FineTuning_VGG16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "_PrjgC87ZbNT",
    "outputId": "391f64c4-f38c-4331-aa4d-dc8e75210087"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.4)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
      "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (3.0.2)\n",
      "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.6.16)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
      "Requirement already satisfied: text-unidecode==1.2 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.2)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n"
     ]
    }
   ],
   "source": [
    "#install depenencies\n",
    "!pip install kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 92,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "LYiwHG4dZuSs",
    "outputId": "4ff30667-a329-4356-ea7d-277d525ae958"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-0f420589-9b00-4a80-9fa2-ce5bc8c46395\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-0f420589-9b00-4a80-9fa2-ce5bc8c46395\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving kaggle.json to kaggle.json\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kaggle.json': b'{\"username\":\"siddharthnaik7\",\"key\":\"28e0f6c07a924550d841c251e1730ac7\"}'}"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#upload the credentials of the kaggle account\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7HPubmLlaW9U"
   },
   "outputs": [],
   "source": [
    "#before importing the dataset we want to use this code\n",
    "# The Kaggle API client expects this file to be in ~/.kaggle,\n",
    "!mkdir -p ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "\n",
    "# This permissions change avoids a warning on Kaggle tool startup.\n",
    "!chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "colab_type": "code",
    "id": "P31EWwjeagb6",
    "outputId": "99075afc-b6e6-439d-91ef-1eac5a14455c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading train_images.zip to /content\n",
      "100% 7.99G/8.01G [00:52<00:00, 112MB/s]\n",
      "100% 8.01G/8.01G [00:53<00:00, 162MB/s]\n",
      "Downloading test_images.zip to /content\n",
      "100% 1.50G/1.50G [00:11<00:00, 159MB/s]\n",
      "100% 1.50G/1.50G [00:11<00:00, 140MB/s]\n",
      "Downloading train.csv to /content\n",
      "  0% 0.00/53.7k [00:00<?, ?B/s]\n",
      "100% 53.7k/53.7k [00:00<00:00, 56.5MB/s]\n",
      "Downloading test.csv to /content\n",
      "  0% 0.00/24.5k [00:00<?, ?B/s]\n",
      "100% 24.5k/24.5k [00:00<00:00, 22.2MB/s]\n",
      "Downloading sample_submission.csv to /content\n",
      "  0% 0.00/28.3k [00:00<?, ?B/s]\n",
      "100% 28.3k/28.3k [00:00<00:00, 25.3MB/s]\n"
     ]
    }
   ],
   "source": [
    "#import the dataset we want to use for our project\n",
    "!kaggle competitions download -c aptos2019-blindness-detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "RGuo0kezccd3",
    "outputId": "5ef6f165-ce4b-407b-d495-7c12ab91e37c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kaggle.json  sample_submission.csv  test_images.zip  train_images.zip\n",
      "sample_data  test.csv\t\t    train.csv\n"
     ]
    }
   ],
   "source": [
    "#lets see the imported files from kaggle competetion\n",
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v9pDZ1bJgOpW"
   },
   "source": [
    "# **Colab Utilization Specifications**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "lSguKDLsc_pb",
    "outputId": "34173ee6-a492-449b-ff68-ea8e6138d98f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "overlay         359G   40G  301G  12% /\n",
      "tmpfs           6.4G     0  6.4G   0% /dev\n",
      "tmpfs           6.4G     0  6.4G   0% /sys/fs/cgroup\n",
      "/dev/sda1       365G   42G  324G  12% /opt/bin\n",
      "tmpfs           6.4G   12K  6.4G   1% /var/colab\n",
      "shm             6.0G  4.0K  6.0G   1% /dev/shm\n",
      "tmpfs           6.4G     0  6.4G   0% /sys/firmware\n"
     ]
    }
   ],
   "source": [
    "#Disk Information of Colab\n",
    "!df -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 975
    },
    "colab_type": "code",
    "id": "k0gyunezdTMD",
    "outputId": "c40148fc-698a-4aab-a789-47de89a80f19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processor\t: 0\n",
      "vendor_id\t: GenuineIntel\n",
      "cpu family\t: 6\n",
      "model\t\t: 63\n",
      "model name\t: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
      "stepping\t: 0\n",
      "microcode\t: 0x1\n",
      "cpu MHz\t\t: 2300.000\n",
      "cache size\t: 46080 KB\n",
      "physical id\t: 0\n",
      "siblings\t: 2\n",
      "core id\t\t: 0\n",
      "cpu cores\t: 1\n",
      "apicid\t\t: 0\n",
      "initial apicid\t: 0\n",
      "fpu\t\t: yes\n",
      "fpu_exception\t: yes\n",
      "cpuid level\t: 13\n",
      "wp\t\t: yes\n",
