{
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    "colab": {
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      "gpuType": "T4"
    },
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      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Dog Breed Prediction"
      ],
      "metadata": {
        "id": "LlAP9FzzMXe0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In this project, we will see how to use Keras and TensorFlow to build, train, and test a Convolutional Neural Network capable of identifying the breed of a dog in a supplied image. This is a supervised learning problem, specifically a multiclass classification problem.**"
      ],
      "metadata": {
        "id": "khh9Bwj2MQ9x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "E7LP-2Ch8vmt",
        "outputId": "79f8a768-e759-4028-aaad-1503efad6873"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a8d9e9e8-ba9d-4177-a400-15dcaffc328d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a8d9e9e8-ba9d-4177-a400-15dcaffc328d\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"adithyasnair123\",\"key\":\"2a054ad83187b3fef16dbbad7f84ed5d\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
      "metadata": {
        "id": "VVtnIn7K84B-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle"
      ],
      "metadata": {
        "id": "G_DE-5Oy9Caf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "p6mzo5qo9PvN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir dog_dataset\n",
        "%cd dog_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOg4Cor89YH9",
        "outputId": "4641ea7e-57c0-458d-8ebb-ab1cc9950005"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/dog_dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets list -s dogbreedidfromcomp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiIAVzaA9hLK",
        "outputId": "abf575ec-e709-4737-f67e-e640d05c26a6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                title                    size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "---------------------------------  ----------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "catherinehorng/dogbreedidfromcomp  dog-breed-id-from-comp  691MB  2020-06-26 03:09:05           4151          8  0.1764706        \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download catherinehorng/dogbreedidfromcomp\n",
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Uew89ID90n2",
        "outputId": "4a826fcc-3245-4124-8e30-27798ce93931"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading dogbreedidfromcomp.zip to /content/dog_dataset\n",
            " 97% 673M/691M [00:04<00:00, 139MB/s]\n",
            "100% 691M/691M [00:04<00:00, 148MB/s]\n",
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip dog_dataset/dogbreedidfromcomp.zip -d dog_dataset\n",
        "!rm dog_dataset/dogbreedidfromcomp.zip\n",
        "!rm dog_dataset/sample_submission.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8EPotvM-J88",
        "outputId": "d1ab853d-ca7a-4c2c-8f9d-7269b72f8248"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.utils import load_img, img_to_array\n",
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "from tqdm import tqdm\n",
        "from keras.preprocessing import image\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
        "from keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "zRvh2-yP-6wq"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels_all = pd.read_csv('dog_dataset/labels.csv')\n",
        "print(labels_all.shape)\n",
        "labels_all.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "I5aGOdFxAHHr",
        "outputId": "72268701-70a7-41dd-c64e-c27595df1aa9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10222, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 id             breed\n",
              "0  000bec180eb18c7604dcecc8fe0dba07       boston_bull\n",
              "1  001513dfcb2ffafc82cccf4d8bbaba97             dingo\n",
              "2  001cdf01b096e06d78e9e5112d419397          pekinese\n",
              "3  00214f311d5d2247d5dfe4fe24b2303d          bluetick\n",
              "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-187de24f-b8e4-439e-8d2f-a0ead059f550\" 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000bec180eb18c7604dcecc8fe0dba07</td>\n",
              "      <td>boston_bull</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>001513dfcb2ffafc82cccf4d8bbaba97</td>\n",
              "      <td>dingo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>001cdf01b096e06d78e9e5112d419397</td>\n",
              "      <td>pekinese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00214f311d5d2247d5dfe4fe24b2303d</td>\n",
