img_name + '.jpg')\n    \n    position_in_list = test_sorted.index(img_name)\n    img_path = test_sorted[position_in_list]\n    \n    img = image.load_img(img_path, target_size = (224,224))\n    x = image.img_to_array(img)\n    x = np.expand_dims(x, axis = 0)\n    x = preprocess_input(x)    \n    test_images = np.vstack((test_images, x))\n        \n    print(index)\n    ","execution_count":31,"outputs":[{"output_type":"stream","text":"0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n162\n163\n164\n165\n166\n167\n168\n169\n170\n171\n172\n173\n174\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Making the xtest data ready\n\nxtestdf = test_images.copy()\nxtestdf = xtestdf[1:]\nxtestdf.shape\nxtestdf","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"array([[[[ 5.06099701e+00, -3.77789993e+01, -6.16800003e+01],\n         [ 1.10609970e+01, -2.37789993e+01, -4.86800003e+01],\n         [ 2.90609970e+01, -1.17789993e+01, -4.26800003e+01],\n         ...,\n         [-8.09390030e+01, -9.57789993e+01, -1.02680000e+02],\n         [-7.89390030e+01, -9.37789993e+01, -1.00680000e+02],\n         [-8.49390030e+01, -9.97789993e+01, -1.06680000e+02]],\n\n        [[ 3.06099701e+00, -3.37789993e+01, -6.06800003e+01],\n         [ 1.70609970e+01, -2.17789993e+01, -4.26800003e+01],\n         [ 2.20609970e+01, -1.17789993e+01, -3.96800003e+01],\n         ...,\n         [-7.69390030e+01, -9.17789993e+01, -9.96800003e+01],\n         [-7.39390030e+01, -8.87789993e+01, -9.56800003e+01],\n         [-8.19390030e+01, -9.67789993e+01, -1.03680000e+02]],\n\n        [[-1.93900299e+00, -4.07789993e+01, -6.16800003e+01],\n         [ 1.30609970e+01, -2.67789993e+01, -5.36800003e+01],\n         [-7.93900299e+00, -4.57789993e+01, -6.26800003e+01],\n         ...,\n         [-7.69390030e+01, -9.17789993e+01, -9.96800003e+01],\n         [-7.89390030e+01, -9.37789993e+01, -1.00680000e+02],\n         [-8.09390030e+01, -9.57789993e+01, -1.02680000e+02]],\n\n        ...,\n\n        [[-5.19390030e+01, -6.57789993e+01, -6.26800003e+01],\n         [-5.09390030e+01, -6.47789993e+01, -6.16800003e+01],\n         [-4.59390030e+01, -5.97789993e+01, -5.66800003e+01],\n         ...,\n         [ 9.50609970e+01,  6.42210007e+01,  5.03199997e+01],\n         [ 8.30609970e+01,  5.22210007e+01,  2.83199997e+01],\n         [ 1.04060997e+02,  6.52210007e+01,  4.43199997e+01]],\n\n        [[-5.29390030e+01, -6.67789993e+01, -6.36800003e+01],\n         [-4.69390030e+01, -6.07789993e+01, -5.76800003e+01],\n         [-4.29390030e+01, -5.67789993e+01, -5.36800003e+01],\n         ...,\n         [ 8.70609970e+01,  5.62210007e+01,  4.23199997e+01],\n         [ 9.60609970e+01,  5.72210007e+01,  3.43199997e+01],\n         [ 1.06060997e+02,  6.72210007e+01,  4.43199997e+01]],\n\n        [[-4.99390030e+01, -6.67789993e+01, -6.26800003e+01],\n         [-4.59390030e+01, -5.97789993e+01, -5.66800003e+01],\n         [-4.19390030e+01, -5.47789993e+01, -5.56800003e+01],\n         ...,\n         [ 9.20609970e+01,  5.32210007e+01,  3.23199997e+01],\n         [ 1.19060997e+02,  8.92210007e+01,  6.33199997e+01],\n         [ 1.08060997e+02,  6.92210007e+01,  4.53199997e+01]]],\n\n\n       [[[ 4.80609970e+01,  2.12210007e+01,  7.23199997e+01],\n         [ 5.10609970e+01,  2.42210007e+01,  7.53199997e+01],\n         [ 5.00609970e+01,  2.52210007e+01,  7.63199997e+01],\n         ...,\n         [ 1.90609970e+01, -6.77899933e+00,  4.23199997e+01],\n         [ 2.30609970e+01, -5.77899933e+00,  4.13199997e+01],\n         [ 2.20609970e+01, -6.77899933e+00,  4.03199997e+01]],\n\n        [[ 4.60609970e+01,  2.12210007e+01,  7.23199997e+01],\n         [ 4.60609970e+01,  2.12210007e+01,  7.23199997e+01],\n         [ 5.00609970e+01,  2.52210007e+01,  7.63199997e+01],\n         ...,\n         [ 1.70609970e+01, -8.77899933e+00,  4.03199997e+01],\n         [ 1.60609970e+01, -6.77899933e+00,  3.93199997e+01],\n         [ 1.00609970e+01, -1.27789993e+01,  3.33199997e+01]],\n\n        [[ 5.00609970e+01,  2.82210007e+01,  7.83199997e+01],\n         [ 4.70609970e+01,  2.52210007e+01,  7.53199997e+01],\n         [ 5.30609970e+01,  2.82210007e+01,  7.93199997e+01],\n         ...,\n         [ 1.70609970e+01, -8.77899933e+00,  4.03199997e+01],\n         [ 1.30609970e+01, -1.27789993e+01,  3.43199997e+01],\n         [ 1.20609970e+01, -1.37789993e+01,  3.33199997e+01]],\n\n        ...,\n\n        [[ 1.80609970e+01,  