{"cells":[{"cell_type":"markdown","metadata":{"id":"i6MKTDtzdF6S"},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9ZSH7XIUMXQr"},"outputs":[],"source":["!mkdir /content/drive\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17995,"status":"ok","timestamp":1747027873126,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"zlMGcLSjNZ-w","outputId":"30c0e493-653b-4a6a-f36e-ea4f78dcf673"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","\n","drive.mount('/content/drive')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2014,"status":"ok","timestamp":1745703806346,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"5vY8pv8WLgua","outputId":"3f1555d3-a8aa-4cde-8721-6b7b4c29352c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting imblearn\n","  Downloading imblearn-0.0-py2.py3-none-any.whl.metadata (355 bytes)\n","Collecting imbalanced-learn (from imblearn)\n","  Downloading imbalanced_learn-0.13.0-py3-none-any.whl.metadata (8.8 kB)\n","Requirement already satisfied: numpy<3,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn->imblearn) (2.0.2)\n","Requirement already satisfied: scipy<2,>=1.10.1 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn->imblearn) (1.14.1)\n","Requirement already satisfied: scikit-learn<2,>=1.3.2 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn->imblearn) (1.6.1)\n","Collecting sklearn-compat<1,>=0.1 (from imbalanced-learn->imblearn)\n","  Downloading sklearn_compat-0.1.3-py3-none-any.whl.metadata (18 kB)\n","Requirement already satisfied: joblib<2,>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn->imblearn) (1.4.2)\n","Requirement already satisfied: threadpoolctl<4,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn->imblearn) (3.6.0)\n","Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n","Downloading imbalanced_learn-0.13.0-py3-none-any.whl (238 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m238.4/238.4 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading sklearn_compat-0.1.3-py3-none-any.whl (18 kB)\n","Installing collected packages: sklearn-compat, imbalanced-learn, imblearn\n","Successfully installed imbalanced-learn-0.13.0 imblearn-0.0 sklearn-compat-0.1.3\n"]}],"source":["!pip install imblearn\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":140133,"status":"ok","timestamp":1745996917033,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"5kmqF7_AlRz5","outputId":"cb4ca7fb-87ea-4452-c6ed-7340e8d48f47"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting torch==2.0.1\n","  Downloading torch-2.0.1-cp311-cp311-manylinux1_x86_64.whl.metadata (24 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch==2.0.1) (3.18.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.11/dist-packages (from torch==2.0.1) (4.13.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.11/dist-packages (from torch==2.0.1) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch==2.0.1) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch==2.0.1) (3.1.6)\n","Collecting nvidia-cuda-nvrtc-cu11==11.7.99 (from torch==2.0.1)\n","  Downloading nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu11==11.7.99 (from torch==2.0.1)\n","  Downloading nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cuda-cupti-cu11==11.7.101 (from torch==2.0.1)\n","  Downloading nvidia_cuda_cupti_cu11-11.7.101-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu11==8.5.0.96 (from torch==2.0.1)\n","  Downloading nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu11==11.10.3.66 (from torch==2.0.1)\n","  Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cufft-cu11==10.9.0.58 (from torch==2.0.1)\n","  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This behaviour is the source of the following dependency conflicts.\n","torchvision 0.21.0+cpu requires torch==2.6.0, but you have torch 2.0.1 which is incompatible.\n","torchaudio 2.6.0+cpu requires torch==2.6.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed cmake-4.0.0 lit-18.1.8 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 torch-2.0.1 triton-2.0.0 wheel-0.45.1\n","Requirement already satisfied: transformers==4.31.0 in /usr/local/lib/python3.11/dist-packages (4.31.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers==4.31.0) (3.18.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in 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requests->transformers==4.31.0) (2025.1.31)\n"]}],"source":["# ✅ تثبيت نسخة مستقرة من torch و transformers\n","!pip install --upgrade torch==2.0.1\n","!pip install --upgrade transformers==4.31.0\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12187,"status":"ok","timestamp":1745686506012,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"FYNJai734jLO","outputId":"368658db-2f87-4a56-b6bd-01d471ed11fd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting transformers==4.31.0\n","  Downloading transformers-4.31.0-py3-none-any.whl.metadata (116 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.9/116.9 kB\u001b[0m \u001b[31m3.3 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tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.31.0)\n","  Downloading tokenizers-0.13.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n","Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.31.0) (0.5.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers==4.31.0) (4.67.1)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0) (2025.3.2)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0) (4.13.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.31.0) (3.4.1)\n","Requirement already satisfied: idna<4,>=2.5 in 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Found existing installation: tokenizers 0.21.1\n","    Uninstalling tokenizers-0.21.1:\n","      Successfully uninstalled tokenizers-0.21.1\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 4.51.3\n","    Uninstalling transformers-4.51.3:\n","      Successfully uninstalled transformers-4.51.3\n","Successfully installed tokenizers-0.13.3 transformers-4.31.0\n"]}],"source":["!pip install --upgrade transformers==4.31.0\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CXdvQ9JIhc2I"},"outputs":[],"source":["# بدي ابدأ بال\n","#EMBEDDING من البدايه\n","#واعمل ARABERT FRO DEEP AGAIN"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nX4R2i-h1MPO","outputId":"e1a0df6e-290a-4511-f4e6-7cdb2c6956e9"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[33mWARNING: Ignoring invalid distribution ~riton 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(/usr/local/lib/python3.11/dist-packages)\u001b[0m\u001b[33m\n","\u001b[0m"]}],"source":["!pip install numpy --upgrade\n","!pip install torch --force-reinstall\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XOolRfjT2HoJ"},"outputs":[],"source":["import numpy\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350,"referenced_widgets":["5806da7a25e04b4eb984fe21f5a67560","495a44147f2c403486e7467964019d30","9177dcb1420a4110bef9aa72fb686514","95433b1db0224152aa816d141ed42dca","fd4323b621ce424ea09094c8dd4d257e","a68f0590ce774ff8a9ed4b1f68bd781d","1be52cf050834eefaea6c024e1d7caa2","fd5366b287a44835b9a8780317fc415d","7cb3c2edbfb24e5b831e36cb761883d1","9e58752eb880403fb8646453e8b446ec","361c3015b4b04a988ed7a7d0cc999a74","88868f346e4a4161a526c748e7385888","c930b36d4e2147dd93b6785e173c70ad","a130a49a1e1f43d7bd93cdbd2a2d2b1c","bc8c436c81324aeba27d9b2ccf7e8b4d","8c34ca1bc5ed48dca6fec81c49664e2e","cb185d27c9554471b04a2878c69a3b53","aef48f723df3436589b9e40cbacac024","be7d0fd22a9e40578d8abf1df134116e","3aa0d27ec6fd4d56b4271e0f1820fe03","e07ad77683e844579efbdd5ab9fc26a6","0cb0b53f9b5244eda49ecc3b20b6ccea","59d9ae437fc5471fb33c9042a71c5723","44f21c8b53a34b0bbb6818698b516696","36b93cb23b6046dfaa533de14f2dc496","4eceebe404f64dd4a4dbb40eac8853c3","fa1541378a1c4afa81c47a19c7c1e769","5e5f248a1bfa4354982203887f769796","336cf126ce174c99a1f6733409b79ca6","c5f0be2838414679a137bc42f684634a","812603c20b034ffcac97cbf591c646a5","e0c1b44a9f3e40de87c39a5a7ac4838b","ef4d05eba61b4660aab0ab24f5da69a8","392944106e51430b8d73ffe3d5c928c2","a58ffdcc2fe2462894c9d16cb81896bb","9b903bf3096142459e7ab80ca3a82318","de2a6d6f5e0349569449a9cff18d5ab7","857e726214184f92a6d7883aa05b6809","cfcf62ab084f49eb8a66e3add4e5306c","dee6856c979d4466b5682d26893f0493","4b3bac8afbac4b34bddb8a4005eb3d5d","1acc9b939d7446ae9e83f0bfdfe72790","e659e19897294c5bb1f9d68943c0f113","2c3daf038947449e897e5a75118e3bd0","e95ca740d95347c28a2e9ff42cb84e3b","28f29bcac77444948c73673bd32dfd3a","7104503498d640e98f043306c63cbef7","dc54d9fd8609451785d4da6e1924b40d","7bf6f19bb18249f386ec1ff97cba922d","5fffa93c9c6642088533dcecd6aaa9d7","a0b0026441a24b29aa222b8fb1b5642e","9a584b60308649f88d7b90352af727ad","515d3ac5843a4ad1827ba488798e2335","b05ddb25974e487a86faf1bc5e55c2a7","95f0a998533e46cdab2d0c2a16f0c1a8","ca8ef99c78a44da1ac33cfbe1eb0a638","916902fa32e94371b5038a89a599f090","62dd457dd842402a9477b1c61a35c7da","134dc0d8099449c3a34cdd37bc1743ba","c8b5fd8d2f784cae81c97a1749e37a40","79bc1d671b8a4ff9a4c3910e0b7d3416","73852b884c2243ca835208bb39d97db8","8169318f4410432c9bdbc35331b99d5b","6e7d861d5845411eafb36ebe024e59f6","66f8eafa9aee446d8f8fc899da318c66","eaecd7e0a2f046119e39f03c382c50d4"]},"executionInfo":{"elapsed":33064,"status":"ok","timestamp":1745611591494,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"Z3-YDzE-hz6b","outputId":"05b2b53c-8f46-4af5-9c42-e4bea76c44b1"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5806da7a25e04b4eb984fe21f5a67560","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/611 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"88868f346e4a4161a526c748e7385888","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/384 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"59d9ae437fc5471fb33c9042a71c5723","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/720k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"392944106e51430b8d73ffe3d5c928c2","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/2.31M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e95ca740d95347c28a2e9ff42cb84e3b","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ca8ef99c78a44da1ac33cfbe1eb0a638","version_major":2,"version_minor":0},"text/plain":["model.safetensors:   0%|          | 0.00/543M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ الملفات موجودة بالفعل، لن تتم إعادة الاستخراج.\n"]}],"source":["# ✅ استيراد المكتبات\n","import os\n","import pickle\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","import torch\n","from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder\n","from transformers import AutoTokenizer, AutoModel\n","\n","# ✅ تحميل نموذج AraBERT\n","model_name = \"aubmindlab/bert-base-arabertv2\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = AutoModel.from_pretrained(model_name)\n","\n","# ✅ تعريف المسارات\n","embedding_output_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/embeddings_DEEP.pkl\"\n","labels_output_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/y_labels.pkl\"\n","encoders_output_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/label_encoders.pkl\"\n","partial_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/arabert_embeddings_partial.pkl\"\n","\n","# ✅ التحقق إذا كانت الملفات النهائية موجودة\n","if os.path.exists(embedding_output_path) and os.path.exists(labels_output_path) and os.path.exists(encoders_output_path):\n","    print(\"✅ الملفات موجودة بالفعل، لن تتم إعادة الاستخراج.