Fruit_Recognition.ipynb

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  "metadata": {
    "colab": {
      "name": "Fruit Recognition.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOexuf+ALIRKT027ymCHw1W",
      "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/dhanyab123/convolutetheworld/blob/main/Fruit_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0GUuuArE6Yw"
      },
      "source": [
        "---\n",
        "---\n",
        "# Fruit Recognition\n",
        "\n",
        "To build a convolution neural network which distinguishes between images of various fruits and vegetables with minimum accuracy of 60% on test data and run time of training below 6 minutes.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBdCuq45IlHq",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "14021aa6-1cb7-4076-c65f-5e93a42334a9"
      },
      "source": [
        "# Install the kaggle library\n",
        "!pip install -q kaggle \n",
        "\n",
        "# To upload the kaggle.json file\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "# Create kaggle folder\n",
        "!mkdir ~/.kaggle\n",
        "\n",
        "# Copy kaggle.json into folder kaggle\n",
        "!cp kaggle.json ~/.kaggle\n",
        "\n",
        "# Set permissions\n",
        "!chmod 600 ~/.kaggle/kaggle.json  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c430f989-d380-4a23-9c5a-308c725145ba\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c430f989-d380-4a23-9c5a-308c725145ba\">\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> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBefaaC7Q2QJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e024c75e-17fc-4767-dc94-d0bb27572247"
      },
      "source": [
        "# To download the required dataset from kaggle\n",
        "!kaggle datasets download -d sshikamaru/fruit-recognition"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading fruit-recognition.zip to /content\n",
            " 91% 93.0M/102M [00:01<00:00, 54.2MB/s]\n",
            "100% 102M/102M [00:01<00:00, 64.2MB/s] \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9p28gScUzVK"
      },
      "source": [
        "# Unzipping the zip files and deleting the zip files\n",
        "!unzip \\*.zip  && rm *.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJ7sov7RViQs"
      },
      "source": [
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLXrMUrrWVNd"
      },
      "source": [
        "# Assigning path for test and train data\n",
        "TRAIN_DATA_PATH = '/content/train/train'\n",
        "TEST_DATA_PATH = '/content/test/test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GUYeaZLalNA"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import models, layers\n",
        "from tensorflow.keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zT1EVbEdN5p"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0Fbxxm_Cm-A"
      },
      "source": [
        "Data pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bu7WKASMWugv"
      },
      "source": [
        "total_image_per = 390\n",
        "labels = []\n",
        "train_image = []\n",
        "img_size = 100\n",
        "tot = {}\n",
        "\n",
        "for category in os.listdir(TRAIN_DATA_PATH):\n",
        "  if category not in tot:\n",
        "    tot.update({category : 0})\n",
        "  for item in os.listdir(TRAIN_DATA_PATH + '/' + category):\n",
        "    if category in tot.keys():\n",
        "      if tot.get(category) >= total_image_per:\n",
        "        continue\n",
        "      y = tot.pop(category) \n",
        "      tot.update({category : y+1})\n",
        "      labels.append(category)\n",
        "\n",
        "    img = image.load_img(TRAIN_DATA_PATH + '/' + category + '/' + str(item), target_size=(img_size, img_size))\n",
        "    img = image.img_to_array(img)\n",
        "    img = img/255.0\n",
        "    train_image.append(img)\n",
        "\n",
        "# Changing data into np arrays\n",
        "label = np.array(labels)\n",
        "X = np.array(train_image) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2Bt38KYM-zL"
      },
      "source": [
        "val = sorted(list(tot.keys()))\n",
        "no = []\n",
        "\n",
        "for i in label:\n",
        "  for j in range(len(val)):\n",
        "    if i == val[j]:\n",
        "      no.append(j)\n",
        "\n",
        "no = np.array(no)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb6uWyxJVap_",
        "outputId": "d2d7dc12-6177-4672-f1ef-31182ad6d785"
      },
      "source": [
        "val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Apple Braeburn',\n",
              " 'Apple Granny Smith',\n",
              " 'Apricot',\n",
              " 'Avocado',\n",
              " 'Banana',\n",
              " 'Blueberry',\n",
              " 'Cactus fruit',\n",
              " 'Cantaloupe',\n",
              " 'Cherry',\n",
              " 'Clementine',\n",
              " 'Corn',\n",
              " 'Cucumber Ripe',\n",
              " 'Grape Blue',\n",
              " 'Kiwi',\n",
              " 'Lemon',\n",
              " 'Limes',\n",
              " 'Mango',\n",
              " 'Onion White',\n",
              " 'Orange',\n",
              " 'Papaya',\n",
              " 'Passion Fruit',\n",
              " 'Peach',\n",
              " 'Pear',\n",
              " 'Pepper Green',\n",
              " 'Pepper Red',\n",
              " 'Pineapple',\n",
              " 'Plum',\n",
              " 'Pomegranate',\n",
              " 'Potato Red',\n",
              " 'Raspberry',\n",
              " 'Strawberry',\n",
              " 'Tomato',\n",
              " 'Watermelon']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkxfMHOPdIAJ",
