malaria.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "malaria.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VISHNUCHARAN27/DEEP-LEARNING/blob/master/malaria.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2x6Yjzy4thW",
        "colab_type": "code",
        "outputId": "a037f3e3-c16e-4560-92f5-af539efe01c3",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e9072078-9917-42a8-ae64-714462a20bfb\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-e9072078-9917-42a8-ae64-714462a20bfb\">\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": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"vishnu2799\",\"key\":\"a3470c3a592aa9d771d440897bc95080\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AZMrwug5GMU",
        "colab_type": "code",
        "outputId": "41bba800-bf09-4a07-eab2-cb2e5cda9532",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!ls ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJa1RC975O7W",
        "colab_type": "code",
        "outputId": "5cf1c755-258c-4341-a3a3-877c4609beda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "!ls -l ~/.kaggle\n",
        "!cat ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 4\n",
            "-rw------- 1 root root 66 Feb 14 07:07 kaggle.json\n",
            "{\"username\":\"vishnu2799\",\"key\":\"a3470c3a592aa9d771d440897bc95080\"}"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mn7Ayl9A5SE5",
        "colab_type": "code",
        "outputId": "a4e9d7bb-c287-4185-b15b-caa91567fb5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "!pip install -q kaggle\n",
        "!pip install -q kaggle-cli"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K    100% |████████████████████████████████| 81kB 5.2MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 5.3MB 5.9MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 92kB 24.7MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 51kB 18.6MB/s \n",
            "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Building wheel for cliff (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Building wheel for pyperclip (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-Ft0Gbf5Xw_",
        "colab_type": "text"
      },
      "source": [
        "# **DOWNLOAD DATSETS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8c0IBWc5WxL",
        "colab_type": "code",
        "outputId": "811ead18-52e8-4bfc-b076-8d4a2fe39ff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "!kaggle datasets download -d iarunava/cell-images-for-detecting-malaria"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading cell-images-for-detecting-malaria.zip to /content\n",
            " 99% 335M/337M [00:05<00:00, 66.3MB/s]\n",
            "100% 337M/337M [00:05<00:00, 64.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QauLgqSz5teA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip cell-images-for-detecting-malaria"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "628dsmcQ5-3T",
        "colab_type": "code",
        "outputId": "3772ee5c-39f5-49f7-b947-014dbed9fe80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mcell_images\u001b[0m/  cell-images-for-detecting-malaria.zip  kaggle.json  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlSdINh57lH3",
        "colab_type": "text"
      },
      "source": [
        "## **CHECK THE IMAGE IN DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWetkZd_6PGj",
        "colab_type": "code",
        "outputId": "a13d878c-e406-4812-c91b-222de5ff98de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "datadir='cell_images'\n",
        "categories=['Parasitized','Uninfected']\n",
        "\n",
        "for category in categories:\n",
        "  path=os.path.join(datadir,category)\n",
        "  for img in os.listdir(path):\n",
        "    img_array=cv2.imread(os.path.join(path,img))\n",
        "    img_array=cv2.cvtColor(img_array,cv2.COLOR_BGR2RGB)\n",
        "    plt.imshow(img_array)\n",
        "    plt.show()\n",
        "    break\n",
        "  break\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dh2DLli0Tx9m6dSvUdb3klsExPHTtDebvGBP1BfE8bTGW9snf6G//5FXwV3tv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\nIAhZEENUEAQhC/8CO48SAwVVgxYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip7C4rR17qIm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_size=120\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3JyQ5Qo86d1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "def dump_pickle(X,y,valid_img_count,pickle_counter,category):\n",
