Modello_from_scratch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Modello_from_scratch.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **ADVANCED MACHINE LEARNING FINAL PROJECT**\n",
        "\n",
        "##### Authors: Dario Della Mura - David Doci\n",
        "\n",
        "##### Course: Advanced Machine Learning\n",
        "\n",
        "##### Master's Degree: Data Science\n",
        "\n",
        "##### Anno accademico: 2020/2021\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8UgyxFP-jSCo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setting Colab GPU environment"
      ],
      "metadata": {
        "id": "IOhcsNuNgNlA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uM_geV2gO9H",
        "outputId": "9b29cd65-a965-4287-a5cf-0c704645e7df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Feb  9 20:04:54 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P8    28W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "  print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "  print('re-execute this cell.')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFvvFgFIgSka",
        "outputId": "3a369983-983d-418f-9419-83ebf8288d7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 13.6 gigabytes of available RAM\n",
            "\n",
            "To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"\n",
            "menu, and then select High-RAM in the Runtime shape dropdown. Then, \n",
            "re-execute this cell.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9zsIu6SgTER",
        "outputId": "b41e0564-b715-4a9c-a5ee-ec33bcdeee97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries and Fruit-360 Dataset"
      ],
      "metadata": {
        "id": "Sez_qVhckM6w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import Fruits-360 dataset from Github\n",
        "\n",
        "!git clone 'https://github.com/Horea94/Fruit-Images-Dataset.git'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0a918lEclRS",
        "outputId": "97d51d63-7eca-4cb8-f38a-75c8570ee474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Fruit-Images-Dataset'...\n",
            "remote: Enumerating objects: 385858, done.\u001b[K\n",
            "remote: Counting objects: 100% (8693/8693), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8672/8672), done.\u001b[K\n",
            "remote: Total 385858 (delta 36), reused 8670 (delta 21), pack-reused 377165\u001b[K\n",
            "Receiving objects: 100% (385858/385858), 2.10 GiB | 26.71 MiB/s, done.\n",
            "Resolving deltas: 100% (1196/1196), done.\n",
            "Checking out files: 100% (90503/90503), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWsa-s8XZN3b"
      },
      "source": [
        "# Generiche\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "import glob\n",
        "import cv2\n",
        "\n",
        "# Keras\n",
        "from keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from keras.layers import Dense, Flatten, Convolution2D, MaxPooling2D, Dropout, Activation, AveragePooling2D\n",
        "from keras.preprocessing import image\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import optimizers \n",
        "import keras\n",
        "\n",
        "# Sklearn\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import class_weight\n",
        "\n",
        "# Setting Seed for project reproducibility\n",
        "np.random.seed(1234)\n",
        "tf.random.set_seed(1234)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6tWOhPYqPGb"
      },
      "source": [
        "## Data pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbFGJT2OZ3_g",
        "outputId": "48478d7e-4ce8-4b34-dcca-8f793ddcda76"
      },
      "source": [
        "# Pre-processing on training images and building images vector and classes vector\n",
        "%%time\n",
        "\n",
        "path_train = '/content/Fruit-Images-Dataset/Training/*'\n",
        "\n",
        "fruit_train = []\n",
        "fruit_label = []\n",
        "for dir_path in glob.glob(path_train):\n",
        "    img_label = dir_path.split(\"/\")[-1]\n",
        "    for image_path in glob.glob(os.path.join(dir_path,\"*.jpg\")):\n",
        "        image = cv2.imread(image_path,cv2.IMREAD_COLOR)\n",
        "        image = cv2.resize(image, (50,50))\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
        "        fruit_train.append(image)\n",
        "        fruit_label.append(img_label)\n",
        "fruit_train = np.array(fruit_train)\n",
        "fruit_label = np.array(fruit_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 16.3 s, sys: 3.13 s, total: 19.4 s\n",
            "Wall time: 19.9 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wwi9gGbBgbzu",
        "outputId": "a0bca475-08c9-4b99-c705-e47d1ce6ba8e"
      },
      "source": [
        "# class codification\n",
        "%%time\n",
        "\n",
        "# numerical label assignment to nominal classes\n",
        "label_to_id = {v:k for k,v in enumerate(np.unique(fruit_label))}\n",
        "id_to_label = {v:k for k,v in label_to_id.items() }\n",
        "# assignment of numerical labels to the reference images\n",
        "fruit_label_id = np.array([label_to_id[i] for i in fruit_label])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 52 ms, sys: 2.78 ms, total: 54.8 ms\n",
            "Wall time: 54.9 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y417K3UTmnal",
        "outputId": "95627f61-eb0f-4680-c1d9-7867c00a1a6c"
      },
      "source": [
        "%%time\n",
        "\n",
        "fruit_label_id.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7 µs, sys: 1 µs, total: 8 µs\n",
            "Wall time: 13.4 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67692,)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EZ5-eU_iCo9",
        "outputId": "a4cb17d8-99e2-4684-8bed-a25c6580cfab"