4.22100067e+00,  4.73199997e+01],\n         [ 2.40609970e+01,  1.02210007e+01,  5.33199997e+01],\n         [ 2.80609970e+01,  1.42210007e+01,  5.73199997e+01],\n         ...,\n         [-7.93900299e+00, -2.47789993e+01,  1.03199997e+01],\n         [-1.19390030e+01, -3.17789993e+01,  4.31999969e+00],\n         [-1.29390030e+01, -3.27789993e+01,  3.31999969e+00]],\n\n        [[ 1.80609970e+01,  4.22100067e+00,  4.73199997e+01],\n         [ 2.30609970e+01,  9.22100067e+00,  5.23199997e+01],\n         [ 2.50609970e+01,  1.22210007e+01,  5.33199997e+01],\n         ...,\n         [-9.93900299e+00, -2.37789993e+01,  9.31999969e+00],\n         [-9.93900299e+00, -2.87789993e+01,  5.31999969e+00],\n         [-1.19390030e+01, -3.07789993e+01,  3.31999969e+00]],\n\n        [[ 1.80609970e+01,  4.22100067e+00,  4.73199997e+01],\n         [ 2.20609970e+01,  8.22100067e+00,  5.13199997e+01],\n         [ 2.40609970e+01,  1.12210007e+01,  5.03199997e+01],\n         ...,\n         [-1.29390030e+01, -2.37789993e+01,  4.31999969e+00],\n         [-1.19390030e+01, -2.87789993e+01,  3.19999695e-01],\n         [-1.59390030e+01, -3.27789993e+01, -3.68000031e+00]]],\n\n\n       [[[ 2.50609970e+01,  9.22100067e+00,  5.23199997e+01],\n         [ 3.00609970e+01,  1.22210007e+01,  5.63199997e+01],\n         [ 2.00609970e+01,  1.12210007e+01,  5.53199997e+01],\n         ...,\n         [ 5.60609970e+01,  3.52210007e+01,  7.53199997e+01],\n         [ 6.00609970e+01,  4.02210007e+01,  7.63199997e+01],\n         [ 5.20609970e+01,  3.62210007e+01,  7.53199997e+01]],\n\n        [[ 2.70609970e+01,  9.22100067e+00,  5.33199997e+01],\n         [ 3.20609970e+01,  1.52210007e+01,  5.73199997e+01],\n         [ 2.70609970e+01,  1.42210007e+01,  5.53199997e+01],\n         ...,\n         [ 5.90609970e+01,  3.72210007e+01,  7.33199997e+01],\n         [ 5.70609970e+01,  3.72210007e+01,  7.33199997e+01],\n         [ 5.20609970e+01,  3.22210007e+01,  6.83199997e+01]],\n\n        [[ 2.50609970e+01,  7.22100067e+00,  5.13199997e+01],\n         [ 2.30609970e+01,  7.22100067e+00,  5.03199997e+01],\n         [ 2.90609970e+01,  1.02210007e+01,  5.63199997e+01],\n         ...,\n         [ 5.90609970e+01,  3.92210007e+01,  7.53199997e+01],\n         [ 5.10609970e+01,  2.92210007e+01,  7.23199997e+01],\n         [ 5.40609970e+01,  3.52210007e+01,  6.93199997e+01]],\n\n        ...,\n\n        [[ 1.80609970e+01,  5.22100067e+00,  3.83199997e+01],\n         [ 2.30609970e+01,  7.22100067e+00,  4.63199997e+01],\n         [ 2.90609970e+01,  1.32210007e+01,  5.53199997e+01],\n         ...,\n         [ 5.30609970e+01,  3.52210007e+01,  6.73199997e+01],\n         [ 4.90609970e+01,  3.22210007e+01,  6.13199997e+01],\n         [ 4.70609970e+01,  3.02210007e+01,  5.93199997e+01]],\n\n        [[ 2.20609970e+01,  6.22100067e+00,  3.83199997e+01],\n         [ 2.30609970e+01,  8.22100067e+00,  4.33199997e+01],\n         [ 3.00609970e+01,  9.22100067e+00,  4.23199997e+01],\n         ...,\n         [ 4.80609970e+01,  3.12210007e+01,  6.03199997e+01],\n         [ 4.60609970e+01,  2.82210007e+01,  6.03199997e+01],\n         [ 5.00609970e+01,  3.02210007e+01,  6.03199997e+01]],\n\n        [[ 1.80609970e+01,  3.22100067e+00,  3.83199997e+01],\n         [ 2.70609970e+01,  1.42210007e+01,  4.73199997e+01],\n         [ 3.20609970e+01,  1.12210007e+01,  4.53199997e+01],\n         ...,\n         [ 4.70609970e+01,  2.62210007e+01,  5.93199997e+01],\n         [ 6.80609970e+01,  4.22210007e+01,  6.73199997e+01],\n         [ 5.00609970e+01,  2.92210007e+01,  6.23199997e+01]]],\n\n\n       ...,\n\n\n       [[[ 3.00609970e+01, -7.77899933e+00,  7.13199997e+01],\n         [ 2.90609970e+01, -8.77899933e+00,  7.03199997e+01],\n         [ 3.50609970e+01, -5.77899933e+00,  7.63199997e+01],\n         ...,\n         [-2.39390030e+01, -5.87789993e+01,  1.33199997e+01],\n         [-2.29390030e+01, -5.77789993e+01,  1.43199997e+01],\n         [-2.19390030e+01, -5.57789993e+01,  1.23199997e+01]],\n\n        [[ 3.10609970e+01, -6.77899933e+00,  7.23199997e+01],\n         [ 3.40609970e+01, -3.77899933e+00,  7.53199997e+01],\n         [ 3.60609970e+01, -4.77899933e+00,  7.73199997e+01],\n         ...,\n         [-2.59390030e+01, -5.97789993e+01,  1.23199997e+01],\n         [-2.39390030e+01, -5.87789993e+01,  1.33199997e+01],\n         [-2.39390030e+01, -5.77789993e+01,  1.03199997e+01]],\n\n        [[ 3.00609970e+01, -7.77899933e+00,  