              "      <td>bluetick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0021f9ceb3235effd7fcde7f7538ed62</td>\n",
              "      <td>golden_retriever</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-187de24f-b8e4-439e-8d2f-a0ead059f550')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-187de24f-b8e4-439e-8d2f-a0ead059f550 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-187de24f-b8e4-439e-8d2f-a0ead059f550');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6533fa5a-919f-40b4-9b5d-db7e6327b010\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6533fa5a-919f-40b4-9b5d-db7e6327b010')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6533fa5a-919f-40b4-9b5d-db7e6327b010 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breeds_all = labels_all[\"breed\"]\n",
        "breeds_counts = breeds_all.value_counts()\n",
        "breeds_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMI9Dx5PAO1g",
        "outputId": "aa0f1243-6ab2-4aff-ed1b-2bacf77e068d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "scottish_deerhound      126\n",
              "maltese_dog             117\n",
              "afghan_hound            116\n",
              "entlebucher             115\n",
              "bernese_mountain_dog    114\n",
              "Name: breed, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CLASS_NAME = [\"scottish_deerhound\",\"maltese_dog\",\"afghan_hound\",\"entlebucher\",\"bernese_mountain_dog\"]\n",
        "labels = labels_all[labels_all['breed'].isin(CLASS_NAME)]\n",
        "labels= labels.reset_index()\n",
        "labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bFuldLScA_PJ",
        "outputId": "ec882c94-81f5-4dc2-86ed-5948fa6ffb0f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index                                id               breed\n",
              "0      9  0042188c895a2f14ef64a918ed9c7b64  scottish_deerhound\n",
              "1     12  00693b8bc2470375cc744a6391d397ec         maltese_dog\n",
              "2     79  01e787576c003930f96c966f9c3e1d44  scottish_deerhound\n",
              "3     80  01ee3c7ff9bcaba9874183135877670e         entlebucher\n",
              "4     88  021b5a49189665c0442c19b5b33e8cf1         entlebucher"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0884aba7-89d1-4d91-b8bd-4ca193742b13\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>id</th>\n",
              "      <th>breed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>0042188c895a2f14ef64a918ed9c7b64</td>\n",
              "      <td>scottish_deerhound</td>\n",
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              "      <th>1</th>\n",
              "      <td>12</td>\n",
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              "      <td>maltese_dog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>79</td>\n",
              "      <td>01e787576c003930f96c966f9c3e1d44</td>\n",
              "      <td>scottish_deerhound</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80</td>\n",
              "      <td>01ee3c7ff9bcaba9874183135877670e</td>\n",
              "      <td>entlebucher</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>88</td>\n",
              "      <td>021b5a49189665c0442c19b5b33e8cf1</td>\n",
              "      <td>entlebucher</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0884aba7-89d1-4d91-b8bd-4ca193742b13')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-0884aba7-89d1-4d91-b8bd-4ca193742b13 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-0884aba7-89d1-4d91-b8bd-4ca193742b13');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-d6b427d4-f328-445d-a6a2-76c1fe184c6d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6b427d4-f328-445d-a6a2-76c1fe184c6d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d6b427d4-f328-445d-a6a2-76c1fe184c6d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Keras-Preprocessing\n",
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "# Creating numpy matrix with zeros\n",
        "X_data = np.zeros((len(labels), 224, 224, 3), dtype='float32')\n",
        "# One hot encoding\n",
        "Y_data = label_binarize(labels['breed'], classes=CLASS_NAME)\n",
        "\n",
        "# Reading and converting image to numpy array and normalizing dataset\n",
        "for i in tqdm(range(len(labels))):\n",
        "    img = image.load_img('dog_dataset/train/%s.jpg' % labels['id'][i], target_size=(224, 224))\n",
        "    img = image.img_to_array(img)\n",
        "    x = np.expand_dims(img.copy(), axis=0)\n",
        "    X_data[i] = x / 255.0\n",