\")\n","else:\n","    # ✅ تحميل البيانات\n","    with open(\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/processed_data.pkl\", \"rb\") as f:\n","        df = pickle.load(f)\n","\n","    # الأعمدة المطلوبة\n","    required_cols = [\"Clean text\", \"POS Tags\", \"Emotional Indicators\", \"Predominant Main Category\", \"Predominant Sub Category\", \"Predominant Fake/Real\"]\n","    df = df[required_cols].dropna().reset_index(drop=True)\n","\n","    # ترميز POS وEmotion\n","    pos_binarizer = MultiLabelBinarizer()\n","    pos_encoded = pos_binarizer.fit_transform(df[\"POS Tags\"])\n","\n","    emo_binarizer = MultiLabelBinarizer()\n","    emo_encoded = emo_binarizer.fit_transform(df[\"Emotional Indicators\"])\n","\n","    # ترميز التصنيفات\n","    main_le = LabelEncoder()\n","    sub_le = LabelEncoder()\n","    rf_le = LabelEncoder()\n","\n","    y_main = main_le.fit_transform(df[\"Predominant Main Category\"])\n","    y_sub = sub_le.fit_transform(df[\"Predominant Sub Category\"])\n","    y_rf = rf_le.fit_transform(df[\"Predominant Fake/Real\"])\n","\n","    # ✅ وظيفة استخراج AraBERT embedding\n","    def get_arabert_embedding(text):\n","        inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=128)\n","        with torch.no_grad():\n","            outputs = model(**inputs)\n","        cls_embedding = outputs.last_hidden_state[:, 0, :].detach().cpu()\n","        return cls_embedding.numpy()\n","\n","    # ✅ استكمال التقدم من ملف التخزين المؤقت إذا وُجد\n","    start_idx = 0\n","    arabert_embeddings = []\n","\n","    if os.path.exists(partial_path):\n","        with open(partial_path, \"rb\") as f:\n","            arabert_embeddings = pickle.load(f)\n","        start_idx = len(arabert_embeddings)\n","        print(f\"🔄 استكمال من العينة رقم: {start_idx}\")\n","\n","    # ✅ متابعة الاستخراج من حيث توقف\n","    for i in tqdm(range(start_idx, len(df)), desc=\"استخراج AraBERT embeddings\"):\n","        text = df.loc[i, \"Clean text\"]\n","        emb = get_arabert_embedding(text)\n","        arabert_embeddings.append(emb)\n","\n","        # نحفظ التقدم بعد كل 100 خطوة\n","        if (i + 1) % 100 == 0 or (i + 1) == len(df):\n","            with open(partial_path, \"wb\") as f:\n","                pickle.dump(arabert_embeddings, f)\n","            print(f\"✅ تم الحفظ المؤقت عند: {i+1}/{len(df)}\")\n","\n","    # ✅ إعادة تشكيل ودمج الميزات\n","    arabert_embeddings = np.array(arabert_embeddings).reshape(len(arabert_embeddings), -1)\n","    X_full = np.concatenate([arabert_embeddings, pos_encoded, emo_encoded], axis=1)\n","    print(\"✅ Final feature shape:\", X_full.shape)\n","\n","    # ✅ حفظ الميزات والليبلات\n","    with open(embedding_output_path, \"wb\") as f:\n","        pickle.dump(X_full, f)\n","\n","    with open(labels_output_path, \"wb\") as f:\n","        y_data = {\n","            \"main\": y_main,\n","            \"sub\": y_sub,\n","            \"rf\": y_rf\n","        }\n","        pickle.dump(y_data, f)\n","\n","    with open(encoders_output_path, \"wb\") as f:\n","        le_data = {\n","            \"main_encoder\": main_le,\n","            \"sub_encoder\": sub_le,\n","            \"rf_encoder\": rf_le\n","        }\n","        pickle.dump(le_data, f)\n","\n","    print(\"✅ تم حفظ: embeddings_DEEP.pkl و y_labels.pkl و label_encoders.pkl\")\n","    print(\"🗑️ يمكنك الآن حذف arabert_embeddings_partial.pkl إذا اكتمل التخزين.\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39379,"status":"ok","timestamp":1745704094752,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"8H9Gyjyt15d_","outputId":"733f9a4c-d642-47a4-d530-72597748dbed"},"outputs":[{"name":"stdout","output_type":"stream","text":["🚀 البدء بمعالجة Sub Category\n","🔍 تطبيق SMOTE كامل على جميع التصنيفات...\n","✅ شكل البيانات بعد SMOTE: (542400, 894)\n","✅ تم حفظ الملف: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_sub-deep-new.pkl\n","    📌 X_train shape: (433920, 894)\n","    📌 y_train shape: (433920,)\n","    📌 X_test shape:  (108480, 894)\n","    📌 y_test shape:  (108480,)\n","✅ ملف Main Category موجود مسبقاً ✅ سيتم تخطيه.\n","✅ ملف Fake/Real موجود مسبقاً ✅ سيتم تخطيه.\n"]}],"source":["# ✅ استيراد المكتبات\n","import os\n","import pickle\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from imblearn.over_sampling import SMOTE\n","from collections import Counter\n","\n","# ✅ تحميل الميزات\n","with open(\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/embeddings_DEEP.pkl\", \"rb\") as f:\n","    X_full = pickle.load(f)\n","\n","# ✅ تحميل الليبلات\n","with open(\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/y_labels.pkl\", \"rb\") as f:\n","    y_data = pickle.load(f)\n","\n","y_main = y_data[\"main\"]\n","y_sub = y_data[\"sub\"]\n","y_rf = y_data[\"rf\"]\n","\n","# ✅ دالة لإزالة التصنيفات قليلة العينات\n","def remove_low_classes(X, y, min_samples=2):\n","    counts = Counter(y)\n","    valid_classes = {label for label, count in counts.items() if count >= min_samples}\n","    mask = np.isin(y, list(valid_classes))\n","    return X[mask], y[mask]\n","\n","# ✅ دالة لتطبيق SMOTE كامل\n","def full_smote(X, y):\n","    print(\"🔍 تطبيق SMOTE كامل على جميع التصنيفات...\")\n","    smote = SMOTE(random_state=42, k_neighbors=1)\n","    X_resampled, y_resampled = smote.fit_resample(X, y)\n","    print(f\"✅ شكل البيانات بعد SMOTE: {X_resampled.shape}\")\n","    return X_resampled, y_resampled\n","\n","# ✅ دالة عامة للتقسيم والحفظ\n","def split_and_save(X, y, name):\n","    path = f\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_{name}-deep-new.pkl\"\n","    if os.path.exists(path):\n","        print(f\"📁 الملف موجود مسبقًا: {path} ✅ سيتم تخطيه\")\n","        return\n","    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n","    with open(path, \"wb\") as f:\n","        pickle.dump((X_train, X_test, y_train, y_test), f)\n","    print(f\"✅ تم حفظ الملف: {path}\")\n","    print(f\"    📌 X_train shape: {X_train.shape}\")\n","    print(f\"    📌 y_train shape: {y_train.shape}\")\n","    print(f\"    📌 X_test shape:  {X_test.shape}\")\n","    print(f\"    📌 y_test shape:  {y_test.shape}\")\n","\n","# ✅ معالجة Sub Category\n","path_sub = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_sub-deep-new.pkl\"\n","if not os.path.exists(path_sub):\n","    print(\"🚀 البدء بمعالجة Sub Category\")\n","    X_sub, y_sub = remove_low_classes(X_full, y_sub)\n","    X_sub_bal, y_sub_bal = full_smote(X_sub, y_sub)\n","    split_and_save(X_sub_bal, y_sub_bal, \"sub\")\n","else:\n","    print(\"✅ ملف Sub Category موجود مسبقاً ✅ سيتم تخطيه.\")\n","\n","# ✅ معالجة Main Category\n","path_main = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_main-deep-new.pkl\"\n","if not os.path.exists(path_main):\n","    print(\"🚀 البدء بمعالجة Main Category\")\n","    X_main, y_main = remove_low_classes(X_full, y_main)\n","    X_main_bal, y_main_bal = full_smote(X_main, y_main)\n","    split_and_save(X_main_bal, y_main_bal, \"main\")\n","else:\n","    print(\"✅ ملف Main Category موجود مسبقاً ✅ سيتم تخطيه.\")\n","\n","# ✅ معالجة Real/Fake\n","path_rf = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_rf-deep-new.pkl\"\n","if not os.path.exists(path_rf):\n","    print(\"🚀 البدء بمعالجة Fake/Real Category\")\n","    X_rf, y_rf = remove_low_classes(X_full, y_rf)\n","    X_rf_bal, y_rf_bal = full_smote(X_rf, y_rf)\n","    split_and_save(X_rf_bal, y_rf_bal, \"rf\")\n","else:\n","    print(\"✅ ملف Fake/Real موجود مسبقاً ✅ سيتم تخطيه.\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":214629,"status":"ok","timestamp":1745870880580,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"Wq-ue_F257OC","outputId":"e080ccac-1444-444e-b4b3-7d2908102187"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.18.0)\n","Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.4.0)\n","Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n","Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (25.2.10)\n","Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.6.0)\n","Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n","Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (18.1.1)\n","Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.4.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (24.2)\n","Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (5.29.4)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.32.3)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (75.2.0)\n","Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\n","Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.0.1)\n","Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.13.2)\n","Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.2)\n","Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.71.0)\n","Requirement already satisfied: tensorboard<2.19,>=2.18 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.18.0)\n","Requirement already satisfied: keras>=3.5.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.8.0)\n","Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.2)\n","Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.13.0)\n","Requirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.4.1)\n","Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n","Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n","Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n","Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.0.9)\n","Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras>=3.5.0->tensorflow) (0.15.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2025.1.31)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.8)\n","Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\n","Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n","Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.18.0)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n","✅ تحميل MAIN من: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_main-deep-new.pkl\n","✅ تحميل SUB من: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_sub-deep-new.pkl\n","✅ تحميل RF من: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_rf-deep-new.pkl\n","✅ تم العثور على نموذج محفوظ: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_model_basic.h5 سيتم تحميله بدون إعادة تدريب.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 148ms/step\n","\n","📊 Main Category Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.93      0.98      0.96       831\n","           1       0.96      1.00      0.98       780\n","           2       0.64      0.59      0.62       813\n","           3       0.84      0.91      0.87       774\n","           4       0.50      0.45      0.47       819\n","           5       0.44      0.34      0.38       800\n","           6       0.62      0.72      0.67       837\n","           7       0.33      0.35      0.34       818\n","           8       0.91      0.94      0.93       781\n","           9       0.84      0.87      0.85       822\n","          10       0.57      0.62      0.59       780\n","          11       0.52      0.53      0.53       806\n","          12       0.38      0.37      0.38       763\n","          13       0.55      0.53      0.54       815\n","          14       0.43      0.49      0.46       801\n","          15       0.74      0.74      0.74       814\n","          16       0.41      0.28      0.33       806\n","          17       0.88      0.96      0.91       785\n","          18       0.96      0.99      0.98       798\n","          19       0.94      0.90      0.92       807\n","          20       0.75      0.76      0.76       825\n","          21       0.91      0.89      0.90       817\n","\n","    accuracy                           0.69     17692\n","   macro avg       0.68      0.69      0.69     17692\n","weighted avg       0.68      0.69      0.69     17692\n","\n","\n","📊 Sub Category Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.02      0.02      0.02       264\n","           1       0.00      0.00      0.00       234\n","           2       0.02      0.01      0.01       220\n","           3       0.02      0.01      0.02       242\n","           4       0.00      0.00      0.00       233\n","           5       0.01      0.03      0.02       224\n","           6       0.02      0.00      0.01       207\n","           7       0.00      0.00      0.00       246\n","           8       0.01      0.01      0.01       252\n","           9       0.02      0.01      0.02       236\n","          10       0.01      0.01      0.01       246\n","          11       0.00      0.00      0.00       253\n","          12       0.00      0.00      0.00       260\n","          13       0.05      0.02      0.03       258\n","          14       0.03      0.00      0.01       217\n","          15       0.02      0.05      0.03       222\n","          16       0.00      0.00      0.00       234\n","          17       0.00      0.00      0.00       213\n","          18       0.00      0.00      0.00       240\n","          19       0.02      0.03      0.03       222\n","          20       0.00      0.00      0.00       225\n","          21       0.01      0.01      0.01       242\n","          22       0.06      0.00      0.01       218\n","          23       0.01      0.01      0.01       250\n","          24       0.02      0.04      0.03       222\n","          25       0.01      0.00      0.01       246\n","          26       0.01      0.01      0.01       233\n","          27       0.02      0.02      0.02       256\n","          28       0.03      0.02      0.02       250\n","          29       0.03      0.01      0.01       250\n","          30       0.02      0.02      0.02       238\n","          31       0.02      0.03      0.02       223\n","          32       0.01      0.00      0.01       250\n","          33       0.01      0.02      0.01       244\n","          34       0.02      0.04      0.03       239\n","          35       0.02      0.03      0.03       240\n","          36       0.01      0.03      0.02       252\n","          37       0.01      0.03      0.01       225\n","          38       0.01      0.01      0.01       234\n","          39       0.01      0.01      0.01       250\n","          40       0.00      0.00      0.00       221\n","          41       0.01      0.01      0.01       250\n","          42       0.01      0.01      0.01       250\n","          43       0.02      0.05      0.02       234\n","          44       0.02      0.04      0.02       229\n","          45       0.01      0.00      0.01       217\n","          46       0.02      0.01      0.01       246\n","          47       0.00      0.00      0.00       253\n","          48       0.00      0.00      0.00       235\n","          49       0.01      0.02      0.01       236\n","          50       0.02      0.02      0.02       