Ratajczyk_approach_1.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"r7sBW8YQSbEz","executionInfo":{"status":"ok","timestamp":1674588636433,"user_tz":-60,"elapsed":6568,"user":{"displayName":"Igor Ratajczyk","userId":"05852146439915468683"}}},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os\n","import cv2\n","from tensorflow import keras\n","from typing import Tuple, Any, Dict, List\n","import json\n","import pandas as pd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":40641,"status":"ok","timestamp":1673090228910,"user":{"displayName":"Igor Ratajczyk","userId":"05852146439915468683"},"user_tz":-60},"id":"9yrF3e0ES8Vk","outputId":"58018117-0c09-48e9-f617-d019843bd276"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","Num GPUs Available:  0\n"]}],"source":["from google.colab import drive, files\n","drive.mount('/content/drive')\n","print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EEU1r9bOSiFz"},"outputs":[],"source":["SEED: int = 123\n","BATCH_SIZE: int = 32\n","VAL_SPLIT: float = .2\n","INPUT_SHAPE: Tuple[int] = (128, 128, 3)\n","IMAGES_PATH: str = \"/content/drive/MyDrive/STUDIA/WDSN/Project/images/processed\"\n","LABELS_PATH: str = \"/content/drive/MyDrive/STUDIA/WDSN/Project/images/labels/labels.json\"\n","IMAGE_COUNT: int = len(os.listdir(IMAGES_PATH))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NkN38_fRmbUx"},"outputs":[],"source":["dataset: pd.DataFrame = pd.read_json(LABELS_PATH).T\n","dataset[\"filename\"] = dataset.index"]},{"cell_type":"code","source":["def compare_history(\n","    histories: List[Dict[str, Any]], \n","    labels: List[str], \n","    epochs: List[int], \n","    metric: str = 'accuracy'\n","    ) -> None:\n","    plt.figure()\n","    for history, label, epoch in zip(histories, labels, epochs):\n","        plt.plot(range(epoch), history.history['acc'], label=label+\" train\")\n","        plt.plot(range(epoch), history.history['val_acc'], label=label+\" validation\")\n","    plt.xlabel('Epochs')\n","    plt.ylabel(metric)\n","    plt.grid()\n","    plt.legend()\n","    plt.show()\n","\n","\n","def decode_img(path: str, filename: str) -> tf.Tensor:\n","    full_path: str = os.path.join(IMAGES_PATH, filename)\n","    img = tf.io.decode_jpeg(tf.io.read_file(full_path), channels=3)\n","    return tf.image.resize(img, INPUT_SHAPE[:2])\n"],"metadata":{"id":"16GhH7OcCcgK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["images = tf.keras.utils.image_dataset_from_directory(\n","    IMAGES_PATH,\n","    labels=None,\n","    label_mode=None,\n","    image_size=INPUT_SHAPE[:2]\n",").cache().prefetch(buffer_size=tf.data.AUTOTUNE)#from_tensor_slices([decode_img(IMAGES_PATH, filename) for filename in os.listdir(IMAGES_PATH)])\n","means: List[float] = tf.data.Dataset.from_tensor_slices([dataset[dataset.index == filename][\"mean\"] for filename in os.listdir(IMAGES_PATH)]).cache().prefetch(buffer_size=tf.data.AUTOTUNE)\n","weights: List[float] = tf.data.Dataset.from_tensor_slices([dataset[dataset.index == filename][\"weight\"] for filename in os.listdir(IMAGES_PATH)]).cache().prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"-kmnXd0vRrNp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1673090943305,"user_tz":-60,"elapsed":8143,"user":{"displayName":"Igor Ratajczyk","userId":"05852146439915468683"}},"outputId":"8c4e6310-2fda-4bd8-b936-f0ef76d328bc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 2025 files belonging to 1 classes.\n"]}]},{"cell_type":"code","source":["ds: tf.data.Dataset = tf.data.Dataset.zip((images, means, weights)).shuffle(SEED)#.batch(BATCH_SIZE)\n","train_dataset = ds.skip(int(IMAGE_COUNT*VAL_SPLIT))#.cache()#.prefetch(buffer_size=tf.data.AUTOTUNE)\n","val_dataset = ds.take(int(IMAGE_COUNT*VAL_SPLIT))#.cache()#.prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"MLcpzdZEEpD2"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g__3_2GaSpcy"},"outputs":[],"source":["model: keras.models = keras.models.Sequential([\n","    tf.keras.layers.InputLayer(input_shape=INPUT_SHAPE, batch_size=BATCH_SIZE),\n","    tf.keras.layers.Conv2D(filters=16, kernel_size=(5, 5), activation='relu'),\n","    tf.keras.layers.MaxPool2D(),\n","    tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),\n","    tf.keras.layers.MaxPool2D(),\n","    tf.keras.layers.GlobalAveragePooling2D(),\n","    tf.keras.layers.Dense(64, activation='relu'),\n","    tf.keras.layers.Dense(64, activation='relu'),\n","    tf.keras.layers.Dense(1, activation='linear'),\n","])\n","\n","model.compile(\n","    optimizer='adam',\n","    loss = tf.keras.losses.MeanSquaredError(),\n","    metrics=[tf.keras.losses.MeanSquaredError()],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":390},"executionInfo":{"elapsed":250,"status":"error","timestamp":1673090945473,"user":{"displayName":"Igor Ratajczyk","userId":"05852146439915468683"},"user_tz":-60},"id":"9-C09npASrHp","outputId":"6efb9064-62bc-4c6c-a0d8-1fee77e71e3d"},"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-34-298964602585>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mmeans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0msample_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m_validate_args\u001b[0;34m(self, y, sample_weights, steps)\u001b[0m\n\u001b[1;32m    745\u001b[0m     \u001b[0;31m# Arguments that shouldn't be passed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_none_or_empty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 747\u001b[0;31m       raise ValueError(\"`y` argument is not supported when using \"\n\u001b[0m\u001b[1;32m    748\u001b[0m                        \"dataset as input.\")\n\u001b[1;32m    749\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_none_or_empty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: `y` argument is not supported when using dataset as input."]}],"source":["history = model.fit(\n","    images, \n","    means,\n","    epochs=10, \n","    sample_weight = weights\n","    )"]},{"cell_type":"code","source":[],"metadata":{"id":"7fo1EdUEY77u"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMmDwJs4w03O9Hpl06o10L7"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}