Image_Recognition_with_CNN.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOC3TlM6xGzQ4BCjRbQRgSO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Melo97/Cactus_Detection_with_CNN/blob/main/Image_Recognition_with_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assessing Climate's Change using Image Recognition Neural Networks to identify local vegetation"
      ],
      "metadata": {
        "id": "PRffwPmZW8te"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a project based on a kaggle competition, described as follow:\n",
        "\n",
        "> *To assess the impact of climate change on Earth's flora and fauna, it is vital to quantify how human activities such as logging, mining, and agriculture are impacting our protected natural areas. Researchers in Mexico have created the VIGIA project, which aims to build a system for autonomous surveillance of protected areas. A first step in such an effort is the ability to recognize the vegetation inside the protected areas. In this competition, you are tasked with creation of an algorithm that can identify a specific type of cactus in aerial imagery.*\n",
        "\n",
        "To access this data, enter the [Aerial Cactus Identification](https://www.kaggle.com/competitions/aerial-cactus-identification/overview/description) competition."
      ],
      "metadata": {
        "id": "QuyCN5T0ax8n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step 1: Downloading Kaggle datasets directly into Colab**\n",
        "\n",
        "*This tutorial is credited to this [Github Repo](https://github.com/bnsreenu/python_for_microscopists/blob/master/Tips_tricks_35_loading_kaggle_data_to_colab.ipynb). Check out the full process here.*\n",
        "1. Install the kaggle python library"
      ],
      "metadata": {
        "id": "zW0qPLrp8bhP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "RFkiDMN6d7xY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d40fb812-a7ce-41d8-f6dc-5fd8d405f5ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.13)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.5.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.16)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mount the Google drive so you can store your kaggle API credentials for future use"
      ],
      "metadata": {
        "id": "5f8PtvOE8i0U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "IsptjnKbt3sk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "578bc7b3-c870-4f71-f527-8f7d795ca398"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make a directory for kaggle at the temporary instance location on Colab drive.\n",
        "\n",
        "Download your kaggle API key (.json file). You can do this by going to your kaggle account page and clicking 'Create new API token' under the API section."
      ],
      "metadata": {
        "id": "fsWb18Qj8l2j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "rJKursbywm3p"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upload the json file to Google Drive and then copy to the temporary location."
      ],
      "metadata": {
        "id": "oc5OMD0G8t5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r '/content/drive/MyDrive/ColabNotebooks/Kaggle-API-credentials' ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "fIs2mibN34mw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the file permissions to read/write to the owner only"
      ],
      "metadata": {
        "id": "dIrO0rvJ8xGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "p5Bm2Ssk5kEP"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Downloading competition data**\n",
        "\n",
        "(If get 403 Forbidden error, click to 'Late Submission' on the Kaggle page for that competition)."
      ],
      "metadata": {
        "id": "n38ozEQY9CNi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = 'danieliglesiasmelo'\n",
        "os.environ['KAGGLE_KEY'] = '1b321f159c020fac8ce6172d889bbfad'\n",
        "\n",
        "! kaggle competitions download -c aerial-cactus-identification"
      ],
      "metadata": {
        "id": "r9Bsb5ho7Xjl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74d617b3-18bd-4608-fe57-a74778e757ab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading aerial-cactus-identification.zip to /content\n",
            " 42% 5.00M/12.0M [00:00<00:00, 19.5MB/s]\n",
            "100% 12.0M/12.0M [00:00<00:00, 40.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip aerial-cactus-identification.zip"
      ],
      "metadata": {
        "id": "zq8rqw0jD7-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "322c818a-c6e3-42ce-dc36-676ac8a23fb3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  aerial-cactus-identification.zip\n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.zip                \n",
            "  inflating: train.csv               \n",
            "  inflating: train.zip               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip train.zip"
      ],
      "metadata": {
        "id": "zQlMViHWjGko",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d9f53ec-fffd-46eb-9899-c57c7753de79"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Importing libraries, loading and exploring Data"
