CNN_Ajwa_or_Rothana_or_SM_classification Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "fe76d1d1ded592430e7548feacfa38dc42f085d9",
        "id": "viY8wRnH0nTa"
      },
      "source": [
        "# Import Library"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2UaMKlSyLFP",
        "outputId": "34b5ab33-fd12-45de-fb22-a8a06e838b1b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Tc8ckqKF0wKn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_height, img_width = 32, 32\n",
        "batch_size = 20\n",
        "\n",
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"/content/drive/MyDrive/image/train\",\n",
        "    image_size = (img_height, img_width),\n",
        "    batch_size = batch_size\n",
        ")\n",
        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"/content/drive/MyDrive/image/validation\",\n",
        "    image_size = (img_height, img_width),\n",
        "    batch_size = batch_size\n",
        ")\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"/content/drive/MyDrive/image/test\",\n",
        "    image_size = (img_height, img_width),\n",
        "    batch_size = batch_size\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TWcQqaB00GL",
        "outputId": "14f85f5a-7722-4207-e47e-215d9125f6ba"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 42 files belonging to 3 classes.\n",
            "Found 9 files belonging to 3 classes.\n",
            "Found 12 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SukkaryMofetiland --->Mofetiland\n",
        "\n",
        "class_names = [\"Ajwa\", \"Rothana\", \"Mofetiland\"]\n",
        "plt.figure(figsize=(10,10))\n",
        "for images, labels in train_ds.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(class_names[labels[i]])\n",
        "    plt.axis(\"off\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "9AB2UXmiCK1Q",
        "outputId": "554ef798-9b67-416f-d5f1-f71c1cfe7528"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Rescaling(1./255))\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(3))\n"
      ],
      "metadata": {
        "id": "9-qjUw9RbYJN"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=\"adam\",\n",
        "    loss=tf.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "IemkOAYACnCR"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(\n",
        "    train_ds,\n",
        "    validation_data = val_ds,\n",
        "    epochs = 30\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgPU4G39CshG",
        "outputId": "f1d1df01-a607-45bb-d09a-0f7b2bcc34db"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "3/3 [==============================] - 6s 349ms/step - loss: 1.0941 - accuracy: 0.4524 - val_loss: 1.0513 - val_accuracy: 0.3333\n",
            "Epoch 2/30\n",
            "3/3 [==============================] - 1s 55ms/step - loss: 1.0256 - accuracy: 0.3571 - val_loss: 1.0026 - val_accuracy: 0.3333\n",
            "Epoch 3/30\n",
            "3/3 [==============================] - 1s 53ms/step - loss: 0.9755 - accuracy: 0.3333 - val_loss: 0.9325 - val_accuracy: 0.4444\n",
            "Epoch 4/30\n",
            "3/3 [==============================] - 0s 60ms/step - loss: 0.8818 - accuracy: 0.5714 - val_loss: 0.8590 - val_accuracy: 0.7778\n",
            "Epoch 5/30\n",
            "3/3 [==============================] - 0s 71ms/step - loss: 0.8086 - accuracy: 0.7381 - val_loss: 0.7798 - val_accuracy: 0.6667\n",
            "Epoch 6/30\n",
            "3/3 [==============================] - 1s 55ms/step - loss: 0.6888 - accuracy: 0.7381 - val_loss: 0.6502 - val_accuracy: 0.8889\n",
            "Epoch 7/30\n",
            "3/3 [==============================] - 1s 48ms/step - loss: 0.5589 - accuracy: 0.9048 - val_loss: 0.5415 - val_accuracy: 0.6667\n",
            "Epoch 8/30\n",
            "3/3 [==============================] - 0s 49ms/step - loss: 0.4609 - accuracy: 0.9048 - val_loss: 0.5238 - val_accuracy: 0.7778\n",
            "Epoch 9/30\n",
            "3/3 [==============================] - 1s 67ms/step - loss: 0.4066 - accuracy: 0.8571 - val_loss: 0.3775 - val_accuracy: 0.8889\n",
            "Epoch 10/30\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.2739 - accuracy: 0.9286 - val_loss: 0.3657 - val_accuracy: 0.8889\n",