        "\n",
        "# Printing train image and one hot encode shape & size\n",
        "print('\\nTrain Images shape: ', X_data.shape, ' size: {:,}'.format(X_data.size))\n",
        "print('One-hot encoded output shape: ', Y_data.shape, ' size: {:,}'.format(Y_data.size))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAlo7IsSB8J2",
        "outputId": "86e76ac4-3491-42c7-e08d-9573e0fdeb95"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting Keras-Preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m812.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from Keras-Preprocessing) (1.23.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from Keras-Preprocessing) (1.16.0)\n",
            "Installing collected packages: Keras-Preprocessing\n",
            "Successfully installed Keras-Preprocessing-1.1.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 588/588 [00:01<00:00, 492.10it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Images shape:  (588, 224, 224, 3)  size: 88,510,464\n",
            "One-hot encoded output shape:  (588, 5)  size: 2,940\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the Model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (5,5), activation ='relu', input_shape = (224,224,3)))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 32, kernel_size = (3,3), activation ='relu', kernel_regularizer = 'l2'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 16, kernel_size = (7,7), activation ='relu', kernel_regularizer = 'l2'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 8, kernel_size = (5,5), activation ='relu', kernel_regularizer = 'l2'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\", kernel_regularizer = 'l2'))\n",
        "model.add(Dense(64, activation = \"relu\", kernel_regularizer = 'l2'))\n",
        "model.add(Dense(len(CLASS_NAME), activation = \"softmax\"))\n",
        "\n",
        "model.compile(loss = 'categorical_crossentropy', optimizer = Adam(0.0001),metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyAtuxFmLbHv",
        "outputId": "ea939ba0-57ec-4eb6-bf87-4adee26b748e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 220, 220, 64)      4864      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 110, 110, 64)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 108, 108, 32)      18464     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 54, 54, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 48, 48, 16)        25104     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 24, 24, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 20, 20, 8)         3208      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 10, 10, 8)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 800)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               102528    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 162,749\n",
            "Trainable params: 162,749\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we will create a network architecture for the model. We have used different types of layers according to their features namely Conv_2d (It is used to create a convolutional kernel that is convolved with the input layer to produce the output tensor), max_pooling2d (It is a downsampling technique which takes out the maximum value over the window defined by poolsize), flatten (It flattens the input and creates a 1D output), Dense (Dense layer produce the output as the dot product of input and kernel).\n"
      ],
      "metadata": {
        "id": "KWvOPM7uLhSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data set into training and testing data sets\n",
        "X_train_and_val, X_test, Y_train_and_val, Y_test = train_test_split(X_data, Y_data, test_size = 0.1)\n",
        "# Splitting the training data set into training and validation data sets\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_train_and_val, Y_train_and_val, test_size = 0.2)"
      ],
      "metadata": {
        "id": "ybJUJ20fLdzp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After defining the network architecture we will start with splitting the test and train data then dividing train data in train and validation data."
      ],
      "metadata": {
        "id": "ODKbNdW5LoPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "epochs = 100\n",
        "batch_size = 128\n",
        "\n",
        "history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs,\n",
        "                    validation_data = (X_val, Y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QU1kzNPnLkV3",