      },
      "source": [
        "%%time\n",
        "\n",
        "fruit_train[1].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 14 µs, sys: 3 µs, total: 17 µs\n",
            "Wall time: 22.2 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 50, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "az2ZOZXAnTrz",
        "outputId": "74e67cc2-fff5-46d2-f0e6-17f3eebfab49"
      },
      "source": [
        "# Splitting images on training and validation sets\n",
        "%%time\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(fruit_train, fruit_label_id, test_size=0.1, random_state=101)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 181 ms, sys: 863 ms, total: 1.04 s\n",
            "Wall time: 893 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tz9I24iPmvoe",
        "outputId": "0351e8f8-f8c1-4e41-8490-53f7c8c536c2"
      },
      "source": [
        "%%time\n",
        "\n",
        "print('Train size: ', len(X_train))\n",
        "print('Test size: ', len(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size:  60922\n",
            "Test size:  6770\n",
            "CPU times: user 273 µs, sys: 63 µs, total: 336 µs\n",
            "Wall time: 221 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzxKL62Im2Oi",
        "outputId": "00778b0d-46f2-4839-bf2a-8dfd9d9bd2d9"
      },
      "source": [
        "%%time\n",
        "# Normalization of train and test(val) sets\n",
        "\n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.85 s, sys: 2.76 s, total: 4.61 s\n",
            "Wall time: 4.64 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdO0C_JUn8lZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8138067-0608-419a-cfaf-4ec4fe495c61"
      },
      "source": [
        "# One-hot-encoding on target variable\n",
        "\n",
        "%%time\n",
        "classes = 131\n",
        "y_train_labels = to_categorical(y_train, classes)\n",
        "y_test_labels = to_categorical(y_test, classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 13.5 ms, sys: 145 µs, total: 13.6 ms\n",
            "Wall time: 14.2 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCj3aRTIq-sS"
      },
      "source": [
        "## Build CNN Model from scratch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uZ4ArzioclK",
        "outputId": "b4fedd5f-c8ef-4fc8-86e0-aeb4747b0561"
      },
      "source": [
        "%%time\n",
        "\n",
        "def build(width, height, depth, classes):\n",
        "    #initialize the model along with the input shape\n",
        "    initializer = tf.keras.initializers.GlorotUniform(seed=1234) \n",
        "    model = Sequential()\n",
        "    inputShape = (height, width, depth)\n",
        "    chanDim = -1\n",
        "    if K.image_data_format() == 'channels_first':\n",
        "        inputShape = (depth, height, width)\n",
        "        chanDim = 1 \n",
        "\n",
        "    model.add(Convolution2D(32, (3,3), padding='same', kernel_initializer=initializer,  input_shape=inputShape))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "    model.add(Convolution2D(64, (3,3), padding='same', kernel_initializer=initializer))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "    model.add(Convolution2D(128, (3,3), padding='same', kernel_initializer=initializer))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "    model.add(Convolution2D(256, (3,3), padding='same', kernel_initializer=initializer))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(32, kernel_initializer=initializer))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.10))\n",
        "\n",
        "    model.add(Dense(classes))\n",
        "    model.add(Activation('softmax'))\n",
        "    \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 6.68 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJuZyMWmrBnM"
      },
      "source": [
        "# building model for (50,50,3) input image\n",
        "import keras.backend as K\n",
        "model = build(50, 50, 3, 131)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIIcK5MHquFG"
      },
      "source": [
        "# set loss function and optimizer of the model and evalutation metrics \n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QV3yhW7BrPKj",
        "outputId": "cbf8126c-6c2f-4d65-ddd4-68ea7f48c96d"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 50, 50, 32)        896       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 50, 50, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 25, 25, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 25, 25, 64)        18496     \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 25, 25, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 12, 12, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 12, 12, 128)       73856     \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 6, 6, 256)         295168    \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 3, 3, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2304)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                73760     \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 131)               4323      \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 131)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 466,499\n",
            "Trainable params: 466,499\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfGxdqcNrQmZ",
        "outputId": "623d9fa8-0f98-473d-8aa1-ba5c438858d9"
      },
      "source": [
        "# Neural network training\n",
        "%%time\n",
        "with tf.device('/device:GPU:0'):  \n",
        "  history = model.fit(X_train, y_train_labels, epochs=40, batch_size=32,  validation_split=0.30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "1333/1333 [==============================] - 36s 20ms/step - loss: 2.4034 - accuracy: 0.4029 - val_loss: 0.6252 - val_accuracy: 0.8505\n",
