{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":40058,"offset":40048,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"Si un representante sindical te ha informado que las sanciones por faltas leves se eliminan de tu expediente en Eulen (o en el sector de seguridad privada, seg\u00fan se aplique) despu\u00e9s de 6 meses, es probable que esta informaci\u00f3n se base en el acuerdo establecido en el convenio colectivo aplicable o en las pol\u00edticas internas de la empresa. Los representantes sindicales suelen estar bien informados sobre estas cuestiones, ya que conocen en detalle tanto la legislaci\u00f3n laboral vigente como los acuerdos espec\u00edficos que afectan a los trabajadores que representan.\n\nEs importante destacar que la legislaci\u00f3n laboral y los convenios colectivos pueden variar y actualizarse, por lo que las pol\u00edticas de disciplina y gesti\u00f3n de sanciones tambi\u00e9n pueden cambiar. Asumiendo esta informaci\u00f3n como correcta, significa que si te han impuesto una sanci\u00f3n por una falta leve, esta deber\u00eda ser eliminada de tu expediente personal en la empresa trascurridos 6 meses desde su imposici\u00f3n, suponiendo que no se cometan otras faltas en ese per\u00edodo que pudieran afectar a esta disposici\u00f3n.\n\nSi tienes m\u00e1s dudas sobre c\u00f3mo esto afecta a tu situaci\u00f3n personal o sobre cualquier aspecto relacionado con la sanci\u00f3n, mi recomendaci\u00f3n es seguir dialogando con tu representante sindical, ya que pueden ofrecerte asesoramiento espec\u00edfico y apoyo en base a tu caso concreto. Tambi\u00e9n pueden guiarte a trav\u00e9s de cualquier proceso de apelaci\u00f3n o explicarte m\u00e1s detalladamente c\u00f3mo este per\u00edodo de tiempo fue determinado de acuerdo con la normativa aplicable.","behavior":"degenerate_repetition","conversation_hash":"300506258ae31782c34e6be1de8d0afa","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4-0125-preview","n_chars":1559,"n_turns_in_conversation":4,"n_words":244,"source_row_offset":40048,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:53:48 GMT","turn_id":"c77ba2d650cc059ab61284b0","turn_index":2,"user_text":"El sindicalista me dice 6 meses"},{"assistant_initiates":false,"asst_text":"En el contexto laboral, los t\u00e9rminos \"falta\" e \"infracci\u00f3n\" suelen utilizarse para referirse a violaciones de las normas laborales o del c\u00f3digo de conducta establecido por una empresa, aunque a veces pueden tener connotaciones ligeramente distintas dependiendo del marco legal o del contexto espec\u00edfico.\n\n**Falta:** En el \u00e1mbito laboral, una falta puede referirse a una conducta inapropiada o a una omisi\u00f3n por parte del empleado que incumple las expectativas de rendimiento, comportamiento, asistencia, o cualquier otra normativa interna de la empresa. Las faltas suelen clasificarse en leves, graves y muy graves, dependiendo de la severidad del incumplimiento o del da\u00f1o causado a la empresa, a los compa\u00f1eros de trabajo o a terceros. Estas clasificaciones determinan la severidad de las sanciones que se pueden aplicar.\n\n**Infracci\u00f3n:** Este t\u00e9rmino tambi\u00e9n se utiliza para describir un acto de incumplimiento o violaci\u00f3n de las normas establecidas (legales, reglamentarias, contractuales, etc.). En el contexto laboral, las infracciones pueden referirse espec\u00edficamente a violaciones de las regulaciones legales o del contrato de trabajo. Al igual que las faltas, las infracciones pueden ser clasificadas seg\u00fan su gravedad y dar lugar a sanciones correspondientes.\n\nAunque en la pr\u00e1ctica los t\u00e9rminos \"falta\" e \"infracci\u00f3n\" pueden utilizarse de manera intercambiable para describir conductas indebidas en el lugar de trabajo, el contexto o el marco legislativo espec\u00edfico podr\u00eda dar a cada palabra matices distintos. En algunos casos, \"infracci\u00f3n\" podr\u00eda usarse para referirse m\u00e1s espec\u00edficamente a violaciones de normativas legales o contractuales, mientras que \"falta\" podr\u00eda tener un uso m\u00e1s gen\u00e9rico dentro de las pol\u00edticas internas de la empresa.\n\nEs importante consultar el convenio colectivo aplicable, el manual de empleado o la normativa interna de la empresa para entender c\u00f3mo se definen y se tratan estas conductas indebidas en un contexto laboral espec\u00edfico. Iguamente, en caso de duda, siempre es recomendable buscar asesoramiento legal o de un representante sindical.","behavior":"degenerate_repetition","conversation_hash":"300506258ae31782c34e6be1de8d0afa","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4-0125-preview","n_chars":2121,"n_turns_in_conversation":4,"n_words":312,"source_row_offset":40049,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:53:48 GMT","turn_id":"d3b941bc8c05618f959b4a18","turn_index":3,"user_text":"\u00bfFaltas e infracci\u00f3n es lo mismo?"},{"assistant_initiates":false,"asst_text":"/imagine prompt: daydreaming office worker on a tropical beach, dressed in business attire, typing on a laptop with sand between their toes, palm trees swaying in the gentle breeze, turquoise ocean in the background, a sunny and relaxed atmosphere, captured with a Canon EOS 5D Mark IV camera, 24-70mm lens, wide-angle shot showcasing the contrast between work and leisure, in a vibrant and colorful style reminiscent of pop art. --ar 16:9 --v 6\n\n/imagine prompt: exhausted commuter asleep on a crowded subway, wearing a suit and tie, surrounded by other passengers reading newspapers and listening to music, dimly lit underground train station with flickering lights, a melancholic and tired atmosphere, photography, black and white, candid shot capturing the sense of mundane routine and urban isolation, with a Fujifilm X-T4 camera, 35mm lens, shallow depth of field focusing on the sleeping commuter's face. --ar 16:9 --v 6\n\n/imagine prompt: stressed office worker in a chaotic and cluttered cubicle, piles of paperwork, overflowing trash bin, flickering fluorescent lights, ringing phone, unfinished coffee cup, crumpled post-it notes, a sense of overwhelming pressure and disorganization, illustration, sketchy and detailed style highlighting the chaotic environment, with a mix of monochrome and bright colors to depict the conflicting emotions of stress and chaos. --ar 1:1 --v 6\n\n/imagine prompt: bored office worker gazing out of a high-rise window, overlooking a bustling cityscape with skyscrapers and traffic below, a stormy sky with dark clouds and lightning in the distance, an eerie and ominous atmosphere, mixed media artwork combining photography and painting techniques, surreal and dream-like interpretation of urban isolation and longing for escape, with a dramatic and moody color palette, emphasizing the contrast between the mundane office setting and the dynamic city outside. --ar 9:16 --v 6","behavior":"degenerate_repetition","conversation_hash":"9a9de29bf68c608abf6cc0b3e9ec4358","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":5712,"n_turns_in_conversation":1,"n_words":741,"source_row_offset":40050,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:54:37 GMT","turn_id":"17f6cb8d37fb9d5b610d2454","turn_index":0,"user_text":"\n                            As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n                            \n                            Please adhere to the structure and formatting below, and follow these guidelines:\n                            \n                            Do not use the words \"description\" or \":\" in any form.