        "outputId": "4206165a-b8e6-4358-8a8e-db8411e8d995"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 23s 2s/step - loss: 5.4303 - accuracy: 0.2128 - val_loss: 5.3889 - val_accuracy: 0.2642\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 1s 314ms/step - loss: 5.3759 - accuracy: 0.2104 - val_loss: 5.3441 - val_accuracy: 0.2075\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 1s 259ms/step - loss: 5.3282 - accuracy: 0.2270 - val_loss: 5.3004 - val_accuracy: 0.2075\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 1s 216ms/step - loss: 5.2829 - accuracy: 0.2648 - val_loss: 5.2562 - val_accuracy: 0.1887\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 5.2373 - accuracy: 0.2435 - val_loss: 5.2120 - val_accuracy: 0.1509\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 1s 201ms/step - loss: 5.1925 - accuracy: 0.2340 - val_loss: 5.1685 - val_accuracy: 0.1604\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 1s 182ms/step - loss: 5.1474 - accuracy: 0.3026 - val_loss: 5.1247 - val_accuracy: 0.1887\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 1s 182ms/step - loss: 5.1024 - accuracy: 0.3073 - val_loss: 5.0810 - val_accuracy: 0.1792\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 5.0581 - accuracy: 0.2719 - val_loss: 5.0385 - val_accuracy: 0.2547\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 5.0132 - accuracy: 0.3688 - val_loss: 4.9962 - val_accuracy: 0.2358\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 4.9662 - accuracy: 0.3712 - val_loss: 4.9514 - val_accuracy: 0.1887\n",
            "Epoch 12/100\n",
            "4/4 [==============================] - 1s 204ms/step - loss: 4.9208 - accuracy: 0.3286 - val_loss: 4.9086 - val_accuracy: 0.2547\n",
            "Epoch 13/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 4.8702 - accuracy: 0.4090 - val_loss: 4.8627 - val_accuracy: 0.2642\n",
            "Epoch 14/100\n",
            "4/4 [==============================] - 1s 186ms/step - loss: 4.8193 - accuracy: 0.4374 - val_loss: 4.8181 - val_accuracy: 0.2642\n",
            "Epoch 15/100\n",
            "4/4 [==============================] - 1s 205ms/step - loss: 4.7660 - accuracy: 0.4184 - val_loss: 4.7697 - val_accuracy: 0.2925\n",
            "Epoch 16/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 4.7162 - accuracy: 0.3593 - val_loss: 4.7254 - val_accuracy: 0.2547\n",
            "Epoch 17/100\n",
            "4/4 [==============================] - 1s 206ms/step - loss: 4.6502 - accuracy: 0.4208 - val_loss: 4.6719 - val_accuracy: 0.3302\n",
            "Epoch 18/100\n",
            "4/4 [==============================] - 1s 215ms/step - loss: 4.5909 - accuracy: 0.4657 - val_loss: 4.6182 - val_accuracy: 0.3585\n",
            "Epoch 19/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 4.5153 - accuracy: 0.4799 - val_loss: 4.5710 - val_accuracy: 0.3302\n",
            "Epoch 20/100\n",
            "4/4 [==============================] - 1s 225ms/step - loss: 4.4373 - accuracy: 0.4917 - val_loss: 4.5036 - val_accuracy: 0.3396\n",
            "Epoch 21/100\n",
            "4/4 [==============================] - 1s 205ms/step - loss: 4.3868 - accuracy: 0.4255 - val_loss: 4.4512 - val_accuracy: 0.3396\n",
            "Epoch 22/100\n",
            "4/4 [==============================] - 1s 206ms/step - loss: 4.3116 - accuracy: 0.4704 - val_loss: 4.4134 - val_accuracy: 0.3585\n",
            "Epoch 23/100\n",
            "4/4 [==============================] - 1s 206ms/step - loss: 4.2408 - accuracy: 0.5130 - val_loss: 4.3637 - val_accuracy: 0.3491\n",
            "Epoch 24/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 4.1798 - accuracy: 0.4870 - val_loss: 4.3248 - val_accuracy: 0.3774\n",
            "Epoch 25/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 4.1426 - accuracy: 0.5012 - val_loss: 4.3117 - val_accuracy: 0.4151\n",
            "Epoch 26/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 4.0727 - accuracy: 0.5437 - val_loss: 4.2647 - val_accuracy: 0.3774\n",
            "Epoch 27/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 4.0498 - accuracy: 0.5296 - val_loss: 4.2217 - val_accuracy: 0.3868\n",
            "Epoch 28/100\n",
            "4/4 [==============================] - 1s 209ms/step - loss: 3.9830 - accuracy: 0.5414 - val_loss: 4.2036 - val_accuracy: 0.4245\n",
            "Epoch 29/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 3.9543 - accuracy: 0.5532 - val_loss: 4.1710 - val_accuracy: 0.4057\n",
            "Epoch 30/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 3.9189 - accuracy: 0.5626 - val_loss: 4.1542 - val_accuracy: 0.4151\n",
            "Epoch 31/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 3.8971 - accuracy: 0.5556 - val_loss: 4.0885 - val_accuracy: 0.4245\n",