        "  \n",
        "  X=np.array(X).reshape(-1,img_size,img_size,3)\n",
        "  \n",
        "  pickle_out=open(os.path.join(datadir,'X_{}_{}'.format(category,pickle_counter) ),\"wb\" )\n",
        "  pickle.dump(X,pickle_out)\n",
        "  pickle_out.close\n",
        "  print('X_{}_{} Generated\\n'.format(category,pickle_counter))\n",
        "  X=[]\n",
        "  \n",
        "  pickle_out=open(os.path.join(datadir,'y_{}_{}'.format(category,pickle_counter) ),\"wb\" )\n",
        "  pickle.dump(y,pickle_out)\n",
        "  pickle_out.close\n",
        "  print('y_{}_{} Generated\\n'.format(category,pickle_counter))\n",
        "                  \n",
        "                  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tphmW8bj74hN",
        "colab_type": "code",
        "outputId": "bb98bbaf-6c0e-4a2d-e9eb-2b7a16f85c14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        }
      },
      "source": [
        "from tqdm import tqdm \n",
        "import pickle\n",
        "\n",
        "\n",
        "def create_training_data():\n",
        "  X=[]\n",
        "  y=[]\n",
        "  valid_img_count=0\n",
        "  invalid_images=0\n",
        "  count=0\n",
        "  for category in categories:\n",
        "    path=os.path.join(datadir,category)\n",
        "    class_num=categories.index(category)\n",
        "    pickle_counter=0\n",
        "    \n",
        "    for img in tqdm(os.listdir(path),desc=category):\n",
        "      try:\n",
        "        img_array=cv2.imread(os.path.join(path,img))\n",
        "        img_array2=cv2.cvtColor(img_array,cv2.COLOR_BGR2RGB)\n",
        "        new_array=cv2.resize(img_array2,(img_size,img_size))\n",
        "        new_array=new_array/255.0\n",
        "        \n",
        "        X.append(new_array)\n",
        "        y.append(class_num)\n",
        "        \n",
        "        count+=1\n",
        "        valid_img_count+=1\n",
        "  \n",
        "        \n",
        "        if count>=2500:\n",
        "          dump_pickle(X,y,valid_img_count,pickle_counter,category)\n",
        "          count=0\n",
        "          pickle_counter+=1\n",
        "          X=[]\n",
        "          y=[]\n",
        "          \n",
        "      \n",
        "      except Exception as e:\n",
        "        invalid_images+=1\n",
        "        pass\n",
        "        \n",
        "    if pickle_counter==5 and count<2500 and category=='Parasitized':\n",
        "      dump_pickle(X,y,valid_img_count,pickle_counter,category)\n",
        "      count=0\n",
        "      X=[]\n",
        "      y=[]\n",
        "      \n",
        "        \n",
        "    if pickle_counter==5 and count<2500 and category=='Uninfected' :\n",
        "      dump_pickle(X,y,valid_img_count,pickle_counter,category)\n",
        "      count=0\n",
        "      X=[]\n",
        "      y=[]\n",
        "  \n",
        "  \n",
        "  return pickle_counter\n",
        "\n",
        "b=create_training_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parasitized:  18%|█▊        | 2470/13780 [00:03<00:16, 673.08it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_0 Generated\n",
            "\n",
            "y_Parasitized_0 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Parasitized:  36%|███▌      | 4955/13780 [00:16<00:08, 1040.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_1 Generated\n",
            "\n",
            "y_Parasitized_1 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Parasitized:  54%|█████▎    | 7398/13780 [00:40<00:06, 1056.94it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_2 Generated\n",
            "\n",
            "y_Parasitized_2 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Parasitized:  72%|███████▏  | 9900/13780 [01:10<00:11, 345.15it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_3 Generated\n",
            "\n",
            "y_Parasitized_3 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Parasitized:  90%|█████████ | 12424/13780 [01:40<00:01, 969.95it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_4 Generated\n",
            "\n",
            "y_Parasitized_4 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Parasitized: 100%|██████████| 13780/13780 [01:58<00:00, 116.18it/s]\n",