239\n","          51       0.01      0.00      0.01       237\n","          52       0.02      0.00      0.01       256\n","          53       0.01      0.04      0.02       237\n","          54       0.00      0.00      0.00       250\n","          55       0.01      0.00      0.01       224\n","          56       0.02      0.02      0.02       259\n","          57       0.00      0.00      0.00       242\n","          58       0.00      0.00      0.00       220\n","          59       0.00      0.00      0.00       218\n","          60       0.01      0.01      0.01       238\n","          61       0.00      0.00      0.00       212\n","          62       0.02      0.04      0.03       245\n","          63       0.00      0.00      0.00       234\n","          64       0.02      0.04      0.02       211\n","          65       0.00      0.00      0.00       224\n","          66       0.02      0.01      0.01       263\n","          67       0.02      0.03      0.02       213\n","          68       0.00      0.00      0.00       218\n","          69       0.01      0.00      0.01       249\n","          70       0.01      0.01      0.01       233\n","          71       0.01      0.01      0.01       209\n","          72       0.03      0.02      0.02       218\n","          73       0.01      0.00      0.01       232\n","          74       0.02      0.02      0.02       230\n","\n","    accuracy                           0.01     17692\n","   macro avg       0.01      0.01      0.01     17692\n","weighted avg       0.01      0.01      0.01     17692\n","\n","\n","📊 Fake/Real Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.50      0.69      0.58      8846\n","           1       0.51      0.31      0.39      8846\n","\n","    accuracy                           0.50     17692\n","   macro avg       0.50      0.50      0.48     17692\n","weighted avg       0.50      0.50      0.48     17692\n","\n","✅ تم حفظ الصورة: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L 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\n","text/plain":["<Figure size 800x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ الصورة: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L 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size 2400x2200 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ الصورة: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_rf_basic.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 800x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["⚠️ لا يوجد ملف محفوظ لرسم Accuracy & Loss.\n"]}],"source":["# ✅ استيراد المكتبات\n","!pip install tensorflow\n","\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.utils import shuffle\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, Bidirectional, LSTM, Reshape\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping\n","\n","# ✅ مسارات الملفات\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/\"\n","model_save_path = os.path.join(base_path, \"cnn+bilstm_model_basic.h5\")\n","acc_loss_save_path = os.path.join(base_path, \"cnn+bilstm_accuracy_loss_basic.png\")\n","\n","paths = {\n","    \"main\": os.path.join(base_path, \"train_test_data_main-deep-new.pkl\"),\n","    \"sub\": os.path.join(base_path, \"train_test_data_sub-deep-new.pkl\"),\n","    \"rf\":  os.path.join(base_path, \"train_test_data_rf-deep-new.pkl\")\n","}\n","\n","# ✅ تحميل البيانات مع فحص وجودها\n","datasets = {}\n","\n","for name, path in paths.items():\n","    if os.path.exists(path):\n","        print(f\"✅ تحميل {name.upper()} من: {path}\")\n","        with open(path, \"rb\") as f:\n","            datasets[name] = pickle.load(f)\n","    else:\n","        print(f\"⚠️ الملف غير موجود: {path} ❌ سيتم تخطي {name.upper()}\")\n","\n","# ✅ التحقق إذا جميع الملفات محملة\n","if not all(k in datasets for k in [\"main\", \"sub\", \"rf\"]):\n","    print(\"⚠️ لم يتم تحميل جميع الملفات المطلوبة. تأكد من وجود جميع الملفات.\")\n","else:\n","    # ✅ تجهيز البيانات\n","    X_train_main, X_test_main, y_train_main, y_test_main = datasets[\"main\"]\n","    X_train_sub, X_test_sub, y_train_sub, y_test_sub = datasets[\"sub\"]\n","    X_train_rf, X_test_rf, y_train_rf, y_test_rf = datasets[\"rf\"]\n","\n","    min_len = min(len(X_train_main), len(y_train_main), len(y_train_sub), len(y_train_rf))\n","    X_train = X_train_main[:min_len]\n","    y_train_main = y_train_main[:min_len]\n","    y_train_sub = y_train_sub[:min_len]\n","    y_train_rf = y_train_rf[:min_len]\n","\n","    min_len_test = min(len(X_test_main), len(y_test_main), len(y_test_sub), len(y_test_rf))\n","    X_test = X_test_main[:min_len_test]\n","    y_test_main = y_test_main[:min_len_test]\n","    y_test_sub = y_test_sub[:min_len_test]\n","    y_test_rf = y_test_rf[:min_len_test]\n","\n","    # ✅ خلط البيانات\n","    X_train, y_train_main, y_train_sub, y_train_rf = shuffle(\n","        X_train, y_train_main, y_train_sub, y_train_rf, random_state=42\n","    )\n","\n","    # ✅ تحقق إذا النموذج محفوظ\n","    if os.path.exists(model_save_path):\n","        print(f\"✅ تم العثور على نموذج محفوظ: {model_save_path} سيتم تحميله بدون إعادة تدريب.\")\n","        model = load_model(model_save_path)\n","        history = None\n","    else:\n","        print(\"🚀 النموذج غير موجود، سيتم بناء وتدريب نموذج جديد.\")\n","        # ✅ بناء النموذج\n","        input_layer = Input(shape=(894,), name=\"input_layer\")\n","        x = Reshape((894, 1))(input_layer)\n","        x = Conv1D(filters=128, kernel_size=3, activation=\"relu\")(x)\n","        x = MaxPooling1D(pool_size=2)(x)\n","        x = Bidirectional(LSTM(64))(x)\n","        x = Dropout(0.3)(x)\n","\n","        main_output = Dense(22, activation=\"softmax\", name=\"main_output\")(x)\n","        sub_output = Dense(75, activation=\"softmax\", name=\"sub_output\")(x)\n","        rf_output = Dense(2, activation=\"softmax\", name=\"rf_output\")(x)\n","\n","        model = Model(inputs=input_layer, outputs=[main_output, sub_output, rf_output])\n","        model.compile(\n","            optimizer=Adam(learning_rate=0.001),\n","            loss={\n","                \"main_output\": \"sparse_categorical_crossentropy\",\n","                \"sub_output\": \"sparse_categorical_crossentropy\",\n","                \"rf_output\": \"sparse_categorical_crossentropy\"\n","            },\n","            metrics={\n","                \"main_output\": \"accuracy\",\n","                \"sub_output\": \"accuracy\",\n","                \"rf_output\": \"accuracy\"\n","            }\n","        )\n","\n","        model.summary()\n","\n","        # ✅ تدريب النموذج\n","        history = model.fit(\n","            X_train,\n","            [y_train_main, y_train_sub, y_train_rf],\n","            validation_data=(X_test, [y_test_main, y_test_sub, y_test_rf]),\n","            epochs=10,\n","            batch_size=64,\n","            callbacks=[EarlyStopping(patience=2, restore_best_weights=True)]\n","        )\n","\n","        # ✅ حفظ النموذج\n","        model.save(model_save_path)\n","        print(f\"✅ تم حفظ النموذج في: {model_save_path}\")\n","\n","    # ✅ التقييم\n","    preds = model.predict(X_test)\n","    preds_main = np.argmax(preds[0], axis=1)\n","    preds_sub = np.argmax(preds[1], axis=1)\n","    preds_rf = np.argmax(preds[2], axis=1)\n","\n","    # ✅ تقارير الأداء\n","    print(\"\\n📊 Main Category Report:\")\n","    print(classification_report(y_test_main, preds_main))\n","\n","    print(\"\\n📊 Sub Category Report:\")\n","    print(classification_report(y_test_sub, preds_sub))\n","\n","    print(\"\\n📊 Fake/Real Report:\")\n","    print(classification_report(y_test_rf, preds_rf))\n","\n","    # ✅ دالة رسم Confusion Matrix\n","    def plot_conf_matrix(y_true, y_pred, title, labels=None, figsize=(8, 6), save_path=None):\n","        cm = confusion_matrix(y_true, y_pred)\n","        if labels is None:\n","            labels = [str(i) for i in range(len(set(y_true)))]\n","        annot = True if cm.shape[0] <= 20 else False\n","        fontsize = 10 if cm.shape[0] <= 20 else 6\n","        figsize = (24, 22) if cm.shape[0] > 30 else figsize\n","        plt.figure(figsize=figsize)\n","        sns.heatmap(cm, annot=annot, fmt=\"d\", cmap=\"Blues\",\n","                    xticklabels=labels, yticklabels=labels, linewidths=0.3)\n","        plt.title(f\"Confusion Matrix - {title}\", fontsize=14)\n","        plt.xlabel(\"Predicted\", fontsize=12)\n","        plt.ylabel(\"Actual\", fontsize=12)\n","        plt.xticks(rotation=90, fontsize=fontsize)\n","        plt.yticks(rotation=0, fontsize=fontsize)\n","        plt.tight_layout()\n","        if save_path:\n","            plt.savefig(save_path, dpi=300)\n","            print(f\"✅ تم حفظ الصورة: {save_path}\")\n","        plt.show()\n","\n","    # ✅ رسم Confusion Matrices\n","    plot_conf_matrix(y_test_main, preds_main, \"Main Category\", save_path=os.path.join(base_path, \"cnn+bilstm_main-basic.png\"))\n","    plot_conf_matrix(y_test_sub, preds_sub, \"Sub Category\", save_path=os.path.join(base_path, \"cnn+bilstm_sub_basic.png\"))\n","    plot_conf_matrix(y_test_rf, preds_rf, \"Fake/Real Category\", save_path=os.path.join(base_path, \"cnn+bilstm_rf_basic.png\"))\n","\n","    # ✅ رسم Accuracy & Loss\n","    if history:\n","        plt.figure(figsize=(14, 5))\n","\n","        plt.subplot(1, 2, 1)\n","        plt.plot(history.history['main_output_accuracy'], label='Main Accuracy')\n","        plt.plot(history.history['sub_output_accuracy'], label='Sub Accuracy')\n","        plt.plot(history.history['rf_output_accuracy'], label='Fake/Real Accuracy')\n","        plt.plot(history.history['val_main_output_accuracy'], label='Val Main Accuracy', linestyle='--')\n","        plt.plot(history.history['val_sub_output_accuracy'], label='Val Sub Accuracy', linestyle='--')\n","        plt.plot(history.history['val_rf_output_accuracy'], label='Val Fake/Real Accuracy', linestyle='--')\n","        plt.title(\"Accuracy\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Accuracy\")\n","        plt.legend()\n","\n","        plt.subplot(1, 2, 2)\n","        plt.plot(history.history['main_output_loss'], label='Main Loss')\n","        plt.plot(history.history['sub_output_loss'], label='Sub Loss')\n","        plt.plot(history.history['rf_output_loss'], label='Fake/Real Loss')\n","        plt.plot(history.history['val_main_output_loss'], label='Val Main Loss', linestyle='--')\n","        plt.plot(history.history['val_sub_output_loss'], label='Val Sub Loss', linestyle='--')\n","        plt.plot(history.history['val_rf_output_loss'], label='Val Fake/Real Loss', linestyle='--')\n","        plt.title(\"Loss\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Loss\")\n","        plt.legend()\n","\n","        plt.tight_layout()\n","        plt.savefig(acc_loss_save_path, dpi=300)\n","        print(f\"✅ تم حفظ رسم Accuracy & Loss: {acc_loss_save_path}\")\n","        plt.show()\n","    else:\n","        if os.path.exists(acc_loss_save_path):\n","            from PIL import Image\n","            img = Image.open(acc_loss_save_path)\n","            plt.figure(figsize=(14, 5))\n","            plt.imshow(img)\n","            plt.axis('off')\n","            plt.title(\"Accuracy & Loss (Loaded)\")\n","            plt.show()\n","        else:\n","            print(\"⚠️ لا يوجد ملف محفوظ لرسم Accuracy & Loss.\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QHsG7EFfjPoB"},"outputs":[],"source":["#نجرب نقسم المودل لاثنين . الاول يعمل مع التصنيفات الفرعيه والرئيسيه والثاني مع ال\n","#rf بس\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":10047,"status":"ok","timestamp":1745690924578,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"LjBJ-vPO2AUk","outputId":"4c4f0de7-b444-422b-c60e-5e8f4a559a61"},"outputs":[{"name":"stdout","output_type":"stream","text":["✅ تم العثور على النموذج. سيتم استخدامه بدون تدريب.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733us/step\n","\n","📊 Fake/Real Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.90      0.97      0.93      8846\n","           1       0.97      0.90      0.93      8846\n","\n","    accuracy                           0.93     17692\n","   macro avg       0.93      0.93      0.93     17692\n","weighted avg       0.93      0.93      0.93     17692\n","\n","\n","🔄 Summary:\n","✅ Accuracy: 0.9321\n","✅ Precision: 0.9344\n","✅ Recall: 0.9321\n","✅ F1-Score: 0.9320\n","✅ تم حفظ Confusion Matrix: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_conf_matrix_rf.