      ],
      "metadata": {
        "id": "l-dKwj6aFk5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing important libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "cVRiA87cFt7t"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cactusDf = pd.read_csv('/content/train.csv')\n",
        "cactusDf.sample(10)"
      ],
      "metadata": {
        "id": "7vbbW4mKGjvy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "a268c804-92a7-43a8-bd86-2b0cd614995c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         id  has_cactus\n",
              "6147   58bbe6b09c21a003e3f2048034df1123.jpg           1\n",
              "9870   8de23ea7369e1e5eb81621b32f999671.jpg           1\n",
              "16069  ea914d3e0118c6ff7088310755e85419.jpg           1\n",
              "8088   741bdab522ed82cf6b69b4c9ac6eea1e.jpg           1\n",
              "6161   58e82092c6067f86fa684ccdca224a6c.jpg           0\n",
              "11124  a0b1a5e47c8de19b3b70b9edf01bd77a.jpg           1\n",
              "9567   89bb8bb27e55906e1576cea1a751c411.jpg           1\n",
              "15484  e1fe16f8f343525b0de31557aad72b55.jpg           1\n",
              "2991   2a464ce658e49d427cdd0bb7454433d0.jpg           1\n",
              "8876   7f811025b10059ea5ed538c5ab5e1057.jpg           1"
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            "text/html": [
              "\n",
              "  <div id=\"df-571151a5-6476-44e0-ae0b-2020a22e56f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th>has_cactus</th>\n",
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              "    <tr>\n",
              "      <th>6147</th>\n",
              "      <td>58bbe6b09c21a003e3f2048034df1123.jpg</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9870</th>\n",
              "      <td>8de23ea7369e1e5eb81621b32f999671.jpg</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>16069</th>\n",
              "      <td>ea914d3e0118c6ff7088310755e85419.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8088</th>\n",
              "      <td>741bdab522ed82cf6b69b4c9ac6eea1e.jpg</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6161</th>\n",
              "      <td>58e82092c6067f86fa684ccdca224a6c.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11124</th>\n",
              "      <td>a0b1a5e47c8de19b3b70b9edf01bd77a.jpg</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9567</th>\n",
              "      <td>89bb8bb27e55906e1576cea1a751c411.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15484</th>\n",
              "      <td>e1fe16f8f343525b0de31557aad72b55.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>2a464ce658e49d427cdd0bb7454433d0.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8876</th>\n",
              "      <td>7f811025b10059ea5ed538c5ab5e1057.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-571151a5-6476-44e0-ae0b-2020a22e56f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-571151a5-6476-44e0-ae0b-2020a22e56f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-571151a5-6476-44e0-ae0b-2020a22e56f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploring Data\n",
        "- First look into images within data:"
      ],
      "metadata": {
        "id": "Y8JGZ34UclS-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#randomly selecting an image from the dataset\n",
        "plt.imshow(plt.imread('train/'+cactusDf['id'].iloc[10696]))"
      ],
      "metadata": {
        "id": "cBW4K4GCft6v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "1eaf3e31-8ea4-4e68-c034-a34e4475654e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb6b6a7b850>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- In human eyes, this image is a lot pixalated. If you look this image from a certain distance, it is much more likely to be a image with a cactus than the other way around (I would guess).\n",
        "\n",
        "- Let's see how this image is classified in the dataset:"
      ],
      "metadata": {
        "id": "rRbrOAuxdDXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('is there a cactus in the image above?')\n",
        "print(cactusDf['has_cactus'].iloc[10696] == 1)"
      ],
      "metadata": {
        "id": "eJPj9yc5pb2Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "357ff637-54b1-49bf-8d39-a8b9992d7720"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "is there a cactus in the image above?\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Going deeper: Ploting a collage of images:"
      ],
      "metadata": {
        "id": "9jh-pEYugGRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "image_dir =  list('train/'+ cactusDf['id'])\n",
        "\n",
        "fig = plt.figure(figsize=(10, 8))\n",
        "for idx, img in enumerate(np.random.choice(image_dir, 9)):\n",
        "    ax = fig.add_subplot(3,3, idx+1, xticks=[], yticks=[])\n",
        "    im = plt.imread(os.path.join(img))\n",
        "    plt.imshow(im)"
      ],
      "metadata": {
        "id": "0B_Ou3FPf9fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "742ffa2f-8f5c-4a16-b7bd-d58511e0ee8a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- How is the data distribuiton is the dataset?"
