Model_4_on_Food_101_data.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP/HDLjXBRqUsYz3/uwsbdq",
      "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/149189/Experiments_on_Food101_dataset/blob/main/Model_4_on_Food_101_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import zipfile\n",
        "from pathlib import Path"
      ],
      "metadata": {
        "id": "z-dqW2ov75xy"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUmA0Mep6t6F",
        "outputId": "c2bb3328-567a-4295-91cb-2ea1fcabe42b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-08-20 10:10:54--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "helper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-08-20 10:10:55 (113 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
      ],
      "metadata": {
        "id": "hxiAkycQ6yzQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vliupLp5GLh",
        "outputId": "61c24a24-c52a-402e-ccd4-a93657312b29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-08-20 10:10:56--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.12.207, 172.253.118.207, 74.125.200.207, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.12.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 519183241 (495M) [application/zip]\n",
            "Saving to: ‘10_food_classes_all_data.zip’\n",
            "\n",
            "10_food_classes_all 100%[===================>] 495.13M  13.7MB/s    in 42s     \n",
            "\n",
            "2024-08-20 10:11:38 (11.9 MB/s) - ‘10_food_classes_all_data.zip’ saved [519183241/519183241]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download and unzip 10 classes of data with all images\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
        "unzip_data(\"10_food_classes_all_data.zip\")\n",
        "\n",
        "# Setup data directories\n",
        "train_dir = \"10_food_classes_all_data/train/\"\n",
        "test_dir = \"10_food_classes_all_data/test/\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "walk_through_dir(\"10_food_classes_all_data\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m98rT81b6gIS",
        "outputId": "7a5c2318-8cc7-4199-d90c-7ad0d56fbfd6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_all_data'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_wings'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/steak'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_curry'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/train'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/grilled_salmon'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ice_cream'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/sushi'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/hamburger'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_wings'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/fried_rice'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/pizza'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ramen'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/steak'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_curry'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = tf.keras.utils.image_dataset_from_directory(\n",
        "    train_dir,\n",
        "    image_size=(224, 224),\n",
        "    batch_size=32\n",
        ")\n",
        "\n",
        "test_data = tf.keras.utils.image_dataset_from_directory(\n",
        "    test_dir,\n",
        "    image_size=(224, 224),\n",
        "    batch_size=32\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKtypLib6jcR",
        "outputId": "57c37777-adc8-4e9c-daa2-6c7e670055b0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7500 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = tf.keras.Sequential([\n",
        "    layers.RandomFlip(\"horizontal_and_vertical\"),\n",
        "    layers.RandomRotation(0.2),\n",
        "    layers.RandomZoom(0.2),\n",
        "    layers.RandomHeight(0.1),\n",
        "    layers.RandomWidth(0.1),\n",
        "], name=\"data_augmentation\")"
      ],
      "metadata": {
        "id": "lrIHVU2l7DGh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeBg44m971uT",
        "outputId": "1f9dabef-ac6c-4ac6-c950-77f221046c49"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "\u001b[1m16705208/16705208\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers[-10:]:\n",
        "    layer.trainable = True"
      ],
      "metadata": {
        "id": "nY1eJBvF8Alo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = layers.Input(shape=(224, 224, 3), name=\"input_layer\")\n",
        "x = data_augmentation(inputs)\n",
        "x = base_model(x)\n",
        "x = layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "model = models.Model(inputs, outputs)"
      ],
      "metadata": {
        "id": "ocsgSxHh8DOh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),  # Lower learning rate for fine-tuning\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "metadata": {
        "id": "6z-PE9-A8Fmp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs/fine_tuning\")\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\"fine_tuned_model.keras\", save_best_only=True)"
      ],
      "metadata": {
        "id": "T5TjUd6-8H4w"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_data,\n",
        "    epochs=5,\n",
        "    validation_data=test_data,\n",
        "    validation_steps=int(0.25 * len(test_data)),\n",
        "    callbacks=[tensorboard_callback, checkpoint_callback]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2ctb2X08KUh",
        "outputId": "b18aa745-68f8-4efa-a78f-2a24ca59fd73"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 440ms/step - accuracy: 0.4059 - loss: 1.7792 - val_accuracy: 0.8487 - val_loss: 0.4647\n",
            "Epoch 2/5\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 413ms/step - accuracy: 0.7823 - loss: 0.6944 - val_accuracy: 0.8273 - val_loss: 0.4912\n",
            "Epoch 3/5\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 401ms/step - accuracy: 0.8453 - loss: 0.4982 - val_accuracy: 0.8355 - val_loss: 0.5098\n",
            "Epoch 4/5\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 392ms/step - accuracy: 0.8729 - loss: 0.4162 - val_accuracy: 0.8454 - val_loss: 0.4764\n",
            "Epoch 5/5\n",
            "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 385ms/step - accuracy: 0.8858 - loss: 0.3613"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self.gen.throw(typ, value, traceback)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m181s\u001b[0m 561ms/step - accuracy: 0.8859 - loss: 0.3612 - val_accuracy: 0.7941 - val_loss: 0.5426\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_fine_Tuned_model = model.evaluate(test_data)\n",
        "results_fine_Tuned_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEIMpXbgRCu1",
        "outputId": "2c6852d1-733d-4041-b283-416bb8abd94c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 67ms/step - accuracy: 0.8410 - loss: 0.4571\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4569302201271057, 0.8460000157356262]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\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.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Show the plots\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "z2DeYjz48NLQ",
        "outputId": "d061ab38-9fb7-4ec0-f329-e500cf8e8a8f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}