            "Epoch 2/40\n",
            "1333/1333 [==============================] - 23s 17ms/step - loss: 1.0664 - accuracy: 0.7098 - val_loss: 0.3015 - val_accuracy: 0.9441\n",
            "Epoch 3/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.8196 - accuracy: 0.7753 - val_loss: 0.1854 - val_accuracy: 0.9775\n",
            "Epoch 4/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.6425 - accuracy: 0.8150 - val_loss: 0.0836 - val_accuracy: 0.9873\n",
            "Epoch 5/40\n",
            "1333/1333 [==============================] - 26s 19ms/step - loss: 0.5023 - accuracy: 0.8505 - val_loss: 0.0738 - val_accuracy: 0.9876\n",
            "Epoch 6/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.4415 - accuracy: 0.8655 - val_loss: 0.0564 - val_accuracy: 0.9919\n",
            "Epoch 7/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.3908 - accuracy: 0.8806 - val_loss: 0.0317 - val_accuracy: 0.9967\n",
            "Epoch 8/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.3708 - accuracy: 0.8862 - val_loss: 0.0329 - val_accuracy: 0.9951\n",
            "Epoch 9/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.2997 - accuracy: 0.9065 - val_loss: 0.0237 - val_accuracy: 0.9947\n",
            "Epoch 10/40\n",
            "1333/1333 [==============================] - 26s 19ms/step - loss: 0.2319 - accuracy: 0.9265 - val_loss: 0.0133 - val_accuracy: 0.9965\n",
            "Epoch 11/40\n",
            "1333/1333 [==============================] - 27s 21ms/step - loss: 0.1993 - accuracy: 0.9384 - val_loss: 0.0233 - val_accuracy: 0.9921\n",
            "Epoch 12/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.1902 - accuracy: 0.9418 - val_loss: 0.0128 - val_accuracy: 0.9987\n",
            "Epoch 13/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.1775 - accuracy: 0.9441 - val_loss: 0.0133 - val_accuracy: 0.9974\n",
            "Epoch 14/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.1533 - accuracy: 0.9538 - val_loss: 0.0050 - val_accuracy: 0.9994\n",
            "Epoch 15/40\n",
            "1333/1333 [==============================] - 28s 21ms/step - loss: 0.1124 - accuracy: 0.9645 - val_loss: 0.0151 - val_accuracy: 0.9960\n",
            "Epoch 16/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.1052 - accuracy: 0.9679 - val_loss: 0.0064 - val_accuracy: 0.9980\n",
            "Epoch 17/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.0820 - accuracy: 0.9741 - val_loss: 0.0180 - val_accuracy: 0.9949\n",
            "Epoch 18/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.0824 - accuracy: 0.9741 - val_loss: 0.0048 - val_accuracy: 0.9992\n",
            "Epoch 19/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0867 - accuracy: 0.9738 - val_loss: 0.0233 - val_accuracy: 0.9936\n",
            "Epoch 20/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.0757 - accuracy: 0.9764 - val_loss: 0.0018 - val_accuracy: 0.9999\n",
            "Epoch 21/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0723 - accuracy: 0.9768 - val_loss: 0.0021 - val_accuracy: 0.9996\n",
            "Epoch 22/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0683 - accuracy: 0.9797 - val_loss: 0.0044 - val_accuracy: 0.9986\n",
            "Epoch 23/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0674 - accuracy: 0.9786 - val_loss: 0.0036 - val_accuracy: 0.9993\n",
            "Epoch 24/40\n",
            "1333/1333 [==============================] - 26s 19ms/step - loss: 0.0615 - accuracy: 0.9813 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 25/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0551 - accuracy: 0.9834 - val_loss: 0.0139 - val_accuracy: 0.9963\n",
            "Epoch 26/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.0536 - accuracy: 0.9839 - val_loss: 0.0017 - val_accuracy: 0.9995\n",
            "Epoch 27/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0457 - accuracy: 0.9860 - val_loss: 0.0025 - val_accuracy: 0.9996\n",
            "Epoch 28/40\n",
            "1333/1333 [==============================] - 28s 21ms/step - loss: 0.0464 - accuracy: 0.9856 - val_loss: 0.0035 - val_accuracy: 0.9995\n",
            "Epoch 29/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0503 - accuracy: 0.9850 - val_loss: 0.0017 - val_accuracy: 0.9997\n",
            "Epoch 30/40\n",
            "1333/1333 [==============================] - 28s 21ms/step - loss: 0.0401 - accuracy: 0.9876 - val_loss: 0.0028 - val_accuracy: 0.9995\n",
            "Epoch 31/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0410 - accuracy: 0.9876 - val_loss: 6.5436e-04 - val_accuracy: 0.9998\n",
            "Epoch 32/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.0399 - accuracy: 0.9886 - val_loss: 7.2470e-04 - val_accuracy: 0.9998\n",
            "Epoch 33/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.0392 - accuracy: 0.9885 - val_loss: 0.0037 - val_accuracy: 0.9994\n",
            "Epoch 34/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0396 - accuracy: 0.9889 - val_loss: 0.0020 - val_accuracy: 0.9995\n",
            "Epoch 35/40\n",
            "1333/1333 [==============================] - 25s 18ms/step - loss: 0.0349 - accuracy: 0.9905 - val_loss: 0.0053 - val_accuracy: 0.9987\n",
            "Epoch 36/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0344 - accuracy: 0.9898 - val_loss: 7.3476e-04 - val_accuracy: 0.9998\n",
            "Epoch 37/40\n",
            "1333/1333 [==============================] - 24s 18ms/step - loss: 0.0333 - accuracy: 0.9908 - val_loss: 0.0042 - val_accuracy: 0.9984\n",
            "Epoch 38/40\n",
            "1333/1333 [==============================] - 25s 19ms/step - loss: 0.0345 - accuracy: 0.9906 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 39/40\n",
