DL2 doggo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled2.ipynb",
      "provenance": [],
      "mount_file_id": "17QQ1BlqEqoGbH6bAz6YMxDpO3ZdfGqCI",
      "authorship_tag": "ABX9TyO3yrBsaTTcmPLAwa0R+ZGv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhi9rocks/DL/blob/master/DL2%20doggo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiwFuA8x7aLm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cc9eb7dc-183d-421c-d0ac-f90eaf0c0363"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yH8H5KJB8zCw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from os.path import join\n",
        "\n",
        "\n",
        "#hot_dog_image_dir = '../input/hot-dog-not-hot-dog/seefood/train/hot_dog'\n",
        "\n",
        "#hot_dog_paths = [join(hot_dog_image_dir,filename) for filename in \n",
        "  #                          ['1000288.jpg',\n",
        "  #                           '127117.jpg']]\n",
        "\n",
        "#not_hot_dog_image_dir = '../input/hot-dog-not-hot-dog/seefood/train/not_hot_dog'\n",
        "#not_hot_dog_paths = [join(not_hot_dog_image_dir, filename) for filename in\n",
        " #                           ['823536.jpg',\n",
        "  #                           '99890.jpg']]\n",
        "\n",
        "#img_paths = hot_dog_paths + not_hot_dog_paths"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87d3-Iam80B8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "196c1f48-62c8-4a7c-a03d-ae7fa7c11a37"
      },
      "source": [
        "cd drive/My\\ Drive/pics"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'drive/My Drive/pics'\n",
            "/content/drive/My Drive/pics\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2Zso-vT9Pf7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3e56e1f2-0e7e-4b62-e288-f4713932a070"
      },
      "source": [
        "ls"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'doggo 2.jpg'  'doggo 4.jpg'   IMG_20191004_184933488.jpg\n",
            "'doggo 3.jpg'   doggo.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fMkbqNj9v0v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_paths=['doggo 4.jpg']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1i0P61t9_Z3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import Image, display\n",
        "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "#from learntools.deep_learning.decode_predictions import decode_predictions\n",
        "import numpy as np\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "068jsQgK-P5q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_size = 224\n",
        "\n",
        "def read_and_prep_images(img_paths, img_height=image_size, img_width=image_size):\n",
        "    imgs = [load_img(img_path, target_size=(img_height, img_width)) for img_path in img_paths]\n",
        "    img_array = np.array([img_to_array(img) for img in imgs])\n",
        "    output = preprocess_input(img_array)\n",
        "    return(output)\n",
        "\n",
        "\n",
        "my_model = ResNet50(weights='imagenet')\n",
        "test_data = read_and_prep_images(img_paths)\n",
        "preds = my_model.predict(test_data)\n",
        "\n",
        "most_likely_labels = decode_predictions(preds, top=3)[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9bRPBjf-RIt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "9a678357-fa98-4126-9d64-04eb61342e31"
      },
      "source": [
        "for i, img_path in enumerate(img_paths):\n",
        "    display(Image(img_path))\n",
        "    print(most_likely_labels[i])"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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FJRQAtFJRQAtFJRQAtFJRQAtFJRQAtFJRQAtFJRQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//9k=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "('n02099712', 'Labrador_retriever', 0.99131376)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWRle6P0-U_9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "95c74734-8a1a-4522-be87-c9c6331ba27d"
      },
      "source": [
        "most_likely_labels"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('n02099712', 'Labrador_retriever', 0.99131376),\n",
              " ('n02099601', 'golden_retriever', 0.005707628),\n",
              " ('n02108422', 'bull_mastiff', 0.0007880731)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fNYGiz0-bn2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}