      ],
      "metadata": {
        "id": "koD2Supugb_Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cactusDf.info()"
      ],
      "metadata": {
        "id": "5ztbfdBxi7aq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e95bada-fc44-4ac5-a382-3619abe7af36"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17500 entries, 0 to 17499\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   id          17500 non-null  object\n",
            " 1   has_cactus  17500 non-null  int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 273.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cactusDf['has_cactus'].value_counts()"
      ],
      "metadata": {
        "id": "CbubDf0to0NL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3a52cf6-9ddf-4dca-db4e-abf65ee853ff"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    13136\n",
              "0     4364\n",
              "Name: has_cactus, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#percentage of data\n",
        "cactusDf['has_cactus'].value_counts(normalize = True)"
      ],
      "metadata": {
        "id": "f2cpPtpFpRyZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fc30c68-7f00-46f4-d996-1814f71ef949"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.750629\n",
              "0    0.249371\n",
              "Name: has_cactus, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Image shapes:\n",
        "\n",
        "*It seems that all of them have the same shape*"
      ],
      "metadata": {
        "id": "PBQ-Jf5ugpc4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Test if all data has the same dimensions:\n",
        "for num in np.random.choice(len(cactusDf), 10):\n",
        "  print(plt.imread('train/'+cactusDf['id'][num]).shape)"
      ],
      "metadata": {
        "id": "rvyjrPXOrZ8U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee3ed13a-a4d5-4d01-810f-177f2e44c74d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n",
            "(32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building the Neural Network"
      ],
      "metadata": {
        "id": "HfsnCuKRg3bA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten"
      ],
      "metadata": {
        "id": "Gv_FzEZXusuS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Pre-work\n",
        "1. One-hot encode predicted variables, so it can be fit to neural network classification algorithm;\n",
        "2. Read image files as arrays;\n",
        "3. Normalize images, to speed trainning;\n",
        "4. Reshape X and Y;"
      ],
      "metadata": {
        "id": "9OYQg-cQj9o6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "#defining y and converting to one-hot encoded labels\n",
        "y = to_categorical(cactusDf['has_cactus'])"
      ],
      "metadata": {
        "id": "Y_J_Oy2-wxIQ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paths = ['train/'+path for path in cactusDf['id']]\n",
        "\n",
        "X = []\n",
        "\n",
        "for path in paths:\n",
        "  img = plt.imread(path)\n",
        "  X.append(img)"
      ],
      "metadata": {
        "id": "9tNA72J4_aM8"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#normalization\n",
        "np.max(X)"
      ],
      "metadata": {
        "id": "6fISSBGX_vBI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48e2a54e-0f5f-4ca7-c29f-f7549c99e16f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = len(cactusDf)\n",
        "X = np.asarray(X)\n",
        "X =X/255\n",
        "X.reshape(batch, 32, 32, 3)"
      ],
      "metadata": {
        "id": "VbgxWkq7BpB2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee247f85-cd05-4615-9489-0d6be2a92ec5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[0.53333333, 0.54117647, 0.49019608],\n",
              "         [0.5254902 , 0.53333333, 0.49019608],\n",
              "         [0.57647059, 0.58039216, 0.54901961],\n",
              "         ...,\n",
              "         [0.59607843, 0.50588235, 0.52941176],\n",
              "         [0.57647059, 0.48627451, 0.50980392],\n",
              "         [0.61568627, 0.5254902 , 0.54901961]],\n",
              "\n",
              "        [[0.48627451, 0.49411765, 0.44313725],\n",
              "         [0.63137255, 0.63921569, 0.59607843],\n",
              "         [0.61960784, 0.62352941, 0.59215686],\n",
              "         ...,\n",
              "         [0.63921569, 0.54901961, 0.57254902],\n",
              "         [0.57647059, 0.49411765, 0.51372549],\n",
              "         [0.56862745, 0.48627451, 0.50588235]],\n",
              "\n",
              "        [[0.63137255, 0.63921569, 0.58823529],\n",
