DeepLearning/ImageRecog/DeepLearningWithKeras_ImageRecog_VGG16.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ngebodh/MachineLearning/blob/master/DeepLearning/ImageRecog/DeepLearningWithKeras_ImageRecog_VGG16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ikdAigw6yTzW"
      },
      "source": [
        "# Image Recognition Using Keras-VGG16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "S4t7l2HGyTzY"
      },
      "source": [
        "## About this project\n",
        "Our goal with this library is to leverage a pretrained model to perform image classification. We will use the VGG16 model trained on the ImageNet dataset to perform some simple image classification. \n",
        "\n",
        "The process will work similar to the image below:\n",
        "\n",
        "____________________________________________________________________________________________________\n",
        "<img src=\"https://miro.medium.com/v2/resize:fit:1200/1*ATIx1SmkEH0FaL_5fMvX2w.jpeg\" width=\"50%\" hight=\"10%\">\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IgcelEt0yTzZ"
      },
      "source": [
        "## Import Libraries \n",
        "\n",
        "Here we import the libraries that we will be using throughout the notebook.\n",
        "\n",
        "The libraries that we will be using include:<br/>\n",
        "* [Numpy](https://www.numpy.org/)\n",
        "* [Keras](https://keras.io/) using the [Tensorflow](https://www.tensorflow.org/) backend:\n",
        "    * Keras-[VGG16](https://neurohive.io/en/popular-networks/vgg16/)\n",
        "    * Keras-image processing library\n",
        "* [Matplotlib](https://matplotlib.org/) <-for plotting and visualizations\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "colab_type": "code",
        "id": "TgugN3mAyTza",
        "outputId": "4bc749fc-58ef-45f7-ca2d-a66b9fc58d68",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ],
      "source": [
        "#Import\n",
        "\n",
        "import numpy as np\n",
        "from keras.applications import vgg16\n",
        "from keras.preprocessing import image\n",
        "\n",
        "#Image visualization\n",
        "%pylab inline\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "colab_type": "code",
        "id": "8x6F6n-HyTzg",
        "outputId": "1354a92e-8d28-4cc4-8891-721d6bb6a7a7",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0820 06:55:47.559752 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0820 06:55:47.580525 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0820 06:55:47.584277 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0820 06:55:47.619110 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0820 06:55:49.527782 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "W0820 06:55:49.529251 139747390875520 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model = vgg16.VGG16(weights='imagenet') #Load the model weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4G6MDgXvyTzk"
      },
      "source": [
        "## Load an Image\n",
        "\n",
        "Now we're going to load an image in and walk through the processing steps so that we can successfully load the image into Keras.\n",
        "We're going to pick an image that's a little tricky to see how the model does at predicting what's in the image. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "colab_type": "code",
        "id": "N1-df8pjyTzl",
        "outputId": "df7fc6de-a1ff-4016-e6ea-7d6d84e9f00e",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F193B021E80>"
            ]
          },
          "execution_count": 3,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imgaddress='panda.jpeg'\n",
        "img = image.load_img(imgaddress,target_size=(224,224)) #Try loading an image with the size specified \n",
        "img #Show image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "9kaJDpH-yTzo",
