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"},"4170eaeb-137f-4f1c-8778-457c5790bfee.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# 1. **Introduction**","metadata":{}},{"cell_type":"markdown","source":"<h3 style = \"color: orange\"> Description </h3>\n\n<ul>\n    In this experiment, you are required to build a convolutional neural network for image classification, possibly with some advanced training tips. Please notice that utilizing external data (or pre-trained model) for training is prohibited. \n</ul>","metadata":{}},{"cell_type":"markdown","source":"# 2. **Importing**","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# libraroes\nimport os\nimport matplotlib.pyplot as plt\nimport cv2\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, BatchNormalization, Dropout, GlobalAveragePooling2D, LeakyReLU\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam, SGD\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom tensorflow.keras.regularizers import l2","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# importing images\ndir = '/kaggle/input/dl-2024-autum-exp-2/food-11_DL_2024'\ntrain_images = os.path.join(dir, 'training/labeled')\ntest_images = os.path.join(dir, 'testing/00')\nvalidation_images = os.path.join(dir, 'validation')","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 3. **Data Visualization**","metadata":{}},{"cell_type":"code","source":"# display images\ndef display_images(image_dir, num_images=5):\n    if os.path.isdir(image_dir) and len(os.listdir(image_dir)) > 0 and os.path.isdir(os.path.join(image_dir, os.listdir(image_dir)[0])):\n        image_paths = [os.path.join(image_dir, subdir, file) \n                       for subdir in os.listdir(image_dir) \n                       if os.path.isdir(os.path.join(image_dir, subdir)) \n                       for file in os.listdir(os.path.join(image_dir, subdir)) \n                       if file.endswith(('.png', '.jpg', '.jpeg'))]\n    else:\n        image_paths = [os.path.join(image_dir, file) \n                       for file in os.listdir(image_dir) \n                       if file.endswith(('.png', '.jpg', '.jpeg'))]\n    \n    image_paths = image_paths[:num_images]  \n    \n    # Display images\n    plt.figure(figsize=(15, 5))\n    for i, image_path in enumerate(image_paths):\n        img = cv2.imread(image_path)\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  \n        plt.subplot(1, num_images, i + 1)\n        plt.imshow(img)\n        plt.axis('off')\n        plt.title(os.path.basename(image_path))\n    plt.show()\n\n# training images\nprint(\"Training Images:\")\ndisplay_images(train_images)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test images\nprint(\"Test Images:\")\ndisplay_images(test_images)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# validation images\nprint(\"Validation Images:\")\ndisplay_images(validation_images)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 4. **Data Preprocessing**","metadata":{}},{"cell_type":"code","source":"# data augmentation\ntrain_datagen = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=40,\n    width_shift_range=0.3,\n    height_shift_range=0.3,\n    shear_range=0.3,\n    zoom_range=0.3,\n    horizontal_flip=True,\n    brightness_range=[0.8, 1.2],\n    fill_mode='nearest'\n)\n\ntrain_generator = train_datagen.flow_from_directory(\n    train_images,\n    target_size=(128, 128),\n    batch_size=64,\n    class_mode='categorical'\n)\n\nval_datagen = ImageDataGenerator(rescale=1./255)\nval_generator = val_datagen.flow_from_directory(\n    validation_images,\n    target_size=(128, 128),\n    batch_size=64,\n    class_mode='categorical'\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 5. **Model training, predicting and evaluation**","metadata":{}},{"cell_type":"code","source":"# CNN Arhitecture\n\nmodel = Sequential([\n    Conv2D(64, (3, 3), padding='same', input_shape=(128, 128, 3)),\n    LeakyReLU(alpha=0.1),\n    BatchNormalization(),\n    MaxPooling2D((2, 2)),\n\n    Conv2D(128, (3, 3), padding='same'),\n    LeakyReLU(alpha=0.1),\n    BatchNormalization(),\n    MaxPooling2D((2, 2)),\n\n    Conv2D(256, (3, 3), padding='same'),\n    LeakyReLU(alpha=0.1),\n    BatchNormalization(),\n    MaxPooling2D((2, 2)),\n\n    Conv2D(512, (3, 3), padding='same'),\n    LeakyReLU(alpha=0.1),\n    BatchNormalization(),\n    MaxPooling2D((2, 2)),\n\n    GlobalAveragePooling2D(),\n\n    Dense(128),\n    LeakyReLU(alpha=0.1),\n    Dropout(0.4),\n\n    Dense(11, activation='softmax')\n])\n\nmodel.summary()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# model compilation\nmodel.compile(optimizer=Adam(learning_rate=0.00003125), \n              loss='categorical_crossentropy', \n              metrics=['accuracy'])\n\n# callbacks\nearly_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\nlr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=15, verbose=1, min_lr=1e-6)\n\n# model training\nhistory = model.fit(\n    train_generator,\n    epochs=300,\n    validation_data=val_generator,\n    callbacks=[early_stopping, lr_scheduler]\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot Training and Validation Accuracy\n\nplt.plot(history.history['accuracy'], label='Train Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.title('Training and Validation Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.legend()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot Training and Validation Loss\n\nplt.plot(history.history['loss'], label='Train Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.title('Training and Validation Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test data augmentation\ntest_datagen = ImageDataGenerator(rescale=1./255)\ntest_generator = test_datagen.flow_from_directory(\n    directory = os.path.dirname(test_images),\n    classes = [os.path.basename(test_images)],\n    target_size = (128, 128),\n    batch_size = 1,\n    class_mode = None,\n    shuffle = False\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# predicting\ntest_predictions = model.predict(test_generator, verbose=1)\npredicted_classes = np.argmax(test_predictions, axis=1)\nclass_labels = {v: k for k, v in train_generator.class_indices.items()}\npredicted_labels = [class_labels[i] for i in predicted_classes]\n\nprint(predicted_labels)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 6. **Submission**","metadata":{}},{"cell_type":"code","source":"# predictions\ntest_ids = [int(os.path.splitext(os.path.basename(f))[0]) for f in test_generator.filenames]\n\nsubmission = pd.DataFrame({\n    'Id': test_ids,\n    'Category': predicted_classes\n})\n\npredictions = submission.sort_values(by='Id')\n\npredictions.to_csv('predictions.csv', index=False)\npredictions.head()","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}