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 800x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ عرض رسم Accuracy & Loss من الملف: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_acc_loss_rf.png\n"]},{"data":{"image/png":"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\n","text/plain":["<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=3000x1200>"]},"metadata":{},"output_type":"display_data"}],"source":["## seperated model for rf catagory only\n","\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.utils import shuffle\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping\n","\n","# ✅ المسارات\n","model_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_rf.h5\"\n","acc_loss_plot = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_acc_loss_rf.png\"\n","conf_matrix_plot = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_conf_matrix_rf.png\"\n","\n","# ✅ تحميل البيانات\n","with open(\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv//DEEP L files/train_test_data_rf-deep-new.pkl\", \"rb\") as f:\n","    X_train_rf, X_test_rf, y_train_rf, y_test_rf = pickle.load(f)\n","\n","X_train_rf, y_train_rf = shuffle(X_train_rf, y_train_rf, random_state=42)\n","\n","# ✅ دالة رسم Confusion Matrix\n","def plot_conf_matrix(y_true, y_pred, title, save_path=None):\n","    cm = confusion_matrix(y_true, y_pred)\n","    labels = [str(i) for i in range(len(np.unique(y_true)))]\n","    plt.figure(figsize=(8, 6))\n","    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n","                xticklabels=labels, yticklabels=labels)\n","    plt.title(f\"Confusion Matrix - {title}\")\n","    plt.xlabel(\"Predicted\")\n","    plt.ylabel(\"Actual\")\n","    plt.tight_layout()\n","    if save_path:\n","        plt.savefig(save_path, dpi=300)\n","        print(f\"✅ تم حفظ Confusion Matrix: {save_path}\")\n","    plt.show()\n","\n","# ✅ تحميل النموذج إذا موجود\n","if os.path.exists(model_path):\n","    print(\"✅ تم العثور على النموذج. سيتم استخدامه بدون تدريب.\")\n","    model_rf = load_model(model_path)\n","    history_rf = None\n","else:\n","    print(\"❌ النموذج غير موجود. سيتم تدريبه الآن...\")\n","\n","    # ✅ بناء وتدريب النموذج\n","    input_layer = Input(shape=(894,), name=\"input_layer\")\n","    x = Dense(256, activation='relu')(input_layer)\n","    x = Dropout(0.3)(x)\n","    x = Dense(128, activation='relu')(x)\n","    x = Dropout(0.3)(x)\n","    output_layer = Dense(2, activation='softmax', name=\"rf_output\")(x)\n","\n","    model_rf = Model(inputs=input_layer, outputs=output_layer)\n","\n","    model_rf.compile(\n","        optimizer=Adam(learning_rate=0.001),\n","        loss=\"sparse_categorical_crossentropy\",\n","        metrics=[\"accuracy\"]\n","    )\n","\n","    model_rf.summary()\n","\n","    history_rf = model_rf.fit(\n","        X_train_rf,\n","        y_train_rf,\n","        validation_data=(X_test_rf, y_test_rf),\n","        epochs=30,\n","        batch_size=64,\n","        callbacks=[EarlyStopping(patience=3, restore_best_weights=True)]\n","    )\n","\n","    model_rf.save(model_path)\n","    print(f\"✅ تم حفظ النموذج: {model_path}\")\n","\n","    # ✅ رسم Accuracy & Loss\n","    plt.figure(figsize=(10, 4))\n","    plt.subplot(1, 2, 1)\n","    plt.plot(history_rf.history['accuracy'], label='Train Accuracy')\n","    plt.plot(history_rf.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","    plt.title(\"Accuracy (Fake/Real)\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend()\n","\n","    plt.subplot(1, 2, 2)\n","    plt.plot(history_rf.history['loss'], label='Train Loss')\n","    plt.plot(history_rf.history['val_loss'], label='Val Loss', linestyle='--')\n","    plt.title(\"Loss (Fake/Real)\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend()\n","\n","    plt.tight_layout()\n","    plt.savefig(acc_loss_plot, dpi=300)\n","    print(f\"✅ تم حفظ رسم Accuracy & Loss: {acc_loss_plot}\")\n","    plt.show()\n","\n","# ✅ التنبؤ والتقييم\n","preds_rf = model_rf.predict(X_test_rf)\n","preds_rf = np.argmax(preds_rf, axis=1)\n","\n","print(\"\\n📊 Fake/Real Report:\")\n","print(classification_report(y_test_rf, preds_rf))\n","\n","accuracy  = accuracy_score(y_test_rf, preds_rf)\n","precision = precision_score(y_test_rf, preds_rf, average='macro', zero_division=0)\n","recall    = recall_score(y_test_rf, preds_rf, average='macro', zero_division=0)\n","f1        = f1_score(y_test_rf, preds_rf, average='macro', zero_division=0)\n","\n","print(f\"\\n🔄 Summary:\")\n","print(f\"✅ Accuracy: {accuracy:.4f}\")\n","print(f\"✅ Precision: {precision:.4f}\")\n","print(f\"✅ Recall: {recall:.4f}\")\n","print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","# ✅ Confusion Matrix\n","plot_conf_matrix(y_test_rf, preds_rf, \"Fake/Real\", save_path=conf_matrix_plot)\n","\n","# ✅ عرض رسم Accuracy & Loss إذا موجود\n","if os.path.exists(acc_loss_plot):\n","    from PIL import Image\n","    print(f\"✅ عرض رسم Accuracy & Loss من الملف: {acc_loss_plot}\")\n","    display(Image.open(acc_loss_plot))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":104580,"status":"ok","timestamp":1745693001755,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"AiYlaKUd1lpU","outputId":"03a17f4f-a270-4238-a28d-f30449554fcd"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["✅ تم العثور على النموذج. سيتم استخدامه بدون تدريب.\n","\u001b[1m1287/1287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 78ms/step\n","\n","🔄 Summary:\n","✅ Accuracy: 0.7778\n","✅ Precision: 0.7819\n","✅ Recall: 0.7778\n","✅ F1-Score: 0.7791\n"]},{"data":{"image/png":"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size 1800x1400 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ عرض رسم Accuracy & Loss من الملف: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_acc_loss_main_attention.png\n"]},{"data":{"image/png":"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<td>0.995468</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.791053</td>\n","      <td>0.746261</td>\n","      <td>0.768004</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.954352</td>\n","      <td>0.960470</td>\n","      <td>0.957401</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.654489</td>\n","      <td>0.669872</td>\n","      <td>0.662091</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.496735</td>\n","      <td>0.528594</td>\n","      <td>0.512170</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.834985</td>\n","      <td>0.764957</td>\n","      <td>0.798439</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.479798</td>\n","      <td>0.405983</td>\n","      <td>0.439815</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.979926</td>\n","      <td>0.991448</td>\n","      <td>0.985654</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.959302</td>\n","      <td>0.969551</td>\n","      <td>0.964400</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.850261</td>\n","      <td>0.782585</td>\n","      <td>0.815021</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.577362</td>\n","      <td>0.594335</td>\n","      <td>0.585726</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.418664</td>\n","      <td>0.438568</td>\n","      <td>0.428385</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.725855</td>\n","      <td>0.646713</td>\n","      <td>0.684002</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.458128</td>\n","      <td>0.546474</td>\n","      <td>0.498417</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.733573</td>\n","      <td>0.763763</td>\n","      <td>0.748363</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.526110</td>\n","      <td>0.575855</td>\n","      <td>0.549860</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.969432</td>\n","      <td>0.948718</td>\n","      <td>0.958963</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>0.993100</td>\n","      <td>0.999466</td>\n","      <td>0.996273</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.962592</td>\n","      <td>0.975962</td>\n","      <td>0.969231</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.911731</td>\n","      <td>0.855692</td>\n","      <td>0.882823</td>\n","      <td>1871.000000</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>0.938786</td>\n","      <td>0.950321</td>\n","      <td>0.944518</td>\n","      <td>1872.000000</td>\n","    </tr>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.777783</td>\n","      <td>0.777783</td>\n","      <td>0.777783</td>\n","      <td>0.777783</td>\n","    </tr>\n","    <tr>\n","      <th>macro avg</th>\n","      <td>0.781856</td>\n","      <td>0.777787</td>\n","      <td>0.779077</td>\n","      <td>41176.000000</td>\n","    </tr>\n","    <tr>\n","      <th>weighted avg</th>\n","      <td>0.781852</td>\n","      <td>0.777783</td>\n","      <td>0.779073</td>\n","      <td>41176.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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0.577362  0.594335  0.585726   1871.000000\n","12             0.418664  0.438568  0.428385   1872.000000\n","13             0.725855  0.646713  0.684002   1871.000000\n","14             0.458128  0.546474  0.498417   1872.000000\n","15             0.733573  0.763763  0.748363   1871.000000\n","16             0.526110  0.575855  0.549860   1872.000000\n","17             0.969432  0.948718  0.958963   1872.000000\n","18             0.993100  0.999466  0.996273   1872.000000\n","19             0.962592  0.975962  0.969231   1872.000000\n","20             0.911731  0.855692  0.882823   1871.000000\n","21             0.938786  0.950321  0.944518   1872.000000\n","accuracy       0.777783  0.777783  0.777783      0.777783\n","macro avg      0.781856  0.777787  0.779077  41176.000000\n","weighted avg   0.781852  0.777783  0.779073  41176.000000"]},"metadata":{},"output_type":"display_data"}],"source":["### seperated model for main catagory only\n","\n","# ✅ استيراد المكتبات\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","from sklearn.utils import shuffle, class_weight\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, Bidirectional, LSTM, Reshape, Attention, Multiply\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n","\n","# ✅ المسارات\n","model_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_main_with_attention.h5\"\n","conf_matrix_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_conf_matrix_main_attention.png\"\n","acc_loss_plot_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/pic/cnn+bilstm_acc_loss_main_attention.png\"\n","\n","# ✅ تحميل البيانات\n","with open(\"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/train_test_data_main-deep-new.pkl\", \"rb\") as f:\n","    X_train, X_test, y_train, y_test = pickle.load(f)\n","\n","X_train, y_train = shuffle(X_train, y_train, random_state=42)\n","\n","# ✅ تحميل أو تدريب النموذج\n","if os.path.exists(model_path):\n","    print(\"✅ تم العثور على النموذج. سيتم استخدامه بدون تدريب.\")\n","    model = load_model(model_path)\n","    history = None\n","else:\n","    print(\"🚀 لم يتم العثور على النموذج. جاري التدريب الآن...\")\n","\n","    weights = class_weight.compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train)\n","    class_weight_main = {i: w for i, w in enumerate(weights)}\n","\n","    input_layer = Input(shape=(894,), name=\"input_layer\")\n","    x = Reshape((894, 1))(input_layer)\n","    x = Conv1D(filters=128, kernel_size=3, activation=\"relu\")(x)\n","    x = MaxPooling1D(pool_size=2)(x)\n","    x = Bidirectional(LSTM(64, return_sequences=True))(x)\n","    att = Attention()([x, x])\n","    x = Multiply()([x, att])\n","    x = LSTM(64)(x)\n","    x = Dropout(0.3)(x)\n","    output = Dense(22, activation=\"softmax\", name=\"main_output\")(x)\n","\n","    model = Model(inputs=input_layer, outputs=output)\n","    model.compile(optimizer=Adam(learning_rate=0.001), loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n","\n","    model.summary()\n","\n","    history = model.fit(\n","        X_train, y_train,\n","        validation_data=(X_test, y_test),\n","        epochs=30,\n","        batch_size=64,\n","        class_weight=class_weight_main,\n","        callbacks=[\n","            EarlyStopping(patience=5, restore_best_weights=True),\n","            ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=1e-6, verbose=1)\n","        ]\n","    )\n","\n","    model.save(model_path)\n","    print(\"✅ تم حفظ النموذج.\")\n","\n","# ✅ التنبؤ والتقييم\n","preds = model.predict(X_test)\n","preds = np.argmax(preds, axis=1)\n","\n","report = classification_report(y_test, preds, output_dict=True, zero_division=0)\n","conf_matrix = confusion_matrix(y_test, preds)\n","accuracy = accuracy_score(y_test, preds)\n","precision = precision_score(y_test, preds, average='macro', zero_division=0)\n","recall = recall_score(y_test, preds, average='macro', zero_division=0)\n","f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","print(f\"\\n🔄 Summary:\")\n","print(f\"✅ Accuracy: {accuracy:.4f}\")\n","print(f\"✅ Precision: {precision:.4f}\")\n","print(f\"✅ Recall: {recall:.4f}\")\n","print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","# ✅ Confusion Matrix\n","plt.figure(figsize=(18, 14))\n","sns.heatmap(conf_matrix, annot=False, cmap=\"Blues\", fmt='d')\n","plt.title(\"Confusion Matrix - Main Category with Attention\")\n","plt.xlabel(\"Predicted\")\n","plt.ylabel(\"Actual\")\n","plt.tight_layout()\n","plt.savefig(conf_matrix_path, dpi=300)\n","plt.show()\n","\n","# ✅ Accuracy & Loss\n","if history:\n","    plt.figure(figsize=(14, 5))\n","    plt.subplot(1, 2, 1)\n","    plt.plot(history.history['accuracy'], label='Train Accuracy')\n","    plt.plot(history.