Final_Project_Assignment.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final Project Assignment.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "-WuZqySaU4ic",
        "1J0xXw_ENU-R",
        "kocxZT7XRnAP"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Phaneesh-Katti/Fruits-n-Veggies-Classifier/blob/main/Final_Project_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WuZqySaU4ic"
      },
      "source": [
        "# Setting up Kaggle\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKfEgcNh81CN",
        "outputId": "702ae299-1e39-4b5f-c223-d2fb08910ac3"
      },
      "source": [
        "! pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3a/e7/3bac01547d2ed3d308ac92a0878fbdb0ed0f3d41fb1906c319ccbba1bfbc/kaggle-1.5.12.tar.gz (58kB)\n",
            "\r\u001b[K     |█████▋                          | 10kB 25.1MB/s eta 0:00:01\r\u001b[K     |███████████▏                    | 20kB 29.7MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 30kB 34.2MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 40kB 26.6MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 51kB 27.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 7.8MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.12-cp37-none-any.whl size=73053 sha256=0b8b6b1b770e0e6108648140ac07d1815e2403089d1e46f458812b07482e269c\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/6a/26/d30b7499ff85a4a4593377a87ecf55f7d08af42f0de9b60303\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Found existing installation: kaggle 1.5.10\n",
            "    Uninstalling kaggle-1.5.10:\n",
            "      Successfully uninstalled kaggle-1.5.10\n",
            "Successfully installed kaggle-1.5.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "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": 91
        },
        "id": "v0Wte-iq-Mmy",
        "outputId": "fbb2713b-cccb-41cd-e823-eba50a16a917"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-da2faec5-7047-41e7-b429-fc842d4a568f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-da2faec5-7047-41e7-b429-fc842d4a568f\">\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"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"insatanic\",\"key\":\"84e54b88ddc3f0018d67d54129b53b61\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwK6FtHd-mAV"
      },
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/ "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbkh2Fzv31gN"
      },
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jbrDHdXWqIV",
        "outputId": "b55a9a1f-905f-485c-8a54-151f0a27e6ea"
      },
      "source": [
        "! kaggle datasets download -d moltean/fruits"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading fruits.zip to /content\n",
            " 98% 744M/760M [00:07<00:00, 112MB/s]\n",
            "100% 760M/760M [00:07<00:00, 103MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcYTitCwX_DR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11bd0a8c-fd41-4428-b703-5a2d564efc23"
      },
      "source": [
        "!unzip fruits.zip -d dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/111_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/112_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/113_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/114_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/115_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/116_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/117_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/119_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/120_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/121_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/122_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/123_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/124_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/125_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/126_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/127_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/128_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/129_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/130_100.jpg  \n",
            "  inflating: dataset/fruits-360/Training/Tomato 3/131_100.jpg  \n",