\n                            Do not place a comma between [ar] and [v].\n                            Write each prompt in one line without using return.\n                            Structure:\n                            [1] = \u5e7b\u60f3\u653e\u5047\u7684\u4e0a\u73ed\u65cf\n                            [2] = a detailed description of [1] with specific imagery details.\n                            [3] = a detailed description of the scene's environment.\n                            [4] = a detailed description of the compositions.\n                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.\n                            [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n                            [v] = Use \"--niji 6\" for Japanese art style, or \"--v 6\" for other styles.\n                            \n                            \n                            Formatting:\n                            Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n                            \n                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n                            \n                            Write your prompts in english.\n                            Do not describe unreal concepts as \"real\" or \"photographic\".\n                            Include one realistic photographic style prompt with lens type and size.\n                            Separate different prompts with two new lines.\n                            Example Prompts:\n                            \n                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6\n                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6\n                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6\n                                                        "},{"assistant_initiates":false,"asst_text":"Your code is quite extensive, covering data preprocessing, model creation, training, evaluation, and plotting of the performance. However, because you're noticing poor performance, there are several aspects and potential areas of improvement I would recommend evaluating:\n\n1. **Data Quality and Quantity**: The quality and quantity of your dataset significantly impact model performance. Consider the following:\n    - Make sure the dataset is large enough to train robust models, especially for a multi-task learning setup.\n    - Check that your data is correctly labeled, especially for complex tasks like defect detection. Incorrect labels can severely impact performance.\n    - Data augmentation can help improve model robustness. You're using `ImageDataGenerator`, but ensure you're leveraging it effectively for both tasks.\n\n2. **Model Architecture Review**: You are doing multi-task learning with both classification and segmentation outputs. Multi-task learning models can be highly effective but also more challenging to tune. Consider the following:\n    - Check if the complexity of your architecture is appropriate for your dataset's size and complexity. Overfitting can be a problem if the model is too large.\n    - Revisit your decision to use global average pooling outputs from intermediate layers (`relu8`, `relu10`, `relu12`) for the classification branch. Make sure these layers indeed capture useful information for classification.\n\n3. **Loss Function and Learning Rate**: Your setup of the loss function and learning rate scheduler is crucial.\n    - Re-evaluate the balance between classification and segmentation losses. You're currently weighting them equally; however, one task might be more challenging than the other and may require adjustment of the loss weights.\n    - You use a starting learning rate of 0.001 with `Adam` and a `ReduceLROnPlateau` scheduler. This setup generally works well, but depending on your specific problem, you might need to fine-tune the learning rate or the reduction factor.\n\n4. **Data Splitting Logic**: Ensure that the logic you use for splitting the data into training, validation, and test sets guarantees that each set is representative of the overall data distribution. Imbalanced splits could bias the model training.\n\n5. **Evaluation and Metrics**: When you evaluate your model, make sure you're looking into both tasks' performance evenly. Sometimes, improving performance on one task can lead to decreased performance on the other. \n\n6. **Training Configuration**: Ensure that the model is trained for a sufficient number of epochs but also include mechanisms to prevent overfitting. Your current setup with `EarlyStopping` and `ReduceLROnPlateau` is good, but double-check if the parameters are set optimally for your case.\n\n7. **Debugging Strategy**:\n    - Start with a simpler model to ensure your pipeline (data loading, preprocessing, augmentation) works correctly.\n    - Incrementally make your model more complex.\n    - Conduct ablation studies by disabling one of the tasks to see if performing that task alone leads to better performance.\n\nFinally, consider employing techniques such as learning rate finding and gradual unfreezing if you're using transfer learning.\n\nRemember, multi-task learning is complex, and finding the right balance between different tasks can require a lot of tuning and experimentation.","behavior":"degenerate_repetition","conversation_hash":"a3c4db1ad810e5d833ffa2140162b9be","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":37321,"n_turns_in_conversation":2,"n_words":3461,"source_row_offset":40051,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:54:43 GMT","turn_id":"8abde827cf07d7099e00dedc","turn_index":0,"user_text":"I am developing multi task learning model for pcb soldering defect image classification using google colab:\u201c\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport cv2\nimport random\nimport tensorflow as tf\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom PIL import ImageTk, Image\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nfrom IPython.display import display, clear_output\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.optimizers import Adam, SGD, RMSprop, AdamW, Adadelta, Adagrad, Adamax, Adafactor, Nadam, Ftrl\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tqdm import tqdm\nimport os\nfrom sklearn.utils import shuffle\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential, Model, load_model\nfrom tensorflow.keras.layers import (\nGlobalAveragePooling2D,\nDropout,\nDense,\nConv2D,\nMaxPooling2D,\nFlatten,\nDropout,\nBatchNormalization,\nActivation,\nconcatenate,\nConv2DTranspose,\nInput,\nReshape,\nUpSampling2D,\n)\nfrom tensorflow.keras.applications import (\nEfficientNetV2B0,\nEfficientNetV2B1,\nEfficientNetV2B2,\nEfficientNetV2B3,\nEfficientNetV2L,\nEfficientNetV2M,\nEfficientNetV2S,\n)\nfrom tensorflow.keras.applications import Xception\nfrom tensorflow.keras.applications import VGG16, VGG19\nfrom tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, ResNetRS50, ResNetRS101\nfrom tensorflow.keras.applications import InceptionResNetV2, ConvNeXtXLarge, ConvNeXtBase, DenseNet121, MobileNetV2, NASNetLarge, NASNetMobile\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport ipywidgets as widgets\nimport io\nfrom PIL import Image\nfrom IPython.display import display, clear_output\nfrom warnings import filterwarnings\nfrom google.colab import drive\ndrive.mount(\u201d/content/gdrive\")\n\ndef load_data(data_folders):\nX_data = [] # Combined data\ny_class_labels = [] # Combined classification labels\ny_seg_labels = [] # Combined segmentation labels\nfor folderPath in data_folders:\nfor label in labels:\nlabel_folder_path = os.path.join(folderPath, label)\nfor filename in tqdm(os.listdir(label_folder_path)):\nif filename.endswith(\u201c.jpg\u201d):\nimg = cv2.imread(os.path.join(label_folder_path, filename))\nimg = cv2.resize(img, (image_size, image_size))\nX_data.append(img)\ny_class_labels.append(label)\nseg_filename = filename.split(\u201c.