            "1333/1333 [==============================] - 26s 20ms/step - loss: 0.0266 - accuracy: 0.9929 - val_loss: 0.0042 - val_accuracy: 0.9992\n",
            "Epoch 40/40\n",
            "1333/1333 [==============================] - 27s 20ms/step - loss: 0.0298 - accuracy: 0.9921 - val_loss: 0.0059 - val_accuracy: 0.9987\n",
            "CPU times: user 15min 32s, sys: 1min 25s, total: 16min 57s\n",
            "Wall time: 17min 25s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HO1soUnks6lh"
      },
      "source": [
        "### Results on training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "78b9EmCAshB6",
        "outputId": "04a6a262-a3e6-4ec0-f748-d6f2ae9530a6"
      },
      "source": [
        "# accuracy and loss chart of the model\n",
        "%%time\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.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()\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 417 ms, sys: 17.7 ms, total: 435 ms\n",
            "Wall time: 483 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVPbNtdPskHr",
        "outputId": "2b0b58c6-5339-43f8-82d9-ef01336901f3"
      },
      "source": [
        "# Performance of the neural network built from scratch on val set\n",
        "\n",
        "score = model.evaluate(X_test, y_test_labels, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.0022673998028039932\n",
            "Test accuracy: 0.999556839466095\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wULxpQns1bS",
        "outputId": "142366d8-b0fa-4c4c-c2a6-696b66adb713"
      },
      "source": [
        "# Use of the previous trained neural network to predict the class of the images of the val set\n",
        "%%time\n",
        "\n",
        "predict_x=model.predict(X_test) \n",
        "classes_x=np.argmax(predict_x,axis=1)\n",
        "print(classification_report(classes_x, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99        59\n",
            "           1       1.00      1.00      1.00        51\n",
            "           2       1.00      1.00      1.00        53\n",
            "           3       1.00      1.00      1.00        52\n",
            "           4       1.00      1.00      1.00        51\n",
            "           5       1.00      1.00      1.00        42\n",
            "           6       0.98      1.00      0.99        46\n",
            "           7       1.00      1.00      1.00        55\n",
            "           8       1.00      1.00      1.00        61\n",
            "           9       1.00      1.00      1.00        46\n",
            "          10       1.00      1.00      1.00        60\n",
            "          11       1.00      1.00      1.00        48\n",
            "          12       1.00      1.00      1.00        61\n",
            "          13       1.00      1.00      1.00        49\n",
            "          14       1.00      1.00      1.00        43\n",
            "          15       1.00      1.00      1.00        43\n",
            "          16       1.00      1.00      1.00        53\n",
            "          17       1.00      1.00      1.00        40\n",
            "          18       1.00      1.00      1.00        51\n",
            "          19       1.00      1.00      1.00        43\n",
            "          20       1.00      1.00      1.00        57\n",
            "          21       1.00      1.00      1.00        65\n",
            "          22       1.00      1.00      1.00        37\n",
            "          23       1.00      1.00      1.00        35\n",
            "          24       1.00      1.00      1.00        51\n",
            "          25       1.00      1.00      1.00        80\n",
            "          26       1.00      1.00      1.00        49\n",
            "          27       1.00      1.00      1.00        70\n",
            "          28       1.00      1.00      1.00        76\n",
            "          29       1.00      1.00      1.00        45\n",
            "          30       1.00      1.00      1.00        45\n",
            "          31       1.00      1.00      1.00        53\n",
            "          32       1.00      1.00      1.00        29\n",
            "          33       1.00      1.00      1.00        60\n",
            "          34       1.00      1.00      1.00        42\n",
            "          35       1.00      1.00      1.00        42\n",
            "          36       1.00      1.00      1.00        46\n",
            "          37       1.00      1.00      1.00        37\n",
            "          38       1.00      1.00      1.00        49\n",
            "          39       1.00      1.00      1.00        52\n",
            "          40       1.00      1.00      1.00        36\n",
            "          41       1.00      1.00      1.00        76\n",
            "          42       1.00      0.97      0.98        32\n",
            "          43       1.00      1.00      1.00        54\n",
            "          44       1.00      1.00      1.00        89\n",
            "          45       1.00      1.00      1.00        51\n",
            "          46       1.00      1.00      1.00        47\n",
            "          47       1.00      1.00      1.00        37\n",
            "          48       1.00      1.00      1.00        57\n",
            "          49       1.00      1.00      1.00        44\n",
            "          50       1.00      1.00      1.00        51\n",
            "          51       1.00      1.00      1.00        59\n",
            "          52       1.00      1.00      1.00        45\n",
            "          53       1.00      1.00      1.00        50\n",
            "          54       1.00      1.00      1.00        40\n",
            "          55       1.00      1.00      1.00        47\n",