            "Epoch 32/100\n",
            "4/4 [==============================] - 1s 185ms/step - loss: 3.8425 - accuracy: 0.5626 - val_loss: 4.0709 - val_accuracy: 0.4340\n",
            "Epoch 33/100\n",
            "4/4 [==============================] - 1s 198ms/step - loss: 3.7894 - accuracy: 0.6170 - val_loss: 4.0543 - val_accuracy: 0.4717\n",
            "Epoch 34/100\n",
            "4/4 [==============================] - 1s 215ms/step - loss: 3.8189 - accuracy: 0.5863 - val_loss: 4.0727 - val_accuracy: 0.4245\n",
            "Epoch 35/100\n",
            "4/4 [==============================] - 1s 212ms/step - loss: 3.7923 - accuracy: 0.5839 - val_loss: 4.0354 - val_accuracy: 0.4057\n",
            "Epoch 36/100\n",
            "4/4 [==============================] - 1s 214ms/step - loss: 3.7227 - accuracy: 0.5816 - val_loss: 4.0213 - val_accuracy: 0.4717\n",
            "Epoch 37/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 3.7023 - accuracy: 0.6028 - val_loss: 3.9727 - val_accuracy: 0.3868\n",
            "Epoch 38/100\n",
            "4/4 [==============================] - 1s 185ms/step - loss: 3.6458 - accuracy: 0.6241 - val_loss: 3.9316 - val_accuracy: 0.4623\n",
            "Epoch 39/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 3.6527 - accuracy: 0.5910 - val_loss: 3.9063 - val_accuracy: 0.4340\n",
            "Epoch 40/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 3.5972 - accuracy: 0.6359 - val_loss: 3.9108 - val_accuracy: 0.4623\n",
            "Epoch 41/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 3.5579 - accuracy: 0.6430 - val_loss: 3.8701 - val_accuracy: 0.4340\n",
            "Epoch 42/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 3.5319 - accuracy: 0.6359 - val_loss: 3.8614 - val_accuracy: 0.4434\n",
            "Epoch 43/100\n",
            "4/4 [==============================] - 1s 205ms/step - loss: 3.5152 - accuracy: 0.6548 - val_loss: 3.8426 - val_accuracy: 0.4717\n",
            "Epoch 44/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 3.4608 - accuracy: 0.6738 - val_loss: 3.8402 - val_accuracy: 0.4811\n",
            "Epoch 45/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 3.4599 - accuracy: 0.6809 - val_loss: 3.8204 - val_accuracy: 0.4151\n",
            "Epoch 46/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 3.4279 - accuracy: 0.6785 - val_loss: 3.7960 - val_accuracy: 0.4811\n",
            "Epoch 47/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 3.3989 - accuracy: 0.6809 - val_loss: 3.8182 - val_accuracy: 0.4057\n",
            "Epoch 48/100\n",
            "4/4 [==============================] - 1s 187ms/step - loss: 3.4053 - accuracy: 0.6690 - val_loss: 3.7917 - val_accuracy: 0.4717\n",
            "Epoch 49/100\n",
            "4/4 [==============================] - 1s 219ms/step - loss: 3.3512 - accuracy: 0.7045 - val_loss: 3.7517 - val_accuracy: 0.4340\n",
            "Epoch 50/100\n",
            "4/4 [==============================] - 1s 220ms/step - loss: 3.3272 - accuracy: 0.6832 - val_loss: 3.7207 - val_accuracy: 0.4528\n",
            "Epoch 51/100\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 3.3105 - accuracy: 0.6809 - val_loss: 3.7326 - val_accuracy: 0.4811\n",
            "Epoch 52/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 3.3015 - accuracy: 0.6785 - val_loss: 3.7219 - val_accuracy: 0.4434\n",
            "Epoch 53/100\n",
            "4/4 [==============================] - 1s 198ms/step - loss: 3.2468 - accuracy: 0.6856 - val_loss: 3.6821 - val_accuracy: 0.4717\n",
            "Epoch 54/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 3.2517 - accuracy: 0.6903 - val_loss: 3.7048 - val_accuracy: 0.4906\n",
            "Epoch 55/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 3.2209 - accuracy: 0.7092 - val_loss: 3.7261 - val_accuracy: 0.4811\n",
            "Epoch 56/100\n",
            "4/4 [==============================] - 1s 211ms/step - loss: 3.2248 - accuracy: 0.6619 - val_loss: 3.6687 - val_accuracy: 0.4717\n",
            "Epoch 57/100\n",
            "4/4 [==============================] - 1s 208ms/step - loss: 3.1960 - accuracy: 0.7045 - val_loss: 3.6921 - val_accuracy: 0.4811\n",
            "Epoch 58/100\n",
            "4/4 [==============================] - 1s 192ms/step - loss: 3.1590 - accuracy: 0.7258 - val_loss: 3.6439 - val_accuracy: 0.4717\n",
            "Epoch 59/100\n",
            "4/4 [==============================] - 1s 208ms/step - loss: 3.1027 - accuracy: 0.7423 - val_loss: 3.6386 - val_accuracy: 0.4906\n",
