Cats&Dogs_classification_from_scratch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMRlLsUX57Q+qpBtDV5WGR1"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
      "metadata": {
        "id": "qUnh86ZUUE5W"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "rzhTUM8FUczN",
        "outputId": "a965a83f-8bd0-4a05-f4a1-9f239703c69b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d95a6854-229e-40c3-b97b-81d90932407f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d95a6854-229e-40c3-b97b-81d90932407f\">\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\":\"nishanikasineshan\",\"key\":\"8d3ca28c3cefdd91fc84a46a594b1404\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "Na8v0R-UUovh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "QGbwrBUqUtSU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "BKpZd25lUxnN"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d samuelcortinhas/cats-and-dogs-image-classification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXZRv5FVU1oa",
        "outputId": "892d2ea9-b1e3-4fc5-d738-97e5bf091250"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading cats-and-dogs-image-classification.zip to /content\n",
            " 76% 49.0M/64.4M [00:00<00:00, 119MB/s]\n",
            "100% 64.4M/64.4M [00:00<00:00, 128MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/cats-and-dogs-image-classification.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_55ejWDV17t",
        "outputId": "7e43cbd8-92c2-4106-9788-917bc2ce9608"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/cats-and-dogs-image-classification.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import required libraries\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import keras.utils as image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Convolution2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten"
      ],
      "metadata": {
        "id": "dkkWBiGCV-hU"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir='/content/train'\n",
        "test_dir='/content/test'"
      ],
      "metadata": {
        "id": "AetBUKBVWeDY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=[]\n",
        "x_test=[]\n",
        "\n",
        "for folder in os.listdir(train_dir):\n",
        "  sub_path=train_dir+\"/\"+folder\n",
        "  for img in os.listdir(sub_path):\n",
        "    img_path=sub_path+\"/\"+img\n",
        "    img_arr=cv2.imread(img_path)\n",
        "    img_arr=cv2.resize(img_arr,(500,500))\n",
        "    x_train.append(img_arr)\n",
        "\n",
        "for folder in os.listdir(test_dir):\n",
        "  sub_path=test_dir+\"/\"+folder\n",
        "  for img in os.listdir(sub_path):\n",
        "    img_path=sub_path+\"/\"+img\n",
        "    img_arr=cv2.imread(img_path)\n",
        "    img_arr=cv2.resize(img_arr,(500,500))\n",
        "    x_test.append(img_arr)"
      ],
      "metadata": {
        "id": "C3IgNhBfb6IP"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "im=Image.fromarray(x_train[0])\n",
        "plt.imshow(im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "sB3p59treyRJ",
        "outputId": "3a153a15-6986-47f3-b962-c3a77eb8367b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f38ecb50280>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "im.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuC-20sTgvBL",
        "outputId": "f4ba0581-9857-41fe-bb20-2a5b9a29b683"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 500)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=np.array(x_train)\n",
        "x_test=np.array(x_test)"
      ],
      "metadata": {
        "id": "F9Nbkvp2g5GV"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=x_train/255.0\n",
        "x_test=x_test/255.0"
      ],
      "metadata": {
        "id": "0B1Cg3CJhDL3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "metadata": {
        "id": "wlxETx8ZizMk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set = train_datagen.flow_from_directory(train_dir,\n",