      "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat arch_capabilities\n",
      "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf\n",
      "bogomips\t: 4600.00\n",
      "clflush size\t: 64\n",
      "cache_alignment\t: 64\n",
      "address sizes\t: 46 bits physical, 48 bits virtual\n",
      "power management:\n",
      "\n",
      "processor\t: 1\n",
      "vendor_id\t: GenuineIntel\n",
      "cpu family\t: 6\n",
      "model\t\t: 63\n",
      "model name\t: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
      "stepping\t: 0\n",
      "microcode\t: 0x1\n",
      "cpu MHz\t\t: 2300.000\n",
      "cache size\t: 46080 KB\n",
      "physical id\t: 0\n",
      "siblings\t: 2\n",
      "core id\t\t: 0\n",
      "cpu cores\t: 1\n",
      "apicid\t\t: 1\n",
      "initial apicid\t: 1\n",
      "fpu\t\t: yes\n",
      "fpu_exception\t: yes\n",
      "cpuid level\t: 13\n",
      "wp\t\t: yes\n",
      "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat arch_capabilities\n",
      "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf\n",
      "bogomips\t: 4600.00\n",
      "clflush size\t: 64\n",
      "cache_alignment\t: 64\n",
      "address sizes\t: 46 bits physical, 48 bits virtual\n",
      "power management:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#CPU Specs\n",
    "!cat /proc/cpuinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 811
    },
    "colab_type": "code",
    "id": "nBcwuHKIdhFZ",
    "outputId": "96d32760-7e0c-4c21-ab7a-e97d1ec1e015"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MemTotal:       13335276 kB\n",
      "MemFree:          149140 kB\n",
      "MemAvailable:   12598856 kB\n",
      "Buffers:           73624 kB\n",
      "Cached:         12226752 kB\n",
      "SwapCached:            0 kB\n",
      "Active:           560580 kB\n",
      "Inactive:       12017788 kB\n",
      "Active(anon):     257148 kB\n",
      "Inactive(anon):      360 kB\n",
      "Active(file):     303432 kB\n",
      "Inactive(file): 12017428 kB\n",
      "Unevictable:           0 kB\n",
      "Mlocked:               0 kB\n",
      "SwapTotal:             0 kB\n",
      "SwapFree:              0 kB\n",
      "Dirty:               960 kB\n",
      "Writeback:             0 kB\n",
      "AnonPages:        277984 kB\n",
      "Mapped:           184732 kB\n",
      "Shmem:               908 kB\n",
      "Slab:             486648 kB\n",
      "SReclaimable:     454720 kB\n",
      "SUnreclaim:        31928 kB\n",
      "KernelStack:        3776 kB\n",
      "PageTables:         4736 kB\n",
      "NFS_Unstable:          0 kB\n",
      "Bounce:                0 kB\n",
      "WritebackTmp:          0 kB\n",
      "CommitLimit:     6667636 kB\n",
      "Committed_AS:    1709652 kB\n",
      "VmallocTotal:   34359738367 kB\n",
      "VmallocUsed:           0 kB\n",
      "VmallocChunk:          0 kB\n",
      "AnonHugePages:         0 kB\n",
      "ShmemHugePages:        0 kB\n",
      "ShmemPmdMapped:        0 kB\n",
      "HugePages_Total:       0\n",
      "HugePages_Free:        0\n",
      "HugePages_Rsvd:        0\n",
      "HugePages_Surp:        0\n",
      "Hugepagesize:       2048 kB\n",
      "DirectMap4k:       90100 kB\n",
      "DirectMap2M:     5152768 kB\n",
      "DirectMap1G:    10485760 kB\n"
     ]
    }
   ],
   "source": [
    "#Memory Specs\n",
    "!cat /proc/meminfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2DwQFQnUeqkt"
   },
   "source": [
    "# **The VM used for Colaboratory**\n",
    "\n",
    "\n",
    "> **RAM:** 13 GB\n",
    "\n",
    "\n",
    "> **CPU:** 2 vCPU (when checking using psutil - so a n1-highmem-2 instance)\n",
    "\n",
    "\n",
    "\n",
    "> **CPU Type:** 2-core Xeon 2.2GHz\n",
    "\n",
    "\n",
    "> **Maximum Lifetime of a VM:** 12 Hours\n",
    "\n",
    "\n",
    "> **Idle VMs Time Out:** After 90 mins\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "colab_type": "code",
    "id": "t5fqtbf3eGbG",
    "outputId": "0fd58ba3-a8a6-4183-9e5d-e6eb87bf797d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "scpustats(ctx_switches=695840, interrupts=788358, soft_interrupts=1167013, syscalls=0)\n",
      "svmem(total=13655322624, available=12902232064, percent=5.5, used=440332288, free=152752128, active=573259776, inactive=12307185664, buffers=75431936, cached=12986806272, shared=929792, slab=498257920)\n"
     ]
    }
   ],
   "source": [
    "from psutil import *\n",
    "print(cpu_count())\n",
    "print(cpu_stats())\n",
    "print(virtual_memory())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fS92ar8EghQo"
   },
   "source": [
    "# **Start**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bv_FyDqg373l"
   },
   "source": [
    "# **Unzip Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "zKrcCu8Mgd0L",