            "          56       1.00      1.00      1.00        56\n",
            "          57       1.00      1.00      1.00        45\n",
            "          58       1.00      1.00      1.00        45\n",
            "          59       1.00      1.00      1.00        51\n",
            "          60       1.00      1.00      1.00        58\n",
            "          61       1.00      1.00      1.00        44\n",
            "          62       1.00      1.00      1.00        40\n",
            "          63       1.00      1.00      1.00        48\n",
            "          64       1.00      1.00      1.00        55\n",
            "          65       1.00      1.00      1.00        38\n",
            "          66       1.00      1.00      1.00        30\n",
            "          67       1.00      1.00      1.00        49\n",
            "          68       1.00      1.00      1.00        83\n",
            "          69       1.00      1.00      1.00        56\n",
            "          70       1.00      0.98      0.99        51\n",
            "          71       1.00      1.00      1.00        53\n",
            "          72       1.00      1.00      1.00        63\n",
            "          73       1.00      1.00      1.00        44\n",
            "          74       1.00      1.00      1.00        42\n",
            "          75       1.00      1.00      1.00        46\n",
            "          76       1.00      1.00      1.00        41\n",
            "          77       1.00      1.00      1.00        57\n",
            "          78       1.00      1.00      1.00        44\n",
            "          79       1.00      1.00      1.00        58\n",
            "          80       1.00      1.00      1.00        54\n",
            "          81       1.00      1.00      1.00        83\n",
            "          82       1.00      1.00      1.00        48\n",
            "          83       1.00      1.00      1.00        45\n",
            "          84       1.00      1.00      1.00        77\n",
            "          85       1.00      1.00      1.00        63\n",
            "          86       1.00      1.00      1.00        56\n",
            "          87       1.00      1.00      1.00        34\n",
            "          88       1.00      1.00      1.00        46\n",
            "          89       1.00      0.98      0.99        56\n",
            "          90       1.00      1.00      1.00        72\n",
            "          91       1.00      1.00      1.00        46\n",
            "          92       1.00      1.00      1.00        52\n",
            "          93       1.00      1.00      1.00        43\n",
            "          94       1.00      1.00      1.00        69\n",
            "          95       1.00      1.00      1.00        60\n",
            "          96       1.00      1.00      1.00        56\n",
            "          97       1.00      1.00      1.00        40\n",
            "          98       1.00      1.00      1.00        45\n",
            "          99       1.00      1.00      1.00        46\n",
            "         100       1.00      1.00      1.00        61\n",
            "         101       1.00      1.00      1.00        46\n",
            "         102       1.00      1.00      1.00        62\n",
            "         103       1.00      1.00      1.00        38\n",
            "         104       1.00      1.00      1.00        86\n",
            "         105       1.00      1.00      1.00        47\n",
            "         106       1.00      1.00      1.00        36\n",
            "         107       1.00      1.00      1.00        41\n",
            "         108       1.00      1.00      1.00        36\n",
            "         109       0.98      1.00      0.99        52\n",
            "         110       1.00      1.00      1.00        41\n",
            "         111       1.00      1.00      1.00        52\n",
            "         112       1.00      1.00      1.00        48\n",
            "         113       1.00      1.00      1.00        51\n",
            "         114       1.00      1.00      1.00        42\n",
            "         115       1.00      1.00      1.00        50\n",
            "         116       1.00      1.00      1.00        54\n",
            "         117       1.00      1.00      1.00        82\n",
            "         118       1.00      1.00      1.00        46\n",
            "         119       1.00      1.00      1.00        41\n",
            "         120       1.00      1.00      1.00        67\n",
            "         121       1.00      1.00      1.00        67\n",
            "         122       1.00      1.00      1.00        81\n",
            "         123       1.00      1.00      1.00        48\n",
            "         124       1.00      1.00      1.00        37\n",
            "         125       1.00      1.00      1.00        77\n",
            "         126       1.00      1.00      1.00        31\n",
            "         127       1.00      1.00      1.00        49\n",
            "         128       1.00      1.00      1.00        50\n",
            "         129       1.00      1.00      1.00        66\n",
            "         130       1.00      1.00      1.00        55\n",
            "\n",
            "    accuracy                           1.00      6770\n",
            "   macro avg       1.00      1.00      1.00      6770\n",
            "weighted avg       1.00      1.00      1.00      6770\n",
            "\n",
            "CPU times: user 1.58 s, sys: 202 ms, total: 1.78 s\n",
            "Wall time: 3.11 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TE7V-wsIKIwh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#save model for future evaluation\n",
        "\n",