\u201d)[0] + \u201c.png\u201d\nseg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0)\nseg_img = cv2.resize(seg_img, (image_size, image_size))\nseg_img = np.where(seg_img > 0, 1, 0) # Convert segmentation mask to binary\ny_seg_labels.append(seg_img)\nX_data = np.array(X_data)\ny_class_labels = np.array(y_class_labels)\ny_seg_labels = np.array(y_seg_labels)\nX_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)\nreturn X_data, y_class_labels, y_seg_labels\n\ndef split_data(X_data, y_class_labels, y_seg_labels, class_data_counts):\nX_train = []\ny_train_class = []\ny_train_seg = []\nX_val = []\ny_val_class = []\ny_val_seg = []\nX_test = []\ny_test_class = []\ny_test_seg = []\n\nfor label, count in class_data_counts.items():\nlabel_indices = np.where(y_class_labels == label)[0]\nclass_X_data = X_data[label_indices]\nclass_y_class_labels = y_class_labels[label_indices]\nclass_y_seg_labels = y_seg_labels[label_indices]\n\ntrain_count = count[0]\nval_count = count[1]\ntest_count = count[2]\n\nclass_X_train = class_X_data[:train_count]\nclass_y_train_class = class_y_class_labels[:train_count]\nclass_y_train_seg = class_y_seg_labels[:train_count]\nclass_X_val = class_X_data[train_count: train_count + val_count]\nclass_y_val_class = class_y_class_labels[train_count: train_count + val_count]\nclass_y_val_seg = class_y_seg_labels[train_count: train_count + val_count]\nclass_X_test = class_X_data[train_count + val_count: train_count + val_count + test_count]\nclass_y_test_class = class_y_class_labels[train_count + val_count: train_count + val_count + test_count]\nclass_y_test_seg = class_y_seg_labels[train_count + val_count: train_count + val_count + test_count]\n\nX_train.extend(class_X_train)\ny_train_class.extend(class_y_train_class)\ny_train_seg.extend(class_y_train_seg)\nX_val.extend(class_X_val)\ny_val_class.extend(class_y_val_class)\ny_val_seg.extend(class_y_val_seg)\nX_test.extend(class_X_test)\ny_test_class.extend(class_y_test_class)\ny_test_seg.extend(class_y_test_seg)\n\n# Convert class labels to categorical\nlabel_encoder = LabelEncoder()\ny_train_class_encoded = label_encoder.fit_transform(y_train_class)\ny_train_class_categorical = to_categorical(y_train_class_encoded)\ny_val_class_encoded = label_encoder.transform(y_val_class)\ny_val_class_categorical = to_categorical(y_val_class_encoded)\ny_test_class_encoded = label_encoder.transform(y_test_class)\ny_test_class_categorical = to_categorical(y_test_class_encoded)\n\nreturn (\nnp.array(X_train),\nnp.array(y_train_class_categorical),\nnp.array(y_train_seg),\nnp.array(X_val),\nnp.array(y_val_class_categorical),\nnp.array(y_val_seg),\nnp.array(X_test),\nnp.array(y_test_class_categorical),\nnp.array(y_test_seg),\n)\n\ndef count_labels(y_class_categorical, label_encoder):\n# Convert one-hot encoded labels back to label encoded\ny_class_labels = np.argmax(y_class_categorical, axis=1)\n# Convert label encoded labels back to original class names\ny_class_names = label_encoder.inverse_transform(y_class_labels)\nunique, counts = np.unique(y_class_names, return_counts=True)\nreturn dict(zip(unique, counts))\n\ndef build_model(input_shape, num_classes):\nnum_filter = 32 # 16/32 best, 8: best classification but no segment\n# Encoder (Done)\ninputs = Input(input_shape)\nconv1 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(inputs)\nbn1 = BatchNormalization()(conv1)\nrelu1 = Activation(\u201crelu\u201d)(bn1)\nconv2 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu1)\nbn2 = BatchNormalization()(conv2)\nrelu2 = Activation(\u201crelu\u201d)(bn2)\ndown1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2)\nconv3 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down1)\nbn3 = BatchNormalization()(conv3)\nrelu3 = Activation(\u201crelu\u201d)(bn3)\nconv4 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu3)\nbn4 = BatchNormalization()(conv4)\nrelu4 = Activation(\u201crelu\u201d)(bn4)\ndown2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4)\nconv5 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down2)\nbn5 = BatchNormalization()(conv5)\nrelu5 = Activation(\u201crelu\u201d)(bn5)\nconv6 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu5)\nbn6 = BatchNormalization()(conv6)\nrelu6 = Activation(\u201crelu\u201d)(bn6)\ndown3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6)\nconv7 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down3)\nbn7 = BatchNormalization()(conv7)\nrelu7 = Activation(\u201crelu\u201d)(bn7)\nconv8 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu7)\nbn8 = BatchNormalization()(conv8)\nrelu8 = Activation(\u201crelu\u201d)(bn8)\n# Middle\ndown4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8)\nconv9 = Conv2D(num_filter * 16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down4)\nbn9 = BatchNormalization()(conv9)\nrelu9 = Activation(\u201crelu\u201d)(bn9)\nconv10 = Conv2D(num_filter * 16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu9)\nbn10 = BatchNormalization()(conv10)\nrelu10 = Activation(\u201crelu\u201d)(bn10)\nup1 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu10)\n# Decoder (Done)\nconcat1 = concatenate([up1, relu8], axis=-1) # , axis=3\nconv11 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat1)\nbn11 = BatchNormalization()(conv11)\nrelu11 = Activation(\u201crelu\u201d)(bn11)\nconv12 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu11)\nbn12 = BatchNormalization()(conv12)\nrelu12 = Activation(\u201crelu\u201d)(bn12)\nup2 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu12)\nconcat2 = concatenate([up2, relu6], axis=-1) # , axis=3\nconv13 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat2)\nbn13 = BatchNormalization()(conv13)\nrelu13 = Activation(\u201crelu\u201d)(bn13)\nconv14 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu13)\nbn14 = BatchNormalization()(conv14)\nrelu14 = Activation(\u201crelu\u201d)(bn14)\nup3 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu14)\nconcat3 = concatenate([up3, relu4], axis=-1) # , axis=3\nconv15 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat3)\nbn15 = BatchNormalization()(conv15)\nrelu15 = Activation(\u201crelu\u201d)(bn15)\nconv16 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu15)\nbn16 = BatchNormalization()(conv16)\nrelu16 = Activation(\u201crelu\u201d)(bn16)\nup4 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu16)\nconcat4 = concatenate([up4, relu2], axis=-1) # , axis=3\nconv17 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat4)\nbn17 = BatchNormalization()(conv17)\nrelu17 = Activation(\u201crelu\u201d)(bn17)\nconv18 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu17)\nbn18 = BatchNormalization()(conv18)\nrelu18 = Activation(\u201crelu\u201d)(bn18)\n# Segmentation branch\nsegmentation_output = Conv2D(1, 1, activation=\u201csigmoid\u201d, name=\u201csegmentation_output\u201d)(relu18) # original\n# Classification branch (Not done)\ngap1 = GlobalAveragePooling2D()(relu8)\ngap2 = GlobalAveragePooling2D()(relu10)\ngap3 = GlobalAveragePooling2D()(relu12)\nconv20 = Conv2D(16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(segmentation_output)\nbn20 = BatchNormalization()(conv20)\nrelu20 = Activation(\u201crelu\u201d)(bn20)\ndown5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20)\nconv21 = Conv2D(32, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down5)\nbn21 = BatchNormalization()(conv21)\nrelu21 = Activation(\u201crelu\u201d)(bn21)\ndown6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21)\nconv22 = Conv2D(64, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down6)\nbn22 = BatchNormalization()(conv22)\nrelu22 = Activation(\u201crelu\u201d)(bn22)\ndown7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22)\nflatten1 = Flatten()(down7)\nconcat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1)\n# FC layers\nfc1 = Dense(1024, activation=\u201crelu\u201d)(concat5)\ndropout1 = Dropout(0.5)(fc1)\nfc2 = Dense(1024, activation=\u201crelu\u201d)(dropout1)\ndropout2 = Dropout(0.5)(fc2)\nclassification_output = Dense(num_classes, activation=\u201csoftmax\u201d, name=\u201cclassification_output\u201d)(dropout2)\n# Define the model\nmodel = Model(inputs=inputs, outputs=[classification_output, segmentation_output])\nreturn model\n\ndef segmentation_loss(y_true, y_pred):\ny_true = tf.cast(y_true, tf.float32)\ny_pred = tf.cast(y_pred, tf.float32)\nbce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)\nsmooth = 1e-5\nintersection = tf.reduce_sum(y_true * y_pred)\nunion = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)\ndice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth)\nsegmentation_loss = bce_loss + 1 * dice_loss\nreturn segmentation_loss\n\ndef train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs):\ncheckpoint = ModelCheckpoint(\n\u201cmultitask_best_weights.h5\u201d,\nmonitor=\u201cval_classification_output_accuracy\u201d,\nsave_best_only=True,\nmode=\u201cmax\u201d,\nverbose=1,)\nreduce_lr = ReduceLROnPlateau(\nmonitor=\u201cval_classification_output_accuracy\u201d,\nfactor=0.3,\npatience=2,\nmin_delta=0.001,\nmode=\u201cauto\u201d,\nverbose=1,)\ntensorboard = TensorBoard(log_dir=\u201clogs\u201d)\nmodel.compile(\noptimizer=Adam(lr=0.001),\nloss={\u201cclassification_output\u201d: \u201ccategorical_crossentropy\u201d, \u201csegmentation_output\u201d: segmentation_loss},\nmetrics={\u201cclassification_output\u201d: \u201caccuracy\u201d, \u201csegmentation_output\u201d: \u201caccuracy\u201d},\nloss_weights={\u201cclassification_output\u201d: 1, \u201csegmentation_output\u201d: 1},)\nhistory = model.fit(\nX_train,\n{\u201cclassification_output\u201d: y_train_class, \u201csegmentation_output\u201d: y_train_seg},\nvalidation_data=(X_val, {\u201cclassification_output\u201d: y_val_class, \u201csegmentation_output\u201d: y_val_seg}),\nepochs=epochs,\nverbose=1,\nbatch_size=batch_size,\ncallbacks=[checkpoint, reduce_lr, tensorboard],)\nreturn history\n\ndef evaluate_model(model, X_test, y_test_class, y_test_seg):\nwith tf.keras.utils.custom_object_scope({\u201csegmentation_loss\u201d: segmentation_loss}):\n# Load the best model weights\nbest_model = load_model(\u201cmultitask_best_weights.h5\u201d)\n# Evaluate the model on test data\ntest_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate(\nX_test, {\u201cclassification_output\u201d: y_test_class, \u201csegmentation_output\u201d: y_test_seg})\nprint(\u201cTest Classification Loss:\u201d, test_class_loss)\nprint(\u201cTest Segmentation Loss:\u201d, test_seg_loss)\nprint(\u201cTest Classification Accuracy:\u201d, test_class_acc)\nprint(\u201cTest Segmentation Accuracy:\u201d, test_seg_acc)\n# Evaluate the model on validation data\nval_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate(\nX_val, {\u2018classification_output\u2019: y_val_class, \u2018segmentation_output\u2019: y_val_seg})\nprint(\u201cValidation Classification Loss:\u201d, val_class_loss)\nprint(\u201cValidation Segmentation Loss:\u201d, val_seg_loss)\nprint(\u201cValidation Classification Accuracy:\u201d, val_class_acc)\nprint(\u201cValidation Segmentation Accuracy:\u201d, val_seg_acc)\n# Evaluate the model on training data\ntrain_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {\u2018classification_output\u2019: y_train_class, \u2018segmentation_output\u2019: y_train_seg})\nprint(\u201cTrain Classification Loss:\u201d, train_class_loss)\nprint(\u201cTrain Segmentation Loss:\u201d, train_seg_loss)\nprint(\u201cTrain Classification Accuracy:\u201d, train_class_acc)\nprint(\u201cTrain Segmentation Accuracy:\u201d, train_seg_acc)\n# Return test classification accuracy\nreturn test_class_acc\n\ndef plot_performance(history):\n# Plot classification accuracy\nclassification_train_accuracy = history.history[\u201cclassification_output_accuracy\u201d]\nclassification_val_accuracy = history.history[\u201cval_classification_output_accuracy\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(classification_train_accuracy, label=\u201cTraining Accuracy\u201d)\nplt.plot(classification_val_accuracy, label=\u201cValidation Accuracy\u201d)\nplt.title(\u201cClassification Accuracy\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cAccuracy\u201d)\nplt.legend()\nplt.show()\n# Plot classification loss\nclassification_train_loss = history.history[\u201cclassification_output_loss\u201d]\nclassification_val_loss = history.history[\u201cval_classification_output_loss\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(classification_train_loss, \u201cb\u201d, label=\u201cTraining Loss\u201d)\nplt.plot(classification_val_loss, \u201cr\u201d, label=\u201cValidation Loss\u201d)\nplt.title(\u201cClassification Loss\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cLoss\u201d)\nplt.legend()\nplt.show()\n# Plot segmentation accuracy\nsegmentation_train_accuracy = history.history[\u201csegmentation_output_accuracy\u201d]\nsegmentation_val_accuracy = history.history[\u201cval_segmentation_output_accuracy\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(segmentation_train_accuracy, label=\u201cTraining Accuracy\u201d)\nplt.plot(segmentation_val_accuracy, label=\u201cValidation Accuracy\u201d)\nplt.title(\u201cSegmentation Accuracy\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cAccuracy\u201d)\nplt.legend()\nplt.show()\n# Plot segmentation loss\nsegmentation_train_loss = history.history[\u201csegmentation_output_loss\u201d]\nsegmentation_val_loss = history.history[\u201cval_segmentation_output_loss\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(segmentation_train_loss, \u201cb\u201d, label=\u201cTraining Loss\u201d)\nplt.plot(segmentation_val_loss, \u201cr\u201d, label=\u201cValidation Loss\u201d)\nplt.title(\u201cSegmentation Loss\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cLoss\u201d)\nplt.legend()\nplt.show()\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport cv2\nimport random\nimport tensorflow as tf\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom PIL import ImageTk, Image\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nfrom IPython.display import display, clear_output\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.optimizers import Adam, SGD, RMSprop, AdamW, Adadelta, Adagrad, Adamax, Adafactor, Nadam, Ftrl\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tqdm import tqdm\nimport os\nfrom sklearn.utils import shuffle\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential, Model, load_model\nfrom tensorflow.keras.layers import (\nGlobalAveragePooling2D,\nDropout,\nDense,\nConv2D,\nMaxPooling2D,\nFlatten,\nDropout,\nBatchNormalization,\nActivation,\nconcatenate,\nConv2DTranspose,\nInput,\nReshape,\nUpSampling2D,\n)\nfrom tensorflow.keras.applications import (\nEfficientNetV2B0,\nEfficientNetV2B1,\nEfficientNetV2B2,\nEfficientNetV2B3,\nEfficientNetV2L,\nEfficientNetV2M,\nEfficientNetV2S,\n)\nfrom tensorflow.keras.applications