Toy_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Toy Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMpBNPKnHuuAfr0iT4s55Vm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NethmiJayasinghe/FYP-Image-Classification/blob/main/Toy_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing Libraries\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import sequential\n",
        "from keras.layers.core import Dense,Flatten,Dropout\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import scipy\n",
        "from skimage.transform import resize\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "plt.style.use('fivethirtyeight')\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "Gjn76V6n5LwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for (dirpath, dirnames, filenames) in os.walk(\"/content/ImageSet/Folded Papers\"):     #change images from jpeg to png\n",
        "  for i in range(300):\n",
        "    im1=Image.open(dirpath+\"/\"+filenames[i])\n",
        "    im1.save(r'/content/ImageSet/Folded Paper/new'+str(i)+'.png')"
      ],
      "metadata": {
        "id": "m4GLQMQZDMmk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def makedata(a):           #Function to read and resize image\n",
        "  try:\n",
        "    img=mpimg.imread(a)\n",
        "    res = resize(img, (64,64))\n",
        "    return res\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "datasetnames=[]\n",
        "for i in range (17):\n",
        "  datasetnames+=[\"dataset\"+str(i)]\n",
        "print (len(datasetnames))\n",
        "\n",
        "foldernames=[\"Building Blocks\",\"Dinosaur\",\"Elephant Stuffed\",\"Pencils\",\"Unicorn\",\"Airplane\",\"Teddy Bear\",\"Balls\",\"Toy Cars\",\"Toy Trucks\",\"Telephone\",\"Tea Cup\",\"Cubes\",\"Lion\",\"Toy Boat\",\"Folded Paper\",\"Empty Bottles\"]\n",
        "for i in range(17):\n",
        "  dataset1=[]\n",
        "  for (dirpath, dirnames, filenames) in os.walk(\"/content/ImageSet/\"+foldernames[i]):   #getting images from the folder\n",
        "    A=len(filenames)\n",
        "    for j in range(A):\n",
        "      singledata=makedata(dirpath+\"/\"+filenames[j]) \n",
        "      try:                                                                   #resizing each image\n",
        "        if singledata.shape == (64,64,3):                                    #reshaping images to store\n",
        "          dataset1.append(singledata)\n",
        "      except:\n",
        "        pass\n",
        "  \n",
        "  datasetnames[i] = np.array(dataset1)\n",
        "\n",
        "print (datasetnames[4].shape)"
      ],
      "metadata": {
        "id": "Rgw635qrACuH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d865a963-163d-46fb-ebd9-0857803db61c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n",
            "(300, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(17):\n",
        "  np.savez(\"dataset\"+str(i)+\".npz\",X=datasetnames[i])"
      ],
      "metadata": {
        "id": "GCdGd0Z6TIVm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=np.load(\"/content/dataset0.npz\") \n",
        "x0=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset1.npz\") \n",
        "x1=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset2.npz\") \n",
        "x2=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset3.npz\") \n",
        "x3=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset4.npz\") \n",
        "x4=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset5.npz\") \n",
        "x5=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset6.npz\") \n",
        "x6=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset7.npz\") \n",
        "x7=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset8.npz\") \n",
        "x8=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset9.npz\") \n",
        "x9=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset10.npz\") \n",
        "x10=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset11.npz\") \n",
        "x11=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset12.npz\") \n",
        "x12=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset13.npz\") \n",
        "x13=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset14.npz\") \n",
        "x14=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset15.npz\") \n",
        "x15=np.array(data[\"X\"]/255)\n",
        "data=np.load(\"/content/dataset16.npz\") \n",
        "x16=np.array(data[\"X\"]/255)\n"
      ],
      "metadata": {
        "id": "P90JNkJQpi0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print (x16.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYjbAqgd4GHK",
        "outputId": "14e44317-6ec7-492f-ae8c-821492248bb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(300, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xx1 = np.concatenate((x0,x1,x2))\n",