7.13199997e+01],\n         [ 3.40609970e+01, -3.77899933e+00,  7.53199997e+01],\n         [ 3.50609970e+01, -5.77899933e+00,  7.63199997e+01],\n         ...,\n         [-3.09390030e+01, -6.27789993e+01,  9.31999969e+00],\n         [-2.49390030e+01, -5.97789993e+01,  1.23199997e+01],\n         [-2.49390030e+01, -5.87789993e+01,  9.31999969e+00]],\n\n        ...,\n\n        [[ 2.06099701e+00, -2.37789993e+01,  4.33199997e+01],\n         [ 1.06099701e+00, -2.47789993e+01,  4.23199997e+01],\n         [ 1.10609970e+01, -1.87789993e+01,  4.83199997e+01],\n         ...,\n         [-1.69390030e+01, -3.87789993e+01,  1.73199997e+01],\n         [-1.79390030e+01, -3.87789993e+01,  1.43199997e+01],\n         [-2.29390030e+01, -4.17789993e+01,  6.31999969e+00]],\n\n        [[ 1.06099701e+00, -2.47789993e+01,  4.23199997e+01],\n         [-1.93900299e+00, -2.77789993e+01,  3.93199997e+01],\n         [ 8.06099701e+00, -1.97789993e+01,  4.73199997e+01],\n         ...,\n         [-1.59390030e+01, -3.67789993e+01,  1.73199997e+01],\n         [-2.19390030e+01, -4.27789993e+01,  1.03199997e+01],\n         [-2.29390030e+01, -4.17789993e+01,  6.31999969e+00]],\n\n        [[ 2.06099701e+00, -2.37789993e+01,  4.33199997e+01],\n         [ 1.06099701e+00, -2.47789993e+01,  4.23199997e+01],\n         [ 5.06099701e+00, -2.07789993e+01,  4.63199997e+01],\n         ...,\n         [-1.99390030e+01, -4.07789993e+01,  1.23199997e+01],\n         [-2.19390030e+01, -4.27789993e+01,  1.03199997e+01],\n         [-2.39390030e+01, -4.27789993e+01,  5.31999969e+00]]],\n\n\n       [[[ 1.50609970e+01,  8.22100067e+00,  6.03199997e+01],\n         [ 2.50609970e+01,  1.52210007e+01,  6.33199997e+01],\n         [ 1.40609970e+01,  8.22100067e+00,  5.83199997e+01],\n         ...,\n         [ 1.70609970e+01,  1.72210007e+01,  6.23199997e+01],\n         [ 2.60609970e+01,  2.02210007e+01,  6.33199997e+01],\n         [ 3.70609970e+01,  2.02210007e+01,  6.53199997e+01]],\n\n        [[ 2.40609970e+01,  9.22100067e+00,  6.13199997e+01],\n         [ 6.06099701e+00,  6.22100067e+00,  5.53199997e+01],\n         [ 1.60609970e+01,  1.22210007e+01,  6.23199997e+01],\n         ...,\n         [ 2.50609970e+01,  2.02210007e+01,  6.63199997e+01],\n         [ 2.90609970e+01,  2.02210007e+01,  6.23199997e+01],\n         [ 3.80609970e+01,  2.12210007e+01,  6.63199997e+01]],\n\n        [[ 1.60609970e+01,  9.22100067e+00,  5.53199997e+01],\n         [ 3.06099701e+00,  2.22100067e+00,  5.33199997e+01],\n         [ 1.10609970e+01,  9.22100067e+00,  5.93199997e+01],\n         ...,\n         [ 3.10609970e+01,  2.22210007e+01,  6.63199997e+01],\n         [ 3.80609970e+01,  2.22210007e+01,  6.53199997e+01],\n         [ 3.70609970e+01,  2.12210007e+01,  5.93199997e+01]],\n\n        ...,\n\n        [[ 1.00609970e+01,  3.22100067e+00,  3.93199997e+01],\n         [ 8.06099701e+00,  5.22100067e+00,  4.53199997e+01],\n         [ 9.06099701e+00,  4.22100067e+00,  4.43199997e+01],\n         ...,\n         [-9.39002991e-01, -1.27789993e+01,  4.13199997e+01],\n         [-1.29390030e+01, -2.37789993e+01,  3.43199997e+01],\n         [-1.89390030e+01, -2.57789993e+01,  3.23199997e+01]],\n\n        [[-4.93900299e+00, -5.77899933e+00,  3.53199997e+01],\n         [-3.93900299e+00, -4.77899933e+00,  3.63199997e+01],\n         [ 9.06099701e+00,  7.22100067e+00,  4.43199997e+01],\n         ...,\n         [-6.93900299e+00, -2.07789993e+01,  3.63199997e+01],\n         [ 6.09970093e-02, -1.77789993e+01,  3.63199997e+01],\n         [-7.93900299e+00, -1.47789993e+01,  3.73199997e+01]],\n\n        [[-5.93900299e+00, -1.17789993e+01,  3.13199997e+01],\n         [ 5.06099701e+00,  1.22100067e+00,  3.83199997e+01],\n         [ 1.20609970e+01,  4.22100067e+00,  4.63199997e+01],\n         ...,\n         [-2.93900299e+00, -1.47789993e+01,  3.63199997e+01],\n         [ 1.80609970e+01,  2.21000671e-01,  4.83199997e+01],\n         [ 4.06099701e+00, -8.77899933e+00,  3.83199997e+01]]],\n\n\n       [[[ 4.06099701e+00,  8.22100067e+00,  5.73199997e+01],\n         [ 8.06099701e+00,  1.22210007e+01,  6.13199997e+01],\n         [ 8.06099701e+00,  1.02210007e+01,  5.93199997e+01],\n         ...,\n         [-4.93900299e+00,  3.22100067e+00,  4.83199997e+01],\n         [-4.93900299e+00,  3.22100067e+00,  4.73199997e+01],\n         [-7.93900299e+00,  2.21000671e-01,  4.43199997e+01]],\n\n        [[ 7.06099701e+00,  