        "                                                 target_size = (64, 64),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'binary')\n",
        "test_set = test_datagen.flow_from_directory(test_dir,\n",
        "                                            target_size = (64, 64),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'binary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYJrF8LCjJFd",
        "outputId": "42ca2747-ee22-46be-bbc7-46092497f9c4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 557 images belonging to 2 classes.\n",
            "Found 140 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Convolution2D(32,3,3,input_shape=(64,64,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "aj1McDpCaseS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "d5J168sykZbh"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit_generator(training_set,epochs=30,validation_data=test_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gD30FwSji5F",
        "outputId": "882df42a-33cf-4e83-a950-c682b51c4ddd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-c78559786341>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(training_set,epochs=30,validation_data=test_set)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "18/18 [==============================] - 9s 467ms/step - loss: 0.7206 - accuracy: 0.5189 - val_loss: 0.7175 - val_accuracy: 0.5143\n",
            "Epoch 2/30\n",
            "18/18 [==============================] - 8s 445ms/step - loss: 0.6733 - accuracy: 0.5512 - val_loss: 0.6842 - val_accuracy: 0.5500\n",
            "Epoch 3/30\n",
            "18/18 [==============================] - 7s 414ms/step - loss: 0.6251 - accuracy: 0.6643 - val_loss: 0.7268 - val_accuracy: 0.5357\n",
            "Epoch 4/30\n",
            "18/18 [==============================] - 9s 486ms/step - loss: 0.6308 - accuracy: 0.6284 - val_loss: 0.6828 - val_accuracy: 0.5857\n",
            "Epoch 5/30\n",
            "18/18 [==============================] - 9s 485ms/step - loss: 0.5946 - accuracy: 0.6912 - val_loss: 0.6816 - val_accuracy: 0.5786\n",
            "Epoch 6/30\n",
            "18/18 [==============================] - 10s 570ms/step - loss: 0.5335 - accuracy: 0.7828 - val_loss: 0.6499 - val_accuracy: 0.6571\n",
            "Epoch 7/30\n",
            "18/18 [==============================] - 8s 455ms/step - loss: 0.4779 - accuracy: 0.8061 - val_loss: 0.6655 - val_accuracy: 0.6143\n",
            "Epoch 8/30\n",
            "18/18 [==============================] - 9s 496ms/step - loss: 0.4399 - accuracy: 0.8456 - val_loss: 0.6690 - val_accuracy: 0.6357\n",
            "Epoch 9/30\n",
            "18/18 [==============================] - 8s 448ms/step - loss: 0.4211 - accuracy: 0.8133 - val_loss: 0.6790 - val_accuracy: 0.6143\n",
            "Epoch 10/30\n",
            "18/18 [==============================] - 7s 410ms/step - loss: 0.3699 - accuracy: 0.8977 - val_loss: 0.6681 - val_accuracy: 0.6071\n",
            "Epoch 11/30\n",
            "18/18 [==============================] - 9s 482ms/step - loss: 0.3456 - accuracy: 0.8977 - val_loss: 0.6864 - val_accuracy: 0.6429\n",
            "Epoch 12/30\n",
            "18/18 [==============================] - 8s 474ms/step - loss: 0.3007 - accuracy: 0.9192 - val_loss: 0.7084 - val_accuracy: 0.6214\n",
            "Epoch 13/30\n",
            "18/18 [==============================] - 7s 425ms/step - loss: 0.2566 - accuracy: 0.9408 - val_loss: 0.6936 - val_accuracy: 0.6500\n",
            "Epoch 14/30\n",
            "18/18 [==============================] - 9s 523ms/step - loss: 0.2222 - accuracy: 0.9569 - val_loss: 0.7766 - val_accuracy: 0.6071\n",
            "Epoch 15/30\n",
            "18/18 [==============================] - 8s 456ms/step - loss: 0.2075 - accuracy: 0.9461 - val_loss: 0.7693 - val_accuracy: 0.6286\n",
            "Epoch 16/30\n",
            "18/18 [==============================] - 9s 482ms/step - loss: 0.1695 - accuracy: 0.9731 - val_loss: 0.7660 - val_accuracy: 0.6571\n",
            "Epoch 17/30\n",