            "Epoch 60/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 3.0857 - accuracy: 0.7281 - val_loss: 3.6156 - val_accuracy: 0.4623\n",
            "Epoch 61/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 3.0756 - accuracy: 0.7518 - val_loss: 3.6094 - val_accuracy: 0.4717\n",
            "Epoch 62/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 3.0440 - accuracy: 0.7565 - val_loss: 3.6011 - val_accuracy: 0.5000\n",
            "Epoch 63/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 3.0080 - accuracy: 0.7754 - val_loss: 3.6227 - val_accuracy: 0.4528\n",
            "Epoch 64/100\n",
            "4/4 [==============================] - 1s 214ms/step - loss: 3.0355 - accuracy: 0.7376 - val_loss: 3.6288 - val_accuracy: 0.5094\n",
            "Epoch 65/100\n",
            "4/4 [==============================] - 1s 231ms/step - loss: 3.0061 - accuracy: 0.7707 - val_loss: 3.6138 - val_accuracy: 0.4623\n",
            "Epoch 66/100\n",
            "4/4 [==============================] - 1s 212ms/step - loss: 2.9846 - accuracy: 0.7470 - val_loss: 3.5687 - val_accuracy: 0.4717\n",
            "Epoch 67/100\n",
            "4/4 [==============================] - 1s 217ms/step - loss: 2.9570 - accuracy: 0.7423 - val_loss: 3.5561 - val_accuracy: 0.4811\n",
            "Epoch 68/100\n",
            "4/4 [==============================] - 1s 191ms/step - loss: 2.9241 - accuracy: 0.7849 - val_loss: 3.5711 - val_accuracy: 0.4623\n",
            "Epoch 69/100\n",
            "4/4 [==============================] - 1s 209ms/step - loss: 2.8983 - accuracy: 0.7707 - val_loss: 3.5821 - val_accuracy: 0.4245\n",
            "Epoch 70/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 2.9123 - accuracy: 0.7730 - val_loss: 3.5509 - val_accuracy: 0.4811\n",
            "Epoch 71/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 2.8671 - accuracy: 0.7896 - val_loss: 3.5351 - val_accuracy: 0.4717\n",
            "Epoch 72/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 2.8505 - accuracy: 0.7967 - val_loss: 3.5266 - val_accuracy: 0.4528\n",
            "Epoch 73/100\n",
            "4/4 [==============================] - 1s 206ms/step - loss: 2.8305 - accuracy: 0.7943 - val_loss: 3.5374 - val_accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "4/4 [==============================] - 1s 211ms/step - loss: 2.8134 - accuracy: 0.7920 - val_loss: 3.5228 - val_accuracy: 0.4906\n",
            "Epoch 75/100\n",
            "4/4 [==============================] - 1s 208ms/step - loss: 2.7719 - accuracy: 0.8038 - val_loss: 3.5201 - val_accuracy: 0.4717\n",
            "Epoch 76/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 2.7564 - accuracy: 0.8274 - val_loss: 3.5409 - val_accuracy: 0.4811\n",
            "Epoch 77/100\n",
            "4/4 [==============================] - 1s 188ms/step - loss: 2.7493 - accuracy: 0.8014 - val_loss: 3.5093 - val_accuracy: 0.4811\n",
            "Epoch 78/100\n",
            "4/4 [==============================] - 1s 208ms/step - loss: 2.7265 - accuracy: 0.8109 - val_loss: 3.5040 - val_accuracy: 0.4717\n",
            "Epoch 79/100\n",
            "4/4 [==============================] - 1s 211ms/step - loss: 2.7368 - accuracy: 0.8156 - val_loss: 3.5743 - val_accuracy: 0.4717\n",
            "Epoch 80/100\n",
            "4/4 [==============================] - 1s 203ms/step - loss: 2.7027 - accuracy: 0.7801 - val_loss: 3.5823 - val_accuracy: 0.4245\n",
            "Epoch 81/100\n",
            "4/4 [==============================] - 1s 217ms/step - loss: 2.7358 - accuracy: 0.7920 - val_loss: 3.5007 - val_accuracy: 0.4717\n",
            "Epoch 82/100\n",
            "4/4 [==============================] - 1s 213ms/step - loss: 2.6588 - accuracy: 0.8463 - val_loss: 3.5086 - val_accuracy: 0.4811\n",
            "Epoch 83/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 2.6317 - accuracy: 0.8322 - val_loss: 3.5193 - val_accuracy: 0.4717\n",
            "Epoch 84/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 2.6423 - accuracy: 0.8487 - val_loss: 3.5471 - val_accuracy: 0.4528\n",
            "Epoch 85/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 2.6165 - accuracy: 0.8274 - val_loss: 3.5029 - val_accuracy: 0.4811\n",
            "Epoch 86/100\n",
            "4/4 [==============================] - 1s 214ms/step - loss: 2.5917 - accuracy: 0.8582 - val_loss: 3.5219 - val_accuracy: 0.4811\n",
            "Epoch 87/100\n",
            "4/4 [==============================] - 1s 209ms/step - loss: 2.5842 - accuracy: 0.8629 - val_loss: 3.5579 - val_accuracy: 0.4245\n",
            "Epoch 88/100\n",
            "4/4 [==============================] - 1s 210ms/step - loss: 2.5706 - accuracy: 0.8392 - val_loss: 3.5786 - val_accuracy: 0.4717\n",