        "outputId": "9c7c1288-d687-4745-d793-7bc52a631b2e",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "execution_count": 4,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert to Numpy array\n",
        "arr = image.img_to_array(img) #Here we convert the image to a numpy array inorder to do further numeric manipulations to it.\n",
        "arr.shape #Height X Width X Color channel (RGB)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "MrEEIcy-yTzq",
        "outputId": "fbd154e5-06ba-4688-edfd-80a80d6677e1",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 224, 224, 3)"
            ]
          },
          "execution_count": 5,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Expand dimension\n",
        "arr = np.expand_dims(arr,axis=0) #Here we add an additional dimension to satisfy the input parameters for keras\n",
        "arr.shape #print out shape of image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "colab_type": "code",
        "id": "LmAAU-uTyTzv",
        "outputId": "258cc4d6-2884-420d-8036-e3e64dc25b1d",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "\n",
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            ]
          },
          "execution_count": 6,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Preprocessing\n",
        "arr = vgg16.preprocess_input(arr) #Normalize data.\n",
        "arr "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Wl7VYWORyTzz"
      },
      "source": [
        "## Predict and Pull Out Predicted Class\n",
        "\n",
        "Now that we've set up out model and image let's now use it to predict what's depicted in the image. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "LTznnfFNyTz0",
        "outputId": "d1b9e103-54c0-412f-cf16-8f7fe7cb86fc",
        "trusted": true
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      "outputs": [
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              "        1.56490842e-07, 8.27659292e-08, 1.04609571e-05, 2.30176511e-07,\n",
              "        1.91370226e-08, 1.81046983e-06, 9.00357975e-07, 5.29871431e-05,\n",
              "        7.40103758e-07, 3.64682023e-07, 5.21576703e-06, 3.12630868e-06,\n",
              "        1.75079506e-07, 1.49572236e-06, 1.39119747e-05, 2.16342323e-06,\n",
              "        5.10878593e-08, 7.57715171e-08, 2.91329430e-04, 7.05078307e-08,\n",
              "        4.76907474e-08, 1.81246867e-06, 3.56844012e-08, 1.39066424e-05,\n",
              "        1.64770972e-06, 1.75037949e-05, 2.87580058e-08, 5.07249661e-06,\n",
              "        2.15312548e-06, 8.98535575e-08, 2.11578185e-06, 5.05718951e-08,\n",
              "        1.54342104e-08, 1.89451919e-06, 1.68519367e-08, 2.59888602e-05,\n",
              "        2.74932461e-07, 2.00012185e-08, 1.97279704e-08, 2.23891875e-05,\n",
              "        2.34228423e-07, 6.33858690e-07, 3.52894244e-06, 6.23561618e-07,\n",
              "        1.59773478e-07, 2.20319460e-04, 3.95545356e-07, 3.39762863e-07,\n",
              "        4.59922553e-08, 4.72152166e-08, 1.57768386e-06, 5.90417631e-06,\n",
              "        8.65793925e-09, 7.61667280e-08, 6.85525734e-08, 1.12686944e-08,\n",
              "        1.39720186e-07, 1.64703977e-07, 1.55557009e-05, 2.25209746e-07,\n",
              "        2.34410757e-09, 1.32394282e-08, 1.03016976e-06, 2.98369230e-07,\n",
              "        7.26395433e-09, 2.41500743e-07, 1.08037386e-06, 5.97784222e-08,\n",
              "        2.72129855e-05, 3.23625699e-07, 2.60866454e-05, 1.82006920e-06,\n",
              "        4.56080656e-04, 2.77210631e-07, 2.13454285e-08, 1.93574422e-07,\n",
              "        1.08476015e-05, 1.02433638e-07, 9.38850553e-06, 1.37056766e-08,\n",
              "        2.66808460e-08, 3.90996036e-07, 2.12439293e-08, 5.76670209e-06,\n",
              "        3.54932149e-06, 1.92957987e-08, 5.46688206e-09, 3.46115507e-06,\n",