        "xx2 = np.concatenate((x3,x4,x5))\n",
        "xx3 = np.concatenate((x6,x7,x8))\n",
        "xx4 = np.concatenate((x9,x10,x11))\n",
        "xx5 = np.concatenate((x12,x13,x14))\n",
        "xx6 = np.concatenate((x15,x16))\n",
        "xx7 = np.concatenate((xx1,xx2,xx3))\n",
        "xx8 = np.concatenate((xx4,xx5,xx6))\n",
        "\n",
        "Xdata = np.concatenate((xx7,xx8))"
      ],
      "metadata": {
        "id": "PHzM-Je54O24"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print (x0[10])"
      ],
      "metadata": {
        "id": "x3vEI2WO59ca"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = plt.imshow(x0[10]*255)        #check assign label with an image\n",
        "print(img)"
      ],
      "metadata": {
        "id": "5UpdtyB99Yft",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "d033e784-76c2-40e4-86a4-199f0189aab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesImage(34.56,20.16;375.84x233.28)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Yzeros= np.zeros(300,float)            #make array of zeros\n",
        "\n",
        "Yones= np.ones(300,float)            #make array of zeros"
      ],
      "metadata": {
        "id": "DMMqoDKl8Ibn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twos=Yones+Yones\n",
        "threes = twos+Yones\n",
        "fours= threes+Yones\n",
        "fives = fours+Yones\n",
        "sixes = fives+Yones\n",
        "sevens = sixes+Yones\n",
        "eights = sevens +Yones\n",
        "nines = eights +Yones\n",
        "tens = nines + Yones\n",
        "elevens = tens + Yones\n",
        "twelews = elevens + Yones\n",
        "thirteens = twelews + Yones\n",
        "Fourteens = thirteens + Yones\n",
        "Fifteens = Fourteens + Yones\n",
        "Sixteens = Fifteens + Yones\n",
        "r=np.concatenate((Yzeros,Yones,twos,threes,fours,fives,sixes,sevens,eights,nines,tens,elevens, twelews,thirteens,Fourteens, Fifteens, Sixteens))\n",
        "Ydata=r.reshape(5100,1)"
      ],
      "metadata": {
        "id": "F6iq8i8h8c50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c= np.arange(Xdata.shape[0])    #Shuffle both datasets in same order\n",
        "np.random.shuffle(c)\n",
        "\n",
        "Xdata = Xdata[c]\n",
        "Ydata = Ydata[c]"
      ],
      "metadata": {
        "id": "-Wh7H7gsYCtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = plt.imshow(Xdata[200]*255)        #check assign label with an image\n",
        "print(foldernames[int(Ydata[200])])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "XFnbhaMbYrr3",
        "outputId": "cbc416a9-aaf8-4f24-b413-69237ca6a86b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cubes\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = Xdata[0:4080]         \n",
        "x_test = Xdata[4080:5100]\n",
        "y_train = Ydata[0:4080]\n",
        "y_test = Ydata[4080:5100]\n",
        "print(x_train.shape)                   #Check datatype\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xe8VaLVrZCd5",
        "outputId": "cf27437e-5116-41ce-97dc-3e4a4f7d833d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4080, 64, 64, 3)\n",
            "(1020, 64, 64, 3)\n",
            "(4080, 1)\n",
            "(1020, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Classification=[\"Building Blocks\",\"Dinosaur\",\"Elephant Stuffed\",\"Pencils\",\"Unicorn\",\"Airplane\",\"Teddy Bear\",\"Balls\",\"Toy Cars\",\"Toy Trucks\",\"Telephone\",\"Tea Cup\",\"Cubes\",\"Lion\",\"Toy Boat\",\"Folded Papers\",\"Empty Bottles\"]         #Data labels\n",
        "y_train_one = to_categorical(y_train)                     #make y dataset to asign data labels\n",
        "y_test_one = to_categorical(y_test)\n",
        "print(y_train_one[3])\n",
        "print(y_test_one.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSBkSTG3ZtDE",
        "outputId": "abdd45f7-0013-4e6b-d0d7-2d849c9c7a4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            "(1020, 17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train[6])\n",
        "print(y_train_one[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYAH1JJ-R5US",
        "outputId": "077b4fb6-7511-46e7-cad2-d6161e378128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4.]\n",
            "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create the model architecture\n",
        "model = tf.keras.Sequential()\n",
        "#Add first layer\n",
        "model.add(Conv2D(64,(5,5), activation = 'relu', input_shape=(64,64,3)))\n",
        "#Add pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "#Add another convolutional layer\n",
        "model.add(Conv2D(64,(5,5), activation = 'relu'))\n",
        "#Add another pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "#Add a flattening layer\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(500, activation = 'relu'))\n",