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(\"model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd-eoFxVrbkI",
        "outputId": "fe1a40fa-0819-4371-fb19-9f0504cc5eac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBNpjmQitxA2"
      },
      "source": [
        "## Testing on unseen images"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#upload pre-trained model\n",
        "from keras.models import model_from_json\n",
        "json_file = open('model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"model.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWkQMF46r-SF",
        "outputId": "0f1447af-13f6-488e-f121-67613a8ed1c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model from disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZakSR206t3TW",
        "outputId": "aa79b622-ee52-4beb-c0e0-8d19ecaf1071"
      },
      "source": [
        "# Pre-processing on training images and building images vector and classes vector\n",
        "%%time\n",
        "\n",
        "path_test = '/content/Fruit-Images-Dataset/Test/*'\n",
        "\n",
        "fruit_test = []\n",
        "fruit_test_label = []\n",
        "for dir_path in glob.glob(path_test):\n",
        "    img_label = dir_path.split(\"/\")[-1]\n",
        "    for image_path in glob.glob(os.path.join(dir_path,\"*.jpg\")):\n",
        "        image = cv2.imread(image_path,cv2.IMREAD_COLOR)\n",
        "        image = cv2.resize(image, (50,50))\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
        "        fruit_test.append(image)\n",
        "        fruit_test_label.append(img_label)\n",
        "fruit_test = np.array(fruit_test)\n",
        "fruit_test_label = np.array(fruit_test_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.36 s, sys: 2.33 s, total: 8.7 s\n",
            "Wall time: 16.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RME8GQD4t_ue",
        "outputId": "b292ec61-2b3b-4c66-c53e-c8f3ad6e48e9"
      },
      "source": [
        "# class codification\n",
        "%%time\n",
        "\n",
        "# numerical label assignment to nominal classes\n",
        "label_to_id_test = {v:k for k,v in enumerate(np.unique(fruit_test_label)) }\n",
        "id_to_label_test = {v:k for k,v in label_to_id_test.items() }\n",
        "# assignment of numerical labels to the reference images\n",
        "fruit_label_test_id = np.array([label_to_id_test[i] for i in fruit_test_label])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 19.4 ms, sys: 347 µs, total: 19.7 ms\n",
            "Wall time: 21.3 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cn0IuGElu0vN",
        "outputId": "3f2ce300-3a6e-4ffd-9d7b-9e698281ea80"
      },
      "source": [
        "%%time\n",
        "\n",
        "fruit_label_test_id.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6 µs, sys: 1e+03 ns, total: 7 µs\n",
            "Wall time: 10 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22688,)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNcH-rLOvU5n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "862755a0-2578-4eff-ee2a-e7f60798fea3"
      },
      "source": [
        "# Normalization of test images\n",
        "%%time\n",
        "fruit_test = fruit_test/255.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 627 ms, sys: 1.65 s, total: 2.28 s\n",
            "Wall time: 1.92 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVZZStcFwr0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fa300ee-cde0-4c13-fe2a-fbfb98e7928b"
      },
      "source": [
        "# One-hot encoding\n",
        "%%time\n",
        "classes=131\n",
        "y_fruit_test_labels = to_categorical(fruit_label_test_id, classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5.01 ms, sys: 2.06 ms, total: 7.07 ms\n",
            "Wall time: 7.12 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ACAdXQmpsuog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7j0VWRcCv5zm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "918e5d2c-9b93-417d-a191-6bd071ac5077"
      },
      "source": [
        "# Using the previous trained neural network to predict the class of test set images\n",
        "%%time\n",
        "pred_test = loaded_model.predict(fruit_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4.76 s, sys: 1.76 s, total: 6.52 s\n",
            "Wall time: 7.55 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_test = np.argmax(pred_test, axis=1)"
      ],
      "metadata": {
        "id": "iXwE3FnbKrga"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlqrYpCxtDSL"
      },
      "source": [
        "### Results on test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance of the neural network built from scratch on the test images not used in the training phase\n",
        "%%time\n",
        "score_test =loaded_model.evaluate(fruit_test, y_fruit_test_labels, verbose=0)\n",
        "print(\"Test loss:\", score_test[0])\n",
        "print(\"Test accuracy:\", score_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-E5m-v90WVS",
        "outputId": "c9f1c710-6ae1-4dab-be63-433c318aed69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.4109913408756256\n",
            "Test accuracy: 0.9619622826576233\n",
            "CPU times: user 5.31 s, sys: 1.21 s, total: 6.52 s\n",
            "Wall time: 6.76 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "cm = confusion_matrix(fruit_label_test_id, pred_test)\n",
        "\n",
        "df_cm = pd.DataFrame(cm)\n",
        "#plt.figure(figsize = (20, 20))\n",
        "#sn.heatmap(df_cm, annot=True)"
      ],
      "metadata": {
        "id": "Vf2gX5vmNnwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cm.to_csv('cf_model_1.csv')"
      ],
      "metadata": {
        "id": "p5GgkigfOsm7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(  fruit_label_test_id, pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ou3SGz4AK2rA",