              "         [0.62745098, 0.63529412, 0.59215686],\n",
              "         [0.61960784, 0.62352941, 0.59215686],\n",
              "         ...,\n",
              "         [0.58823529, 0.50588235, 0.5254902 ],\n",
              "         [0.63137255, 0.54901961, 0.56862745],\n",
              "         [0.50588235, 0.42352941, 0.44313725]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.5372549 , 0.43529412, 0.47843137],\n",
              "         [0.77254902, 0.67058824, 0.71372549],\n",
              "         [0.43137255, 0.32941176, 0.37254902],\n",
              "         ...,\n",
              "         [0.62352941, 0.51764706, 0.58431373],\n",
              "         [0.56862745, 0.4627451 , 0.52941176],\n",
              "         [0.61176471, 0.50588235, 0.57254902]],\n",
              "\n",
              "        [[0.58823529, 0.48627451, 0.52941176],\n",
              "         [0.6627451 , 0.56078431, 0.60392157],\n",
              "         [0.58823529, 0.48627451, 0.5372549 ],\n",
              "         ...,\n",
              "         [0.65490196, 0.54901961, 0.61568627],\n",
              "         [0.59607843, 0.48235294, 0.55294118],\n",
              "         [0.59607843, 0.48235294, 0.56078431]],\n",
              "\n",
              "        [[0.71764706, 0.61568627, 0.65882353],\n",
              "         [0.45098039, 0.34901961, 0.39215686],\n",
              "         [0.64705882, 0.54509804, 0.59607843],\n",
              "         ...,\n",
              "         [0.70196078, 0.58823529, 0.65882353],\n",
              "         [0.55686275, 0.44313725, 0.52156863],\n",
              "         [0.54509804, 0.43137255, 0.50980392]]],\n",
              "\n",
              "\n",
              "       [[[0.4627451 , 0.42745098, 0.44705882],\n",
              "         [0.42352941, 0.38823529, 0.40784314],\n",
              "         [0.40784314, 0.37254902, 0.39215686],\n",
              "         ...,\n",
              "         [0.61960784, 0.51764706, 0.56078431],\n",
              "         [0.54117647, 0.43921569, 0.48235294],\n",
              "         [0.48235294, 0.38039216, 0.42352941]],\n",
              "\n",
              "        [[0.52156863, 0.48627451, 0.50588235],\n",
              "         [0.48235294, 0.44705882, 0.46666667],\n",
              "         [0.45882353, 0.42352941, 0.44313725],\n",
              "         ...,\n",
              "         [0.52941176, 0.42745098, 0.47058824],\n",
              "         [0.46666667, 0.36470588, 0.40784314],\n",
              "         [0.45490196, 0.35294118, 0.39607843]],\n",
              "\n",
              "        [[0.58823529, 0.55294118, 0.56470588],\n",
              "         [0.49019608, 0.45490196, 0.46666667],\n",
              "         [0.41960784, 0.38431373, 0.40392157],\n",
              "         ...,\n",
              "         [0.38823529, 0.28627451, 0.32941176],\n",
              "         [0.40392157, 0.30196078, 0.34509804],\n",
              "         [0.49019608, 0.38823529, 0.43137255]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.80392157, 0.74509804, 0.76470588],\n",
              "         [0.82352941, 0.77647059, 0.79215686],\n",
              "         [0.88235294, 0.83529412, 0.85098039],\n",
              "         ...,\n",
              "         [0.7254902 , 0.67843137, 0.71764706],\n",
              "         [0.81568627, 0.76470588, 0.80392157],\n",
              "         [0.96078431, 0.90980392, 0.94901961]],\n",
              "\n",
              "        [[0.43529412, 0.37647059, 0.39607843],\n",
              "         [0.4627451 , 0.41568627, 0.43137255],\n",
              "         [0.50588235, 0.45882353, 0.4745098 ],\n",
              "         ...,\n",
              "         [0.44313725, 0.39215686, 0.43137255],\n",
              "         [0.54901961, 0.49803922, 0.5372549 ],\n",
              "         [0.71764706, 0.66666667, 0.70588235]],\n",
              "\n",
              "        [[0.36470588, 0.30588235, 0.3254902 ],\n",
              "         [0.25490196, 0.20784314, 0.22352941],\n",
              "         [0.23529412, 0.18823529, 0.20392157],\n",
              "         ...,\n",
              "         [0.40784314, 0.35686275, 0.39607843],\n",
              "         [0.38039216, 0.31764706, 0.36078431],\n",
              "         [0.49411765, 0.43137255, 0.4745098 ]]],\n",
              "\n",
              "\n",
              "       [[[0.61960784, 0.54509804, 0.61960784],\n",
              "         [0.61960784, 0.54509804, 0.61960784],\n",
              "         [0.62352941, 0.55294118, 0.61568627],\n",