              "        4.60707452e-06, 8.13636859e-07, 7.05712333e-09, 7.61370202e-08,\n",
              "        1.58197563e-05, 1.59634112e-07, 4.25841016e-08, 8.63675780e-08,\n",
              "        3.15127153e-07, 1.37078018e-07, 8.26236146e-09, 7.20345440e-07,\n",
              "        7.70038412e-07, 5.76401953e-06, 1.76408343e-04, 6.24262839e-06,\n",
              "        1.91485956e-08, 5.60556614e-08, 2.70809073e-06, 9.02508020e-07,\n",
              "        2.95503586e-08, 1.55460680e-06, 1.79604989e-08, 1.41473531e-04,\n",
              "        7.05745742e-06, 5.13960731e-06, 8.36539414e-07, 4.59378988e-08,\n",
              "        1.47361149e-07, 1.56827085e-02, 5.41640084e-08, 3.66196460e-08,\n",
              "        2.05718635e-07, 5.28138003e-07, 2.88077587e-07, 1.40117641e-06,\n",
              "        6.73106251e-06, 1.06973950e-07, 4.91104792e-08, 4.73942492e-07,\n",
              "        3.94559038e-07, 2.09052200e-06, 2.24188443e-05, 3.96605492e-06,\n",
              "        5.94256699e-06, 6.54013110e-09, 1.66611756e-07, 2.93421021e-08,\n",
              "        1.59431323e-07, 4.18593390e-06, 1.75135465e-05, 5.42386829e-07,\n",
              "        2.94390543e-06, 3.06368929e-06, 1.10581439e-07, 1.37935990e-06,\n",
              "        4.67533425e-08, 2.87463394e-07, 1.09019925e-06, 3.78070943e-08,\n",
              "        1.95305256e-05, 2.49075583e-07, 1.88775157e-05, 2.43823506e-05,\n",
              "        1.42132376e-06, 1.68880410e-06, 1.35500656e-07, 9.74761178e-07,\n",
              "        4.05859140e-07, 7.13980057e-07, 5.38881864e-07, 2.40283862e-05,\n",
              "        1.32920263e-07, 6.72690078e-07, 2.31001380e-07, 5.29700870e-08,\n",
              "        3.78082837e-06, 1.23375179e-02, 7.97549365e-06, 4.27160302e-07,\n",
              "        6.40690558e-08, 3.79926405e-07, 2.92688952e-07, 4.44884591e-08,\n",
              "        3.11600665e-08, 1.39019543e-08, 1.04999273e-07, 1.77168033e-06,\n",
              "        1.93643405e-06, 8.88002978e-06, 4.42980927e-05, 2.48587895e-07,\n",
              "        1.17284152e-07, 6.74648604e-08, 4.71922789e-07, 1.98075540e-07,\n",
              "        5.25653661e-07, 2.69821498e-06, 3.84169937e-07, 4.97369570e-07,\n",
              "        1.24303801e-06, 8.41954346e-08, 2.03592112e-08, 4.53609594e-09,\n",
              "        7.37740535e-08, 2.57440161e-06, 3.67120805e-07, 2.96055891e-08,\n",
              "        4.95262095e-04, 4.38857199e-07, 9.76660317e-07, 5.29256283e-09,\n",
              "        6.05305331e-07, 1.11715795e-04, 4.06630285e-08, 1.13087867e-06,\n",
              "        9.70791945e-08, 2.07313428e-06, 1.46531249e-07, 6.29572794e-09,\n",
              "        4.29507281e-06, 7.06429546e-07, 1.90187802e-06, 8.96300136e-08,\n",
              "        3.10500106e-08, 4.11359746e-09, 2.76818866e-07, 1.82589592e-05,\n",
              "        2.08955339e-06, 1.29551552e-08, 4.04326011e-06, 1.43453362e-05,\n",
              "        4.76714958e-05, 3.86165802e-08, 1.10205278e-07, 6.61844552e-08,\n",
              "        3.30068275e-08, 2.15795843e-08, 2.05103703e-07, 3.29985816e-08,\n",
              "        5.43825900e-06, 1.20013965e-05, 5.67736333e-06, 1.09627951e-08,\n",
              "        9.58354804e-07, 4.88013825e-07, 7.25631082e-07, 5.84639508e-07,\n",
              "        2.96557209e-06, 2.47400664e-07, 5.33883394e-06, 5.80091744e-08,\n",
              "        1.04031699e-06, 5.33550838e-03, 1.83874741e-04, 3.44726857e-07,\n",
              "        5.14731846e-06, 1.81518001e-09, 5.55293305e-08, 3.86142405e-04,\n",
              "        2.38253932e-07, 1.85367881e-07, 2.17458478e-08, 2.51325895e-04,\n",
              "        4.90558065e-08, 1.98287512e-06, 1.05115441e-06, 1.89473880e-07,\n",