        "outputId": "d3f410c8-3d52-4b8f-e1d9-83e6845b5d52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.69      0.82       164\n",
            "           1       1.00      0.30      0.47       148\n",
            "           2       1.00      0.86      0.92       160\n",
            "           3       0.88      1.00      0.93       164\n",
            "           4       0.93      1.00      0.96       161\n",
            "           5       1.00      1.00      1.00       164\n",
            "           6       0.57      0.89      0.69       152\n",
            "           7       0.92      0.85      0.88       164\n",
            "           8       0.95      1.00      0.97       164\n",
            "           9       1.00      1.00      1.00       144\n",
            "          10       1.00      1.00      1.00       166\n",
            "          11       0.88      1.00      0.93       164\n",
            "          12       1.00      1.00      1.00       219\n",
            "          13       1.00      1.00      1.00       164\n",
            "          14       1.00      1.00      1.00       143\n",
            "          15       0.91      1.00      0.95       166\n",
            "          16       1.00      0.89      0.94       166\n",
            "          17       0.96      1.00      0.98       152\n",
            "          18       0.95      0.86      0.90       166\n",
            "          19       0.84      1.00      0.91       150\n",
            "          20       0.96      0.95      0.96       154\n",
            "          21       0.85      1.00      0.92       166\n",
            "          22       1.00      1.00      1.00       164\n",
            "          23       0.84      0.99      0.91       164\n",
            "          24       0.94      0.88      0.91       166\n",
            "          25       0.96      1.00      0.98       234\n",
            "          26       1.00      1.00      1.00       164\n",
            "          27       1.00      1.00      1.00       246\n",
            "          28       1.00      0.70      0.82       246\n",
            "          29       1.00      1.00      1.00       164\n",
            "          30       1.00      1.00      1.00       164\n",
            "          31       0.99      1.00      1.00       164\n",
            "          32       0.98      0.88      0.93       153\n",
            "          33       1.00      1.00      1.00       166\n",
            "          34       0.94      1.00      0.97       166\n",
            "          35       1.00      0.37      0.54       150\n",
            "          36       0.85      0.82      0.83       154\n",
            "          37       1.00      1.00      1.00       130\n",
            "          38       1.00      0.80      0.89       156\n",
            "          39       0.99      1.00      0.99       166\n",
            "          40       1.00      0.80      0.89       156\n",
            "          41       1.00      1.00      1.00       234\n",
            "          42       0.88      1.00      0.93        99\n",
            "          43       1.00      1.00      1.00       166\n",
            "          44       0.98      1.00      0.99       328\n",
            "          45       1.00      0.96      0.98       164\n",
            "          46       0.90      1.00      0.95       166\n",
            "          47       1.00      1.00      1.00       166\n",
            "          48       1.00      1.00      1.00       164\n",
            "          49       1.00      1.00      1.00       158\n",
            "          50       1.00      1.00      1.00       166\n",
            "          51       1.00      1.00      1.00       164\n",
            "          52       1.00      1.00      1.00       166\n",
            "          53       0.99      1.00      0.99       157\n",
            "          54       1.00      1.00      1.00       166\n",
            "          55       1.00      1.00      1.00       166\n",
            "          56       0.99      1.00      0.99       156\n",
            "          57       1.00      1.00      1.00       157\n",
            "          58       1.00      1.00      1.00       166\n",
            "          59       1.00      1.00      1.00       164\n",
            "          60       1.00      1.00      1.00       166\n",
            "          61       1.00      0.98      0.99       166\n",
            "          62       1.00      1.00      1.00       166\n",
            "          63       1.00      1.00      1.00       166\n",
            "          64       0.98      1.00      0.99       166\n",
            "          65       1.00      0.98      0.99       142\n",
            "          66       0.91      1.00      0.95       102\n",
            "          67       0.98      0.99      0.99       166\n",
            "          68       1.00      1.00      1.00       246\n",
            "          69       0.99      1.00      1.00       164\n",
            "          70       0.85      0.96      0.90       164\n",
            "          71       0.99      0.92      0.95       160\n",
            "          72       1.00      1.00      1.00       218\n",
            "          73       1.00      0.92      0.96       178\n",
            "          74       0.96      1.00      0.98       150\n",
            "          75       1.00      1.00      1.00       155\n",
            "          76       0.67      1.00      0.80       146\n",
            "          77       1.00      1.00      1.00       160\n",
            "          78       1.00      1.00      1.00       164\n",
            "          79       0.99      1.00      0.99       166\n",
            "          80       1.00      1.00      1.00       164\n",
            "          81       0.82      1.00      0.90       246\n",