              "         ...,\n",
              "         [0.65098039, 0.57647059, 0.59215686],\n",
              "         [0.55686275, 0.48235294, 0.50588235],\n",
              "         [0.52156863, 0.44705882, 0.47058824]],\n",
              "\n",
              "        [[0.6627451 , 0.58039216, 0.65490196],\n",
              "         [0.56862745, 0.49411765, 0.56862745],\n",
              "         [0.67843137, 0.60784314, 0.67058824],\n",
              "         ...,\n",
              "         [0.58823529, 0.51372549, 0.52941176],\n",
              "         [0.46666667, 0.39215686, 0.41568627],\n",
              "         [0.54117647, 0.46666667, 0.49019608]],\n",
              "\n",
              "        [[0.68627451, 0.60392157, 0.67843137],\n",
              "         [0.63137255, 0.54901961, 0.62352941],\n",
              "         [0.56862745, 0.48627451, 0.55294118],\n",
              "         ...,\n",
              "         [0.69411765, 0.61960784, 0.63529412],\n",
              "         [0.57254902, 0.49803922, 0.52156863],\n",
              "         [0.55294118, 0.47843137, 0.50196078]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.71372549, 0.61568627, 0.69019608],\n",
              "         [0.63137255, 0.54117647, 0.61176471],\n",
              "         [0.6       , 0.50980392, 0.57254902],\n",
              "         ...,\n",
              "         [0.58039216, 0.47843137, 0.51372549],\n",
              "         [0.60392157, 0.50196078, 0.5372549 ],\n",
              "         [0.51372549, 0.41176471, 0.45490196]],\n",
              "\n",
              "        [[0.54901961, 0.45098039, 0.5254902 ],\n",
              "         [0.60784314, 0.51764706, 0.58823529],\n",
              "         [0.47843137, 0.38823529, 0.45882353],\n",
              "         ...,\n",
              "         [0.54509804, 0.44313725, 0.47843137],\n",
              "         [0.56862745, 0.46666667, 0.50196078],\n",
              "         [0.53333333, 0.43137255, 0.46666667]],\n",
              "\n",
              "        [[0.56470588, 0.4745098 , 0.55294118],\n",
              "         [0.56078431, 0.47058824, 0.54901961],\n",
              "         [0.57254902, 0.48235294, 0.55294118],\n",
              "         ...,\n",
              "         [0.63921569, 0.5372549 , 0.57254902],\n",
              "         [0.63137255, 0.52941176, 0.56470588],\n",
              "         [0.59607843, 0.49411765, 0.52941176]]],\n",
              "\n",
              "\n",
              "       ...,\n",
              "\n",
              "\n",
              "       [[[0.39215686, 0.35686275, 0.37647059],\n",
              "         [0.51764706, 0.48235294, 0.50196078],\n",
              "         [0.86666667, 0.83137255, 0.85098039],\n",
              "         ...,\n",
              "         [0.47843137, 0.43529412, 0.46666667],\n",
              "         [0.47058824, 0.42745098, 0.45882353],\n",
              "         [0.50196078, 0.45490196, 0.49411765]],\n",
              "\n",
              "        [[0.45490196, 0.41176471, 0.43529412],\n",
              "         [0.5254902 , 0.48235294, 0.50588235],\n",
              "         [0.74901961, 0.71372549, 0.73333333],\n",
              "         ...,\n",
              "         [0.48627451, 0.44313725, 0.4745098 ],\n",
              "         [0.48235294, 0.43921569, 0.47058824],\n",
              "         [0.44313725, 0.4       , 0.43137255]],\n",
              "\n",
              "        [[0.52941176, 0.48627451, 0.50980392],\n",
              "         [0.5254902 , 0.48235294, 0.50588235],\n",
              "         [0.32156863, 0.27843137, 0.30196078],\n",
              "         ...,\n",
              "         [0.54509804, 0.50196078, 0.53333333],\n",
              "         [0.59607843, 0.55294118, 0.58431373],\n",
              "         [0.43529412, 0.39215686, 0.42352941]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.59607843, 0.55686275, 0.58823529],\n",
              "         [0.39215686, 0.35294118, 0.38431373],\n",
              "         [0.47058824, 0.44313725, 0.47058824],\n",
              "         ...,\n",
              "         [0.47058824, 0.42745098, 0.44313725],\n",
              "         [0.38431373, 0.34901961, 0.36078431],\n",
              "         [0.50980392, 0.4745098 , 0.48627451]],\n",
              "\n",
              "        [[0.64705882, 0.62352941, 0.63921569],\n",
              "         [0.4627451 , 0.43921569, 0.45490196],\n",
              "         [0.5254902 , 0.49803922, 0.5254902 ],\n",
              "         ...,\n",
              "         [0.36470588, 0.3254902 , 0.32941176],\n",