        "outputId": "c626caff-69d7-4205-b999-4b055ec068c2"
      },
      "source": [
        "# Checking the shape of the training image data\n",
        "X[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "FctLHgiKdTlR",
        "outputId": "d4d5e03e-3553-4993-833b-88cf4767a6b7"
      },
      "source": [
        "# Viewing the train data and its resp. label\n",
        "plt.imshow(X[90])\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "\n",
        "print(label[90])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Banana\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9yCAM7Jejk-",
        "outputId": "e102aa20-bd21-48f9-d4ef-55223448791e"
      },
      "source": [
        "# Checking the total number of possible labels\n",
        "np.unique(no)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGGwYGOMe2m0"
      },
      "source": [
        "# Splitting data into test and train sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, no, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tu8qhmIKD7mI"
      },
      "source": [
        "Creating the Convolution Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1wJEhK3etWc"
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(16,(3,3), activation='relu', input_shape = (100,100,3)))\n",
        "model.add(layers.MaxPool2D((2,2)))\n",
        "model.add(layers.Conv2D(32,(3,3), activation='relu'))\n",
        "model.add(layers.MaxPool2D((2,2)))\n",
        "model.add(layers.Conv2D(64,(3,3), activation='relu'))\n",
        "model.add(layers.MaxPool2D((2,2)))\n",
        "model.add(layers.Conv2D(64,(3,3), activation='relu'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKt0g2iQfe-V",
        "outputId": "e8aa2b90-3369-41ca-f7fa-63bcb7fde0a0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 98, 98, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 49, 49, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 47, 47, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 21, 21, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 8, 8, 64)          36928     \n",
            "=================================================================\n",
            "Total params: 60,512\n",
            "Trainable params: 60,512\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbWC2qaogI_g"
      },
      "source": [
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(150, activation='relu'))\n",
        "model.add(layers.Dense(33))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aREOKYZAgOrW",
        "outputId": "dd553aca-581f-460a-99b2-b1d0cb46b679"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 98, 98, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 49, 49, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 47, 47, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 21, 21, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 8, 8, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 150)               614550    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 33)                4983      \n",
            "=================================================================\n",
            "Total params: 680,045\n",
            "Trainable params: 680,045\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sOTGMWWgUWZ"
      },
      "source": [
        "# Adding to Loss function\n",
        "model.compile(optimizer='adam', loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uzNd7F4TZBH",
        "outputId": "6798df66-7f00-4e99-ba0a-379749206433"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9652,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0QCkFERD3XA",
        "outputId": "95631e45-3600-4931-8491-96c90c391365"
      },
      "source": [
        "# Training the model\n",
        "model.fit(X_train, y_train, epochs=2, batch_size=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "97/97 [==============================] - 77s 784ms/step - loss: 0.8479 - accuracy: 0.7598\n",
            "Epoch 2/2\n",
            "97/97 [==============================] - 76s 784ms/step - loss: 0.0596 - accuracy: 0.9818\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fabf8d69d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWgrzPebv-tL",
        "outputId": "f5ff1242-b7f2-4a8d-dafa-caac24b30dc1"
      },
      "source": [
        "# Testing the model\n",
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "101/101 [==============================] - 8s 76ms/step - loss: 0.2165 - accuracy: 0.9251\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.21654634177684784, 0.925108790397644]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Dw-iWcwymx_",
        "outputId": "94917a2c-1daf-4dd9-dac7-4b2fa5318e23"
      },
      "source": [
        "# Checking size of testing dataset\n",
        "len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3218"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYyVXUTuEfz_"
      },
      "source": [
        "Viewing output results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-229vtXtqTm"
      },
      "source": [
        "# Predicting output for the test data\n",
        "predictions = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "hruX52TK8AtX",
        "outputId": "545abb93-f30b-4dc2-97fc-247ded6ead8a"
      },
      "source": [
        "# To get the prediction for the data and the input\n",
        "num = 90\n",
        "\n",
        "print(val[np.argmax(predictions[num])])\n",
        "\n",
        "plt.imshow(X_test[num])\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "val[y_test[num]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Grape Blue\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Grape Blue'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}