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","    plt.title(\"Accuracy\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend()\n","\n","    plt.subplot(1, 2, 2)\n","    plt.plot(history.history['loss'], label='Train Loss')\n","    plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","    plt.title(\"Loss\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend()\n","\n","    plt.tight_layout()\n","    plt.savefig(acc_loss_plot_path, dpi=300)\n","    plt.show()\n","    # ✅ عرض رسم Accuracy & Loss إذا موجود\n","if os.path.exists(acc_loss_plot_path):\n","    from PIL import Image\n","    print(f\"✅ عرض رسم Accuracy & Loss من الملف: {acc_loss_plot_path}\")\n","    display(Image.open(acc_loss_plot_path))\n","\n","# ✅ عرض التقرير كنص\n","report_df = pd.DataFrame(report).transpose()\n","display(report_df)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":15432893,"status":"ok","timestamp":1745720529816,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"Xt4NS4am1evP","outputId":"8b644207-aa4d-4407-d431-2203a1735040"},"outputs":[{"name":"stdout","output_type":"stream","text":["✅ تم تحميل بيانات التدريب والاختبار.\n","🚀 لم يتم العثور على النموذج. سيتم تدريبه الآن...\n"]},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"functional_1\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">892</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ reshape_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling1d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)      │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ bidirectional_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │ max_pooling1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ attention_1         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Attention</span>)         │                   │            │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply_1          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │                   │            │ attention_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multiply_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ lstm_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ sub_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">75</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,875</span> │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_1 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m892\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m512\u001b[0m │ reshape_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling1d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ bidirectional_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │     \u001b[38;5;34m98,816\u001b[0m │ max_pooling1d_1[\u001b[38;5;34m…\u001b[0m │\n","│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ attention_1         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","│ (\u001b[38;5;33mAttention\u001b[0m)         │                   │            │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply_1          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","│ (\u001b[38;5;33mMultiply\u001b[0m)          │                   │            │ attention_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ multiply_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │     \u001b[38;5;34m49,408\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ lstm_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ sub_output (\u001b[38;5;33mDense\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m)        │      \u001b[38;5;34m4,875\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">153,611</span> (600.04 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m153,611\u001b[0m (600.04 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">153,611</span> (600.04 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m153,611\u001b[0m (600.04 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Epoch 1/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m443s\u001b[0m 65ms/step - accuracy: 0.0584 - loss: 4.0670 - val_accuracy: 0.2728 - val_loss: 2.9129 - learning_rate: 5.0000e-04\n","Epoch 2/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.2927 - loss: 2.8210 - val_accuracy: 0.4683 - val_loss: 2.0522 - learning_rate: 5.0000e-04\n","Epoch 3/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.4382 - loss: 2.1709 - val_accuracy: 0.5659 - val_loss: 1.6459 - learning_rate: 5.0000e-04\n","Epoch 4/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.5255 - loss: 1.8085 - val_accuracy: 0.6364 - val_loss: 1.3596 - learning_rate: 5.0000e-04\n","Epoch 5/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.5889 - loss: 1.5570 - val_accuracy: 0.6801 - val_loss: 1.1879 - learning_rate: 5.0000e-04\n","Epoch 6/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.6306 - loss: 1.3929 - val_accuracy: 0.7115 - val_loss: 1.0693 - learning_rate: 5.0000e-04\n","Epoch 7/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.6649 - loss: 1.2592 - val_accuracy: 0.7299 - val_loss: 0.9960 - learning_rate: 5.0000e-04\n","Epoch 8/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.6911 - loss: 1.1559 - val_accuracy: 0.7560 - val_loss: 0.8894 - learning_rate: 5.0000e-04\n","Epoch 9/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.7117 - loss: 1.0772 - val_accuracy: 0.7604 - val_loss: 0.8719 - learning_rate: 5.0000e-04\n","Epoch 10/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7295 - loss: 1.0106 - val_accuracy: 0.7818 - val_loss: 0.7980 - learning_rate: 5.0000e-04\n","Epoch 11/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7414 - loss: 0.9584 - val_accuracy: 0.7920 - val_loss: 0.7615 - learning_rate: 5.0000e-04\n","Epoch 12/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7547 - loss: 0.9132 - val_accuracy: 0.7966 - val_loss: 0.7355 - learning_rate: 5.0000e-04\n","Epoch 13/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.7660 - loss: 0.8644 - val_accuracy: 0.8064 - val_loss: 0.7022 - learning_rate: 5.0000e-04\n","Epoch 14/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7757 - loss: 0.8282 - val_accuracy: 0.8100 - val_loss: 0.6817 - learning_rate: 5.0000e-04\n","Epoch 15/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7807 - loss: 0.8026 - val_accuracy: 0.8163 - val_loss: 0.6645 - learning_rate: 5.0000e-04\n","Epoch 16/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.7887 - loss: 0.7746 - val_accuracy: 0.8252 - val_loss: 0.6273 - learning_rate: 5.0000e-04\n","Epoch 17/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.7951 - loss: 0.7492 - val_accuracy: 0.8308 - val_loss: 0.6031 - learning_rate: 5.0000e-04\n","Epoch 18/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8012 - loss: 0.7228 - val_accuracy: 0.8317 - val_loss: 0.5981 - learning_rate: 5.0000e-04\n","Epoch 19/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8058 - loss: 0.7062 - val_accuracy: 0.8404 - val_loss: 0.5689 - learning_rate: 5.0000e-04\n","Epoch 20/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8116 - loss: 0.6879 - val_accuracy: 0.8410 - val_loss: 0.5616 - learning_rate: 5.0000e-04\n","Epoch 21/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8168 - loss: 0.6652 - val_accuracy: 0.8462 - val_loss: 0.5442 - learning_rate: 5.0000e-04\n","Epoch 22/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8196 - loss: 0.6552 - val_accuracy: 0.8453 - val_loss: 0.5478 - learning_rate: 5.0000e-04\n","Epoch 23/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8231 - loss: 0.6421 - val_accuracy: 0.8549 - val_loss: 0.5118 - learning_rate: 5.0000e-04\n","Epoch 24/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8265 - loss: 0.6258 - val_accuracy: 0.8526 - val_loss: 0.5194 - learning_rate: 5.0000e-04\n","Epoch 25/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8303 - loss: 0.6106 - val_accuracy: 0.8560 - val_loss: 0.5055 - learning_rate: 5.0000e-04\n","Epoch 26/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8336 - loss: 0.5980 - val_accuracy: 0.8628 - val_loss: 0.4839 - learning_rate: 5.0000e-04\n","Epoch 27/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8355 - loss: 0.5925 - val_accuracy: 0.8630 - val_loss: 0.4830 - learning_rate: 5.0000e-04\n","Epoch 28/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8385 - loss: 0.5801 - val_accuracy: 0.8626 - val_loss: 0.4824 - learning_rate: 5.0000e-04\n","Epoch 29/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8419 - loss: 0.5691 - val_accuracy: 0.8658 - val_loss: 0.4718 - learning_rate: 5.0000e-04\n","Epoch 30/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8443 - loss: 0.5594 - val_accuracy: 0.8690 - val_loss: 0.4579 - learning_rate: 5.0000e-04\n","Epoch 31/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m438s\u001b[0m 65ms/step - accuracy: 0.8458 - loss: 0.5507 - val_accuracy: 0.8684 - val_loss: 0.4635 - learning_rate: 5.0000e-04\n","Epoch 32/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - accuracy: 0.8484 - loss: 0.5432\n","Epoch 32: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8484 - loss: 0.5432 - val_accuracy: 0.8691 - val_loss: 0.4587 - learning_rate: 5.0000e-04\n","Epoch 33/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m440s\u001b[0m 65ms/step - accuracy: 0.8679 - loss: 0.4702 - val_accuracy: 0.8864 - val_loss: 0.3952 - learning_rate: 2.5000e-04\n","Epoch 34/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m442s\u001b[0m 65ms/step - accuracy: 0.8708 - loss: 0.4549 - val_accuracy: 0.8887 - val_loss: 0.3858 - learning_rate: 2.5000e-04\n","Epoch 35/35\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m439s\u001b[0m 65ms/step - accuracy: 0.8720 - loss: 0.4500 - val_accuracy: 0.8899 - val_loss: 0.3839 - learning_rate: 2.5000e-04\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ النموذج.\n","\u001b[1m3390/3390\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 17ms/step\n","\n","📊 Sub Category Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      1.00      1446\n","           1       0.82      0.74      0.78      1447\n","           2       0.99      1.00      0.99      1447\n","           3       0.97      0.99      0.98      1446\n","           4       0.99      0.99      0.99      1446\n","           5       0.96      0.99      0.97      1446\n","           6       1.00      1.00      1.00      1446\n","           7       0.99      0.98      0.98      1446\n","           8       0.98      1.00      0.99      1446\n","           9       1.00      1.00      1.00      1446\n","          10       0.98      1.00      0.99      1446\n","          11       0.99      1.00      0.99      1447\n","          12       0.98      0.93      0.96      1446\n","          13       0.75      0.88      0.81      1446\n","          14       0.99      0.97      0.98      1447\n","          15       0.99      1.00      0.99      1447\n","          16       0.95      0.98      0.96      1446\n","          17       0.96      1.00      0.98      1446\n","          18       0.96      0.99      0.98      1446\n","          19       0.60      0.56      0.58      1446\n","          20       0.54      0.67      0.60      1447\n","          21       0.78      0.86      0.82      1447\n","          22       0.99      1.00      0.99      1446\n","          23       0.97      1.00      0.98      1447\n","          24       0.99      1.00      0.99      1447\n","          25       0.97      0.99      0.98      1446\n","          26       0.97      1.00      0.98      1447\n","          27       0.91      0.99      0.95      1447\n","          28       0.99      1.00      0.99      1446\n","          29       0.81      0.79      0.80      1447\n","          30       0.99      1.00      0.99      1446\n","          31       0.89      0.94      0.92      1446\n","          32       0.92      0.91      0.91      1447\n","          33       0.97      0.98      0.97      1446\n","          34       1.00      1.00      1.00      1446\n","          35       0.94      0.98      0.96      1446\n","          36       0.25      0.07      0.11      1446\n","          37       0.51      0.45      0.48      1447\n","          38       0.81      0.75      0.78      1446\n","          39       0.45      0.60      0.51      1446\n","          40       0.97      0.99      0.98      1446\n","          41       1.00      1.00      1.00      1447\n","          42       0.33      0.21      0.25      1446\n","          43       0.97      0.99      0.98      1447\n","          44       0.89      0.91      0.90      1446\n","          45       1.00      1.00      1.00      1446\n","          46       0.99      0.99      0.99      1447\n","          47       0.97      1.00      0.98      1446\n","          48       0.97      0.99      0.98      1447\n","          49       0.74      0.81      0.78      1446\n","          50       0.92      0.98      0.95      1446\n","          51       0.57      0.57      0.57      1447\n","          52       0.31      0.22      0.26      1447\n","          53       1.00      1.00      1.00      1446\n","          54       0.99      0.99      0.99      1446\n","          55       0.80      0.72      0.76      1447\n","          56       1.00      1.00      1.00      1446\n","          57       0.99      1.00      0.99      1447\n","          58       0.94      0.96      0.95      1446\n","          59       0.36      0.29      0.32      1447\n","          60       1.00      1.00      1.00      1447\n","          61       0.97      0.97      0.97      1446\n","          62       1.00      1.00      1.00      1446\n","          63       1.00      1.00      1.00      1446\n","          64       1.00      1.00      1.00      1446\n","          65       0.99      1.00      1.00      1447\n","          66       0.87      0.95      0.91      1446\n","          67       0.98      0.98      0.98      1447\n","          68       1.00      1.00      1.00      1447\n","          69       0.95      0.96      0.95      1446\n","          70       0.78      0.84      0.81      1447\n","          71       1.00      1.00      1.00      1447\n","          72       0.98      1.00      0.99      1446\n","          73       0.93      0.90      0.92      1447\n","          74       0.44      0.57      0.50      1446\n","\n","    accuracy                           0.89    108480\n","   macro avg       0.88      0.89      0.88    108480\n","weighted avg       0.88      0.89      0.88    108480\n","\n","\n","🔄 Summary:\n","✅ Accuracy: 0.8899\n","✅ Precision: 0.8813\n","✅ Recall: 0.8899\n","✅ F1-Score: 0.8839\n","✅ تم حفظ Confusion Matrix: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_conf_matrix_sub.png\n"]},{"data":{"image/png":"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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ رسم Accuracy & Loss: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/cnn+bilstm_acc_loss_sub.