1.12210007e+01,  6.13199997e+01],\n         [ 5.06099701e+00,  9.22100067e+00,  5.93199997e+01],\n         [ 4.06099701e+00,  8.22100067e+00,  5.73199997e+01],\n         ...,\n         [-5.93900299e+00,  2.22100067e+00,  4.73199997e+01],\n         [-3.93900299e+00,  4.22100067e+00,  4.83199997e+01],\n         [-5.93900299e+00,  2.22100067e+00,  4.63199997e+01]],\n\n        [[ 2.06099701e+00,  5.22100067e+00,  5.73199997e+01],\n         [ 5.06099701e+00,  8.22100067e+00,  6.03199997e+01],\n         [ 1.00609970e+01,  1.72210007e+01,  6.53199997e+01],\n         ...,\n         [-3.93900299e+00,  4.22100067e+00,  4.93199997e+01],\n         [-4.93900299e+00,  3.22100067e+00,  4.73199997e+01],\n         [-5.93900299e+00,  2.22100067e+00,  4.63199997e+01]],\n\n        ...,\n\n        [[-2.29390030e+01, -7.77899933e+00,  3.23199997e+01],\n         [-1.89390030e+01, -3.77899933e+00,  3.63199997e+01],\n         [-1.79390030e+01, -2.77899933e+00,  3.73199997e+01],\n         ...,\n         [-1.59390030e+01, -1.77899933e+00,  3.53199997e+01],\n         [-1.69390030e+01, -3.77899933e+00,  3.53199997e+01],\n         [-1.69390030e+01, -3.77899933e+00,  3.53199997e+01]],\n\n        [[-2.19390030e+01, -6.77899933e+00,  3.33199997e+01],\n         [-1.39390030e+01,  1.22100067e+00,  4.13199997e+01],\n         [-1.49390030e+01, -1.77899933e+00,  3.83199997e+01],\n         ...,\n         [-1.79390030e+01, -3.77899933e+00,  3.33199997e+01],\n         [-1.69390030e+01, -2.77899933e+00,  3.43199997e+01],\n         [-1.59390030e+01, -1.77899933e+00,  3.53199997e+01]],\n\n        [[-2.09390030e+01, -5.77899933e+00,  3.43199997e+01],\n         [-1.69390030e+01, -1.77899933e+00,  3.83199997e+01],\n         [-1.59390030e+01, -3.77899933e+00,  3.33199997e+01],\n         ...,\n         [-1.59390030e+01, -1.77899933e+00,  3.53199997e+01],\n         [-1.69390030e+01, -1.77899933e+00,  3.23199997e+01],\n         [-1.89390030e+01, -3.77899933e+00,  3.03199997e+01]]]])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## plotting the images\n\nimport matplotlib.pyplot as plt\nfig, axes = plt.subplots(5,5, figsize=(8,8))\n\nfor i,ax in enumerate(axes.flat):\n    ax.imshow(xtestdf[i])","execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x576 with 25 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## making the ytest data ready\n\nytestdf = testdata_copy.target\nytestdf.head()","execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"0    1\n1    0\n2    0\n3    0\n4    0\nName: target, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# ResNet50 model\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.resnet50 import ResNet50\n\nimg_rows, img_cols = 224, 224\n\n\nresnet = ResNet50(weights = 'imagenet',\n                      include_top = False,\n                      input_shape  =  (img_rows,  img_cols, 3))\n\n## lets freeze the last  4 layers as they  are set to be trainable by  default\nfor layer in  resnet.layers:\n  layer.trainable = False\n\n## lets look at our layers\nfor (i, layer) in enumerate(resnet.layers):\n  print(str(i)  + \" \"  +  layer.__class__.__name__, layer.trainable)","execution_count":76,"outputs":[{"output_type":"stream","text":"0 InputLayer False\n1 ZeroPadding2D False\n2 Conv2D False\n3 BatchNormalization False\n4 Activation False\n5 ZeroPadding2D False\n6 MaxPooling2D False\n7 Conv2D False\n8 BatchNormalization False\n9 Activation False\n10 Conv2D False\n11 BatchNormalization False\n12 Activation False\n13 Conv2D False\n14 Conv2D False\n15 BatchNormalization False\n16 BatchNormalization False\n17 Add False\n18 Activation False\n19 Conv2D False\n20 BatchNormalization False\n21 Activation False\n22 Conv2D False\n23 BatchNormalization False\n24 Activation False\n25 Conv2D False\n26 BatchNormalization False\n27 Add False\n28 Activation False\n29 Conv2D False\n30 BatchNormalization False\n31 Activation False\n32 Conv2D False\n33 BatchNormalization False\n34 Activation False\n35 Conv2D False\n36 BatchNormalization False\n37 Add False\n38 Activation False\n39 Conv2D False\n40 BatchNormalization False\n41 Activation False\n42 Conv2D False\n43 BatchNormalization False\n44 Activation False\n45 Conv2D False\n46 Conv2D False\n47 BatchNormalization False\n48 BatchNormalization False\n49 Add False\n50 Activation False\n51 Conv2D False\n52 BatchNormalization False\n53 Activation False\n54 Conv2D False\n55 BatchNormalization False\n56 