              "        1.15158309e-06, 1.69682278e-06, 7.51792413e-07, 9.05999258e-08,\n",
              "        2.11496390e-05, 2.63543797e-07, 2.37221093e-06, 2.13503561e-08,\n",
              "        6.15924682e-06, 3.40474315e-08, 3.20646043e-09, 6.14307325e-08,\n",
              "        1.45927561e-05, 8.64352216e-08, 1.43941094e-07, 5.15253191e-07,\n",
              "        6.36787192e-08, 1.10993224e-08, 2.80374434e-06, 6.52698742e-04,\n",
              "        1.50569051e-08, 1.22862764e-06, 1.08190960e-07, 3.12823225e-08,\n",
              "        3.62867247e-09, 4.03211828e-07, 3.97046342e-08, 1.65756101e-05,\n",
              "        1.71216607e-06, 1.03340312e-06, 6.63309265e-08, 6.99213203e-08,\n",
              "        5.04177979e-06, 5.02478542e-07, 1.48223538e-04, 5.60587971e-03,\n",
              "        1.22078291e-07, 1.86379359e-03, 2.41065627e-05, 3.56008854e-06,\n",
              "        2.09815774e-08, 1.45322669e-08, 8.51550794e-08, 4.63173137e-06,\n",
              "        7.07669523e-09, 7.60982232e-07, 1.78681694e-05, 5.82860002e-06,\n",
              "        6.26186036e-09, 6.71652529e-08, 3.74762585e-08, 2.02472563e-08,\n",
              "        3.52314004e-07, 6.38601023e-07, 2.49788741e-07, 4.97702857e-08,\n",
              "        8.95248853e-09, 3.09092769e-07, 6.68525715e-08, 6.17728847e-06,\n",
              "        3.55519774e-07, 4.82555379e-06, 4.56816724e-06, 1.00632030e-06,\n",
              "        5.42093858e-06, 2.09320729e-06, 3.83092839e-07, 4.86991212e-06,\n",
              "        2.43694722e-05, 2.56681062e-07, 8.63832668e-07, 6.09998722e-07,\n",
              "        1.07966014e-06, 3.76456956e-06, 1.74969591e-06, 1.18637536e-06,\n",
              "        1.50784388e-07, 1.34870800e-06, 9.96130325e-07, 1.12748012e-06,\n",
              "        2.89275135e-07, 1.01199373e-06, 3.10480566e-08, 1.45067531e-07,\n",
              "        1.26558336e-06, 3.39497774e-06, 3.42935181e-07, 9.83362384e-07,\n",
              "        3.81136658e-07, 1.72278953e-06, 6.00352860e-06, 1.54326791e-08,\n",
              "        3.96012638e-07, 1.47352722e-07, 1.79122722e-07, 6.93016375e-07,\n",
              "        8.36267500e-05, 2.56906588e-07, 3.51594025e-07, 8.25545783e-08,\n",
              "        5.67604445e-07, 1.08268534e-06, 1.89774516e-06, 2.16050283e-03,\n",
              "        4.29603785e-01, 1.07624714e-04, 1.10562395e-08, 5.84981933e-07,\n",
              "        7.06443331e-08, 2.95814175e-07, 4.58169644e-08, 3.93046840e-08,\n",
              "        1.51907518e-08, 2.29506000e-07, 4.63015475e-08, 1.30545716e-08,\n",
              "        5.95132610e-09, 3.17670242e-07, 3.65797881e-08, 3.37072038e-06,\n",
              "        4.28877343e-08, 1.46307002e-07, 9.32429156e-09, 4.02610453e-07,\n",
              "        2.71421470e-07, 2.34199042e-08, 3.92000175e-08, 3.31181809e-07,\n",
              "        7.20081275e-08, 9.55431823e-09, 3.60028992e-07, 4.12938306e-07,\n",
              "        3.22377531e-07, 2.96478007e-08, 1.23911505e-07, 1.00578827e-05]],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 7,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Predict\n",
        "preds = model.predict(arr)\n",
        "preds #Prints out all the prections for a number of classes. We just want to top predctions. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "colab_type": "code",
        "id": "tLzkJoMCyTz5",
        "outputId": "aa4e293f-f7b3-4734-f957-30b9eff21ab3",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
            "40960/35363 [==================================] - 0s 0us/step\n",
            "-coffee_mug: 50.948304 % likelihood\n",
            "-cup: 42.960379 % likelihood\n",
            "-pitcher: 1.568271 % likelihood\n",
            "-saltshaker: 1.233752 % likelihood\n",
            "-coffeepot: 0.845757 % likelihood\n",
            "-water_jug: 0.560588 % likelihood\n",