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1400x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# ✅ استيراد المكتبات\n","## seperated model for sub catagory only\n","# ✅ استيراد المكتبات\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.utils import shuffle, class_weight\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, Bidirectional, LSTM, Reshape, Attention, Multiply\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n","import matplotlib.image as mpimg\n","\n","# ✅ المسارات\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files\"\n","model_path = os.path.join(base_path, \"cnn+bilstm_sub.h5\")\n","conf_matrix_path = os.path.join(base_path, \"cnn+bilstm_conf_matrix_sub.png\")\n","acc_loss_path = os.path.join(base_path, \"cnn+bilstm_acc_loss_sub.png\")\n","data_path = os.path.join(base_path, \"train_test_data_sub-deep-new.pkl\")\n","\n","# ✅ تحميل البيانات\n","if os.path.exists(data_path):\n","    with open(data_path, \"rb\") as f:\n","        X_train, X_test, y_train, y_test = pickle.load(f)\n","    X_train, y_train = shuffle(X_train, y_train, random_state=42)\n","    print(\"✅ تم تحميل بيانات التدريب والاختبار.\")\n","else:\n","    raise FileNotFoundError(f\"❌ لم يتم العثور على بيانات التدريب: {data_path}\")\n","\n","# ✅ تحميل أو تدريب النموذج\n","if os.path.exists(model_path):\n","    print(\"✅ تم العثور على النموذج. سيتم استخدامه بدون تدريب.\")\n","    model = load_model(model_path)\n","    history = None\n","else:\n","    print(\"🚀 لم يتم العثور على النموذج. سيتم تدريبه الآن...\")\n","\n","    # ✅ حساب class_weight للفئة الفرعية\n","    weights = class_weight.compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train)\n","    class_weight_sub = {i: w for i, w in enumerate(weights)}\n","\n","    # ✅ بناء النموذج\n","    input_layer = Input(shape=(894,), name=\"input_layer\")\n","    x = Reshape((894, 1))(input_layer)\n","    x = Conv1D(filters=128, kernel_size=3, activation=\"relu\")(x)\n","    x = MaxPooling1D(pool_size=2)(x)\n","    x = Bidirectional(LSTM(64, return_sequences=True))(x)\n","    att = Attention()([x, x])\n","    x = Multiply()([x, att])\n","    x = Dropout(0.4)(x)  # زيادة خفيفة للدروب آوت\n","    x = LSTM(64)(x)\n","    x = Dropout(0.4)(x)\n","    output = Dense(75, activation=\"softmax\", name=\"sub_output\")(x)\n","\n","    model = Model(inputs=input_layer, outputs=output)\n","\n","    model.compile(\n","        optimizer=Adam(learning_rate=0.0005),  # تخفيض Learning Rate\n","        loss=\"sparse_categorical_crossentropy\",\n","        metrics=[\"accuracy\"]\n","    )\n","\n","    model.summary()\n","\n","    history = model.fit(\n","        X_train, y_train,\n","        validation_data=(X_test, y_test),\n","        epochs=35,  # زيادة Epochs\n","        batch_size=64,\n","        class_weight=class_weight_sub,\n","        callbacks=[\n","            EarlyStopping(patience=5, restore_best_weights=True),\n","            ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=2, verbose=1, min_lr=1e-6)\n","        ]\n","    )\n","\n","    model.save(model_path)\n","    print(\"✅ تم حفظ النموذج.\")\n","\n","# ✅ التنبؤ\n","preds = model.predict(X_test)\n","preds = np.argmax(preds, axis=1)\n","\n","# ✅ تقييم الأداء\n","print(\"\\n📊 Sub Category Report:\")\n","print(classification_report(y_test, preds))\n","\n","accuracy = accuracy_score(y_test, preds)\n","precision = precision_score(y_test, preds, average='macro', zero_division=0)\n","recall = recall_score(y_test, preds, average='macro', zero_division=0)\n","f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","print(f\"\\n🔄 Summary:\")\n","print(f\"✅ Accuracy: {accuracy:.4f}\")\n","print(f\"✅ Precision: {precision:.4f}\")\n","print(f\"✅ Recall: {recall:.4f}\")\n","print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","# ✅ رسم Confusion Matrix\n","def plot_conf_matrix(y_true, y_pred, title, save_path):\n","    cm = confusion_matrix(y_true, y_pred)\n","    labels = [str(i) for i in range(len(np.unique(y_true)))]\n","    figsize = (24, 20) if len(labels) > 30 else (10, 7)\n","    annot = True if len(labels) <= 20 else False\n","    fontsize = 10 if len(labels) <= 20 else 6\n","\n","    plt.figure(figsize=figsize)\n","    sns.heatmap(cm, annot=annot, fmt=\"d\", cmap=\"Blues\",\n","                xticklabels=labels, yticklabels=labels, linewidths=0.3)\n","    plt.title(f\"Confusion Matrix - {title}\", fontsize=14)\n","    plt.xlabel(\"Predicted\", fontsize=12)\n","    plt.ylabel(\"Actual\", fontsize=12)\n","    plt.xticks(rotation=90, fontsize=fontsize)\n","    plt.yticks(rotation=0, fontsize=fontsize)\n","    plt.tight_layout()\n","    plt.savefig(save_path, dpi=300)\n","    print(f\"✅ تم حفظ Confusion Matrix: {save_path}\")\n","    plt.show()\n","\n","plot_conf_matrix(y_test, preds, \"Sub Category\", conf_matrix_path)\n","\n","# ✅ رسم Accuracy & Loss\n","if history is not None:\n","    plt.figure(figsize=(14, 5))\n","\n","    plt.subplot(1, 2, 1)\n","    plt.plot(history.history['accuracy'], label='Train Accuracy')\n","    plt.plot(history.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","    plt.title(\"Accuracy\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Accuracy\")\n","    plt.legend()\n","\n","    plt.subplot(1, 2, 2)\n","    plt.plot(history.history['loss'], label='Train Loss')\n","    plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","    plt.title(\"Loss\")\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.legend()\n","\n","    plt.tight_layout()\n","    plt.savefig(acc_loss_path, dpi=300)\n","    print(f\"✅ تم حفظ رسم Accuracy & Loss: {acc_loss_path}\")\n","    plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zDfwqc2_SUhz"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SaHAxmhd37mV"},"outputs":[],"source":["# اجرب مودل جديد مع ال cnn +BiGRU"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":16499788,"status":"ok","timestamp":1745751214777,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"7aTj-GjaKmUZ","outputId":"1c17be11-b001-4cfc-9c8a-791a5be0c8df"},"outputs":[{"name":"stdout","output_type":"stream","text":["❌ [TRAINING] MAIN model not found. Training...\n"]},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"functional\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">892</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ main_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,838</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m892\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │           \u001b[38;5;34m512\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling1d (\u001b[38;5;33mMaxPooling1D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m74,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ main_output (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m)             │         \u001b[38;5;34m2,838\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,846</span> (304.09 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m77,846\u001b[0m (304.09 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,846</span> (304.09 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m77,846\u001b[0m (304.09 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m2574/2574\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 35ms/step - accuracy: 0.1872 - loss: 2.7198 - val_accuracy: 0.4169 - val_loss: 1.9552 - learning_rate: 0.0010\n","Epoch 2/30\n","\u001b[1m2574/2574\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 35ms/step - accuracy: 0.4215 - loss: 1.9515 - val_accuracy: 0.5136 - val_loss: 1.6250 - learning_rate: 0.0010\n","Epoch 3/30\n","\u001b[1m2574/2574\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 35ms/step - accuracy: 0.4989 - loss: 1.6883 - val_accuracy: 0.5568 - val_loss: 1.4794 - learning_rate: 0.0010\n","Epoch 4/30\n","\u001b[1m2574/2574\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 35ms/step - accuracy: 0.5379 - loss: 1.5470 - val_accuracy: 0.5960 - val_loss: 1.3428 - learning_rate: 0.0010\n","Epoch 5/30\n","\u001b[1m2574/2574\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 35ms/step - accuracy: 0.5707 - loss: 1.4364 - val_accuracy: 0.6132 - val_loss: 1.2826 - learning_rate: 0.0010\n","Epoch 6/30\n","\u001b[1m2574/2574\u001b[0m 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This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ النموذج.\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1400x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\u001b[1m1287/1287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 11ms/step\n","\n","🔄 Summary:\n","✅ Accuracy: 0.7330\n","✅ Precision: 0.7321\n","✅ Recall: 0.7330\n","✅ F1-Score: 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["❌ [TRAINING] SUB model not found. Training...\n"]},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"functional_1\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">892</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ reshape_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling1d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)      │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ bidirectional_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │ max_pooling1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ attention           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Attention</span>)         │                   │            │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n","│                     │                   │            │ attention[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ gru_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │     <span style=\"color: #00af00; text-decoration-color: #00af00\">37,248</span> │ multiply[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gru_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ sub_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">75</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,875</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_1 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m892\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m512\u001b[0m │ reshape_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling1d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)      │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ bidirectional_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │     \u001b[38;5;34m74,496\u001b[0m │ max_pooling1d_1[\u001b[38;5;34m…\u001b[0m │\n","│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ attention           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","│ (\u001b[38;5;33mAttention\u001b[0m)         │                   │            │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply (\u001b[38;5;33mMultiply\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n","│                     │                   │            │ attention[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ gru_2 (\u001b[38;5;33mGRU\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │     \u001b[38;5;34m37,248\u001b[0m │ multiply[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ gru_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ sub_output (\u001b[38;5;33mDense\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m)        │      \u001b[38;5;34m4,875\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,131</span> (457.54 