              "         [0.36078431, 0.3254902 , 0.32941176],\n",
              "         [0.47843137, 0.44313725, 0.44705882]],\n",
              "\n",
              "        [[0.41568627, 0.39215686, 0.40784314],\n",
              "         [0.56862745, 0.54509804, 0.56078431],\n",
              "         [0.51764706, 0.49019608, 0.51764706],\n",
              "         ...,\n",
              "         [0.48627451, 0.44705882, 0.45098039],\n",
              "         [0.48627451, 0.45098039, 0.45490196],\n",
              "         [0.44313725, 0.40784314, 0.41176471]]],\n",
              "\n",
              "\n",
              "       [[[0.57254902, 0.51372549, 0.5254902 ],\n",
              "         [0.63137255, 0.57254902, 0.58431373],\n",
              "         [0.6       , 0.54117647, 0.55294118],\n",
              "         ...,\n",
              "         [0.61568627, 0.55686275, 0.56862745],\n",
              "         [0.58431373, 0.5254902 , 0.5372549 ],\n",
              "         [0.56470588, 0.50588235, 0.51764706]],\n",
              "\n",
              "        [[0.58823529, 0.52941176, 0.54117647],\n",
              "         [0.70588235, 0.64705882, 0.65882353],\n",
              "         [0.71764706, 0.65882353, 0.67058824],\n",
              "         ...,\n",
              "         [0.59215686, 0.53333333, 0.54509804],\n",
              "         [0.59607843, 0.5372549 , 0.54901961],\n",
              "         [0.60392157, 0.54509804, 0.55686275]],\n",
              "\n",
              "        [[0.69019608, 0.63137255, 0.64313725],\n",
              "         [0.79607843, 0.7372549 , 0.74901961],\n",
              "         [0.80392157, 0.74509804, 0.75686275],\n",
              "         ...,\n",
              "         [0.59607843, 0.5372549 , 0.54901961],\n",
              "         [0.60784314, 0.54901961, 0.56078431],\n",
              "         [0.61960784, 0.56078431, 0.57254902]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.49019608, 0.43137255, 0.44313725],\n",
              "         [0.49411765, 0.43529412, 0.44705882],\n",
              "         [0.68627451, 0.62745098, 0.63921569],\n",
              "         ...,\n",
              "         [0.59607843, 0.5372549 , 0.54901961],\n",
              "         [0.58039216, 0.52156863, 0.53333333],\n",
              "         [0.55686275, 0.49803922, 0.50980392]],\n",
              "\n",
              "        [[0.43529412, 0.37647059, 0.38823529],\n",
              "         [0.47058824, 0.41176471, 0.42352941],\n",
              "         [0.60784314, 0.54901961, 0.56078431],\n",
              "         ...,\n",
              "         [0.57254902, 0.51372549, 0.5254902 ],\n",
              "         [0.6       , 0.54117647, 0.55294118],\n",
              "         [0.56470588, 0.50588235, 0.51764706]],\n",
              "\n",
              "        [[0.49803922, 0.43921569, 0.45098039],\n",
              "         [0.4627451 , 0.40392157, 0.41568627],\n",
              "         [0.43137255, 0.37254902, 0.38431373],\n",
              "         ...,\n",
              "         [0.56078431, 0.50196078, 0.51372549],\n",
              "         [0.62745098, 0.56862745, 0.58039216],\n",
              "         [0.56862745, 0.50980392, 0.52156863]]],\n",
              "\n",
              "\n",
              "       [[[0.69019608, 0.58039216, 0.63921569],\n",
              "         [0.58431373, 0.4745098 , 0.53333333],\n",
              "         [0.60392157, 0.49411765, 0.55294118],\n",
              "         ...,\n",
              "         [0.69019608, 0.56078431, 0.63529412],\n",
              "         [0.61960784, 0.49019608, 0.56470588],\n",
              "         [0.63529412, 0.50588235, 0.58039216]],\n",
              "\n",
              "        [[0.6627451 , 0.55294118, 0.61176471],\n",
              "         [0.62352941, 0.51372549, 0.57254902],\n",
              "         [0.54117647, 0.43137255, 0.49019608],\n",
              "         ...,\n",
              "         [0.68627451, 0.55686275, 0.63137255],\n",
              "         [0.5254902 , 0.39607843, 0.47058824],\n",
              "         [0.59607843, 0.46666667, 0.54117647]],\n",
              "\n",
              "        [[0.67843137, 0.56862745, 0.62745098],\n",
              "         [0.68627451, 0.57647059, 0.63529412],\n",
              "         [0.57647059, 0.46666667, 0.52156863],\n",
              "         ...,\n",
              "         [0.69019608, 0.56862745, 0.63921569],\n",
              "         [0.54901961, 0.42745098, 0.49803922],\n",