            "-teapot: 0.533551 % likelihood\n",
            "-espresso_maker: 0.225793 % likelihood\n",
            "-espresso: 0.216050 % likelihood\n",
            "-whiskey_jug: 0.186379 % likelihood\n"
          ]
        }
      ],
      "source": [
        "# Predictions for most likely classes. \n",
        "\n",
        "prediction_classes =vgg16.decode_predictions(preds, top=10)\n",
        "for imagenet_id, name, likelihood in prediction_classes[0]:\n",
        "    print(\"-{}: {:2f} % likelihood\".format(name, likelihood*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kQTaYRhuyTz9"
      },
      "source": [
        "## Combine into a function!\n",
        "\n",
        "Here we'll use another image to test how the model performs at prediction and combine all the previous steps into a function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "wzsSwpR5yTz_",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def imgClasser(locc):\n",
        "    imgName = locc\n",
        "    #Re- Load in the sample image we want to predict\n",
        "    img =image.load_img(imgName, target_size=(224,224)) #Load in the image and resize\n",
        "    \n",
        "    #Convert image to array (flatten)\n",
        "    x=image.img_to_array(img)\n",
        "\n",
        "    #The neural network is actually expecting (a list) more than 1 image so we will trick it\n",
        "    #Add a 4th dimension to the array\n",
        "\n",
        "    x= np.expand_dims(x,axis=0)\n",
        "\n",
        "    #Normalize data to 0-1 instead of 0-255\n",
        "    x=vgg16.preprocess_input(x)\n",
        "    \n",
        "    \n",
        "    #Now we run the normalized data through the network and predict\n",
        "    predictions =model.predict(x)\n",
        "\n",
        "    #We will get back a predictions object with 1000 element array. \n",
        "    #Each element reps a probability that the input matches each of the 1000 objects \n",
        "    #that the network was trained on.\n",
        "\n",
        "    #Here we use a function to tell us the names of the objects that \n",
        "    #the network predicted. We only want to top 10 so we ask for ony 10\n",
        "\n",
        "    prediction_classes =vgg16.decode_predictions(predictions, top=10)\n",
        "\n",
        "    #Print out all the predictions\n",
        "    for imagenet_id, name, likelihood in prediction_classes[0]:\n",
        "        print(\"-{}: {:2f} % likelihood\".format(name, likelihood*100))\n",
        "    #Look at loaded image\n",
        "    plt.figure()\n",
        "    plt.imshow(img) \n",
        "    plt.title('Selected Image')\n",
        "    plt.show()  # display it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "colab_type": "code",
        "id": "8ubtk_ZdyT0D",
        "outputId": "6d8ebfb8-0f84-4f41-dece-fd76bee44c81",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-ant: 83.244991 % likelihood\n",
            "-tick: 9.916326 % likelihood\n",
            "-ground_beetle: 1.015474 % likelihood\n",
            "-barn_spider: 0.821714 % likelihood\n",
            "-cockroach: 0.662146 % likelihood\n",
            "-long-horned_beetle: 0.640894 % likelihood\n",
            "-lacewing: 0.417204 % likelihood\n",
            "-scorpion: 0.339317 % likelihood\n",
            "-centipede: 0.336908 % likelihood\n",
            "-mantis: 0.327309 % likelihood\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "imgClasser('ant.jpeg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The model was able to classify the image as containing an **ant** with **83.2% likelihood**"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "include_colab_link": true,
      "name": "DeepLearningWithKeras-ImageRecog-VGG16.ipynb",
      "provenance": [],
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}