            "18/18 [==============================] - 9s 477ms/step - loss: 0.1664 - accuracy: 0.9659 - val_loss: 0.7441 - val_accuracy: 0.6714\n",
            "Epoch 18/30\n",
            "18/18 [==============================] - 9s 480ms/step - loss: 0.1403 - accuracy: 0.9803 - val_loss: 0.8473 - val_accuracy: 0.6286\n",
            "Epoch 19/30\n",
            "18/18 [==============================] - 7s 410ms/step - loss: 0.1281 - accuracy: 0.9749 - val_loss: 0.8311 - val_accuracy: 0.6500\n",
            "Epoch 20/30\n",
            "18/18 [==============================] - 7s 408ms/step - loss: 0.1015 - accuracy: 0.9874 - val_loss: 0.9054 - val_accuracy: 0.6214\n",
            "Epoch 21/30\n",
            "18/18 [==============================] - 9s 496ms/step - loss: 0.0872 - accuracy: 0.9964 - val_loss: 1.0071 - val_accuracy: 0.5929\n",
            "Epoch 22/30\n",
            "18/18 [==============================] - 9s 535ms/step - loss: 0.0944 - accuracy: 0.9874 - val_loss: 0.9813 - val_accuracy: 0.5857\n",
            "Epoch 23/30\n",
            "18/18 [==============================] - 8s 434ms/step - loss: 0.0791 - accuracy: 0.9946 - val_loss: 0.9283 - val_accuracy: 0.6500\n",
            "Epoch 24/30\n",
            "18/18 [==============================] - 9s 505ms/step - loss: 0.0622 - accuracy: 0.9982 - val_loss: 1.0244 - val_accuracy: 0.6429\n",
            "Epoch 25/30\n",
            "18/18 [==============================] - 8s 426ms/step - loss: 0.0497 - accuracy: 0.9982 - val_loss: 1.0166 - val_accuracy: 0.6571\n",
            "Epoch 26/30\n",
            "18/18 [==============================] - 7s 416ms/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 1.0209 - val_accuracy: 0.6429\n",
            "Epoch 27/30\n",
            "18/18 [==============================] - 7s 420ms/step - loss: 0.0435 - accuracy: 1.0000 - val_loss: 1.0834 - val_accuracy: 0.6500\n",
            "Epoch 28/30\n",
            "18/18 [==============================] - 7s 413ms/step - loss: 0.0407 - accuracy: 1.0000 - val_loss: 1.0806 - val_accuracy: 0.6429\n",
            "Epoch 29/30\n",
            "18/18 [==============================] - 9s 524ms/step - loss: 0.0340 - accuracy: 1.0000 - val_loss: 1.1254 - val_accuracy: 0.6643\n",
            "Epoch 30/30\n",
            "18/18 [==============================] - 9s 519ms/step - loss: 0.0307 - accuracy: 1.0000 - val_loss: 1.1899 - val_accuracy: 0.6357\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f38ec1d97f0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rH41tIeAmeT8",
        "outputId": "46855153-863d-4374-fb4a-37b7ab53e0f5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 1s 287ms/step - loss: 1.1899 - accuracy: 0.6357\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.189937949180603, 0.6357142925262451]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W78Zd2veoVxH",
        "outputId": "d48e429b-9072-4f9a-97d8-e39f4f2b3234"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cats': 0, 'dogs': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_img=image.load_img('/content/bull_dog.jpg',target_size=(64,64))\n",
        "predict_modified = image.img_to_array(predict_img)\n",
        "predict_modified = predict_modified / 255\n",
        "predict_modified = np.expand_dims(predict_modified, axis = 0)\n",
        "prediction= model.predict(predict_modified)\n",
        "if prediction[0][0] >= 0.5:\n",
        "  pred = 'dog'\n",
        "  probability = prediction[0][0]\n",
        "  print (\"probability = \" + str(probability))\n",
        "  print(\"Prediction = \" + pred)\n",
        "else:\n",
        "  pred = 'cat'\n",
        "  probability = 1 - prediction[0][0]\n",
        "  print (\"probability = \" + str(probability))\n",
        "  print(\"Prediction = \" + pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaPZ2s7Rmkuy",
        "outputId": "8f4adb58-18d6-48bc-9f10-a9548d1fb840"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 31ms/step\n",
            "probability = 0.9892852\n",
            "Prediction = dog\n"
          ]
        }
      ]
    }
  ]
}