import Xception\nfrom tensorflow.keras.applications import VGG16, VGG19\nfrom tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, ResNetRS50, ResNetRS101\nfrom tensorflow.keras.applications import InceptionResNetV2, ConvNeXtXLarge, ConvNeXtBase, DenseNet121, MobileNetV2, NASNetLarge, NASNetMobile\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport ipywidgets as widgets\nimport io\nfrom PIL import Image\nfrom IPython.display import display, clear_output\nfrom warnings import filterwarnings\nfrom google.colab import drive\ndrive.mount(\u201c/content/gdrive\u201d)\n\ndef load_data(data_folders):\nX_data = [] # Combined data\ny_class_labels = [] # Combined classification labels\ny_seg_labels = [] # Combined segmentation labels\nfor folderPath in data_folders:\nfor label in labels:\nlabel_folder_path = os.path.join(folderPath, label)\nfor filename in tqdm(os.listdir(label_folder_path)):\nif filename.endswith(\u201c.jpg\u201d):\nimg = cv2.imread(os.path.join(label_folder_path, filename))\nimg = cv2.resize(img, (image_size, image_size))\nX_data.append(img)\ny_class_labels.append(label)\nseg_filename = filename.split(\u201c.\u201d)[0] + \u201c.png\u201d\nseg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0)\nseg_img = cv2.resize(seg_img, (image_size, image_size))\nseg_img = np.where(seg_img > 0, 1, 0) # Convert segmentation mask to binary\ny_seg_labels.append(seg_img)\nX_data = np.array(X_data)\ny_class_labels = np.array(y_class_labels)\ny_seg_labels = np.array(y_seg_labels)\nX_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)\nreturn X_data, y_class_labels, y_seg_labels\n\ndef split_data(X_data, y_class_labels, y_seg_labels, class_data_counts):\nX_train = []\ny_train_class = []\ny_train_seg = []\nX_val = []\ny_val_class = []\ny_val_seg = []\nX_test = []\ny_test_class = []\ny_test_seg = []\n\nfor label, count in class_data_counts.items():\nlabel_indices = np.where(y_class_labels == label)[0]\nclass_X_data = X_data[label_indices]\nclass_y_class_labels = y_class_labels[label_indices]\nclass_y_seg_labels = y_seg_labels[label_indices]\n\ntrain_count = count[0]\nval_count = count[1]\ntest_count = count[2]\n\nclass_X_train = class_X_data[:train_count]\nclass_y_train_class = class_y_class_labels[:train_count]\nclass_y_train_seg = class_y_seg_labels[:train_count]\nclass_X_val = class_X_data[train_count: train_count + val_count]\nclass_y_val_class = class_y_class_labels[train_count: train_count + val_count]\nclass_y_val_seg = class_y_seg_labels[train_count: train_count + val_count]\nclass_X_test = class_X_data[train_count + val_count: train_count + val_count + test_count]\nclass_y_test_class = class_y_class_labels[train_count + val_count: train_count + val_count + test_count]\nclass_y_test_seg = class_y_seg_labels[train_count + val_count: train_count + val_count + test_count]\n\nX_train.extend(class_X_train)\ny_train_class.extend(class_y_train_class)\ny_train_seg.extend(class_y_train_seg)\nX_val.extend(class_X_val)\ny_val_class.extend(class_y_val_class)\ny_val_seg.extend(class_y_val_seg)\nX_test.extend(class_X_test)\ny_test_class.extend(class_y_test_class)\ny_test_seg.extend(class_y_test_seg)\n\n# Convert class labels to categorical\nlabel_encoder = LabelEncoder()\ny_train_class_encoded = label_encoder.fit_transform(y_train_class)\ny_train_class_categorical = to_categorical(y_train_class_encoded)\ny_val_class_encoded = label_encoder.transform(y_val_class)\ny_val_class_categorical = to_categorical(y_val_class_encoded)\ny_test_class_encoded = label_encoder.transform(y_test_class)\ny_test_class_categorical = to_categorical(y_test_class_encoded)\n\nreturn (\nnp.array(X_train),\nnp.array(y_train_class_categorical),\nnp.array(y_train_seg),\nnp.array(X_val),\nnp.array(y_val_class_categorical),\nnp.array(y_val_seg),\nnp.array(X_test),\nnp.array(y_test_class_categorical),\nnp.array(y_test_seg),\n)\n\ndef count_labels(y_class_categorical, label_encoder):\n# Convert one-hot encoded labels back to label encoded\ny_class_labels = np.argmax(y_class_categorical, axis=1)\n# Convert label encoded labels back to original class names\ny_class_names = label_encoder.inverse_transform(y_class_labels)\nunique, counts = np.unique(y_class_names, return_counts=True)\nreturn dict(zip(unique, counts))\n\ndef build_model(input_shape, num_classes):\nnum_filter = 32 # 16/32 best, 8: best classification but no segment\n# Encoder (Done)\ninputs = Input(input_shape)\nconv1 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(inputs)\nbn1 = BatchNormalization()(conv1)\nrelu1 = Activation(\u201crelu\u201d)(bn1)\nconv2 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu1)\nbn2 = BatchNormalization()(conv2)\nrelu2 = Activation(\u201crelu\u201d)(bn2)\ndown1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2)\nconv3 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down1)\nbn3 = BatchNormalization()(conv3)\nrelu3 = Activation(\u201crelu\u201d)(bn3)\nconv4 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu3)\nbn4 = BatchNormalization()(conv4)\nrelu4 = Activation(\u201crelu\u201d)(bn4)\ndown2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4)\nconv5 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down2)\nbn5 = BatchNormalization()(conv5)\nrelu5 = Activation(\u201crelu\u201d)(bn5)\nconv6 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu5)\nbn6 = BatchNormalization()(conv6)\nrelu6 = Activation(\u201crelu\u201d)(bn6)\ndown3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6)\nconv7 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down3)\nbn7 = BatchNormalization()(conv7)\nrelu7 = Activation(\u201crelu\u201d)(bn7)\nconv8 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu7)\nbn8 = BatchNormalization()(conv8)\nrelu8 = Activation(\u201crelu\u201d)(bn8)\n# Middle\ndown4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8)\nconv9 = Conv2D(num_filter * 16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down4)\nbn9 = BatchNormalization()(conv9)\nrelu9 = Activation(\u201crelu\u201d)(bn9)\nconv10 = Conv2D(num_filter * 16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu9)\nbn10 = BatchNormalization()(conv10)\nrelu10 = Activation(\u201crelu\u201d)(bn10)\nup1 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu10)\n# Decoder (Done)\nconcat1 = concatenate([up1, relu8], axis=-1) # , axis=3\nconv11 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat1)\nbn11 = BatchNormalization()(conv11)\nrelu11 = Activation(\u201crelu\u201d)(bn11)\nconv12 = Conv2D(num_filter * 8, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu11)\nbn12 = BatchNormalization()(conv12)\nrelu12 = Activation(\u201crelu\u201d)(bn12)\nup2 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu12)\nconcat2 = concatenate([up2, relu6], axis=-1) # , axis=3\nconv13 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat2)\nbn13 = BatchNormalization()(conv13)\nrelu13 = Activation(\u201crelu\u201d)(bn13)\nconv14 = Conv2D(num_filter * 4, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu13)\nbn14 = BatchNormalization()(conv14)\nrelu14 = Activation(\u201crelu\u201d)(bn14)\nup3 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu14)\nconcat3 = concatenate([up3, relu4], axis=-1) # , axis=3\nconv15 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat3)\nbn15 = BatchNormalization()(conv15)\nrelu15 = Activation(\u201crelu\u201d)(bn15)\nconv16 = Conv2D(num_filter * 2, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu15)\nbn16 = BatchNormalization()(conv16)\nrelu16 = Activation(\u201crelu\u201d)(bn16)\nup4 = UpSampling2D(size=(2, 2), interpolation=\u201cbilinear\u201d)(relu16)\nconcat4 = concatenate([up4, relu2], axis=-1) # , axis=3\nconv17 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(concat4)\nbn17 = BatchNormalization()(conv17)\nrelu17 = Activation(\u201crelu\u201d)(bn17)\nconv18 = Conv2D(num_filter * 1, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(relu17)\nbn18 = BatchNormalization()(conv18)\nrelu18 = Activation(\u201crelu\u201d)(bn18)\n# Segmentation branch\nsegmentation_output = Conv2D(1, 1, activation=\u201csigmoid\u201d, name=\u201csegmentation_output\u201d)(relu18) # original\n# Classification branch (Not done)\ngap1 = GlobalAveragePooling2D()(relu8)\ngap2 = GlobalAveragePooling2D()(relu10)\ngap3 = GlobalAveragePooling2D()(relu12)\nconv20 = Conv2D(16, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(segmentation_output)\nbn20 = BatchNormalization()(conv20)\nrelu20 = Activation(\u201crelu\u201d)(bn20)\ndown5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20)\nconv21 = Conv2D(32, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down5)\nbn21 = BatchNormalization()(conv21)\nrelu21 = Activation(\u201crelu\u201d)(bn21)\ndown6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21)\nconv22 = Conv2D(64, 3, activation=\u201clinear\u201d, padding=\u201csame\u201d, strides=1)(down6)\nbn22 = BatchNormalization()(conv22)\nrelu22 = Activation(\u201crelu\u201d)(bn22)\ndown7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22)\nflatten1 = Flatten()(down7)\nconcat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1)\n# FC layers\nfc1 = Dense(1024, activation=\u201crelu\u201d)(concat5)\ndropout1 = Dropout(0.5)(fc1)\nfc2 = Dense(1024, activation=\u201crelu\u201d)(dropout1)\ndropout2 = Dropout(0.5)(fc2)\nclassification_output = Dense(num_classes, activation=\u201csoftmax\u201d, name=\u201cclassification_output\u201d)(dropout2)\n# Define the model\nmodel = Model(inputs=inputs, outputs=[classification_output, segmentation_output])\nreturn model\n\ndef segmentation_loss(y_true, y_pred):\ny_true = tf.cast(y_true, tf.float32)\ny_pred = tf.cast(y_pred, tf.float32)\nbce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)\nsmooth = 1e-5\nintersection = tf.reduce_sum(y_true * y_pred)\nunion = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)\ndice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth)\nsegmentation_loss = bce_loss + 1 * dice_loss\nreturn segmentation_loss\n\ndef train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs):\ncheckpoint = ModelCheckpoint(\n\u201cmultitask_best_weights.h5\u201d,\nmonitor=\u201cval_classification_output_accuracy\u201d,\nsave_best_only=True,\nmode=\u201cmax\u201d,\nverbose=1,)\nreduce_lr = ReduceLROnPlateau(\nmonitor=\u201cval_classification_output_accuracy\u201d,\nfactor=0.3,\npatience=2,\nmin_delta=0.001,\nmode=\u201cauto\u201d,\nverbose=1,)\ntensorboard = TensorBoard(log_dir=\u201clogs\u201d)\nmodel.compile(\noptimizer=Adam(lr=0.001),\nloss={\u201cclassification_output\u201d: \u201ccategorical_crossentropy\u201d, \u201csegmentation_output\u201d: segmentation_loss},\nmetrics={\u201cclassification_output\u201d: \u201caccuracy\u201d, \u201csegmentation_output\u201d: \u201caccuracy\u201d},\nloss_weights={\u201cclassification_output\u201d: 1, \u201csegmentation_output\u201d: 1},)\nhistory = model.fit(\nX_train,\n{\u201cclassification_output\u201d: y_train_class, \u201csegmentation_output\u201d: y_train_seg},\nvalidation_data=(X_val, {\u201cclassification_output\u201d: y_val_class, \u201csegmentation_output\u201d: y_val_seg}),\nepochs=epochs,\nverbose=1,\nbatch_size=batch_size,\ncallbacks=[checkpoint, reduce_lr, tensorboard],)\nreturn history\n\ndef evaluate_model(model, X_test, y_test_class, y_test_seg):\nwith tf.keras.utils.custom_object_scope({\u201csegmentation_loss\u201d: segmentation_loss}):\n# Load the best model weights\nbest_model = load_model(\u201cmultitask_best_weights.h5\u201d)\n# Evaluate the model on test data\ntest_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate(\nX_test, {\u201cclassification_output\u201d: y_test_class, \u201csegmentation_output\u201d: y_test_seg})\nprint(\u201cTest Classification Loss:\u201d, test_class_loss)\nprint(\u201cTest Segmentation Loss:\u201d, test_seg_loss)\nprint(\u201cTest Classification Accuracy:\u201d, test_class_acc)\nprint(\u201cTest Segmentation Accuracy:\u201d, test_seg_acc)\n# Evaluate the model on validation data\nval_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate(\nX_val, {\u2018classification_output\u2019: y_val_class, \u2018segmentation_output\u2019: y_val_seg})\nprint(\u201cValidation Classification Loss:\u201d, val_class_loss)\nprint(\u201cValidation Segmentation Loss:\u201d, val_seg_loss)\nprint(\u201cValidation Classification Accuracy:\u201d, val_class_acc)\nprint(\u201cValidation Segmentation Accuracy:\u201d, val_seg_acc)\n# Evaluate the model on training data\ntrain_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {\u2018classification_output\u2019: y_train_class, \u2018segmentation_output\u2019: y_train_seg})\nprint(\u201cTrain Classification Loss:\u201d, train_class_loss)\nprint(\u201cTrain Segmentation Loss:\u201d, train_seg_loss)\nprint(\u201cTrain Classification Accuracy:\u201d, train_class_acc)\nprint(\u201cTrain Segmentation Accuracy:\u201d, train_seg_acc)\n# Return test classification accuracy\nreturn test_class_acc\n\ndef plot_performance(history):\n# Plot classification accuracy\nclassification_train_accuracy = history.history[\u201cclassification_output_accuracy\u201d]\nclassification_val_accuracy = history.history[\u201cval_classification_output_accuracy\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(classification_train_accuracy, label=\u201cTraining Accuracy\u201d)\nplt.plot(classification_val_accuracy, label=\u201cValidation Accuracy\u201d)\nplt.title(\u201cClassification Accuracy\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cAccuracy\u201d)\nplt.legend()\nplt.show()\n# Plot classification loss\nclassification_train_loss = history.history[\u201cclassification_output_loss\u201d]\nclassification_val_loss = history.history[\u201cval_classification_output_loss\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(classification_train_loss, \u201cb\u201d, label=\u201cTraining Loss\u201d)\nplt.plot(classification_val_loss, \u201cr\u201d, label=\u201cValidation Loss\u201d)\nplt.title(\u201cClassification Loss\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cLoss\u201d)\nplt.legend()\nplt.show()\n# Plot segmentation accuracy\nsegmentation_train_accuracy = history.history[\u201csegmentation_output_accuracy\u201d]\nsegmentation_val_accuracy = history.history[\u201cval_segmentation_output_accuracy\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(segmentation_train_accuracy, label=\u201cTraining Accuracy\u201d)\nplt.plot(segmentation_val_accuracy, label=\u201cValidation Accuracy\u201d)\nplt.title(\u201cSegmentation Accuracy\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cAccuracy\u201d)\nplt.legend()\nplt.show()\n# Plot segmentation loss\nsegmentation_train_loss = history.history[\u201csegmentation_output_loss\u201d]\nsegmentation_val_loss = history.history[\u201cval_segmentation_output_loss\u201d]\nplt.figure(figsize=(7, 3))\nplt.plot(segmentation_train_loss, \u201cb\u201d, label=\u201cTraining Loss\u201d)\nplt.plot(segmentation_val_loss, \u201cr\u201d, label=\u201cValidation Loss\u201d)\nplt.title(\u201cSegmentation Loss\u201d)\nplt.xlabel(\u201cEpochs\u201d)\nplt.ylabel(\u201cLoss\u201d)\nplt.legend()\nplt.show()\n# Set image size\nimage_size = 224\n# Define labels\nlabels = [\u201cbridge\u201d, \u201cexcess\u201d, \u201cgood\u201d]\n# Set