              "         [0.6       , 0.47843137, 0.54901961]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.30980392, 0.27058824, 0.30588235],\n",
              "         [0.35686275, 0.31764706, 0.35294118],\n",
              "         [0.51372549, 0.4745098 , 0.50980392],\n",
              "         ...,\n",
              "         [0.50588235, 0.51372549, 0.49411765],\n",
              "         [0.44313725, 0.4627451 , 0.43921569],\n",
              "         [0.48627451, 0.50588235, 0.48235294]],\n",
              "\n",
              "        [[0.42352941, 0.38431373, 0.41960784],\n",
              "         [0.54509804, 0.50588235, 0.54117647],\n",
              "         [0.49411765, 0.45490196, 0.49019608],\n",
              "         ...,\n",
              "         [0.55686275, 0.56470588, 0.54509804],\n",
              "         [0.5254902 , 0.54509804, 0.52156863],\n",
              "         [0.51764706, 0.5372549 , 0.51372549]],\n",
              "\n",
              "        [[0.62745098, 0.58823529, 0.62352941],\n",
              "         [0.61568627, 0.57647059, 0.61176471],\n",
              "         [0.50588235, 0.46666667, 0.50196078],\n",
              "         ...,\n",
              "         [0.50588235, 0.5254902 , 0.50196078],\n",
              "         [0.41960784, 0.43921569, 0.41568627],\n",
              "         [0.36470588, 0.38431373, 0.36078431]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "id": "wnIOJ5gx9YYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "619463fc-44b3-4ab4-c457-a23bbb20c2f2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17500, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### spliting data (train & validation) | build Network, Compile and Fit"
      ],
      "metadata": {
        "id": "-I6zPpoqlY2t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.18, random_state=42)"
      ],
      "metadata": {
        "id": "OlECnS6VwpBO"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating Neural Network model:\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "#adding layers\n",
        "model.add(Conv2D(filters=32, kernel_size = (4,4), strides=(1,1), padding = 'valid',\n",
        "                 input_shape=(32,32,3), activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size = (4,4), strides=(1,1), padding = 'valid',\n",
        "                 input_shape=(32,32,3), activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "#Dense Layers\n",
        "model.add(Dense(units = 512, activation = \"relu\"))\n",
        "\n",
        "model.add(Dense(units = 2, activation = \"softmax\"))\n",
        "\n",
        "#Compile\n",
        "model.compile(\n",
        "    loss = 'categorical_crossentropy',\n",
        "    optimizer = 'adam',\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "6SciglCi-dyl"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "_4GSKDNlAh97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e2b0200-072e-4c18-c64c-401744014b50"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 29, 29, 32)        1568      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 14, 14, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 11, 11, 32)        16416     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 5, 5, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 800)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 512)               410112    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 2)                 1026      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 429,122\n",
            "Trainable params: 429,122\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "e_stop = EarlyStopping(min_delta = 0.05,\n",
        "                       patience = 10,\n",
        "                       restore_best_weights= True)"
      ],
      "metadata": {
        "id": "Sk4EkM522LqZ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting the model\n",
        "model.fit(x= X_train, y = y_train,\n",
        "          validation_data= (X_val, y_val),\n",
        "          callbacks = e_stop,\n",
        "          batch_size=512, epochs = 20)"
      ],
      "metadata": {
        "id": "tvKJWvruDCzl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb758651-743d-4593-95a6-93e3fe2646f6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "29/29 [==============================] - 25s 821ms/step - loss: 0.3721 - accuracy: 0.8123 - val_loss: 0.3647 - val_accuracy: 0.8606\n",
            "Epoch 2/20\n",
            "29/29 [==============================] - 21s 728ms/step - loss: 0.2182 - accuracy: 0.9162 - val_loss: 0.1631 - val_accuracy: 0.9410\n",
            "Epoch 3/20\n",