Activation False\n57 Conv2D False\n58 BatchNormalization False\n59 Add False\n60 Activation False\n61 Conv2D False\n62 BatchNormalization False\n63 Activation False\n64 Conv2D False\n65 BatchNormalization False\n66 Activation False\n67 Conv2D False\n68 BatchNormalization False\n69 Add False\n70 Activation False\n71 Conv2D False\n72 BatchNormalization False\n73 Activation False\n74 Conv2D False\n75 BatchNormalization False\n76 Activation False\n77 Conv2D False\n78 BatchNormalization False\n79 Add False\n80 Activation False\n81 Conv2D False\n82 BatchNormalization False\n83 Activation False\n84 Conv2D False\n85 BatchNormalization False\n86 Activation False\n87 Conv2D False\n88 Conv2D False\n89 BatchNormalization False\n90 BatchNormalization False\n91 Add False\n92 Activation False\n93 Conv2D False\n94 BatchNormalization False\n95 Activation False\n96 Conv2D False\n97 BatchNormalization False\n98 Activation False\n99 Conv2D False\n100 BatchNormalization False\n101 Add False\n102 Activation False\n103 Conv2D False\n104 BatchNormalization False\n105 Activation False\n106 Conv2D False\n107 BatchNormalization False\n108 Activation False\n109 Conv2D False\n110 BatchNormalization False\n111 Add False\n112 Activation False\n113 Conv2D False\n114 BatchNormalization False\n115 Activation False\n116 Conv2D False\n117 BatchNormalization False\n118 Activation False\n119 Conv2D False\n120 BatchNormalization False\n121 Add False\n122 Activation False\n123 Conv2D False\n124 BatchNormalization False\n125 Activation False\n126 Conv2D False\n127 BatchNormalization False\n128 Activation False\n129 Conv2D False\n130 BatchNormalization False\n131 Add False\n132 Activation False\n133 Conv2D False\n134 BatchNormalization False\n135 Activation False\n136 Conv2D False\n137 BatchNormalization False\n138 Activation False\n139 Conv2D False\n140 BatchNormalization False\n141 Add False\n142 Activation False\n143 Conv2D False\n144 BatchNormalization False\n145 Activation False\n146 Conv2D False\n147 BatchNormalization False\n148 Activation False\n149 Conv2D False\n150 Conv2D False\n151 BatchNormalization False\n152 BatchNormalization False\n153 Add False\n154 Activation False\n155 Conv2D False\n156 BatchNormalization False\n157 Activation False\n158 Conv2D False\n159 BatchNormalization False\n160 Activation False\n161 Conv2D False\n162 BatchNormalization False\n163 Add False\n164 Activation False\n165 Conv2D False\n166 BatchNormalization False\n167 Activation False\n168 Conv2D False\n169 BatchNormalization False\n170 Activation False\n171 Conv2D False\n172 BatchNormalization False\n173 Add False\n174 Activation False\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n## Let us  create a function to build our top layer\n\ndef addTopMobileNetLayer(bottom_model, num_classes):\n\n  top_model = bottom_model.output\n  top_model = GlobalAveragePooling2D()(top_model)\n  top_model = (Dense(2048, activation = 'relu'))(top_model)\n  top_model = (Dense(2048, activation = 'relu'))(top_model)\n  top_model = (Dense(1024, activation = 'relu'))(top_model)\n  top_model = (Dense(num_classes, activation  = 'softmax'))(top_model)\n\n  return top_model","execution_count":77,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Dense, GlobalAveragePooling2D, Dropout, Activation,  Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.models import Model\n\nnum_classes = 2\n\nFC_head = addTopMobileNetLayer(resnet, num_classes)\n\nmodel  = Model(inputs =  resnet.input, outputs = FC_head)\n\nprint(model.summary())","execution_count":78,"outputs":[{"output_type":"stream","text":"Model: \"model_1\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n__________________________________________________________________________________________________\nconv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_2[0][0]                    \n__________________________________________________________________________________________________\nconv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n__________________________________________________________________________________________________\nconv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n__________________________________________________________________________________________________\nconv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n__________________________________________________________________________________________________\npool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n__________________________________________________________________________________________________\npool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n__________________________________________________________________________________________________\nconv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n__________________________________________________________________________________________________\nconv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n__________________________________________________________________________________________________\nconv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n                                                                 conv2_block1_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n__________________________________________________________________________________________________\nconv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n__________________________________________________________________________________________________\nconv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n                                                                 conv2_block2_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n__________________________________________________________________________________________________\nconv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n__________________________________________________________________________________________________\nconv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n                                                                 conv2_block3_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n__________________________________________________________________________________________________\nconv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n__________________________________________________________________________________________________\nconv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n__________________________________________________________________________________________________\nconv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n                                                                 conv3_block1_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n__________________________________________________________________________________________________\nconv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n__________________________________________________________________________________________________\nconv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n                                                                 conv3_block2_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n__________________________________________________________________________________________________\nconv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n__________________________________________________________________________________________________\nconv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n                                                                 conv3_block3_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n__________________________________________________________________________________________________\nconv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n__________________________________________________________________________________________________\nconv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n                                                                 conv3_block4_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n                                                                 conv4_block1_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n                                                                 conv4_block2_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n                                                                 conv4_block3_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n                                                                 conv4_block4_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n__________________________________________________________________________________________________\n","name":"stdout"},{"output_type":"stream","text":"conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n                                                                 