    "outputId": "8e6e3893-c5f5-4050-9952-02191ffae930"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!unzip train_images.zip -d ./Train_Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Eycej2e2g-Vk",
    "outputId": "71d45888-ae71-48d9-ae7b-9743fdcb81f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!unzip test_images.zip -d ./Test_Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bQ_QtMdF4C5T"
   },
   "source": [
    "# **Delete Zip Files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lnVZdHqao57J"
   },
   "outputs": [],
   "source": [
    "!rm -rf test_images.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0C6TDe-GpCyW"
   },
   "outputs": [],
   "source": [
    "!rm -rf train_images.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Iw5FHIGb4I8j"
   },
   "source": [
    "# **Load Training and Test Information in Dataframes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Rd4hUOAPpIEJ",
    "outputId": "d7f16df6-001d-4cd0-b183-d81ac360458b"
   },
   "outputs": [
    {
     "data": {
      "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>id_code</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000c1434d8d7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001639a390f0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0024cdab0c1e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002c21358ce6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>005b95c28852</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_code  diagnosis\n",
       "0  000c1434d8d7          2\n",
       "1  001639a390f0          4\n",
       "2  0024cdab0c1e          1\n",
       "3  002c21358ce6          0\n",
       "4  005b95c28852          0"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_train = pd.read_csv('train.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "57-4ClMspYgK",
    "outputId": "5f66fd7b-1bca-4a11-afb6-f18d2693ce42"
   },
   "outputs": [
    {
     "data": {
      "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>id_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005cfc8afb6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>003f0afdcd15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>006efc72b638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00836aaacf06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>009245722fa4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_code\n",
       "0  0005cfc8afb6\n",
       "1  003f0afdcd15\n",
       "2  006efc72b638\n",
       "3  00836aaacf06\n",
       "4  009245722fa4"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kT2LbBF12zk3"
   },
   "source": [
    "# **Base Line**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "colab_type": "code",
    "id": "RaibCVoi22_Z",
    "outputId": "052a75e5-f860-4836-ca8d-c64b866c403f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1805\n",
      "2     999\n",
      "1     370\n",
      "4     295\n",
      "3     193\n",
      "Name: diagnosis, dtype: int64\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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IiGTDkQwREcmGIUNERLJhyBARkWz+0r+TuXmzFHo9D0kREUmhVCrg4mJXp+f8pUNGrxcM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\nZMOQISIi2TBkiIhINv8PHgQ3qvD3TAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "Class_Frequency_Count = df_train['diagnosis'].value_counts()\n",
    "print(str(Class_Frequency_Count) + \"\\n\")\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.barplot(Class_Frequency_Count.index,Class_Frequency_Count.values,alpha=0.9)\n",
    "plt.title(\"Frequency Distribution of Classes\")\n",
    "plt.ylabel(\"Number of Occurances\", fontsize=12)\n",
    "#plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Classes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M6edlOHG3ys4"
   },
   "source": [
    "# **Directory Structure Creation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y2gncbKq0gaE"
   },
   "outputs": [],
   "source": [
    "#!rm -rf Validation_Dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q8oCp3uMrnfC"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "train_keyword = 'Training_Dir'\n",
    "validation_keyword = 'Validation_Dir'\n",
    "keywords = [train_keyword,validation_keyword]\n",
    "dir_list = df_train['diagnosis'].tolist()\n",
    "\n",
    "for keyword in keywords:\n",
    "  if(not os.path.exists(keyword)):\n",
    "    os.mkdir(keyword)\n",
    "  for class_dir in dir_list:\n",
    "    if(not os.path.exists(keyword+'/'+str(class_dir))):\n",
    "      os.mkdir(keyword+'/'+str(class_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nXbM4Q0xpj7x"
   },
   "outputs": [],
   "source": [
    "split = int(0.8 * len(df_train))\n",
    "\n",
    "train_dataframe = df_train.iloc[:split]\n",
    "val_dataframe = df_train.iloc[split:]\n",
    "\n",
    "train_images = train_dataframe['id_code'].tolist()\n",