            "Uninfected:   0%|          | 0/13780 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Parasitized_5 Generated\n",
            "\n",
            "y_Parasitized_5 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected:  18%|█▊        | 2477/13780 [00:30<00:20, 539.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_0 Generated\n",
            "\n",
            "y_Uninfected_0 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected:  36%|███▌      | 4974/13780 [00:42<00:13, 649.58it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_1 Generated\n",
            "\n",
            "y_Uninfected_1 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected:  54%|█████▍    | 7485/13780 [01:20<00:07, 836.60it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_2 Generated\n",
            "\n",
            "y_Uninfected_2 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected:  72%|███████▏  | 9948/13780 [01:50<00:04, 869.56it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_3 Generated\n",
            "\n",
            "y_Uninfected_3 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected:  90%|████████▉ | 12397/13780 [02:20<00:01, 869.96it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_4 Generated\n",
            "\n",
            "y_Uninfected_4 Generated\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Uninfected: 100%|██████████| 13780/13780 [02:34<00:00, 89.06it/s] \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_Uninfected_5 Generated\n",
            "\n",
            "y_Uninfected_5 Generated\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrhoFoAnJlPD",
        "colab_type": "code",
        "outputId": "62eaf566-5ab4-4a45-eec8-0c68497ef3a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "%ls cell_images"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mParasitized\u001b[0m/     X_Parasitized_5  y_Parasitized_0  y_Uninfected_1\n",
            "\u001b[01;34mUninfected\u001b[0m/      X_Uninfected_0   y_Parasitized_1  y_Uninfected_2\n",
            "X_Parasitized_0  X_Uninfected_1   y_Parasitized_2  y_Uninfected_3\n",
            "X_Parasitized_1  X_Uninfected_2   y_Parasitized_3  y_Uninfected_4\n",
            "X_Parasitized_2  X_Uninfected_3   y_Parasitized_4  y_Uninfected_5\n",
            "X_Parasitized_3  X_Uninfected_4   y_Parasitized_5\n",
            "X_Parasitized_4  X_Uninfected_5   y_Uninfected_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fA2o7jn5LoKk",
        "colab_type": "code",
        "outputId": "a059ada9-8c4f-43c7-86bc-9833eea32730",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        }
      },
      "source": [
        "import random \n",
        "import pickle\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "for i in range(b+1):\n",
        "  \n",
        "  print('--------------------------------------\\n ')\n",
        "  print('Processing-part{}'.format(i))\n",
        "  \n",
        "  pickle_in=open(os.path.join(datadir,'X_{}_{}'.format(categories[0],i)) ,'rb')\n",
        "  Xpara=pickle.load(pickle_in)\n",
        "  pickle_in.close\n",
        "  \n",
        "  pickle_in=open(os.path.join(datadir,'X_{}_{}'.format(categories[1],i)) ,'rb')\n",
        "  Xuninf=pickle.load(pickle_in)\n",
        "  pickle_in.close\n",
        "  \n",
        "  pickle_in=open(os.path.join(datadir,'y_{}_{}'.format(categories[0],i)) ,'rb')\n",
        "  ypara=pickle.load(pickle_in)\n",
        "  pickle_in.close\n",
        "  \n",
        "  pickle_in=open(os.path.join(datadir,'y_{}_{}'.format(categories[1],i)) ,'rb')\n",
        "  yuninf=pickle.load(pickle_in)\n",
        "  pickle_in.close\n",
        "  \n",
        "  X=np.concatenate((Xpara,Xuninf),axis=0)  #https://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html\n",
        "  y=np.concatenate((ypara,yuninf),axis=0)  \n",
        "  \n",
        "  print('\\nConcatenated successfully-{}'.format(i))\n",
        "  \n",
        "  BSHUFFLE=[]\n",
        "  \n",
        "  x=0\n",
        "  for label in y:\n",
        "    BSHUFFLE.append([X[x],label])\n",
        "    x+=1\n",
        "    \n",
        "  random.shuffle(BSHUFFLE)\n",
        "  print(\"\\nShuffled Successfully-{}\".format(i))\n",
        "  \n",
        "  X=[]\n",
        "  y=[]\n",
        "  for item in BSHUFFLE:\n",
        "    X.append(item[0])\n",
        "    y.append(item[1])\n",
        "    \n",