        "#Add a dropout layer\n",
        "model.add(Dropout(0.5))\n",
        "#Add a layer with 250 neurons\n",
        "model.add(Dense(250, activation = 'relu'))\n",
        "#Add a layer with 17 neurons\n",
        "model.add(Dense(17, activation = 'softmax'))"
      ],
      "metadata": {
        "id": "C2UkSQbwZ67O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the model\n",
        "model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "cXN4EiYFaCsS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train the model\n",
        "hist = model.fit(x_train, y_train_one, batch_size = 256, epochs = 15, validation_split = 0.2)"
      ],
      "metadata": {
        "id": "lgtmkYd-aJsH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate the model using test data set\n",
        "model.evaluate(x_test, y_test_one)[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vtwnqz_dtPKE",
        "outputId": "94c16975-e758-4db6-dd78-6781281c1add"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 4s 116ms/step - loss: 0.5670 - accuracy: 0.8324\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8323529362678528"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the models accuracy\n",
        "plt.plot(hist.history['accuracy'])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "jxQXyHIguJrg",
        "outputId": "b393b6ec-5251-4d56-e8c8-37c103bc5c3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing the models loss\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "dr2cMILMuRuZ",
        "outputId": "d866c14f-4c74-4573-b29c-59c98c745815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Test the model with an example\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JJe_JZZZuW9m",
        "outputId": "d67785f0-62b2-454b-e0a5-3333be5e8b76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-434209aa-9e87-4d5f-9ebf-a4db75010e50\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-434209aa-9e87-4d5f-9ebf-a4db75010e50\">\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 src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 0001_L.png to 0001_L (1).png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_image = plt.imread('0001_L (1).png')\n",
        "img=plt.imshow(new_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "A8MGnBBquq_r",
        "outputId": "bbd484be-2191-472b-a879-a206a08d90bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Resize the image\n",
        "from skimage.transform import resize\n",
        "resized_image = resize(new_image,(64,64,3))/255\n",
        "img = plt.imshow(resized_image*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "CjMFHpV7u0R2",
        "outputId": "ff613f92-3f2a-4bc8-fcc2-5df45b38ba94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the models predictions\n",
        "probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\n",
        "predictions = probability_model.predict(np.array([resized_image]))\n",
        "#Show the predictions\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzIKLHROu7-p",
        "outputId": "d0b3929c-5196-482d-99df-99eb3ade2721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.05349347, 0.053494  , 0.0534934 , 0.0534934 , 0.0534934 ,\n",
              "        0.0534934 , 0.05349354, 0.0534934 , 0.05372817, 0.14333333,\n",
              "        0.05351488, 0.05351184, 0.05349497, 0.05349749, 0.05397847,\n",
              "        0.0534994 , 0.0534934 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index=int(np.argmax(predictions,axis=1))\n",
        "print(Classification[index])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PFk-v0_VKqP",
        "outputId": "06809c09-3497-4b09-dfa0-a89a033f5ac3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Toy Trucks\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_index = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
        "for i in range(17):\n",
        "  print(Classification[list_index[i]],':', round(predictions[0][list_index[i]]*100,2), '%')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bQAksKLu-Yh",
        "outputId": "38f7b5c4-7d14-41b1-9466-9d3291db7547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building Blocks : 5.35 %\n",
            "Dinosaur : 5.35 %\n",
            "Elephant Stuffed : 5.35 %\n",
            "Pencils : 5.35 %\n",
            "Unicorn : 5.35 %\n",
            "Airplane : 5.35 %\n",
            "Teddy Bear : 5.35 %\n",
            "Balls : 5.35 %\n",
            "Toy Cars : 5.37 %\n",
            "Toy Trucks : 14.33 %\n",
            "Telephone : 5.35 %\n",
            "Tea Cup : 5.35 %\n",
            "Cubes : 5.35 %\n",
            "Lion : 5.35 %\n",
            "Toy Boat : 5.4 %\n",
            "Folded Papers : 5.35 %\n",
            "Empty Bottles : 5.35 %\n"
          ]
        }
      ]
    }
  ]
}