    "train_labels = train_dataframe['diagnosis'].tolist()\n",
    "val_images = val_dataframe['id_code'].tolist()\n",
    "val_labels = val_dataframe['diagnosis'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "42MdRPIiuFwa"
   },
   "outputs": [],
   "source": [
    "import shutil as cf\n",
    "image_list = []\n",
    "class_list = []\n",
    "for keyword in keywords:\n",
    "  if(not os.path.exists(keyword)):\n",
    "    os.mkdir(keyword)\n",
    "  if keyword == train_keyword:\n",
    "    image_list = train_images\n",
    "    class_list = train_labels\n",
    "  elif keyword == validation_keyword: \n",
    "    image_list = val_images\n",
    "    class_list = val_labels\n",
    "  for image_info in image_list:\n",
    "    cl = class_list[image_list.index(image_info)]\n",
    "    cf.copy('Train_Images/'+str(image_info)+'.png', keyword+'/'+str(cl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H2meYmoF2L5V"
   },
   "outputs": [],
   "source": [
    "!rm -rf Train_Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "by8NP_t0M8rX"
   },
   "source": [
    "# **Fine Tuning Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "vNQKBQIYMUsz",
    "outputId": "a6805273-1922-42f5-d215-0c778c955bec"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0714 23:14:38.759129 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0714 23:14:38.795075 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0714 23:14:38.808318 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0714 23:14:38.850255 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 1s 0us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0714 23:14:40.391715 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0714 23:14:40.392945 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import VGG16\n",
    " \n",
    "\n",
    "vgg_conv = VGG16(weights='imagenet',include_top=False,input_shape=(224, 224, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "U7SZ8F_2MdGv",
    "outputId": "68e00ea2-af5f-49ec-9805-c1683236e753"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.input_layer.InputLayer object at 0x7f4ba3dbf278> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f68fe80> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f68ed68> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x7f4b7f6b3da0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f6b3e10> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f663748> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x7f4b7f6069b0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f606e10> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f6265f8> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f5c3630> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x7f4b7f5ddb38> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f576198> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f58e710> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f5a4a58> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x7f4b7f53e9b0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f53eef0> True\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f4f9438> True\n",
      "<keras.layers.convolutional.Conv2D object at 0x7f4b7f4f9cc0> True\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x7f4b7f526898> True\n"
     ]
    }
   ],
   "source": [
    "# Freeze the layers except the last 2 layers\n",
    "for layer in vgg_conv.layers[:-4]:\n",
    "    layer.trainable = False\n",
    "\n",
    "# check the trainable status of the individual layers\n",
    "for layer in vgg_conv.layers:\n",
    "    print(layer, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "ip3Vf6-OMkXo",
    "outputId": "6244a0ba-1660-4466-aa02-96f46e2db27e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n",
      "Found 2929 images belonging to 5 classes.\n",
      "Found 733 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Activation\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.optimizers import SGD\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "print(tf.__version__)\n",
    "\n",
    "train_dir = 'Training_Dir'\n",
    "validation_dir = 'Validation_Dir'\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=10,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    " \n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    " \n",
    "# Change the batchsize according to your system RAM\n",
    "train_batchsize = 100\n",
    "val_batchsize = 60\n",
    " \n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(224, 224),\n",
    "        batch_size=train_batchsize,\n",
    "        class_mode='categorical')\n",
    " \n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(224, 224),\n",
    "        batch_size=val_batchsize,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "colab_type": "code",
    "id": "PfEZNUHPMVMa",