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            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberries6.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberries7.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberries8.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberries9.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry2.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry3.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry4.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry5.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/raspberry_apple.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/salak.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/salak2.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/strawberries.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/strawberries2.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/strawberries3.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/strawberries4.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/strawberries5.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tangelo.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tomatoes1.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tomatoes2.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tomatoes3.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tomatoes4.jpg  \n",
            "  inflating: dataset/fruits-360/test-multiple_fruits/tomatoes5.jpg  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1J0xXw_ENU-R"
      },
      "source": [
        "# Making Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "626CzQavNXnp"
      },
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyLorVhVOMFF"
      },
      "source": [
        "training_path = \"/content/dataset/fruits-360/Training\"\n",
        "testing_path = \"/content/dataset/fruits-360/Test\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iopUQg-kOOps",
        "outputId": "662ccd0a-6bf2-4d35-dac6-0c8e055c8ad9"
      },
      "source": [
        "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    training_path,\n",
        "    image_size = (100,100),\n",
        "    batch_size = 32\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 67692 files belonging to 131 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZKeU-WJOYIq",
        "outputId": "a314c50e-f6ea-4763-9f03-4ae08e02340f"
      },
      "source": [
        "test_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    testing_path,\n",
        "    image_size = (100,100),\n",
        "    batch_size = 32\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 22688 files belonging to 131 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKQpHwocQ9BS"
      },
      "source": [
        "# Augmenting Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pPYeyP_Q-cI"
      },
      "source": [
        "dataGen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range=5,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgfG5PPGRcPy",
        "outputId": "8d702c3f-4135-4117-a807-6cec53015a49"
      },
      "source": [
        "train_dataset_aug = dataGen.flow_from_directory(\n",
        "    training_path,\n",
        "    target_size = (100,100),\n",
        "    batch_size = 32,\n",
        "    shuffle=True,\n",
        "    seed = 420\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 67692 images belonging to 131 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UA6v9c5IRgi0",
        "outputId": "fd23a49a-a52e-4f0b-e4c1-47bb72b51a23"
      },
      "source": [
        "test_dataset_aug = dataGen.flow_from_directory(\n",
        "    testing_path,\n",
        "    target_size = (100,100),\n",
        "    batch_size = 32,\n",
        "    shuffle=True,\n",
        "    seed = 420\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 22688 images belonging to 131 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kocxZT7XRnAP"
      },
      "source": [
        "# Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwpbLb1eRoLd",
        "outputId": "3a277994-dae2-425d-84dc-1deff201b46c"
      },
      "source": [
        "pretrained_model = tf.keras.applications.InceptionV3(input_shape=(100,100,3),\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yln3ViK_R41d"
      },
      "source": [
        "pretrained_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPO1DfyVR8Yi"
      },
      "source": [
        "def create_model():\n",
        "  TLmodel_aug = tf.keras.models.Sequential()\n",
        "\n",
        "  TLmodel_aug.add(pretrained_model)\n",
        "\n",
        "  TLmodel_aug.add(tf.keras.layers.GlobalAveragePooling2D())\n",
        "\n",
        "  TLmodel_aug.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
        "  TLmodel_aug.add(tf.keras.layers.Dense(512, activation='relu'))\n",
        "  TLmodel_aug.add(tf.keras.layers.Dense(131, activation='softmax'))\n",
        "\n",
        "  TLmodel_aug.compile(optimizer = 'adam',\n",
        "                loss='categorical_crossentropy',\n",
        "                metrics = ['accuracy'])\n",
        "  return TLmodel_aug\n",
        "\n",
        "TLmodel_aug = create_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0oXsRJPSG12",