        "  X=np.array(X).reshape(-1,img_size,img_size,3)\n",
        "\n",
        "  pickle_out=open(os.path.join(datadir,'X_{}'.format(i)),'wb')\n",
        "  pickle.dump(X,pickle_out)\n",
        "  pickle_out.close\n",
        "  \n",
        "  pickle_out=open(os.path.join(datadir,'y_{}'.format(i)),'wb')\n",
        "  pickle.dump(y,pickle_out)\n",
        "  pickle_out.close\n",
        "  \n",
        "  print(\"\\nSaved sucessfully-{}\".format(i))\n",
        "  \n",
        "  \n",
        "  \n",
        "  \n",
        "  \n",
        "  \n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------\n",
            " \n",
            "Processing-part0\n",
            "\n",
            "Concatenated successfully-0\n",
            "\n",
            "Shuffled Successfully-0\n",
            "\n",
            "Saved sucessfully-0\n",
            "--------------------------------------\n",
            " \n",
            "Processing-part1\n",
            "\n",
            "Concatenated successfully-1\n",
            "\n",
            "Shuffled Successfully-1\n",
            "\n",
            "Saved sucessfully-1\n",
            "--------------------------------------\n",
            " \n",
            "Processing-part2\n",
            "\n",
            "Concatenated successfully-2\n",
            "\n",
            "Shuffled Successfully-2\n",
            "\n",
            "Saved sucessfully-2\n",
            "--------------------------------------\n",
            " \n",
            "Processing-part3\n",
            "\n",
            "Concatenated successfully-3\n",
            "\n",
            "Shuffled Successfully-3\n",
            "\n",
            "Saved sucessfully-3\n",
            "--------------------------------------\n",
            " \n",
            "Processing-part4\n",
            "\n",
            "Concatenated successfully-4\n",
            "\n",
            "Shuffled Successfully-4\n",
            "\n",
            "Saved sucessfully-4\n",
            "--------------------------------------\n",
            " \n",
            "Processing-part5\n",
            "\n",
            "Concatenated successfully-5\n",
            "\n",
            "Shuffled Successfully-5\n",
            "\n",
            "Saved sucessfully-5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEXz5uRzUe5Q",
        "colab_type": "code",
        "outputId": "1e9b5f3c-d5c8-4a58-8f39-6d092b0c4610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GksybIwU4Sh",
        "colab_type": "code",
        "outputId": "e03d9304-fc7a-451e-98db-b1fda0a2465d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2271
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense,Activation,MaxPooling2D,Dropout,Conv2D,Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "model=Sequential()\n",
        "\n",
        "model.add(Conv2D (64,(3,3),input_shape=(120,120,3))   )\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D (64,(3,3))   )\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D (64,(3,3))   )\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D (64,(3,3))   )\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "epochs=10\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  print(\"EPOCHS-{}\".format(epoch))\n",
        "  for i in range(b+1):\n",
        "    X=pickle.load(open(os.path.join (datadir,'X_{}'.format(i) ),'rb' ))\n",
        "    y=pickle.load(open(os.path.join(datadir,'y_{}'.format(i) ),'rb' ))\n",
        "    \n",
        "    model.fit(X,np.array(y),batch_size=32,epochs=1,validation_split=0.1)\n",
        "    model.save(\"Malaria.model\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EPOCHS-0\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.6183 - acc: 0.6320 - val_loss: 0.4155 - val_acc: 0.8160\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 229s 51ms/sample - loss: 0.2808 - acc: 0.9047 - val_loss: 0.2035 - val_acc: 0.9500\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.2108 - acc: 0.9376 - val_loss: 0.2509 - val_acc: 0.9240\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 229s 51ms/sample - loss: 0.2032 - acc: 0.9416 - val_loss: 0.1423 - val_acc: 0.9480\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1661 - acc: 0.9496 - val_loss: 0.1832 - val_acc: 0.9440\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 116s 50ms/sample - loss: 0.1963 - acc: 0.9374 - val_loss: 0.1265 - val_acc: 0.9727\n",