    "outputId": "fc4261cf-2ba2-49fa-ba88-63be2a23d8fc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0714 23:15:14.622193 139963654322048 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 7, 7, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              25691136  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 5)                 5125      \n",
      "=================================================================\n",
      "Total params: 42,510,149\n",
      "Trainable params: 34,874,885\n",
      "Non-trainable params: 7,635,264\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "model = keras.models.Sequential()\n",
    " \n",
    "# Add the vgg convolutional base model\n",
    "model.add(vgg_conv)\n",
    " \n",
    "# Add new custom layers\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(1024, activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.5))\n",
    "model.add(keras.layers.Dense(1024, activation='relu'))\n",
    "model.add(keras.layers.Dense(1024, activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.5))\n",
    "model.add(keras.layers.Dense(5, activation= tf.nn.softmax))\n",
    " \n",
    "# Show a summary of the model. Check the number of trainable parameters\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91
    },
    "colab_type": "code",
    "id": "DPT7M2SqOKD_",
    "outputId": "7d1f904b-b535-42da-fadd-5bc617bec731"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0714 23:15:20.398178 139963654322048 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Regular Model\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=keras.optimizers.RMSprop(lr=2e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "iR7pmgqXOUjH",
    "outputId": "db7ba07a-4db7-4667-8258-73167d580ff3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0714 23:15:29.924794 139963654322048 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "30/29 [==============================] - 425s 14s/step - loss: 1.3575 - acc: 0.4963 - val_loss: 0.9633 - val_acc: 0.6794\n",
      "Epoch 2/10\n",
      "30/29 [==============================] - 392s 13s/step - loss: 0.9499 - acc: 0.6662 - val_loss: 0.8563 - val_acc: 0.7244\n",
      "Epoch 3/10\n",
      "30/29 [==============================] - 403s 13s/step - loss: 0.9467 - acc: 0.6756 - val_loss: 0.8990 - val_acc: 0.7203\n",
      "Epoch 4/10\n",
      "30/29 [==============================] - 397s 13s/step - loss: 0.8608 - acc: 0.7030 - val_loss: 0.8446 - val_acc: 0.7312\n",
      "Epoch 5/10\n",
      "30/29 [==============================] - 394s 13s/step - loss: 0.8326 - acc: 0.6995 - val_loss: 0.7548 - val_acc: 0.7258\n",
      "Epoch 6/10\n",
      "30/29 [==============================] - 395s 13s/step - loss: 0.8183 - acc: 0.7079 - val_loss: 0.8341 - val_acc: 0.7353\n",
      "Epoch 7/10\n",
      "30/29 [==============================] - 392s 13s/step - loss: 0.8143 - acc: 0.7150 - val_loss: 0.7469 - val_acc: 0.7394\n",
      "Epoch 8/10\n",
      "30/29 [==============================] - 394s 13s/step - loss: 0.7744 - acc: 0.7147 - val_loss: 0.7686 - val_acc: 0.7271\n",
      "Epoch 9/10\n",
      "30/29 [==============================] - 393s 13s/step - loss: 0.7830 - acc: 0.7179 - val_loss: 0.7671 - val_acc: 0.7449\n",
      "Epoch 10/10\n",
      "30/29 [==============================] - 389s 13s/step - loss: 0.7536 - acc: 0.7220 - val_loss: 0.7870 - val_acc: 0.7435\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=train_generator.samples/train_generator.batch_size ,\n",
    "      epochs=10,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=validation_generator.samples/validation_generator.batch_size,\n",
    "      verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "id": "ua3-CUVZAnXQ",
    "outputId": "8cbbd044-b081-4a10-e9b3-b2ccae5cdc19"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wJE7vx8iBYcSF+9Mr3A+9DKyLbqLDEsuyZcuYNWsWM2bMYP369Tz11FO8/fbb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\nYiEiIkn9B8+6EZIi5O4uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mMwMH9q71k/qFlynWaDSYzRc/2vLC5bRaba1n3l+pgQNvplOnzuzc+Rsff/wBa9as5Pnn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\nKVNo3bq1PUq0uYBRt4DFQs6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RdLjKHxe9bYbsHMXZ/1bqO2xll/gPDQ0lPT0ds9kMgNls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\nVB4iImKaykNEREz7NyufAPua121hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    " \n",
    "epochs = range(len(acc))\n",
    " \n",
    "plt.plot(epochs, acc, 'b', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    " \n",
    "plt.figure()\n",
    " \n",
    "plt.plot(epochs, loss, 'b', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "9QEYs1daA3r9",
    "outputId": "07897035-8e33-41aa-9347-d83a430b79e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.7448840366440125\n",
      "Training Accuracy 0.7203823853450735\n"