            "29/29 [==============================] - 22s 766ms/step - loss: 0.1612 - accuracy: 0.9393 - val_loss: 0.1754 - val_accuracy: 0.9356\n",
            "Epoch 4/20\n",
            "29/29 [==============================] - 22s 750ms/step - loss: 0.1441 - accuracy: 0.9454 - val_loss: 0.1366 - val_accuracy: 0.9486\n",
            "Epoch 5/20\n",
            "29/29 [==============================] - 21s 719ms/step - loss: 0.1341 - accuracy: 0.9489 - val_loss: 0.1132 - val_accuracy: 0.9606\n",
            "Epoch 6/20\n",
            "29/29 [==============================] - 24s 819ms/step - loss: 0.1283 - accuracy: 0.9513 - val_loss: 0.3770 - val_accuracy: 0.8413\n",
            "Epoch 7/20\n",
            "29/29 [==============================] - 21s 733ms/step - loss: 0.1679 - accuracy: 0.9370 - val_loss: 0.1312 - val_accuracy: 0.9502\n",
            "Epoch 8/20\n",
            "29/29 [==============================] - 22s 755ms/step - loss: 0.1168 - accuracy: 0.9562 - val_loss: 0.1404 - val_accuracy: 0.9422\n",
            "Epoch 9/20\n",
            "29/29 [==============================] - 21s 725ms/step - loss: 0.1154 - accuracy: 0.9573 - val_loss: 0.1352 - val_accuracy: 0.9479\n",
            "Epoch 10/20\n",
            "29/29 [==============================] - 20s 697ms/step - loss: 0.1025 - accuracy: 0.9613 - val_loss: 0.1387 - val_accuracy: 0.9454\n",
            "Epoch 11/20\n",
            "29/29 [==============================] - 22s 769ms/step - loss: 0.1004 - accuracy: 0.9629 - val_loss: 0.4544 - val_accuracy: 0.8241\n",
            "Epoch 12/20\n",
            "29/29 [==============================] - 21s 718ms/step - loss: 0.1985 - accuracy: 0.9263 - val_loss: 0.1235 - val_accuracy: 0.9565\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb6b0230b80>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicts = model.predict(X_val)\n",
        "class_predictions = np.argmax(predicts, axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_W8MpQJp2u7v",
        "outputId": "a83a3646-73f4-4353-80dc-0c26af4db206"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99/99 [==============================] - 1s 13ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Assessing Predictions:\n",
        "\n",
        "1. Confusion Matrix;\n",
        "2. Scores: Accuracy, Precision, Recall and F1-score;"
      ],
      "metadata": {
        "id": "f4RC-aLSl3S4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "#reverting y_val to binary, to fit classification_report and confusion_matrix\n",
        "y_val_binary = np.argmax(y_val, axis=1)\n",
        "\n",
        "#creating confusion matrix\n",
        "sns.heatmap(\n",
        "    confusion_matrix(y_val_binary, class_predictions),\n",
        "    annot = True,\n",
        "    fmt = 'd',\n",
        "    cmap = 'Blues'\n",
        ")\n",
        "\n",
        "#naming axis\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')"
      ],
      "metadata": {
        "id": "GpFiVvSfBMgp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "07ea9037-87c0-4f14-a15f-8a61a2e4731d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(50.722222222222214, 0.5, 'True Labels')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_val_binary, class_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoX5552cT_Lj",
        "outputId": "6c22986a-26f8-47b6-8f59-22f2996f015f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.82      0.87       786\n",
            "           1       0.94      0.98      0.96      2364\n",
            "\n",
            "    accuracy                           0.94      3150\n",
            "   macro avg       0.94      0.90      0.92      3150\n",
            "weighted avg       0.94      0.94      0.94      3150\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**\n",
        "\n",
        "The model had a\n",
        "Considering the results of the **Confusion Matrix** and the **Classification Report**, it is clear that:\n",
        "- The model had a great performance predicting both presence and absence of cactus in images, specially the ones that it IS present. Considering the importance of the task, the model should be deployed, as it rarely classifies wrongly an image that actually has cacuts.\n",
        "- Although it's great performance, the 'absence of cactus' in image, is what might ask for improvement. Since the data has much more cactus images than the opposite, it leans into the'has cactus' response. One way to imporove is Generating 'absence of cactus' Images from the ones in dataset, so it evens the classes.\n",
        "\n"
      ],
      "metadata": {
        "id": "80nvuQAtmH62"
      }
    }
  ]
}