data folders\ndata_folders = [\n\u201c/content/gdrive/MyDrive/FYP_4/4 Dataset Ratio 60 20 20/jit0/f_dip/train\u201d,\n\u201c/content/gdrive/MyDrive/FYP_4/4 Dataset Ratio 60 20 20/jit0/f_dip/val\u201d,\n\u201c/content/gdrive/MyDrive/FYP_4/4 Dataset Ratio 60 20 20/jit0/f_dip/test\u201d,]\n\n# Load data\nX_data, y_class_labels, y_seg_labels = load_data(data_folders)\n# Define train:val:test ratio for each class\nclass_data_counts = {\n\u201cbridge\u201d: [40, 80, 80],\n\u201cexcess\u201d: [40, 80, 80],\n\u201cgood\u201d: [40, 80, 80],}\n\n# Split data\nX_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, X_test, y_test_class, y_test_seg = split_data(\nX_data, y_class_labels, y_seg_labels, class_data_counts)\n\u2018\u2019\u2018\nprint(\u201cNumber of train images:\u201d, len(X_train))\nprint(\u201cNumber of train binary masks:\u201d, len(y_train_seg))\nprint(\u201cNumber of validation images:\u201d, len(X_val))\nprint(\u201cNumber of validation binary masks:\u201d, len(y_val_seg))\nprint(\u201cNumber of test images:\u201d, len(X_test))\nprint(\u201cNumber of test binary masks:\u201d, len(y_test_seg))\n\u2019\u2018\u2019\n# Initialize the label encoder\nlabel_encoder = LabelEncoder()\nlabel_encoder.fit(y_class_labels)\n# Count the number of images of each class in the train, validation, and test sets\ntrain_counts = count_labels(y_train_class, label_encoder)\nval_counts = count_labels(y_val_class, label_encoder)\ntest_counts = count_labels(y_test_class, label_encoder)\nprint(\u201cTrain counts: \u201c, train_counts,\u201d Total in train set:\u201d, sum(train_counts.values()))\nprint(\u201cValidation counts:\u201d, val_counts, \" Total in validation set:\u201c, sum(val_counts.values()))\nprint(\u201cTest counts: \u201c, test_counts,\u201d Total in test set:\u201d, sum(test_counts.values()))\n# Build model\ninput_shape = (image_size, image_size, 3)\nnum_classes = len(labels)\nmodel = build_model(input_shape, num_classes)\nmodel.summary()\n\n# Train model n times\ntest_class_acc_list = []\nfor i in range(5):\nprint(f\u201d\\nTrain {i+1}:\\n\")\nmodel = build_model(input_shape, num_classes)\nbatch_size = 64\nepochs = 50\nhistory = train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs)\n# Evaluate model on test data\ntest_class_acc = evaluate_model(model, X_test, y_test_class, y_test_seg)\nplot_performance(history)\ntest_class_acc_list.append(test_class_acc)\n\n# Calculate average test classification accuracy\naverage_test_class_acc = sum(test_class_acc_list) / len(test_class_acc_list)\nprint(\u201cTest Classification Accuracy List:\u201d, test_class_acc_list)\nprint(\u201cAverage Test Classification Accuracy:\u201d, average_test_class_acc)\n\"\nCheck my code if there is any error lead to the poor performance of the model."},{"assistant_initiates":false,"asst_text":"The `split_data` function you've outlined doesn't explicitly mention how the data is shuffled before splitting. However, given how you've described it, if you're directly slicing the `X_data`, `y_class_labels`, and `y_seg_labels` arrays without any prior shuffling, then the function will select the first 40 images (and corresponding labels) for training, the next 80 for validation, and the final 80 for testing for each class. This approach could potentially introduce bias if your data is ordered in any way (e.g., if similar images are grouped together).\n\nTo ensure that the images selected for training, validation, and testing are randomized, you should shuffle the data within each class before splitting it. But according to your description, after loading the data, there is a shuffle operation which is applied to the entire dataset:\n\n```python\nX_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)\n```\n\nThis operation ensures that the data is mixed before the split operation. However, if your aim is to assign images to training, validation, and test sets entirely randomly, you would need to ensure that the shuffling occurs within each class before you perform the split. This is because shuffling after splitting wouldn't change which items are in the training, validation, or test sets; it would only change their order within those sets.\n\nIf you need to ensure that the selection within each class is random (and distinct) for the train, validation, and test sets, consider updating your data loading or split logic to shuffle the indices within each class before splitting. This might look something like:\n\n1. For each class:\n    - Generate an array of indices for that class's data.\n    - Shuffle the array of indices.\n    - Use the first portion of the shuffled indices for training, the next for validation, and the last for testing, based on your specified counts.\n\nThis could be implemented in a slightly modified version of the splitting part of your `split_data` function to enforce the randomness within each class:\n\n```python\nimport numpy as np\n\nnp.random.seed(101)  # For reproducibility\nfor label, count in class_data_counts.items():\n    # Get indices for all items of this class\n    label_indices = np.where(y_class_labels == label)[0]\n    # Shuffle indices\n    np.random.shuffle(label_indices)\n    \n    train_count, val_count, test_count = count\n    # Use shuffled indices to determine train/val/test split\n    class_X_train = X_data[label_indices[:train_count]]\n    class_y_train_class = y_class_labels[label_indices[:train_count]]\n    class_y_train_seg = y_seg_labels[label_indices[:train_count]]\n    \n    class_X_val = X_data[label_indices[train_count:train_count+val_count]]\n    class_y_val_class = y_class_labels[label_indices[train_count:train_count+val_count]]\n    class_y_val_seg = y_seg_labels[label_indices[train_count:train_count+val_count]]\n    \n    class_X_test = X_data[label_indices[train_count+val_count:train_count+val_count+test_count]]\n    class_y_test_class = y_class_labels[label_indices[train_count+val_count:train_count+val_count+test_count]]\n    class_y_test_seg = y_seg_labels[label_indices[train_count+val_count:train_count+val_count+test_count]]\n    \n    # Append to aggregate train/val/test arrays\n    # Similar to your existing code, but ensure these interior arrays are properly initialized and appended to\n```\n\nThis ensures that for each class, the data is randomly selected for each of the training, validation, and test sets according to your prescribed counts, making your dataset more robust for model training and evaluation.","behavior":"degenerate_repetition","conversation_hash":"a3c4db1ad810e5d833ffa2140162b9be","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":3925,"n_turns_in_conversation":2,"n_words":505,"source_row_offset":40052,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:54:43 GMT","turn_id":"c76f126ccf5a850f4ce8ece7","turn_index":1,"user_text":"In my split_data function, am I using the data in my directory random or in order, for example if my \nclass_data_counts = {\n    \"bridge\": [40, 80, 80],\n    \"excess\": [40, 80, 80],\n    \"good\": [40, 80, 80],}\nthe function will load the first 40 train image or random 40 train images ?"