            "EPOCHS-1\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1609 - acc: 0.9478 - val_loss: 0.1297 - val_acc: 0.9520\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1820 - acc: 0.9458 - val_loss: 0.1363 - val_acc: 0.9640\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1714 - acc: 0.9440 - val_loss: 0.2218 - val_acc: 0.9220\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 242s 54ms/sample - loss: 0.1806 - acc: 0.9458 - val_loss: 0.1226 - val_acc: 0.9540\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1473 - acc: 0.9567 - val_loss: 0.1512 - val_acc: 0.9520\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 114s 50ms/sample - loss: 0.1778 - acc: 0.9366 - val_loss: 0.1168 - val_acc: 0.9688\n",
            "EPOCHS-2\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1493 - acc: 0.9513 - val_loss: 0.1361 - val_acc: 0.9560\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1581 - acc: 0.9487 - val_loss: 0.1337 - val_acc: 0.9680\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1521 - acc: 0.9493 - val_loss: 0.1889 - val_acc: 0.9420\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1657 - acc: 0.9511 - val_loss: 0.1154 - val_acc: 0.9640\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1399 - acc: 0.9549 - val_loss: 0.1520 - val_acc: 0.9480\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 115s 50ms/sample - loss: 0.1629 - acc: 0.9461 - val_loss: 0.1139 - val_acc: 0.9805\n",
            "EPOCHS-3\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 228s 51ms/sample - loss: 0.1487 - acc: 0.9527 - val_loss: 0.1292 - val_acc: 0.9540\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 228s 51ms/sample - loss: 0.1517 - acc: 0.9502 - val_loss: 0.1246 - val_acc: 0.9640\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.1470 - acc: 0.9516 - val_loss: 0.1929 - val_acc: 0.9440\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1536 - acc: 0.9516 - val_loss: 0.1077 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1314 - acc: 0.9591 - val_loss: 0.1693 - val_acc: 0.9600\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 115s 50ms/sample - loss: 0.1598 - acc: 0.9453 - val_loss: 0.1165 - val_acc: 0.9609\n",
            "EPOCHS-4\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1391 - acc: 0.9533 - val_loss: 0.1304 - val_acc: 0.9620\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1403 - acc: 0.9538 - val_loss: 0.1172 - val_acc: 0.9700\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1426 - acc: 0.9524 - val_loss: 0.1869 - val_acc: 0.9480\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1456 - acc: 0.9527 - val_loss: 0.1161 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1287 - acc: 0.9613 - val_loss: 0.1450 - val_acc: 0.9560\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 114s 50ms/sample - loss: 0.1505 - acc: 0.9461 - val_loss: 0.1093 - val_acc: 0.9688\n",
            "EPOCHS-5\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 228s 51ms/sample - loss: 0.1395 - acc: 0.9556 - val_loss: 0.1215 - val_acc: 0.9580\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.1388 - acc: 0.9560 - val_loss: 0.1224 - val_acc: 0.9680\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.1354 - acc: 0.9549 - val_loss: 0.1658 - val_acc: 0.9460\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 228s 51ms/sample - loss: 0.1462 - acc: 0.9502 - val_loss: 0.1150 - val_acc: 0.9680\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1276 - acc: 0.9584 - val_loss: 0.1172 - val_acc: 0.9600\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 114s 49ms/sample - loss: 0.1471 - acc: 0.9483 - val_loss: 0.1029 - val_acc: 0.9688\n",
            "EPOCHS-6\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1357 - acc: 0.9531 - val_loss: 0.1215 - val_acc: 0.9580\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1303 - acc: 0.9564 - val_loss: 0.1115 - val_acc: 0.9700\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1360 - acc: 0.9551 - val_loss: 0.1733 - val_acc: 0.9400\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1338 - acc: 0.9573 - val_loss: 0.1059 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1128 - acc: 0.9638 - val_loss: 0.1175 - val_acc: 0.9580\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 115s 50ms/sample - loss: 0.1462 - acc: 0.9444 - val_loss: 0.1039 - val_acc: 0.9648\n",