     ]
    }
   ],
   "source": [
    "# Max Accuracy\n",
    "\n",
    "print(\"Validation Accuracy:\", max(history.history['val_acc']))\n",
    "print(\"Training Accuracy\", max(history.history['acc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "11laAxwtCjNA"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "test_list=df_test['id_code'].tolist()\n",
    "test_image = []\n",
    "for i in test_list:\n",
    "    img = image.load_img('Test_Images/'+i+'.png',target_size=(224,224,3))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    test_image.append(img)\n",
    "X = np.array(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bzGUeFucFGmf"
   },
   "outputs": [],
   "source": [
    "pred_class = []\n",
    "for class_prob in y_prob:\n",
    "  class_prob = class_prob.tolist()\n",
    "  pred_class.append(class_prob.index(max(class_prob)))\n",
    "df_test['diagnosis'] = pred_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bHElmd82LBxe"
   },
   "source": [
    "# **Base Line Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "colab_type": "code",
    "id": "rpJXrdeDFNRj",
    "outputId": "f297c8d9-00e2-458e-9dc7-b1855ce8d0a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1805\n",
      "2     999\n",
      "1     370\n",
      "4     295\n",
      "3     193\n",
      "Name: diagnosis, dtype: int64\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Classes')"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiGTDkQwREcmGIUNERLJhyBARkWz+0r+TuXmzFHo9D0kREUmhVCrg4mJXp+f8pUNGrxcM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\nZMOQISIi2TBkiIhINv8PHgQ3qvD3TAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Class_Frequency_Count = df_train['diagnosis'].value_counts()\n",
    "print(str(Class_Frequency_Count) + \"\\n\")\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.barplot(Class_Frequency_Count.index,Class_Frequency_Count.values,alpha=0.9)\n",
    "plt.title(\"Frequency Distribution of Classes\")\n",
    "plt.ylabel(\"Number of Occurances\", fontsize=12)\n",
    "#plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Classes\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i-P13K-3LIfZ"
   },
   "source": [
    "# **Predicted Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "id": "63rZWOWBHShk",
    "outputId": "ed99eba1-128f-4e27-f4e6-5f48b975d575"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    971\n",
      "0    713\n",
      "1    244\n",
      "Name: diagnosis, dtype: int64\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Classes')"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mTMCIESNgYWGBv/3tb/jXv/5l+Dl/f39MmTIFGo0GrVq1woYNG/Jtd/z48cjIyDCUQbdu\n3d44/ibVqlWrMH/+fLRr1w7W1taYNGlSkafAS2FjY4MtW7Zg2bJlWLhwIQDAzc3N8LgdOHAACxYs\ngE6nQ/369RESEgLgVQF7e3vD29sbOp0O3333HWxtbfNt28vLC1u2bMG6desQGhoKU1NT1KlTBx9/\n/PEbOZo2bYpBgwahX79+UKlU6NevH5o1a2YYv379OpYsWYKMjAzUqFEDgYGBcHJywi+//IKQkBA8\nf/4c5ubm6NChg2H37Oeff441a9bA398fKSkpUKvVGDp0KNq0aYPIyEgsWrQIKSkpsLGxwahRoyTN\nuIydQgh+aBkVbu3atXj69ClWrFhRrjmuXLmC6dOn4+zZs5J2WRFR+eNcjiqFvLw87Ny5E/7+/iwY\nokqEJUMVXmRkJFq2bImEhASMGDGivOMQUTFwdxkREcmGMxkiIpINS4aIiGTzTkomODgY3t7eaNSo\nER48eGBY/vjxYwwcOBBdu3bFwIED8eTJk1KPERFRxfFOjslcvXoVrq6uGDp0KMLCwgzv6B4+fDj6\n9euHXr16ITw8HAcOHMDOnTtLNVYcyckZ0Ot5SIqISAqlUgE7O6ti/cw7PfDv7e1tKJmkpCR07doV\nly9fhkqlgk6ng5eXF06ePAkhRInG7O3ti5UnKSmdJUNEJJFSqYCDQ9WiV3xNub3j//frJP3+SY4q\nlQqOjo6IiYmBEKJEY8UtGSIiktef+rIyxW1kIiIqnnIrGbVajbi4OOh0OsNur/j4eKjVagghSjRW\nXNxdRkQkXUl2l5XbKcwODg5wc3PDkSNHAABHjhyBm5sb7O3tSzxGREQVyzs58L9o0SKcPHkSiYmJ\nsLOzg62tLY4ePYrIyEjMmjULL168gLW1NYKDg1GvXj0AKPFYcXAmQ0QkXUlmMn/qy8qwZIiIpKtU\nu8uIiMj4/anPLiOiiq1KVRVUKr4WlpNOp0dWuk627bNkiKjCUqmUmLl/UXnHMGrB/nMByFcyfIlA\nRESyYckQEZFsWDJERCQblgwREcmGJUNERLJhyRARkWxYMkREJBuWDBERyYYlQ0REsmHJEBGRbFgy\nREQkG5YMERHJhiVDRESyYckQEZFsWDJERCQblgwREcmGJUNERLJhyRARkWxYMkREJBuWDBERyYYl\nQ0REsmHJEBGRbFgyREQkG5YMERHJhiVDRESyYckQEZFsWDJERCQblgwREcmGJUNERLIpUclERUXh\nt99+K+ssRERkZCSVzN/+9jdcu3YNAHDgwAH06NEDvr6+2Ldvn6zhiIiocpNUMpcuXUKzZs0AANu3\nb8e2bduwb98+fPXVV2US4uzZs+jduzd69eoFPz8/nDx5EgDw+PFjDBw4EF27dsXAgQPx5MkTw88U\nNkZERBWDpJLJy8uDmZkZ4uLikJKSAk9PTzRo0ACJiYmlDiCEwIwZMxASEoLw8HCEhIRg5syZ0Ov1\nmD9/PoYMGYKIiAgMGTIE8+bNM/xcYWNERFQxSCoZNzc3bNy4EevXr0eHDh0AAHFxcahatWrZhFAq\nkZaWBgBIS0uDo6MjkpOTcffuXfj6+gIAfH19cffuXWi1WiQlJRU4RkREFYeJlJUWL16M0NBQmJiY\nYMaMGQCA69evo2fPnqUOoFAosHr1aowfPx6WlpbIyMjApk2bEBMTAycnJ6hUKgCASqWCo6MjYmJi\nIIQocMze3r7UmYiIqGxIKpn33nsPK1euzLesW7du6NatW6kDvHz5Ehs3bsSGDRvg6emJ//znP5g6\ndSpCQkJKve2iODiUzUyMiOSRnZcDpVJR3jGMmkKhQI0a1WTbvqSSEUJg3759OHr0KLRaLQ4fPowr\nV64gISEB3bt3L1WAe/fuIT4+Hp6engAAT09PVKlSBebm5oiLi4NOp4NKpYJOp0N8fDzUajWEEAWO\nFUdSUjr0elGq/EQkn6o2pnyOykwIgYSENEnrKpWKYr84l3RMJjQ0FPv378eAAQMQExMDAHB2dsbm\nzZuLdWNv4+zsjNjYWDx69AgAEBkZiaSkJNSuXRtubm44cuQIAODIkSNwc3ODvb09HBwcChwjIqKK\nQyGEKPJlQvv27XHw4EHY29ujZcuWuHLlCoQQaNWqFa5cuVLqEIcOHcJXX30FheLVtHjy5Mnw8fFB\nZGQkZs2ahRcvXsDa2hrBwcGoV68eABQ6JhVnMkQVW1UbU8zcv6i8Yxi1YP+5SE/Nk7RuSWYyknaX\n6XQ6WFlZAYChCDIyMmBpaVmsGyuIn58f/Pz83lhev379At/wWdgYERFVDJJ2l7Vv3x5Lly5Fbm4u\ngFf78EJDQ9GxY0dZwxERUeUmqWRmz56NhIQEeHp6Ii0tDR4eHoiOjkZAQIDc+YiIqBKTtLusatWq\nWL9+PZKSkvD8+XOo1WrUqFFD7mxERFTJSSqZH374Aa6urqhbty4cHBwAAI8ePUJMTAzatGkja0Ai\nIqq8JO0uCwwMNBz4/52VlRUCAwNlCUVERMZBUskkJSXB0dEx3zJHR0ckJCTIEoqIiIyDpJKpVasW\nLl26lG/Z5cuXUbNmTVlCERGRcZB0TGbixImYNGkS/P39UatWLURFReGbb77BkiVL5M5HRESVmKSZ\njI+PD7Zu3YrMzEycP38emZmZ2Lx5M3x8fOTOR0RElZikmQwAuLu7w93dXc4sRERkZCSVTG5uLg4e\nPIh79+4hMzMz39i7uCQ/ERFVTpJKZtasWbh//z46duyI6tWry52JiIiMhKSSuXDhAk6fPg1ra2u5\n8xARkRGRVDJqtdpwccw/oyqWZlCpJJ0jQSWk0+mRlfnn/T9GZKwklUzv3r0xfvx4DB8+3HBZmd99\n8MEHsgSrSFQqJaYFh5d3DKP2xcxe5R2BiGQgqWT++c9/AgBWrVqVb7lCocDp06fLPhURERkFSSVz\n5swZuXMQEZER4oEGIiKSjaSZTHp6OtauXYsrV64gOTkZQgjD2Llz5+TKRkRElZykmcyCBQtw9+5d\njB8/HikpKZg7dy7UajVGjBghczwiIqrMJM1kLl68iGPHjsHOzg4qlQo+Pj74y1/+grFjx7JoiIio\nQJJmMnq9HtWqVQMAWFpaIi0tDTVq1MDTp09lDUdERJWbpJlM48aNceXKFXzwwQfQaDRYsGABrKys\nUKdOHZnjERFRZSZpJrNo0SK4uroCAObMmQMLCwu8ePGCF8ckIqJCFTmT0el0+OabbzBu3DgAgIOD\nAxYvXix7MCIiqvyKnMmoVCrs3r0bJiaSP3qGiIgIgMTdZb1798aePXvkzkJEREZG0vTk1q1b+Oc/\n/4ktW7bA2dkZCoXCMLZr1y7ZwhERUeUmqWQGDBiAAQMGyJ2FiIiMjKSS6dOnj9w5iIjICEkqmf37\n9xc45u/vX2ZhiIjIuEgqmfDw/B/YlZiYiKioKHh4eLBkiIioQJJK5h//+Mcby/bv34/IyMgyD0RE\nRMajxJ8n07dvXxw4cKAssxARkZGRNJPR6/X5vs/KysKhQ4cMF80srZycHCxZsgSXLl2Cubk5mjdv\njqCgIDx+/BizZs1CSkoKbG1tERwcbLheWmFjRERUMUgqmSZNmuR7bwwAODk5ITAwsExCLF++HObm\n5oiIiIBCoUBiYiIAYP78+RgyZAh69eqF8PBwzJs3Dzt37ixyjIiIKgZJJXP69Ol831epUgX29vZl\nEiAjIwPffvstzp8/byiy6tWrIykpCXfv3sW2bdsAAL6+vggKCoJWq4UQosCxsspFRESlJ6lkTExM\nYGFhARsbG8Oy1NRUZGdnw8nJqVQBoqKiYGtri3Xr1uHy5cuwsrLClClTYGFhAScnJ6hUKgCvrqHm\n6OiImJgYCCEKHCtOyTg4VJW0XnZOHpTKEh++IgkUCqBGjbLZ/UrGIzsvB0qlougVqcQUCoWszz1J\nJTN+/HgsWbIkX8nExsZi7ty52LdvX6kC6HQ6REVFoUmTJpg5cyZu3ryJsWPHIjQ0tFTblSIpKR16\nvShyvarVLN44LkVlSwggISGtvGNQBVPVxlTSc5RKTggh+bmnVCokvzj/naSSefz4MRo1apRvWaNG\njfDo0aNi3djbqNVqmJiYwNfXFwDw/vvvw87ODhYWFoiLi4NOp4NKpYJOp0N8fDzUajWEEAWOERFR\nxSFpH5CDg8MbH7X89OlT2NraljqAvb09vLy8cPHiRQCvCi0pKQl16tSBm5sbjhw5AgA4cuQI3Nzc\nYG9vDwcHhwLHiIio4pA0k+nXrx8mTZqEadOmoVatWnj27BlCQ0PRv3//MgmxcOFCfP755wgODoaJ\niQlCQkJgbW2NBQsWYNasWdiwYQOsra0RHBxs+JnCxoiIqGKQVDKffvopTExMEBwcjNjYWKjVavj7\n+2PkyJFlEqJWrVpvvapA/fr1CzzmU9gYERFVDJJKRqlUYsyYMRgzZozceYiIyIhIOiazadMm3Lp1\nK9+yW7du4auvvpIlFBERGQdJJbNz5078z//8T75l9evXx44dO2QJRURExkFSyeTl5cHEJP+eNVNT\nU+Tm5soSioiIjIOkkmnatCl2796db9nevXvRpEkTWUIREZFxkHTgf/bs2Rg5ciQOHTqEWrVqISoq\nCgkJCYZrhxEREb2NpJJp0KABIiIicO7cOcTExKBLly7o0KEDrKys5M5HRESVmKSSAYCEhAS4uLig\nadOm/NwWIiKSpMiSOXnyJJYtW4bo6GgAr67YqVarMWPGDHTr1k32gEREVHkVWjLnzp3D7NmzMXbs\nWHz00UdwdHREfHw8jh07hrlz58Lc3BwdO3Z8V1mJiKiSKbRkNmzYgMDAQPTo0cOwrGbNmvj000/h\n4uKCDRs2sGSIiKhAhZ7C/PDhQ3Tu3PmtY126dMGvv/4qSygiIjIOhZaMmZkZ0tPT3zr24sULmJmZ\nyRKKiIiMQ6El065dO6xcufKtY6tWrULbtm1lCUVERMah0GMy06dPx+DBg9GzZ0907doVNWrUQEJC\nAk6ePIn09PQ3rgJARET0ukJLxsnJCQcPHsS2bdtw4cIFJCcnw87ODt7e3hgxYkSZfDImEREZryLf\nJ2NjY4OpU6di6tSp7yIPEREZEUkXyCQiIioJlgwREcmGJUNERLIpsGQGDBhg+HrdunXvJAwRERmX\nAkvmyZMnyMnJAQBs3br1nQUiIiLjUeDZZZ06dULXrl3h6uqKnJwcDB069K3r7dq1S7ZwRERUuRVY\nMkuXLsXVq1fx/Plz3L59G/7+/u8yFxERGYFC3yej0Wig0WiQl5eHPn36vKtMRERkJCR9Mqa/vz8u\nX76Mb7/9FvHx8XB0dESvXr3QunVrufMREVElJukU5n379mHq1KmoUaMGOnfuDEdHR3z22Wf4+uuv\n5c5HRESVmKSZzObNm7Ft2zY0btzYsOyjjz7C5MmT853qTERE9DpJM5mUlBTUr18/37J69eohNTVV\nllBERGQcJJVMixYtsGzZMmRlZQEAMjMzERISAg8PD1nDERFR5SZpd9nChQsxbdo0aDQa2NjYIDU1\nFR4eHgV+oBkREREgsWQcHR2xa9cuxMbGGs4uc3Z2ljsbERFVcpJK5nfOzs4sFyIikqxCXYV53bp1\naNSoER48eAAAuHHjBvz8/NC1a1eMGjUKSUlJhnULGyMiooqhwpTMzz//jBs3bsDV1RUAoNfrMX36\ndMybNw8RERHQaDRYsWJFkWNERFRxFFkyer0ely5dQm5urmwhcnNzERgYiAULFhiW3blzB+bm5tBo\nNACAQYMG4cSJE0WOERFRxVFkySiVSowfPx5mZmayhQgNDYWfnx9q1qxpWBYTEwMXFxfD9/b29tDr\n9UhJSSl0jIiIKg5JB/5btmyJGzduoHnz5mUe4Pr167hz5w4CAgLKfNtFcXCoKmm97Jw8KJUVZs+i\nUVIogBo1qpV3DKpgsvNyoFQqyjuGUVMoFLI+9ySVjIuLCz755BN06tQJzs7OUCj++6BPmTKlVAGu\nXLmCyMhIdOrUCQAQGxuL0aNH4+OPP0Z0dLRhPa1WC6VSCVtbW6jV6gLHiiMpKR16vShyvarVLKDX\n64u1bSoeIYCEhLTyjkEVTFUbU0nPUSo5IYTk555SqZD84tzwM1JWysnJgY+PDxQKBeLi4hAbG2v4\nV1qffvopfvjhB5w5cwZnzpyBs7MztmzZgjFjxiA7OxtXr14FAOzduxfdunUDADRr1qzAMSIiqjgk\nzWSWLl0qd443KJVKhISEYP6Ks0UvAAAN5klEQVT8+cjJyYGrqyuWL19e5BgREVUckt+MGRkZiRMn\nTiApKQnz5s3Do0ePkJubm+/KzGXhzJkzhq9btGiBw4cPv3W9wsaIiKhikLS77Pjx4xg6dCji4uLw\n7bffAgAyMjKwbNkyWcMREVHlJmkms2bNGmzfvh2NGzfG8ePHAQCNGzfG/fv3ZQ1HRESVm6SZjFar\nRaNGjQDAcGaZQqHId5YZERHRH0kqmaZNmyI8PDzfsqNHj8Ld3V2WUEREZBwk7S6bM2cORo8ejf37\n9yMzMxOjR4/G48ePsXXrVrnzERFRJSapZOrXr4/jx4/j7Nmz6NChA9RqNTp06AArKyu58xERUSUm\n+RTmKlWqwNPTEzVr1oSTkxMLhoiIiiSpZKKjoxEQEICbN2/C2toaL168wPvvv4/ly5cbLs1PRET0\nR5IO/M+cORNNmzbFlStXcOnSJfz0009o1qwZZs2aJXc+IiKqxCTNZH7++Wds3boVpqamAAArKysE\nBATAy8tL1nBERFS5SZrJNG/eHLdu3cq37M6dO/Dw8JAlFBERGYcCZzKhoaGGr2vVqoVPP/0UHTp0\ngLOzM2JjY3H+/Hn4+vq+k5BERFQ5FVgyf7yMf5cuXQC8eve/mZkZOnfujJycHHnTERFRpVZgyZTH\n5f2JiMi4SH6fTFZWFp4+fYrMzMx8y1u0aFHmoYiIyDhIKplvv/0WgYGBMDU1hYWFhWG5QqHAuXPn\n5MpGRESVnKSSWb58OdauXYs2bdrInYeIiIyIpFOYTU1N0apVK7mzEBGRkZFUMlOmTMGyZcug1Wrl\nzkNEREZE0u6yOnXqYM2aNdi9e7dhmRACCoUC9+7dky0cERFVbpJKZsaMGejVqxe6d++e78A/ERFR\nYSSVTEpKCqZMmcKPWyYiomKRdEymb9++b3z8MhERUVEkzWRu3bqFXbt24csvv0T16tXzje3atUuW\nYEREVPlJKpkBAwZgwIABcmchIiIjI6lk+vTpI3cOIiIyQpJKZv/+/QWO+fv7l1kYIiIyLpJK5o8H\n/RMTExEVFQUPDw+WDBERFUhSyfzjH/94Y9n+/fsRGRlZ5oGIypKNlQpKlaSTKKkU9Do9UjN05R2D\nKiDJl/r/o759+6J169aYOXNmWeYhKlNKlRJ3Nn1e3jGMXrNPlwBgydCbJJWMXq/P931WVhYOHTqE\natWqyRKKiIiMg6SSadKkyRvv9ndyckJQUJAsoYiIyDhIKpnTp0/n+75KlSqwt7eXJRARERkPSSXj\n6uoqdw4iIjJChZbMxx9/XOhFMRUKBXbs2FGqAMnJyZgxYwaePXsGMzMz1K5dG4GBgbC3t8eNGzcw\nb9485OTkwNXVFcuXL4eDgwMAFDpGREQVQ6Hndvr5+aFnz55v/GvVqhV++eUX3Lhxo9QBFAoFxowZ\ng4iICBw+fBi1atXCihUroNfrMX36dMybNw8RERHQaDRYsWIFABQ6RkREFUehJdO/f/98/3x8fPDo\n0SNs27YNXbp0QURERKkD2NrawsvLy/B98+bNER0djTt37sDc3BwajQYAMGjQIJw4cQIACh0jIqKK\nQ9IxmfT0dGzevBm7du1Chw4dcPDgQbz33ntlHkav12PPnj3w9vZGTEwMXFxcDGP29vbQ6/VISUkp\ndMzW1rbMcxERUckUWjLZ2dnYsWMHtm7dCi8vL+zevRsNGjSQLUxQUBAsLS0xbNgwnDp1Srbb+Z2D\nQ1VJ62Xn5EGp5LvG5aRQADVqlP37rnS52VAq+WF7cpPr8cvOy+HjJzOFQiHLY/e7QkvG29sber0e\nY8aMQbNmzZCYmIjExMR863zwwQdlEiQ4OBhPnz5FWFgYlEol1Go1oqOjDeNarRZKpRK2traFjhVH\nUlI69HpR5HpVq1m88YZUKltCAAkJaWW+XTtrU0mPMZWOXI9fVRs+fnITQkh+7JRKheQX578rtGQs\nLCwAAHv27HnruEKheOM9NCWxatUq3LlzB5s2bYKZmRkAoFmzZsjOzsbVq1eh0Wiwd+9edOvWrcgx\nIiKqOAotmTNnzsge4OHDh9i4cSPq1KmDQYMGAQBq1qyJ9evXIyQkBPPnz893mjIAKJXKAseIiKji\nKPEFMstKgwYN8Msvv7x1rEWLFjh8+HCxx4iIqGLg0WwiIpINS4aIiGTDkiEiItmwZIiISDYsGSIi\nkg1LhoiIZMOSISIi2bBkiIhINiwZIiKSDUuGiIhkw5IhIiLZsGSIiEg2LBkiIpINS4aIiGTDkiEi\nItmwZIiISDYsGSIikg1LhoiIZMOSISIi2bBkiIhINiwZIiKSDUuGiIhkw5IhIiLZsGSIiEg2LBki\nIpINS4aIiGTDkiEiItmwZIiISDYsGSIikg1LhoiIZMOSISIi2bBkiIhINiwZIiKSDUuGiIhkU6lL\n5vHjxxg4cCC6du2KgQMH4smTJ+UdiYiIXlOpS2b+/PkYMmQIIiIiMGTIEMybN6+8IxER0WtMyjtA\nSSUlJeHu3bvYtm0bAMDX1xdBQUHQarWwt7eXtA2lUiFpPYUCcLCxLHFWKppCIf3xKOaWYVbNTobt\nUn4KWR4/BRSwt7It8+3SfymK8diV5DGutCUTExMDJycnqFQqAIBKpYKjoyNiYmIkl4ydnZXk21s8\ntXuJcpJ0Fuamsmy3yYi/y7Jdys/B3FyW7S7oFSDLdum/LBzkeeyASr67jIiIKrZKWzJqtRpxcXHQ\n6XQAAJ1Oh/j4eKjV6nJORkREv6u0JePg4AA3NzccOXIEAHDkyBG4ublJ3lVGRETyUwghRHmHKKnI\nyEjMmjULL168gLW1NYKDg1GvXr3yjkVERP9fpS4ZIiKq2Crt7jIiIqr4WDJERCQblgwREcmGJUNE\nRLJhyRgZXjS08goODoa3tzcaNWqEBw8elHccKobk5GR88skn6Nq1K3r27ImJEydCq9WWd6wKgSVj\nZHjR0MqrU6dO2LVrF1xdXcs7ChWTQqHAmDFjEBERgcOHD6NWrVpYsWJFeceqEFgyRuT3i4b6+voC\neHXR0Lt37/IVVSWh0Wh4xYpKytbWFl5eXobvmzdvjujo6HJMVHGwZIxIYRcNJaJ3Q6/XY8+ePfD2\n9i7vKBUCS4aIqAwFBQXB0tISw4YNK+8oFUKlvdQ/ven1i4aqVCpeNJToHQsODsbTp08RFhYGpZKv\n4QHOZIwKLxpKVH5WrVqFO3fuYP369TAzMyvvOBUGr11mZHjR0Mpr0aJFOHnyJBITE2FnZwdbW1sc\nPXq0vGORBA8fPoSvry/q1KkDCwsLAEDNmjWxfv36ck5W/lgyREQkG+4uIyIi2bBkiIhINiwZIiKS\nDUuGiIhkw5IhIiLZsGSIZLJ27VoEBASUdwyicsV3/BOV0uHDh7Ft2zY8fvwYVlZWaNy4McaOHVve\nsYgqBJYMUSls27YNmzZtwsKFC9G2bVuYmpriwoULOH36NCwtLcs7HlG54+4yohJKS0vDmjVrMG/e\nPHTp0gWWlpYwNTWFt7c3Zs6c+cb6kydPRps2beDp6YmhQ4fi4cOHhrHz58+je/fu8PDwQLt27bBl\nyxYAgFarxf/93/9Bo9GgVatWGDJkCPR6PQAgLi4OkyZNQuvWreHt7Y2dO3catnfr1i307dsXLVq0\nwF//+lcsXbpU5nuD6O04kyEqoevXryMnJwedO3eWtP6HH36IJUuWwMzMDMuXL0dAQADCw8MBAHPm\nzMHq1auh0WiQmpqK3377DcCrmZKTkxMuXboEALh58yYUCgX0ej3GjRsHb29vrFy5EnFxcRgxYgTq\n1q2Ldu3aYfHixRg+fDh69+6NjIyMfIVG9C5xJkNUQikpKbCzs4OJibTXav7+/qhatSrMzMwwadIk\n3L9/H2lpaQAAExMT/Prrr0hPT4eNjQ2aNm1qWJ6QkIDo6GiYmppCo9FAoVDg9u3b0Gq1mDhxIszM\nzFCrVi0MGDAAx44dM/zcs2fPoNVqYWVlhebNm8tzJxAVgSVDVEK2trZITk7Gy5cvi1xXp9NhxYoV\n8PHxQYsWLQwfaJWcnAwAWLNmDc6fP4+OHTti2LBhuH79OgBg9OjRqF27NkaNGoVOnTph06ZNAIDn\nz58jPj4eGo3G8C8sLAyJiYkAgMWLF+PJkyf46KOP0K9fP5w9e1aOu4CoaIKISuTFixfi/fffF8eP\nH3/r+Jo1a8Rnn30mhBDi4MGDolu3buLZs2dCr9eL1NRU0bBhQ/HkyZN8P5Obmyu2bdsmPvzwwze2\n98svv4jWrVuLH3/8UVy7dk107ty5yIw6nU4cP35cNGvWTGRkZJTgtyQqHc5kiEqoWrVqmDx5MgID\nA/Hdd98hKysLeXl5OH/+PEJCQvKtm5GRATMzM9jZ2SErKwurVq0yjOXm5uLQoUNIS0uDqakprKys\nDB94dfbsWTx9+hRCCFSrVg0qlQoKhQLu7u6wsrLCpk2bkJ2dDZ1OhwcPHuDWrVsAgPDwcGi1WiiV\nSlhbWwMAP0SLygUP/BOVwqhRo1C9enVs2LABAQEBsLKyQtOmTTF27FhcvHjRsF7v3r3xww8/oF27\ndrC1tcWUKVOwZ88ew3h4eDiCgoKg0+lQt25dLF++HADw9OlTBAUFQavVwtraGoMHD0br1q0BAGFh\nYQgODkanTp2Qm5uLunXrYurUqQCACxcuYNmyZcjOzoaLiwu++OILw+ecEL1L/DwZIiKSDefPREQk\nG5YMERHJhiVDRESyYckQEZFsWDJERCQblgwREcmGJUNERLJhyRARkWxYMkREJJv/B+Afs0zOZuiV\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Class_Test_Frequency_Count = df_test['diagnosis'].value_counts()\n",
    "print(str(Class_Test_Frequency_Count) + \"\\n\")\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.barplot(Class_Test_Frequency_Count.index,Class_Test_Frequency_Count.values,alpha=0.9)\n",
    "plt.title(\"Frequency Distribution of Test Classes\")\n",
    "plt.ylabel(\"Number of Occurances\", fontsize=12)\n",
    "#plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Classes\", fontsize=12)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Project_V1.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}