            "          82       1.00      1.00      1.00       164\n",
            "          83       1.00      0.93      0.96       164\n",
            "          84       0.98      0.74      0.84       232\n",
            "          85       0.93      0.99      0.96       166\n",
            "          86       0.99      1.00      0.99       234\n",
            "          87       1.00      1.00      1.00       102\n",
            "          88       0.99      1.00      1.00       166\n",
            "          89       0.92      1.00      0.96       222\n",
            "          90       0.99      0.83      0.90       237\n",
            "          91       0.76      1.00      0.86       166\n",
            "          92       1.00      0.87      0.93       166\n",
            "          93       0.99      1.00      0.99       148\n",
            "          94       1.00      0.93      0.96       234\n",
            "          95       0.99      1.00      0.99       222\n",
            "          96       0.88      1.00      0.94       222\n",
            "          97       1.00      1.00      1.00       164\n",
            "          98       0.91      1.00      0.95       164\n",
            "          99       1.00      0.99      1.00       166\n",
            "         100       1.00      1.00      1.00       163\n",
            "         101       0.99      1.00      1.00       166\n",
            "         102       1.00      1.00      1.00       151\n",
            "         103       1.00      1.00      1.00       142\n",
            "         104       0.97      1.00      0.99       304\n",
            "         105       0.96      1.00      0.98       164\n",
            "         106       1.00      1.00      1.00       153\n",
            "         107       0.79      1.00      0.88       150\n",
            "         108       1.00      0.96      0.98       151\n",
            "         109       0.99      0.62      0.76       150\n",
            "         110       1.00      0.99      1.00       150\n",
            "         111       1.00      1.00      1.00       166\n",
            "         112       0.98      0.99      0.99       164\n",
            "         113       1.00      1.00      1.00       166\n",
            "         114       1.00      1.00      1.00       164\n",
            "         115       1.00      1.00      1.00       162\n",
            "         116       1.00      0.96      0.98       164\n",
            "         117       0.97      0.91      0.94       246\n",
            "         118       1.00      1.00      1.00       166\n",
            "         119       0.99      1.00      0.99       166\n",
            "         120       1.00      1.00      1.00       246\n",
            "         121       1.00      1.00      1.00       225\n",
            "         122       0.96      1.00      0.98       246\n",
            "         123       1.00      1.00      1.00       160\n",
            "         124       0.99      1.00      0.99       164\n",
            "         125       1.00      1.00      1.00       228\n",
            "         126       1.00      1.00      1.00       127\n",
            "         127       0.91      1.00      0.95       153\n",
            "         128       0.92      1.00      0.96       158\n",
            "         129       1.00      1.00      1.00       249\n",
            "         130       0.92      1.00      0.96       157\n",
            "\n",
            "    accuracy                           0.96     22688\n",
            "   macro avg       0.97      0.96      0.96     22688\n",
            "weighted avg       0.97      0.96      0.96     22688\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/cf_model_1.csv')"
      ],
      "metadata": {
        "id": "TAj6lNg0P8pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['Unnamed: 0'])"
      ],
      "metadata": {
        "id": "oTcdapxiQdt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "PRWcFrPxQmIG",
        "outputId": "5b0e77d1-2908-403c-c77b-e97c72e66860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>126</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 131 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d5556f0-a02c-4ee3-ae3b-572e405fab53')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       0   1    2    3    4  5    6   7  ...  123  124  125  126  127  128  129  130\n",
              "0    113   0    0    0    0  0    0  12  ...    0    0    0    0    0    0    0    0\n",
              "1      0  45    0    0    0  0  103   0  ...    0    0    0    0    0    0    0    0\n",
              "2      0   0  137   23    0  0    0   0  ...    0    0    0    0    0    0    0    0\n",
              "3      0   0    0  164    0  0    0   0  ...    0    0    0    0    0    0    0    0\n",
              "4      0   0    0    0  161  0    0   0  ...    0    0    0    0    0    0    0    0\n",
              "..   ...  ..  ...  ...  ... ..  ...  ..  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
              "126    0   0    0    0    0  0    0   0  ...    0    0    0  127    0    0    0    0\n",
              "127    0   0    0    0    0  0    0   0  ...    0    0    0    0  153    0    0    0\n",
              "128    0   0    0    0    0  0    0   0  ...    0    0    0    0    0  158    0    0\n",
              "129    0   0    0    0    0  0    0   0  ...    0    0    0    0    0    0  249    0\n",
              "130    0   0    0    0    0  0    0   0  ...    0    0    0    0    0    0    0  157\n",
              "\n",
              "[131 rows x 131 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_excel('cf_model_1.xlsx')"
      ],
      "metadata": {
        "id": "WzGSCDasQJLJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}