        "outputId": "6463c36f-c286-4d43-f5f3-24b29a1672f1"
      },
      "source": [
        "history4 = TLmodel_aug.fit(train_dataset_aug, validation_data=test_dataset_aug, epochs = 30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "2116/2116 [==============================] - 286s 118ms/step - loss: 1.1974 - accuracy: 0.6998 - val_loss: 0.7967 - val_accuracy: 0.7952\n",
            "Epoch 2/30\n",
            "2116/2116 [==============================] - 246s 116ms/step - loss: 0.2410 - accuracy: 0.9253 - val_loss: 0.7774 - val_accuracy: 0.8356\n",
            "Epoch 3/30\n",
            "2116/2116 [==============================] - 245s 116ms/step - loss: 0.1750 - accuracy: 0.9473 - val_loss: 0.7979 - val_accuracy: 0.8357\n",
            "Epoch 4/30\n",
            "2116/2116 [==============================] - 245s 116ms/step - loss: 0.1462 - accuracy: 0.9546 - val_loss: 0.7082 - val_accuracy: 0.8586\n",
            "Epoch 5/30\n",
            "2116/2116 [==============================] - 247s 117ms/step - loss: 0.1207 - accuracy: 0.9662 - val_loss: 0.9336 - val_accuracy: 0.8432\n",
            "Epoch 6/30\n",
            "2116/2116 [==============================] - 247s 117ms/step - loss: 0.1286 - accuracy: 0.9653 - val_loss: 0.8088 - val_accuracy: 0.8592\n",
            "Epoch 7/30\n",
            "2116/2116 [==============================] - 247s 117ms/step - loss: 0.1253 - accuracy: 0.9678 - val_loss: 1.0217 - val_accuracy: 0.8531\n",
            "Epoch 8/30\n",
            "2116/2116 [==============================] - 252s 119ms/step - loss: 0.1096 - accuracy: 0.9718 - val_loss: 1.0587 - val_accuracy: 0.8495\n",
            "Epoch 9/30\n",
            "2116/2116 [==============================] - 251s 118ms/step - loss: 0.1082 - accuracy: 0.9741 - val_loss: 0.9893 - val_accuracy: 0.8669\n",
            "Epoch 10/30\n",
            "2116/2116 [==============================] - 248s 117ms/step - loss: 0.0882 - accuracy: 0.9785 - val_loss: 1.0166 - val_accuracy: 0.8607\n",
            "Epoch 11/30\n",
            "2116/2116 [==============================] - 253s 119ms/step - loss: 0.0975 - accuracy: 0.9768 - val_loss: 1.3054 - val_accuracy: 0.8603\n",
            "Epoch 12/30\n",
            "2116/2116 [==============================] - 250s 118ms/step - loss: 0.0995 - accuracy: 0.9795 - val_loss: 1.2804 - val_accuracy: 0.8490\n",
            "Epoch 13/30\n",
            "2116/2116 [==============================] - 248s 117ms/step - loss: 0.1143 - accuracy: 0.9767 - val_loss: 1.2585 - val_accuracy: 0.8678\n",
            "Epoch 14/30\n",
            "2116/2116 [==============================] - 248s 117ms/step - loss: 0.0907 - accuracy: 0.9813 - val_loss: 1.4484 - val_accuracy: 0.8606\n",
            "Epoch 15/30\n",
            "2116/2116 [==============================] - 248s 117ms/step - loss: 0.1080 - accuracy: 0.9801 - val_loss: 1.3151 - val_accuracy: 0.8715\n",
            "Epoch 16/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.0897 - accuracy: 0.9821 - val_loss: 1.4516 - val_accuracy: 0.8702\n",
            "Epoch 17/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.1051 - accuracy: 0.9817 - val_loss: 1.3948 - val_accuracy: 0.8737\n",
            "Epoch 18/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.0995 - accuracy: 0.9827 - val_loss: 1.6287 - val_accuracy: 0.8615\n",
            "Epoch 19/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.1162 - accuracy: 0.9822 - val_loss: 1.7435 - val_accuracy: 0.8703\n",
            "Epoch 20/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.1071 - accuracy: 0.9827 - val_loss: 1.6962 - val_accuracy: 0.8731\n",
            "Epoch 21/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.0856 - accuracy: 0.9863 - val_loss: 1.8221 - val_accuracy: 0.8767\n",
            "Epoch 22/30\n",
            "2116/2116 [==============================] - 250s 118ms/step - loss: 0.0938 - accuracy: 0.9852 - val_loss: 1.6613 - val_accuracy: 0.8812\n",
            "Epoch 23/30\n",
            "2116/2116 [==============================] - 252s 119ms/step - loss: 0.0962 - accuracy: 0.9858 - val_loss: 1.8324 - val_accuracy: 0.8754\n",
            "Epoch 24/30\n",
            "2116/2116 [==============================] - 254s 120ms/step - loss: 0.0873 - accuracy: 0.9865 - val_loss: 1.9898 - val_accuracy: 0.8757\n",
            "Epoch 25/30\n",
            "2116/2116 [==============================] - 254s 120ms/step - loss: 0.0907 - accuracy: 0.9872 - val_loss: 2.0049 - val_accuracy: 0.8742\n",
            "Epoch 26/30\n",
            "2116/2116 [==============================] - 250s 118ms/step - loss: 0.0997 - accuracy: 0.9869 - val_loss: 1.9286 - val_accuracy: 0.8781\n",
            "Epoch 27/30\n",