            "Epoch 89/100\n",
            "4/4 [==============================] - 1s 191ms/step - loss: 2.5663 - accuracy: 0.8558 - val_loss: 3.5197 - val_accuracy: 0.4434\n",
            "Epoch 90/100\n",
            "4/4 [==============================] - 1s 208ms/step - loss: 2.5293 - accuracy: 0.8582 - val_loss: 3.5285 - val_accuracy: 0.4811\n",
            "Epoch 91/100\n",
            "4/4 [==============================] - 1s 192ms/step - loss: 2.5096 - accuracy: 0.8582 - val_loss: 3.5321 - val_accuracy: 0.5000\n",
            "Epoch 92/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 2.5308 - accuracy: 0.8487 - val_loss: 3.5419 - val_accuracy: 0.4811\n",
            "Epoch 93/100\n",
            "4/4 [==============================] - 1s 190ms/step - loss: 2.5220 - accuracy: 0.8511 - val_loss: 3.5216 - val_accuracy: 0.4528\n",
            "Epoch 94/100\n",
            "4/4 [==============================] - 1s 194ms/step - loss: 2.4496 - accuracy: 0.8913 - val_loss: 3.5205 - val_accuracy: 0.4717\n",
            "Epoch 95/100\n",
            "4/4 [==============================] - 1s 229ms/step - loss: 2.4277 - accuracy: 0.8936 - val_loss: 3.5276 - val_accuracy: 0.4434\n",
            "Epoch 96/100\n",
            "4/4 [==============================] - 1s 223ms/step - loss: 2.4208 - accuracy: 0.8913 - val_loss: 3.5340 - val_accuracy: 0.4906\n",
            "Epoch 97/100\n",
            "4/4 [==============================] - 1s 227ms/step - loss: 2.3910 - accuracy: 0.9102 - val_loss: 3.5803 - val_accuracy: 0.4623\n",
            "Epoch 98/100\n",
            "4/4 [==============================] - 1s 192ms/step - loss: 2.3870 - accuracy: 0.8960 - val_loss: 3.5755 - val_accuracy: 0.4623\n",
            "Epoch 99/100\n",
            "4/4 [==============================] - 1s 207ms/step - loss: 2.3716 - accuracy: 0.9007 - val_loss: 3.5596 - val_accuracy: 0.4528\n",
            "Epoch 100/100\n",
            "4/4 [==============================] - 1s 189ms/step - loss: 2.3569 - accuracy: 0.9007 - val_loss: 3.5563 - val_accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will train our model on 100 epochs and a batch size of 128. You can try using more number of epochs to increase accuracy. During each epochs we can see how the model is performing by viewing the training and validation accuracy."
      ],
      "metadata": {
        "id": "g4MfIlH-LsvI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training history\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.plot(history.history['accuracy'], color='r')\n",
        "plt.plot(history.history['val_accuracy'], color='b')\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'val'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "lzcR_1DyLtAr",
        "outputId": "cc37f1f4-2170-4479-ad58-85dd740a8d5e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "score = model.evaluate(X_test, Y_test)\n",
        "print('Accuracy over the test set: \\n ', round((score[1]*100), 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0Ruk-ODLxrR",
        "outputId": "900be331-7827-4131-bdbf-a1fd685e8c66"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 1s 476ms/step\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 3.5276 - accuracy: 0.5254\n",
            "Accuracy over the test set: \n",
            "  52.54 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting image to compare\n",
        "plt.imshow(X_test[50,:,:,:])\n",
        "plt.show()\n",
        "\n",
        "# Finding max value from predition list and comaparing original value vs predicted\n",
        "print(\"Originally : \",labels['breed'][np.argmax(Y_test[50])])\n",
        "print(\"Predicted : \",labels['breed'][np.argmax(Y_pred[50])])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "hcMN9ryJL0v1",
        "outputId": "c180ff25-f9f4-4e4d-c10b-4d4384d3a32e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Originally :  entlebucher\n",
            "Predicted :  entlebucher\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion:"
      ],
      "metadata": {
        "id": "hNCpPyRsL5Sq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We started with downloading the dataset creating the model and finding out the predictions using the model. We can optimize different hyper parameters in order to tune this model for a higher accuracy. This model can be used to predict different breeds of dogs which can be further used by different NGO's working on saving animals and for educational purposes also."
      ],
      "metadata": {
        "id": "0L-EmR9KL23Y"
      }
    }
  ]
}