            "EPOCHS-7\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1248 - acc: 0.9593 - val_loss: 0.1235 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1302 - acc: 0.9573 - val_loss: 0.1133 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 228s 51ms/sample - loss: 0.1229 - acc: 0.9589 - val_loss: 0.1816 - val_acc: 0.9400\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.1289 - acc: 0.9584 - val_loss: 0.0960 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1110 - acc: 0.9627 - val_loss: 0.1190 - val_acc: 0.9620\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 115s 50ms/sample - loss: 0.1278 - acc: 0.9535 - val_loss: 0.1021 - val_acc: 0.9648\n",
            "EPOCHS-8\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1252 - acc: 0.9587 - val_loss: 0.1165 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1182 - acc: 0.9602 - val_loss: 0.1259 - val_acc: 0.9700\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1157 - acc: 0.9607 - val_loss: 0.1685 - val_acc: 0.9500\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1214 - acc: 0.9589 - val_loss: 0.1014 - val_acc: 0.9600\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.0986 - acc: 0.9673 - val_loss: 0.1229 - val_acc: 0.9600\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 115s 50ms/sample - loss: 0.1279 - acc: 0.9566 - val_loss: 0.0883 - val_acc: 0.9688\n",
            "EPOCHS-9\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1131 - acc: 0.9602 - val_loss: 0.1267 - val_acc: 0.9620\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 227s 50ms/sample - loss: 0.1139 - acc: 0.9584 - val_loss: 0.0974 - val_acc: 0.9700\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 226s 50ms/sample - loss: 0.1100 - acc: 0.9633 - val_loss: 0.1738 - val_acc: 0.9440\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 224s 50ms/sample - loss: 0.1123 - acc: 0.9611 - val_loss: 0.1043 - val_acc: 0.9660\n",
            "Train on 4500 samples, validate on 500 samples\n",
            "4500/4500 [==============================] - 225s 50ms/sample - loss: 0.1011 - acc: 0.9669 - val_loss: 0.1175 - val_acc: 0.9660\n",
            "Train on 2302 samples, validate on 256 samples\n",
            "2302/2302 [==============================] - 116s 51ms/sample - loss: 0.1190 - acc: 0.9592 - val_loss: 0.0985 - val_acc: 0.9648\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7Tz9AtR-GIb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"Malaria.model\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Mp02AYc-IcZ",
        "colab_type": "code",
        "outputId": "1a7970ba-0f8b-412b-a551-600995435e53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "import keras\n",
        "from keras.models import load_model\n",
        "from keras.utils import CustomObjectScope\n",
        "from keras.initializers import glorot_uniform\n",
        "\n",
        "with CustomObjectScope({'GlorotUniform': glorot_uniform()}):\n",
        "        model = load_model(\"Malaria.model\")\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 118, 118, 64)      1792      \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 118, 118, 64)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 59, 59, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 57, 57, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 57, 57, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 26, 26, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 26, 26, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 11, 11, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1601      \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 114,177\n",
            "Trainable params: 114,177\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7E1212KWOYVN",
        "colab_type": "code",
        "outputId": "7dae03df-6fa5-45cc-eff8-97a4862085f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import cv2\n",
        "import tensorflow as tf\n",
        "\n",
        "def prepare(filepath):\n",
        "  img_size=120\n",
        "  a=cv2.imread(filepath)\n",
        "  a1=cv2.resize(a,(img_size,img_size),interpolation = cv2.INTER_AREA)\n",
        "  return a1.reshape(-1,img_size,img_size,3)\n",
        "\n",
        "prediction=model.predict( [  prepare('cell_images/Uninfected/C100P61ThinF_IMG_20150918_144104_cell_128.png' )  ]   )\n",
        "print(categories  [  int (prediction[0][0])   ]  )\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninfected\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}