conv4_block5_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n__________________________________________________________________________________________________\nconv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n__________________________________________________________________________________________________\nconv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n                                                                 conv4_block6_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n__________________________________________________________________________________________________\nconv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n__________________________________________________________________________________________________\nconv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n__________________________________________________________________________________________________\nconv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n                                                                 conv5_block1_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n__________________________________________________________________________________________________\nconv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n__________________________________________________________________________________________________\nconv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n                                                                 conv5_block2_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n__________________________________________________________________________________________________\nconv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n__________________________________________________________________________________________________\nconv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n__________________________________________________________________________________________________\nconv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n__________________________________________________________________________________________________\nconv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n                                                                 conv5_block3_3_bn[0][0]          \n__________________________________________________________________________________________________\nconv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048)         0           conv5_block3_out[0][0]           \n__________________________________________________________________________________________________\ndense_36 (Dense)                (None, 2048)         4196352     global_average_pooling2d_1[0][0] \n__________________________________________________________________________________________________\ndense_37 (Dense)                (None, 2048)         4196352     dense_36[0][0]                   \n__________________________________________________________________________________________________\ndense_38 (Dense)                (None, 1024)         2098176     dense_37[0][0]                   \n__________________________________________________________________________________________________\ndense_39 (Dense)                (None, 2)            2050        dense_38[0][0]                   \n==================================================================================================\nTotal params: 34,080,642\nTrainable params: 10,492,930\nNon-trainable params: 23,587,712\n__________________________________________________________________________________________________\nNone\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\n\nearlystop = EarlyStopping(monitor = 'val_loss', \n                          min_delta = 0, \n                          patience = 3,\n                          verbose = 1,\n                          restore_best_weights = True)\n\ncallbacks = [earlystop]\n\n\nmodel.compile(loss = 'binary_crossentropy', optimizer = Adam(lr = 0.009), metrics = ['accuracy'])","execution_count":81,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"epochs = 10\n\n\nhistory = model.fit(xtraindf, \n          ytraindf,\n          batch_size = 16,\n          epochs = epochs,\n          verbose = 1,\n          callbacks = callbacks,\n          