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m117,131\u001b[0m (457.54 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">117,131</span> (457.54 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,131\u001b[0m (457.54 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m426s\u001b[0m 62ms/step - accuracy: 0.1227 - loss: 3.7388 - val_accuracy: 0.4960 - val_loss: 1.9711 - learning_rate: 0.0010\n","Epoch 2/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.5018 - loss: 1.9340 - val_accuracy: 0.6396 - val_loss: 1.3686 - learning_rate: 0.0010\n","Epoch 3/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m423s\u001b[0m 62ms/step - accuracy: 0.6153 - loss: 1.4608 - val_accuracy: 0.6999 - val_loss: 1.1224 - learning_rate: 0.0010\n","Epoch 4/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.6720 - loss: 1.2301 - val_accuracy: 0.7397 - val_loss: 0.9752 - learning_rate: 0.0010\n","Epoch 5/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7080 - loss: 1.0835 - val_accuracy: 0.7713 - val_loss: 0.8428 - learning_rate: 0.0010\n","Epoch 6/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7337 - loss: 0.9827 - val_accuracy: 0.7809 - val_loss: 0.8007 - learning_rate: 0.0010\n","Epoch 7/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7527 - loss: 0.9056 - val_accuracy: 0.7975 - val_loss: 0.7386 - learning_rate: 0.0010\n","Epoch 8/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7680 - loss: 0.8427 - val_accuracy: 0.8054 - val_loss: 0.7033 - learning_rate: 0.0010\n","Epoch 9/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7795 - loss: 0.7983 - val_accuracy: 0.8181 - val_loss: 0.6590 - learning_rate: 0.0010\n","Epoch 10/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m421s\u001b[0m 62ms/step - accuracy: 0.7891 - loss: 0.7608 - val_accuracy: 0.8248 - val_loss: 0.6321 - learning_rate: 0.0010\n","Epoch 11/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.7956 - loss: 0.7346 - val_accuracy: 0.8183 - val_loss: 0.6458 - learning_rate: 0.0010\n","Epoch 12/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.8019 - loss: 0.7042 - val_accuracy: 0.8334 - val_loss: 0.5967 - learning_rate: 0.0010\n","Epoch 13/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m423s\u001b[0m 62ms/step - accuracy: 0.8076 - loss: 0.6834 - val_accuracy: 0.8419 - val_loss: 0.5628 - learning_rate: 0.0010\n","Epoch 14/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.8117 - loss: 0.6659 - val_accuracy: 0.8414 - val_loss: 0.5627 - learning_rate: 0.0010\n","Epoch 15/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.8178 - loss: 0.6438 - val_accuracy: 0.8429 - val_loss: 0.5519 - learning_rate: 0.0010\n","Epoch 16/30\n","\u001b[1m6780/6780\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m422s\u001b[0m 62ms/step - accuracy: 0.8218 - loss: 0.6295 - val_accuracy: 0.8453 - val_loss: 0.5470 - learning_rate: 0.0010\n","Epoch 17/30\n","\u001b[1m6780/6780\u001b[0m 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This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ النموذج.\n"]},{"data":{"image/png":"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size 1400x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\u001b[1m3390/3390\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 17ms/step\n","\n","🔄 Summary:\n","✅ Accuracy: 0.8903\n","✅ Precision: 0.8797\n","✅ Recall: 0.8903\n","✅ F1-Score: 0.8837\n"]},{"data":{"image/png":"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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["❌ [TRAINING] RF model not found. Training...\n"]},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"functional_2\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ reshape_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">892</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">446</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ bidirectional_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">74,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ rf_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">258</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ reshape_2 (\u001b[38;5;33mReshape\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m892\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │           \u001b[38;5;34m512\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling1d_2 (\u001b[38;5;33mMaxPooling1D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m446\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m74,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ rf_output (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m258\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">75,266</span> (294.01 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m75,266\u001b[0m (294.01 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">75,266</span> (294.01 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m75,266\u001b[0m (294.01 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 35ms/step - accuracy: 0.6558 - loss: 0.6182 - val_accuracy: 0.6593 - val_loss: 0.5854 - learning_rate: 0.0010\n","Epoch 2/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 34ms/step - accuracy: 0.7418 - loss: 0.5153 - val_accuracy: 0.8361 - val_loss: 0.3447 - learning_rate: 0.0010\n","Epoch 3/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.8632 - loss: 0.3019 - val_accuracy: 0.8892 - val_loss: 0.2499 - learning_rate: 0.0010\n","Epoch 4/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 35ms/step - accuracy: 0.8886 - loss: 0.2550 - val_accuracy: 0.8922 - val_loss: 0.2486 - learning_rate: 0.0010\n","Epoch 5/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.8957 - loss: 0.2392 - val_accuracy: 0.9000 - val_loss: 0.2292 - learning_rate: 0.0010\n","Epoch 6/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.8872 - loss: 0.2546 - val_accuracy: 0.8954 - val_loss: 0.2393 - learning_rate: 0.0010\n","Epoch 7/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9044 - loss: 0.2183 - val_accuracy: 0.9074 - val_loss: 0.2107 - learning_rate: 0.0010\n","Epoch 8/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9128 - loss: 0.2027 - val_accuracy: 0.9165 - val_loss: 0.1974 - learning_rate: 0.0010\n","Epoch 9/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9182 - loss: 0.1920 - val_accuracy: 0.9196 - val_loss: 0.1917 - learning_rate: 0.0010\n","Epoch 10/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9180 - loss: 0.1932 - val_accuracy: 0.9174 - val_loss: 0.2002 - learning_rate: 0.0010\n","Epoch 11/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 34ms/step - accuracy: 0.9213 - loss: 0.1864 - val_accuracy: 0.9257 - val_loss: 0.1844 - learning_rate: 0.0010\n","Epoch 12/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9240 - loss: 0.1772 - val_accuracy: 0.9144 - val_loss: 0.1981 - learning_rate: 0.0010\n","Epoch 13/30\n","\u001b[1m1105/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9224 - loss: 0.1826\n","Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9224 - loss: 0.1826 - val_accuracy: 0.9234 - val_loss: 0.1857 - learning_rate: 0.0010\n","Epoch 14/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9320 - loss: 0.1628 - val_accuracy: 0.9227 - val_loss: 0.1829 - learning_rate: 5.0000e-04\n","Epoch 15/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9303 - loss: 0.1628 - val_accuracy: 0.9233 - val_loss: 0.1851 - learning_rate: 5.0000e-04\n","Epoch 16/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9315 - loss: 0.1592 - val_accuracy: 0.9259 - val_loss: 0.1778 - learning_rate: 5.0000e-04\n","Epoch 17/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9325 - loss: 0.1575 - val_accuracy: 0.9257 - val_loss: 0.1815 - learning_rate: 5.0000e-04\n","Epoch 18/30\n","\u001b[1m1105/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9349 - loss: 0.1548\n","Epoch 18: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9349 - loss: 0.1548 - val_accuracy: 0.9275 - val_loss: 0.1800 - learning_rate: 5.0000e-04\n","Epoch 19/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9375 - loss: 0.1488 - val_accuracy: 0.9292 - val_loss: 0.1774 - learning_rate: 2.5000e-04\n","Epoch 20/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 35ms/step - accuracy: 0.9377 - loss: 0.1466 - val_accuracy: 0.9295 - val_loss: 0.1874 - learning_rate: 2.5000e-04\n","Epoch 21/30\n","\u001b[1m1105/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9400 - loss: 0.1416\n","Epoch 21: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9400 - loss: 0.1416 - val_accuracy: 0.9288 - val_loss: 0.1806 - learning_rate: 2.5000e-04\n","Epoch 22/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9399 - loss: 0.1387 - val_accuracy: 0.9292 - val_loss: 0.1836 - learning_rate: 1.2500e-04\n","Epoch 23/30\n","\u001b[1m1105/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9431 - loss: 0.1338\n","Epoch 23: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9431 - loss: 0.1338 - val_accuracy: 0.9280 - val_loss: 0.1821 - learning_rate: 1.2500e-04\n","Epoch 24/30\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 35ms/step - accuracy: 0.9427 - loss: 0.1342 - val_accuracy: 0.9283 - val_loss: 0.1841 - learning_rate: 6.2500e-05\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ النموذج.\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1400x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 11ms/step\n","\n","🔄 Summary:\n","✅ Accuracy: 0.9292\n","✅ Precision: 0.9335\n","✅ Recall: 0.9292\n","✅ F1-Score: 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# ✅ استيراد المكتبات\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","from sklearn.utils import shuffle, class_weight\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, Bidirectional, GRU, Reshape, Attention, Multiply\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n","\n","# ✅ المسارات الأساسية\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/\"\n","\n","# ✅ دالة عامة لتدريب أو تحميل نموذج\n","\n","def train_or_load_model(name, X_train, y_train, X_test, y_test, num_classes, use_attention=False):\n","    model_path = os.path.join(base_path, f\"CNN + BiGRU_model_all_{name}.h5\")\n","    conf_matrix_path = os.path.join(base_path, f\"CNN + BiGRU_conf_matrix_all_{name}.png\")\n","    acc_loss_path = os.path.join(base_path, f\"CNN + BiGRU_accuracy_loss_all_{name}.png\")\n","    comparison_path = os.path.join(base_path, \"all_models_comparison.csv\")\n","\n","    X_train, y_train = shuffle(X_train, y_train, random_state=42)\n","    weights = class_weight.compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train)\n","    class_weight_dict = {i: w for i, w in enumerate(weights)}\n","\n","    if os.path.exists(model_path):\n","        print(f\"✅ [LOADED] {name.upper()} model loaded from disk\")\n","        model = load_model(model_path)\n","        history = None\n","    else:\n","        print(f\"❌ [TRAINING] {name.upper()} model not found. Training...\")\n","        input_layer = Input(shape=(894,), name=\"input_layer\")\n","        x = Reshape((894, 1))(input_layer)\n","        x = Conv1D(filters=128, kernel_size=3, activation=\"relu\")(x)\n","        x = MaxPooling1D(pool_size=2)(x)\n","        x = Bidirectional(GRU(64, return_sequences=use_attention))(x)\n","        if use_attention:\n","            att = Attention()([x, x])\n","            x = Multiply()([x, att])\n","            x = GRU(64)(x)\n","        else:\n","            x = Dropout(0.3)(x)\n","        x = Dropout(0.3)(x)\n","        output = Dense(num_classes, activation=\"softmax\", name=f\"{name}_output\")(x)\n","\n","        model = Model(inputs=input_layer, outputs=output)\n","        model.compile(optimizer=Adam(learning_rate=0.001), loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n","        model.summary()\n","\n","        history = model.fit(\n","            X_train, y_train,\n","            validation_data=(X_test, y_test),\n","            epochs=30,\n","            batch_size=64,\n","            class_weight=class_weight_dict,\n","            callbacks=[EarlyStopping(patience=5, restore_best_weights=True),\n","                       ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=1e-6, verbose=1)]\n","        )\n","\n","        model.save(model_path)\n","        print(\"✅ تم حفظ النموذج.\")\n","\n","        # ✅ رسم Accuracy & Loss\n","        if history:\n","            plt.figure(figsize=(14, 5))\n","            plt.subplot(1, 2, 1)\n","            plt.plot(history.history['accuracy'], label='Train Accuracy')\n","            plt.plot(history.