            "Epoch 11/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.2423 - accuracy: 0.9286 - val_loss: 0.3249 - val_accuracy: 0.8889\n",
            "Epoch 12/30\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.1927 - accuracy: 0.9286 - val_loss: 0.3515 - val_accuracy: 0.6667\n",
            "Epoch 13/30\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.2054 - accuracy: 0.8810 - val_loss: 0.3416 - val_accuracy: 0.8889\n",
            "Epoch 14/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.1762 - accuracy: 0.9524 - val_loss: 0.3859 - val_accuracy: 0.8889\n",
            "Epoch 15/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.2166 - accuracy: 0.9048 - val_loss: 0.3480 - val_accuracy: 0.7778\n",
            "Epoch 16/30\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.1711 - accuracy: 0.9048 - val_loss: 0.4475 - val_accuracy: 0.6667\n",
            "Epoch 17/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.2567 - accuracy: 0.8810 - val_loss: 0.3497 - val_accuracy: 0.7778\n",
            "Epoch 18/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.2361 - accuracy: 0.9048 - val_loss: 0.5726 - val_accuracy: 0.8889\n",
            "Epoch 19/30\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3177 - accuracy: 0.8810 - val_loss: 0.3732 - val_accuracy: 0.8889\n",
            "Epoch 20/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.1479 - accuracy: 0.9524 - val_loss: 0.3877 - val_accuracy: 0.7778\n",
            "Epoch 21/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.1643 - accuracy: 0.9286 - val_loss: 0.4215 - val_accuracy: 0.7778\n",
            "Epoch 22/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.1929 - accuracy: 0.9286 - val_loss: 0.3795 - val_accuracy: 0.7778\n",
            "Epoch 23/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.1294 - accuracy: 0.9762 - val_loss: 0.3828 - val_accuracy: 0.8889\n",
            "Epoch 24/30\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.1482 - accuracy: 0.9286 - val_loss: 0.3731 - val_accuracy: 0.7778\n",
            "Epoch 25/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.1278 - accuracy: 0.9048 - val_loss: 0.5482 - val_accuracy: 0.7778\n",
            "Epoch 26/30\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.3225 - accuracy: 0.8810 - val_loss: 0.7448 - val_accuracy: 0.6667\n",
            "Epoch 27/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4137 - accuracy: 0.7143 - val_loss: 0.4220 - val_accuracy: 0.7778\n",
            "Epoch 28/30\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.1667 - accuracy: 0.9524 - val_loss: 0.3538 - val_accuracy: 0.8889\n",
            "Epoch 29/30\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.1606 - accuracy: 0.9286 - val_loss: 0.3783 - val_accuracy: 0.8889\n",
            "Epoch 30/30\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.1782 - accuracy: 0.9286 - val_loss: 0.3659 - val_accuracy: 0.8889\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6659e3b7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWyctYJ4C1Ra",
        "outputId": "09881d7f-e129-4460-bdf2-d756a464325b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 186ms/step - loss: 0.2326 - accuracy: 0.9167\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.23260264098644257, 0.9166666865348816]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for images, labels in test_ds.take(1):\n",
        "  classifications = model(images)\n",
        "  \n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    index = numpy.argmax(classifications[i])\n",
        "    plt.title(\"Pred: \" + class_names[index] + \" | Real: \" + class_names[labels[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "-52pnJjTC_5M",
        "outputId": "d2e1ee39-acea-44fd-f3b6-1bf85db03829"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with open(\"DFModel.tflite\", 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FyjwRvvDNL7",
        "outputId": "368f4230-acd6-43e1-eea2-b9dc5bce9943"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpy07w0l6b/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "CNN_Ajwa_or_Rothana_or_SM_classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}