            "2116/2116 [==============================] - 250s 118ms/step - loss: 0.0985 - accuracy: 0.9878 - val_loss: 2.2428 - val_accuracy: 0.8779\n",
            "Epoch 28/30\n",
            "2116/2116 [==============================] - 249s 118ms/step - loss: 0.0998 - accuracy: 0.9874 - val_loss: 2.6516 - val_accuracy: 0.8667\n",
            "Epoch 29/30\n",
            "2116/2116 [==============================] - 250s 118ms/step - loss: 0.1158 - accuracy: 0.9863 - val_loss: 2.1182 - val_accuracy: 0.8833\n",
            "Epoch 30/30\n",
            "2116/2116 [==============================] - 251s 119ms/step - loss: 0.1045 - accuracy: 0.9873 - val_loss: 2.3196 - val_accuracy: 0.8823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "DXeLqZlfSfkC",
        "outputId": "ba044ba0-182d-4115-b64b-3c3384155eed"
      },
      "source": [
        "plt.plot(history4.history['accuracy'], label='accuracy')\n",
        "plt.plot(history4.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('CNN + Transfer Learning + Data Aug.')\n",
        "plt.ylim([0.8, 1])\n",
        "plt.legend(loc='lower right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8054b43c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTB5NCKi7G8T"
      },
      "source": [
        "# Saving the weights\n",
        "!mkdir -p saved_model\n",
        "TLmodel_aug.save_weights('saved_model/my_model')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2R0pWC45TwZ",
        "outputId": "2e2becf4-0913-4380-bd2a-b57f4471372a"
      },
      "source": [
        "# Downloading the weights\n",
        "!zip -r /content/weights.zip /content/saved_model\n",
        "# files.download(\"/content/weights.zip\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/saved_model/ (stored 0%)\n",
            "  adding: content/saved_model/checkpoint (deflated 40%)\n",
            "  adding: content/saved_model/my_model.index (deflated 79%)\n",
            "  adding: content/saved_model/my_model.data-00000-of-00001 (deflated 12%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7iEjGVel1_y"
      },
      "source": [
        "# Classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MgJEX0Z8cx_"
      },
      "source": [
        "fruits = np.array(['Apple Braeburn',\n",
        " 'Grape Blue',\n",
        " 'Pear Monster',\n",
        " 'Apple Crimson Snow',\n",
        " 'Grapefruit Pink',\n",
        " 'Pear Red',\n",
        " 'Apple Golden 1',\n",
        " 'Grapefruit White',\n",
        " 'Pear Stone',\n",
        " 'Apple Golden 2',\n",
        " 'Grape Pink',\n",
        " 'Pear Williams',\n",
        " 'Apple Golden 3',\n",
        " 'Grape White',\n",
        " 'Pepino',\n",
        " 'Apple Granny Smith',\n",
        " 'Grape White 2',\n",
        " 'Pepper Green',\n",
        " 'Apple Pink Lady',\n",
        " 'Grape White 3',\n",
        " 'Pepper Orange',\n",
        " 'Apple Red 1',\n",
        " 'Grape White 4',\n",
        " 'Pepper Red',\n",
        " 'Apple Red 2',\n",
        " 'Guava',\n",
        " 'Pepper Yellow',\n",
        " 'Apple Red 3',\n",
        " 'Hazelnut',\n",
        " 'Physalis',\n",
        " 'Apple Red Delicious',\n",
        " 'Huckleberry',\n",
        " 'Physalis with Husk',\n",
        " 'Apple Red Yellow 1',\n",
        " 'Kaki',\n",
        " 'Pineapple',\n",
        " 'Apple Red Yellow 2',\n",
        " 'Kiwi',\n",
        " 'Pineapple Mini',\n",
        " 'Apricot',\n",
        " 'Kohlrabi',\n",
        " 'Pitahaya Red',\n",
        " 'Avocado',\n",
        " 'Kumquats',\n",
        " 'Plum',\n",
        " 'Avocado ripe',\n",
        " 'Lemon',\n",
        " 'Plum 2',\n",
        " 'Banana',\n",
        " 'Lemon Meyer',\n",
        " 'Plum 3',\n",
        " 'Banana Lady Finger',\n",
        " 'Limes',\n",
        " 'Pomegranate',\n",
        " 'Banana Red',\n",
        " 'Lychee',\n",
        " 'Pomelo Sweetie',\n",
        " 'Beetroot',\n",
        " 'Mandarine',\n",
        " 'Potato Red',\n",
        " 'Blueberry',\n",
        " 'Mango',\n",
        " 'Potato Red Washed',\n",
        " 'Cactus fruit',\n",
        " 'Mango Red',\n",
        " 'Potato Sweet',\n",
        " 'Cantaloupe 1',\n",
        " 'Mangostan',\n",
        " 'Potato White',\n",
        " 'Cantaloupe 2',\n",
        " 'Maracuja',\n",
        " 'Quince',\n",
        " 'Carambula',\n",
        " 'Melon Piel de Sapo',\n",
        " 'Rambutan',\n",
        " 'Cauliflower',\n",
        " 'Mulberry',\n",
        " 'Raspberry',\n",
        " 'Cherry 1',\n",
        " 'Nectarine',\n",
        " 'Redcurrant',\n",
        " 'Cherry 2',\n",
        " 'Nectarine Flat',\n",
        " 'Salak',\n",
        " 'Cherry Rainier',\n",
        " 'Nut Forest',\n",
        " 'Strawberry',\n",
        " 'Cherry Wax Black',\n",