validation_data = (xtestdf, ytestdf)\n          )","execution_count":82,"outputs":[{"output_type":"stream","text":"Epoch 1/10\n69/69 [==============================] - 103s 1s/step - loss: 7.6646 - accuracy: 0.5455 - val_loss: 7.6623 - val_accuracy: 0.5714\nEpoch 2/10\n69/69 [==============================] - 110s 2s/step - loss: 7.6646 - accuracy: 0.5455 - val_loss: 7.6623 - val_accuracy: 0.5714\nEpoch 3/10\n69/69 [==============================] - 100s 1s/step - loss: 7.6646 - accuracy: 0.5455 - val_loss: 7.6623 - val_accuracy: 0.5714\nEpoch 4/10\n69/69 [==============================] - ETA: 0s - loss: 7.6646 - accuracy: 0.5455Restoring model weights from the end of the best epoch.\n69/69 [==============================] - 101s 1s/step - loss: 7.6646 - accuracy: 0.5455 - val_loss: 7.6623 - val_accuracy: 0.5714\nEpoch 00004: early stopping\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### My CNN"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nimport tensorflow\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout\nfrom keras.optimizers import Adam, RMSprop\nfrom tensorflow.keras.callbacks import TensorBoard","execution_count":87,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(24,3,3, input_shape = (224,224,3), activation = 'relu'))\nmodel.add(Conv2D(36,3,3, activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size = (2,2)))\nmodel.add(Conv2D(36,3,3, activation = 'relu'))\nmodel.add(Conv2D(36,3,3,  activation = 'relu'))\n\nmodel.add(Flatten())\nmodel.add(Dense(units = 2028, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(units = 1024, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(units = 512, activation = 'relu'))\nmodel.add(Dense(units = 1, activation = 'sigmoid'))","execution_count":120,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss = 'binary_crossentropy', optimizer = RMSprop(lr = 0.005), metrics = ['accuracy'])\nepochs = 10\n\n\nhistory = model.fit(xtraindf, \n          ytraindf,\n          batch_size = 64,\n          epochs = epochs,\n          verbose = 1,\n          validation_data = (xtestdf, ytestdf))","execution_count":121,"outputs":[{"output_type":"stream","text":"Epoch 1/10\n18/18 [==============================] - 3s 163ms/step - loss: 104.3361 - accuracy: 0.5100 - val_loss: 0.6415 - val_accuracy: 0.5771\nEpoch 2/10\n18/18 [==============================] - 3s 174ms/step - loss: 0.6919 - accuracy: 0.5427 - val_loss: 0.6876 - val_accuracy: 0.5486\nEpoch 3/10\n18/18 [==============================] - 3s 172ms/step - loss: 0.6930 - accuracy: 0.5382 - val_loss: 0.6724 - val_accuracy: 0.5657\nEpoch 4/10\n18/18 [==============================] - 3s 157ms/step - loss: 0.6973 - accuracy: 0.5364 - val_loss: 0.6958 - val_accuracy: 0.5543\nEpoch 5/10\n18/18 [==============================] - 3s 154ms/step - loss: 0.6911 - accuracy: 0.5582 - val_loss: 0.7021 - val_accuracy: 0.4229\nEpoch 6/10\n18/18 [==============================] - 3s 153ms/step - loss: 0.7430 - accuracy: 0.5091 - val_loss: 0.8298 - val_accuracy: 0.5714\nEpoch 7/10\n18/18 [==============================] - 3s 155ms/step - loss: 0.7840 - accuracy: 0.5127 - val_loss: 0.6897 - val_accuracy: 0.5771\nEpoch 8/10\n18/18 [==============================] - 3s 156ms/step - loss: 0.7208 - accuracy: 0.5418 - val_loss: 0.6900 - val_accuracy: 0.6514\nEpoch 9/10\n18/18 [==============================] - 3s 150ms/step - loss: 0.6950 - accuracy: 0.5218 - val_loss: 0.7353 - val_accuracy: 0.4229\nEpoch 10/10\n18/18 [==============================] - 3s 148ms/step - loss: 0.7391 - accuracy: 0.5245 - val_loss: 0.8404 - val_accuracy: 0.6400\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot of validation vs training accuracy over the epochs\n\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.legend(['training', 'validation'])\nplt.title('Loss')\nplt.xlabel('Epoch')","execution_count":123,"outputs":[{"output_type":"execute_result","execution_count":123,"data":{"text/plain":"Text(0.5, 0, 'Epoch')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ypred = model.predict(xtestdf)\nevaluation = model.evaluate(xtestdf, ytestdf)\nprint('Test accuracy : {:.3f}'.format(evaluation[1]))","execution_count":124,"outputs":[{"output_type":"stream","text":"6/6 [==============================] - 0s 18ms/step - loss: 0.8404 - accuracy: 0.6400\nTest accuracy : 0.640\n","name":"stdout"}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}