},{"assistant_initiates":false,"asst_text":"A chemistry AI solver is a type of artificial intelligence system designed to assist with solving complex chemistry-related problems. This AI can help with tasks such as predicting chemical reactivity, analyzing molecular structures, identifying potential chemical reactions, and suggesting optimal reaction conditions. It uses data-driven algorithms and machine learning techniques to generate solutions to chemistry problems and can significantly speed up the process of experimentation and research in the field of chemistry.","behavior":"degenerate_repetition","conversation_hash":"51170578b6b97a86ad68e19941617a87","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":548,"n_turns_in_conversation":6,"n_words":75,"source_row_offset":40053,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:55:29 GMT","turn_id":"ecbdb73c69deb2ecff58e4ab","turn_index":0,"user_text":"chemistry ai solver"},{"assistant_initiates":false,"asst_text":"To use a chemistry AI solver to solve a chemistry problem, you typically need to provide the AI system with relevant information or input related to the problem you are trying to solve. Here are some general steps you can take to effectively use a chemistry AI solver:\n\n1. Define the problem: Clearly articulate the chemistry problem you are trying to solve, such as predicting the reactivity of a compound, identifying a suitable reaction pathway, or optimizing reaction conditions.\n\n2. Input data: Provide the AI solver with relevant data, such as molecular structures, reaction conditions, and any other pertinent information needed to address the problem.\n\n3. Select the appropriate AI tool: Choose an AI solver that is specifically designed for solving chemistry-related problems. There are various AI tools and platforms available, such as ChemPred, ChemAI, and Reaxys, that can assist with different types of chemistry problems.\n\n4. Run the AI solver: Input the data into the AI solver and run the algorithms to generate a solution or prediction. The AI solver will use machine learning and data analysis techniques to process the input data and provide you with results.\n\n5. Evaluate the results: Review the output provided by the AI solver and assess the accuracy and reliability of the solution. It's important to critically analyze the results to ensure they align with known chemical principles and experimental data.\n\nBy following these steps, you can effectively utilize a chemistry AI solver to help solve complex chemistry problems and aid in your research and experimentation efforts.","behavior":"degenerate_repetition","conversation_hash":"51170578b6b97a86ad68e19941617a87","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":1635,"n_turns_in_conversation":6,"n_words":260,"source_row_offset":40054,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:55:29 GMT","turn_id":"bc8123c05daf2e511780a177","turn_index":1,"user_text":"how to solver chemistry throe ai "},{"assistant_initiates":false,"asst_text":"Sure, I can provide an example for each type of chemistry problem mentioned:\n\n1. Predicting the reactivity of a compound: \n   Problem: Determine the likelihood of a given compound undergoing a substitution reaction with a specific reagent.\n   \n2. Identifying a suitable reaction pathway:\n   Problem: Find the most efficient and selective pathway for the conversion of a starting material to a desired product, considering all possible reaction mechanisms.\n   \n3. Optimizing reaction conditions:\n   Problem: Determine the ideal temperature, pressure, solvent, and catalyst for a chemical reaction to maximize yield and minimize unwanted byproducts.\n\nThese are just examples, and actual chemistry problems can vary in complexity and scope. When using a chemistry AI solver, it's important to clearly define the problem you want to solve to ensure that the AI system can effectively generate a solution.","behavior":"degenerate_repetition","conversation_hash":"51170578b6b97a86ad68e19941617a87","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":1115,"n_turns_in_conversation":6,"n_words":163,"source_row_offset":40055,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:55:29 GMT","turn_id":"4a5abad9d3b76d8da494d8b8","turn_index":2,"user_text":"1. Define the problem: Clearly articulate the chemistry problem you are trying to solve, such as predicting the reactivity of a compound, identifying a suitable reaction pathway, or optimizing reaction conditions.\n"},{"assistant_initiates":false,"asst_text":"Indeed, providing the AI solver with accurate and relevant data is crucial for obtaining meaningful results. Here are some examples of the type of data you may need to input for different chemistry problems:\n\n1. Predicting the reactivity of a compound:\n   - Input: Molecular structure of the compound, chemical properties, information about potential reagents, and reaction conditions.\n   \n2. Identifying a suitable reaction pathway:\n   - Input: Molecular structures of the reactants and products, potential intermediates, knowledge of possible reaction mechanisms, and any constraints or preferences for the reaction.\n   \n3. Optimizing reaction conditions:\n   - Input: Information about the starting materials, desired products, potential catalysts, solvent preferences, temperature range, pressure range, and any other relevant parameters that can affect the reaction outcome.\n\nBy providing comprehensive and accurate input data to the AI solver, you can help improve the accuracy and reliability of the solutions generated by the AI system for the specific chemistry problem you are trying to solve.","behavior":"degenerate_repetition","conversation_hash":"51170578b6b97a86ad68e19941617a87","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":1278,"n_turns_in_conversation":6,"n_words":182,"source_row_offset":40056,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:55:29 GMT","turn_id":"b0a8735ed04f186508bc438a","turn_index":3,"user_text":"2. Input data: Provide the AI solver with relevant data, such as molecular structures, reaction conditions, and any other pertinent information needed to address the problem.\n"},{"assistant_initiates":false,"asst_text":"Exactly, selecting the right AI tool or platform is essential for effectively solving chemistry problems using AI technology. Here are some popular AI tools and platforms that are specifically designed for solving chemistry-related problems:\n\n1. ChemPred: A tool that uses predictive modeling and machine learning algorithms to predict chemical properties, reactivity, and toxicity of compounds.\n\n2. ChemAI: An AI platform that combines deep learning and data analytics to assist with drug discovery, molecular design, and reaction prediction.\n\n3. Reaxys: A comprehensive chemistry database and search tool that provides access to chemical reactions, properties, and synthesis information to aid in solving complex chemistry problems.\n\nThese AI tools leverage advanced algorithms and data analysis techniques to assist scientists and researchers in various areas of chemistry, from drug discovery to materials science to chemical synthesis. By choosing the appropriate AI tool for your specific chemistry problem, you can enhance your ability to tackle complex challenges and accelerate your research efforts.","behavior":"degenerate_repetition","conversation_hash":"51170578b6b97a86ad68e19941617a87","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":1385,"n_turns_in_conversation":6,"n_words":198,"source_row_offset":40057,"source_shard":"turns-00018.parquet","timestamp":"Sat, 16 Mar 2024 14:55:29 GMT","turn_id":"153aa4e90438813d9bc26608","turn_index":4,"user_text":"3. Select the appropriate AI tool: Choose an AI solver that is specifically designed for solving chemistry-related problems. There are various AI tools and platforms available, such as ChemPred, ChemAI, and Reaxys, that can assist with different types of chemistry problems.\n"}],"shard":{"index":18,"name":"turns-00018.parquet","rows":78782}}