        " 'Nut Pecan',\n",
        " 'Strawberry Wedge',\n",
        " 'Cherry Wax Red',\n",
        " 'Onion Red',\n",
        " 'Tamarillo',\n",
        " 'Cherry Wax Yellow',\n",
        " 'Onion Red Peeled',\n",
        " 'Tangelo',\n",
        " 'Chestnut',\n",
        " 'Onion White',\n",
        " 'Tomato 1',\n",
        " 'Clementine',\n",
        " 'Orange',\n",
        " 'Tomato 2',\n",
        " 'Cocos',\n",
        " 'Papaya',\n",
        " 'Tomato 3',\n",
        " 'Corn',\n",
        " 'Passion Fruit',\n",
        " 'Tomato 4',\n",
        " 'Corn Husk',\n",
        " 'Peach',\n",
        " 'Tomato Cherry Red',\n",
        " 'Cucumber Ripe',\n",
        " 'Peach 2',\n",
        " 'Tomato Heart',\n",
        " 'Cucumber Ripe 2',\n",
        " 'Peach Flat',\n",
        " 'Tomato Maroon',\n",
        " 'Dates',\n",
        " 'Pear',\n",
        " 'Tomato not Ripened',\n",
        " 'Eggplant',\n",
        " 'Pear 2',\n",
        " 'Tomato Yellow',\n",
        " 'Fig',\n",
        " 'Pear Abate',\n",
        " 'Walnut',\n",
        " 'Ginger Root',\n",
        " 'Pear Forelle',\n",
        " 'Watermelon',\n",
        " 'Granadilla',\n",
        " 'Pear Kaiser'])\n",
        "\n",
        "fruits.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRJXEYcovTIv"
      },
      "source": [
        "# Upload and Classify"
      ]
    },
    {
      "cell_type": "code",
      "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": 111
        },
        "id": "s_KVDsEsl585",
        "outputId": "c5d7ffed-8c30-4a93-b9f9-9b50e4959a92"
      },
      "source": [
        "# Upload the file you want to test and put it in my_tests/test\n",
        "# There can only be one file in the folder at a time\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-37407c3b-c9f2-4893-94af-35bc23304296\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-37407c3b-c9f2-4893-94af-35bc23304296\">\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 image_2021-03-21_130031.png to image_2021-03-21_130031.png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnOXj5OMjVMz"
      },
      "source": [
        "# Uploading the weights\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDaZMZOl4RvV",
        "outputId": "97feaeda-a78c-4483-9ac4-2671acbcd363"
      },
      "source": [
        "# Loading the saved weights and creating the model\n",
        "model = create_model()\n",
        "!unzip \"/content/weights.zip\" -d \"/content\"\n",
        "model.load_weights('/content/saved_model/my_model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/weights.zip\n",
            "   creating: /content/content/saved_model/\n",
            "  inflating: /content/content/saved_model/checkpoint  \n",
            "  inflating: /content/content/saved_model/my_model.index  \n",
            "  inflating: /content/content/saved_model/my_model.data-00000-of-00001  \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f8036a3c5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Uo4ENnHXc_2",
        "outputId": "8aa32c9c-4b49-4a23-e06b-b650387e7dd5"
      },
      "source": [
        "# Loading the image\n",
        "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_dir = \"/content/my_tests\"\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "        test_dir,\n",
        "        target_size=(100, 100),\n",
        "        color_mode=\"rgb\",\n",
        "        shuffle = False,\n",
        "        class_mode='categorical',\n",
        "        batch_size=1)\n",
        "\n",
        "filenames = test_generator\n",
        "nb_samples = len(filenames)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O4hoBw6KRQa",
        "outputId": "001b6681-7135-48f8-cb0d-a4e052c3a170"
      },
      "source": [
        "# Finding the probabilities of the classes\n",
        "prob = TLmodel_aug.predict_generator(test_generator,steps = np.ceil(nb_samples))[0];"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSGH0KV0PmqQ",
        "outputId": "824f2761-e314-4751-b9ab-1de3aa941942"
      },
      "source": [
        "# Finding the top 3 classes\n",
        "temp = sorted(prob)[::-1]\n",
        "top3 = []\n",
        "\n",
        "for j in range(3):\n",
        "  for i in range(len(fruits)):\n",
        "      if temp[j] == prob[i] and temp[j] != 0:\n",
        "        top3.append([fruits[i], prob[i]])\n",
        "        break\n",
        "  \n",
        "for i in top3:\n",
        "  print(f\"{i[0]}: {(i[1]*100).round(2)}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Apple Red 2: 6.52%\n",
            "Apple Red Yellow 2: 5.8%\n",
            "Corn Husk: 4.51%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUh0gU97sgpF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}