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","            plt.title(f\"Accuracy ({name})\")\n","            plt.xlabel(\"Epoch\")\n","            plt.ylabel(\"Accuracy\")\n","            plt.legend()\n","\n","            plt.subplot(1, 2, 2)\n","            plt.plot(history.history['loss'], label='Train Loss')\n","            plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","            plt.title(f\"Loss ({name})\")\n","            plt.xlabel(\"Epoch\")\n","            plt.ylabel(\"Loss\")\n","            plt.legend()\n","\n","            plt.tight_layout()\n","            plt.savefig(acc_loss_path, dpi=300)\n","            plt.show()\n","\n","    # ✅ التنبؤ والتقييم\n","    preds = model.predict(X_test)\n","    preds = np.argmax(preds, axis=1)\n","\n","    report = classification_report(y_test, preds, output_dict=True, zero_division=0)\n","    conf_matrix = confusion_matrix(y_test, preds)\n","    accuracy = accuracy_score(y_test, preds)\n","    precision = precision_score(y_test, preds, average='macro', zero_division=0)\n","    recall = recall_score(y_test, preds, average='macro', zero_division=0)\n","    f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","    print(f\"\\n🔄 Summary:\")\n","    print(f\"✅ Accuracy: {accuracy:.4f}\")\n","    print(f\"✅ Precision: {precision:.4f}\")\n","    print(f\"✅ Recall: {recall:.4f}\")\n","    print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","    # ✅ Confusion Matrix\n","    plt.figure(figsize=(24, 20))\n","    sns.heatmap(conf_matrix, annot=False, fmt=\"d\", cmap=\"Blues\")\n","    plt.title(f\"Confusion Matrix - {name.upper()}\")\n","    plt.xlabel(\"Predicted\")\n","    plt.ylabel(\"Actual\")\n","    plt.tight_layout()\n","    plt.savefig(conf_matrix_path, dpi=300)\n","    plt.show()\n","\n","    # ✅ حفظ النتائج\n","    results_df = pd.DataFrame({\n","        \"Model\": [name],\n","        \"Accuracy\": [accuracy],\n","        \"Precision\": [precision],\n","        \"Recall\": [recall],\n","        \"F1_Score\": [f1]\n","    })\n","\n","    if not os.path.exists(comparison_path):\n","        results_df.to_csv(comparison_path, index=False)\n","    else:\n","        results_df.to_csv(comparison_path, mode='a', header=False, index=False)\n","\n","# ✅ تنفيذ للنماذج الثلاثة\n","\n","try:\n","    with open(os.path.join(base_path, \"train_test_data_main-deep-new.pkl\"), \"rb\") as f:\n","        X_train_main, X_test_main, y_train_main, y_test_main = pickle.load(f)\n","    train_or_load_model(\"main\", X_train_main, y_train_main, X_test_main, y_test_main, num_classes=22)\n","except Exception as e:\n","    print(f\"⚠️ خطأ في نموذج MAIN: {e}\")\n","\n","try:\n","    with open(os.path.join(base_path, \"train_test_data_sub-deep-new.pkl\"), \"rb\") as f:\n","        X_train_sub, X_test_sub, y_train_sub, y_test_sub = pickle.load(f)\n","    train_or_load_model(\"sub\", X_train_sub, y_train_sub, X_test_sub, y_test_sub, num_classes=75, use_attention=True)\n","except Exception as e:\n","    print(f\"⚠️ خطأ في نموذج SUB: {e}\")\n","\n","try:\n","    with open(os.path.join(base_path, \"train_test_data_rf-deep-new.pkl\"), \"rb\") as f:\n","        X_train_rf, X_test_rf, y_train_rf, y_test_rf = pickle.load(f)\n","    train_or_load_model(\"rf\", X_train_rf, y_train_rf, X_test_rf, y_test_rf, num_classes=2)\n","except Exception as e:\n","    print(f\"⚠️ خطأ في نموذج RF: {e}\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1736909,"status":"ok","timestamp":1746292661148,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"zFggyKqbPPVc","outputId":"ba67df18-db75-49e7-f4b8-9bf22b0fe2d5"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل MAIN\n","✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل MAIN. سيتم تحميله بدون إعادة تدريب.\n","\u001b[1m1287/1287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m392s\u001b[0m 304ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99      1871\n","           1       0.99      1.00      0.99      1871\n","           2       0.83      0.76      0.79      1872\n","           3       0.96      0.97      0.96      1872\n","           4       0.69      0.71      0.70      1872\n","           5       0.54      0.54      0.54      1871\n","           6       0.83      0.80      0.81      1872\n","           7       0.53      0.39      0.45      1872\n","           8       0.98      0.99      0.98      1871\n","           9       0.96      0.97      0.97      1872\n","          10       0.83      0.82      0.83      1872\n","          11       0.65      0.56      0.60      1871\n","          12       0.39      0.51      0.44      1872\n","          13       0.73      0.67      0.70      1871\n","          14       0.47      0.53      0.50      1872\n","          15       0.73      0.76      0.75      1871\n","          16       0.57      0.58      0.58      1872\n","          17       0.97      0.95      0.96      1872\n","          18       0.99      1.00      1.00      1872\n","          19       0.95      0.98      0.97      1872\n","          20       0.90      0.88      0.89      1871\n","          21       0.95      0.95      0.95      1872\n","\n","    accuracy                           0.79     41176\n","   macro avg       0.79      0.79      0.79     41176\n","weighted avg       0.79      0.79      0.79     41176\n","\n","\n","🔄 Summary for MAIN:\n","✅ Accuracy: 0.7877\n","✅ Precision: 0.7919\n","✅ Recall: 0.7877\n","✅ F1-Score: 0.7884\n","✅ عرض رسم Accuracy & Loss المخزن: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/CNN + Bilstm_main_finetuned_acc_loss1.png\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABEkAAAGrCAYAAAA8UcljAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA7LlJREFUeJzs3XeYE1Xbx/HvpCfbKwssdelFQJBelmJBuhTBRrFgR0SwFyyvFZUH+4OiSBNEEX1UQEBFBFQU6QhIL9v7pue8fwwJxF2qIMX7c125YCeTyZkku5n5zX3O0ZRSCiGEEEIIIYQQQoh/OcPZboAQQgghhBBCCCHEuUBCEiGEEEIIIYQQQggkJBFCCCGEEEIIIYQAJCQRQgghhBBCCCGEACQkEUIIIYQQQgghhAAkJBFCCCGEEEIIIYQAJCQRQgghhBBCCCGEACQkEUIIIYQQQgghhAAkJBFCCCGEEEIIIYQAJCQRQghxHkhPTyc9Pf1sN+OM+/bbb9E0jW+//fZsN0WIEzZ79mzi4+MpLi4+200p15n4+6FpGk888UTo57feeouqVavidrtP6/MIIYT450lIIoQQ57n3338fTdPQNI0ffvihzP1KKapUqYKmafTs2bPcbeTn52Oz2dA0jU2bNpW7zrBhw4iMjAxblp6ejqZp9OrVq8z6O3fuRNM0XnrppePuQ7D9f72lpKQc97FnwrBhw47apiNvw4YNOyvtO9PeeOMNNE2jVatWZ7sp56UVK1bQqVMnoqOjSU5Opnv37ixfvvyEH38yvztnm9/v5/HHH+euu+4K+/tQvXr1o/69uRANGzYMj8fD22+/fbabIoQQ4m8yne0GCCGEOD1sNhszZsygffv2Ycu/++479u7di9VqPepj58yZEwolpk+fztNPP31Sz/3FF1+wevVqmjdvfkptB7j00ku54YYbwpbZ7XYAFi5ceMrbPRUjR46kW7duoZ937NjBY489xi233EKHDh1Cy9PS0k7r83bs2BGn04nFYjmt2z1Z06dPp3r16vz0009s27aNWrVqndX2nE92797N5ZdfTkJCAuPHjycQCLBo0SIWL15Mu3btznbzTrvPP/+cLVu2cMstt5ztppxVNpuNoUOH8vLLL3PXXXehadrZbpIQQohTJCGJEEJcIK688krmzJnDf/7zH0ymw3/eZ8yYQfPmzcnOzj7qY6dNm8aVV15JtWrVmDFjxkmFJFWrVqWoqIjx48czf/78U25/nTp1uO6668q9758ODdq0aUObNm1CP//yyy889thjtGnT5qhtPB0MBgM2m+2Mbf9E7Nixgx9//JFPPvmEkSNHMn36dB5//PGz2qajKSkpISIi4mw3I8z//vc/ioqKWLx4MZdccgkAY8aMuWC7YUyZMoV27dpRuXLls92Us27QoEG88MILLF26lC5dupzt5gghhDhF0t1GCCEuEEOGDCEnJ4dFixaFlnk8Hj7++GOuueaaoz5u9+7dLFu2jMGDBzN48ODQSfKJioqKYvTo0Xz++ef8+uuvf2sfjuavYwoEx+6YPXs2zzzzDKmpqdhsNrp27cq2bdvKPH7VqlVcccUVxMTE4HA46NSp00l1fyjPE088Ue7V4mD3p507d4aWBbse/PDDD7Rs2RKbzUbNmjWZOnVq2GPLG5MkPT2dRo0asXHjRjp37ozD4aBy5cq88MILZZ57165d9O7dm4iICJKTkxk9ejQLFiw4qXFOpk+fTlxcHD169GDAgAFMnz693PXy8/MZPXo01atXx2q1kpqayg033BAWxrlcLp544gnq1KmDzWajYsWKXHXVVWzfvv2o+wuHu5u8//77oWXB7l7bt2/nyiuvJCoqimuvvRaAZcuWMXDgQKpWrYrVaqVKlSqMHj0ap9NZpt2bN29m0KBBJCUlYbfbqVu3Lg8//DAAS5cuRdM0Pv300zKPmzFjBpqmsWLFimO+fgaDfmillApbfqxKrlOVmZnJjTfeSIUKFbDZbDRp0oQPPvigzHqzZs2iefPmREVFER0dTePGjZk4cWLofq/Xy/jx46lduzY2m42EhATat28f9rekPC6Xi6+//jqs6upk+Hw+nnrqKdLS0rBarVSvXp2HHnqoTKD02Wef0aNHDypVqoTVaiUtLY2nnnoKv99fZpvvvPMOaWlp2O12WrZsybJly8p9brfbzeOPP06tWrVCn5lx48aVeW63283o0aNJSkoiKiqK3r17s3fv3nK32bx5c+Lj4/nss89O6fUQQghxbpCQRAghLhDVq1enTZs2zJw5M7Tsq6++oqCggMGDBx/1cTNnziQiIoKePXvSsmVL0tLSjnpifDSjRo0iLi4ubCDDk+VyucjOzg67He/q+3PPPcenn37Kfffdx4MPPsjKlStDJ85BS5YsoWPHjhQWFvL444/zf//3f+Tn59OlSxd++umnU27vydq2bRsDBgzg0ksvZcKECcTFxTFs2DA2bNhw3Mfm5eVxxRVX0KRJEyZMmEC9evW4//77+eqrr0LrlJSU0KVLF7755hvuvvtuHn74YX788Ufuv//+k2rn9OnTueqqq7BYLAwZMoStW7fy888/h61TXFxMhw4dmDRpEpdddhkTJ07k1ltvZfPmzaETSL/fT8+ePRk/fjzNmzdnwoQJjBo1ioKCAtavX39SbQry+XxcfvnlJCcn89JLL9G/f39A7y5WWlrKbbfdxqRJk7j88suZNGlSme5ba9eupVWrVixZsoSbb76ZiRMn0rdvXz7//HNAD6SqVKlS7ud/+vTppKWlhVUYleeqq64iJiaGsWPH4vF4Tmk/T4TT6SQ9PZ0PP/yQa6+9lhdffJGYmBiGDRsWFoAsWrSIIUOGEBcXx/PPP89zzz1Henp6WEj4xBNPMH78eDp37sxrr73Gww8/TNWqVY8beq5evRqPx8PFF198Svtw00038dhjj3HxxRfzyiuv0KlTJ5599tkyf6/ef/99IiMjuffee5k4cSLNmzfnscce44EHHghb791332XkyJGkpKTwwgsv0K5dO3r37s2ePXvC1gsEAvTu3ZuXXnqJXr16MWnSJPr27csrr7zC1VdfXaaNr776KpdddhnPPfccZrOZHj16HHWfLr744r8dwAohhDjLlBBCiPPalClTFKB+/vln9dprr6moqChVWlqqlFJq4MCBqnPnzkoppapVq6Z69OhR5vGNGzdW1157bejnhx56SCUmJiqv1xu23tChQ1VERETYsk6dOqmGDRsqpZQaP368AtTq1auVUkrt2LFDAerFF1887j4A5d6mTJkSep5OnTqF1l+6dKkCVP369ZXb7Q4tnzhxogLUunXrlFJKBQIBVbt2bXX55ZerQCAQWq+0tFTVqFFDXXrppcdtm1JK/fzzz2HtUUqpxx9/XJX3NRp8P3bs2BFaVq1aNQWo77//PrQsMzNTWa1WNWbMmDL7tXTp0tCyTp06KUBNnTo1tMztdquUlBTVv3//0LIJEyYoQM2bNy+0zOl0qnr16pXZ5tH88ssvClCLFi1SSumvX2pqqho1alTYeo899pgC1CeffFJmG8HX+b333lOAevnll4+6Tnn7q9Thz86Rr/fQoUMVoB544IEy2wt+3o/07LPPKk3T1K5du0LLOnbsqKKiosKWHdkepZR68MEHldVqVfn5+aFlmZmZymQyqccff7zM8/zVjz/+qOLi4pTFYlEDBw5UPp/vuI/5qxP53Xn11VcVoKZNmxZa5vF4VJs2bVRkZKQqLCxUSik1atQoFR0dfcx2NGnSpNy/DcczefLksN+3Ix3t703QmjVrFKBuuummsOX33XefAtSSJUtCy8p7f0eOHKkcDodyuVxKKX3fk5OTVdOmTcP+JrzzzjsKCPv78eGHHyqDwaCWLVsWts233npLAWr58uVhbbz99tvD1rvmmmsUUO7n4ZZbblF2u/2o+y2EEOLcJ5UkQghxARk0aBBOp5MvvviCoqIivvjii2N2tVm7di3r1q1jyJAhoWVDhgwhOzubBQsWnNRzB6tJxo8ff0pt79OnD4sWLQq7XX755cd8zPDhw8PGKwkOqvrnn38CsGbNGrZu3co111xDTk5OqEKlpKSErl278v333xMIBE6pvSerQYMGYYO+JiUlUbdu3VBbjyUyMjJsLBSLxULLli3DHvv1119TuXJlevfuHVpms9m4+eabT7iN06dPp0KFCnTu3BnQZx26+uqrmTVrVljXhrlz59KkSRP69etXZhvBLkhz584lMTGRu+6666jrnIrbbrutzLLgAL+gV9RkZ2fTtm1blFL89ttvAGRlZfH9998zYsQIqlatetT23HDDDbjdbj7++OPQso8++gifz3fc8Wh27drFlVdeyY033si8efP49NNPufnmm8O63owcOZIqVaqc3E6X48svvyQlJSXsd9dsNnP33XdTXFzMd999B0BsbCwlJSXH7DoTGxvLhg0b2Lp160m1IScnB4C4uLhTaj/AvffeG7Z8zJgxgD62S9CR729RURHZ2dl06NCB0tJSNm/eDOjjBmVmZnLrrbeG/U0YNmwYMTExYc8xZ84c6tevT7169cIq14LjiCxdujSsjXfffXfY4++5556j7ldcXBxOp5PS0tLjvwhCCCHOSTJwqxBCXECSkpLo1q0bM2bMoLS0FL/fz4ABA466/rRp04iIiKBmzZqhsTxsNhvVq1dn+vTpxywr/6uYmBjuueceHn/8cX777beTPnFKTU096bEN/nqyG3zOvLw8gNBJ39ChQ4+6jYKCAiIiIsjNzQ1bnpSUhNFoPKn2nExbQW9vsK3HkpqaWiZYiIuLY+3ataGfd+3aRVpaWpn1TnRmGr/fz6xZs+jcuTM7duwILW/VqhUTJkxg8eLFXHbZZQBs37491NXlaLZv307dunXDBhH+u0wmE6mpqWWW7969m8cee4z58+eXeT0LCgqAw8FZo0aNjvkc9erV45JLLmH69OnceOONgB4etW7d+riv5bPPPovBYODpp5/GarXy3nvvMXToUKKiokJdYNavX39aplbetWsXtWvXDo2BElS/fv3Q/QC33347s2fPpnv37lSuXJnLLruMQYMGccUVV4Qe8+STT9KnTx/q1KlDo0aNuOKKK7j++uu56KKLTqgt6i/jr5xo+w0GQ5nXNCUlhdjY2FD7ATZs2MAjjzzCkiVLKCwsDFs/+P4G169du3bY/WazmZo1a4Yt27p1K5s2bSIpKanctmVmZoa18a+zWNWtW/eo+xV8LWR2GyGEOH9JSCKEEBeYa665hptvvpmDBw/SvXt3YmNjy11PKcXMmTMpKSmhQYMGZe7PzMykuLiYyMjIE37uUaNG8corrzB+/HheffXVU9yDE3e0ECN4ohKsEnnxxRdp2rRpuetGRkayfPnyUPVE0I4dO6hevfpRn/toJ0HlDSZ5Im09lr/z2BO1ZMkSDhw4wKxZs5g1a1aZ+6dPnx4KSU6Xk30NrVZrmVDA7/dz6aWXkpuby/3330+9evWIiIhg3759DBs27JQqhW644QZGjRrF3r17cbvdrFy5ktdee+24j/vxxx9p2rRpaJDW66+/noyMDMaOHUtUVBSDBw9mxYoVzJ0796TbdKqSk5NZs2YNCxYs4KuvvuKrr75iypQp3HDDDaFBXjt27Mj27dv57LPPWLhwIZMnT+aVV17hrbfe4qabbjrqthMSEgA9lCwvvDoRxwsT8vPz6dSpE9HR0Tz55JOkpaVhs9n49ddfuf/++0/p/Q0EAjRu3JiXX3653Pv/TqVPXl4eDocjrPpFCCHE+UVCEiGEuMD069ePkSNHsnLlSj766KOjrvfdd9+xd+9ennzyydDV56C8vDxuueUW5s2bd1JT3garSZ544oljVm/8U4JXgKOjo49ZpdKkSZMy3RFSUlKOue1g1Up+fn5YEHXkFfB/UrVq1di4cSNKqbATz/Jm+ynP9OnTSU5O5vXXXy9z3yeffMKnn37KW2+9hd1uJy0t7biDr6alpbFq1Sq8Xi9ms7ncdY58DY90Mq/hunXr+OOPP/jggw/CBmr96/sZrCY4kUFjBw8ezL333svMmTNxOp2YzeYyA3qWR9O0MoOE3nfffWRkZPDMM88wffp0mjVrRp8+fU5k146pWrVqrF27lkAgEBYcBbufVKtWLbTMYrHQq1cvevXqRSAQ4Pbbb+ftt9/m0UcfDVVyxMfHM3z4cIYPH05xcTEdO3bkiSeeOGZIUq9ePUAPFBs3bnzS7Q8EAmzdujXs709GRgb5+fmh9n/77bfk5OTwySef0LFjx9B6R1Y7Hbm/W7duDZt+1+v1smPHDpo0aRJalpaWxu+//07Xrl2PGdIE2xisigrasmXLUR+zY8eOMn9PhRBCnF9kTBIhhLjAREZG8uabb/LEE0/Qq1evo64X7GozduxYBgwYEHa7+eabqV279knPcgN6f/3Y2FiefPLJv7Mbp0Xz5s1JS0vjpZdeori4uMz9WVlZgH6y3q1bt7CbzWY75raDAcz3338fWlZSUlLuFKz/hMsvv5x9+/Yxf/780DKXy8V///vf4z7W6XTyySef0LNnzzKfhQEDBnDnnXdSVFQU2nb//v35/fffy50qN1jd0r9/f7Kzs8utwAiuU61aNYxGY9hrCPDGG2+c8H4Hq2yOrKpRSoXN8AJ696mOHTvy3nvvsXv37nLbE5SYmEj37t2ZNm0a06dP54orriAxMfG4benWrRtbt27lww8/DFv+3HPP0aBBA3bu3Env3r3LVMOciiuvvJKDBw+GBaE+n49JkyYRGRlJp06dgMPjhgQZDIZQN5rg7FF/XScyMpJatWodd3ap5s2bY7FY+OWXX06p/UCZirNgdUewq19576/H4ynzGWnRogVJSUm89dZbYbMKvf/++2VCuEGDBrFv375yfzecTiclJSUAdO/eHYD//Oc/Yescq0ru119/pW3btke9XwghxLlPKkmEEOICdLwqDrfbzdy5c7n00kuPGgb07t2biRMnkpmZSXJy8gk/d0xMDKNGjTrlAVxPJ4PBwOTJk+nevTsNGzZk+PDhVK5cmX379rF06VKio6ND07+erMsuu4yqVaty4403MnbsWIxGI++99x5JSUllTsL/CSNHjuS1115jyJAhjBo1iooVKzJ9+vTQ+3usK+bz58+nqKgobNDXI7Vu3ZqkpCSmT5/O1VdfzdixY/n4448ZOHAgI0aMoHnz5uTm5jJ//nzeeustmjRpwg033MDUqVO59957+emnn+jQoQMlJSV888033H777fTp04eYmBgGDhzIpEmT0DSNtLQ0vvjii9CYECeiXr16pKWlcd9997Fv3z6io6OZO3duuWO9/Oc//6F9+/ZcfPHF3HLLLdSoUYOdO3fyv//9jzVr1oSte8MNN4TG83nqqadOqC0PPvgg8+bNY+jQoSxatIi2bdtSXFzMzJkz2bFjB5dccglPP/00bdq0OaGuS4sXL8blcpVZ3rdvX2655Rbefvtthg0bxurVq6levToff/wxy5cv59VXXyUqKgrQp7DNzc2lS5cupKamsmvXLiZNmkTTpk1DFQ8NGjQgPT2d5s2bEx8fzy+//MLHH3/MnXfeecz22Ww2LrvsMr755ptyQ9Ft27bx9NNPl1nerFkzevTowdChQ3nnnXdCXWp++uknPvjgA/r27Rvq/ta2bVvi4uIYOnQod999N5qm8eGHH5YJtsxmM08//TQjR46kS5cuXH311ezYsYMpU6aUGZPk+uuvZ/bs2dx6660sXbqUdu3a4ff72bx5M7Nnz2bBggW0aNGCpk2bMmTIEN544w0KCgpo27YtixcvPmp11urVq8nNzT0tlUJCCCHOon9+Qh0hhBCn05FTAB/LkVNyzp07VwHq3XffPer63377rQLUxIkTlVLHnwL4SHl5eSomJuakpgC+4447jnr/0aYAnjNnTth65U0dq5RSv/32m7rqqqtUQkKCslqtqlq1amrQoEFq8eLFx22bUuVPAayUUqtXr1atWrVSFotFVa1aVb388stHnQK4vOlQj7Zff50CuLzXeOjQoapatWphy/7880/Vo0cPZbfbVVJSkhozZkzovV65cuVR969Xr17KZrOpkpKSo64zbNgwZTabVXZ2tlJKqZycHHXnnXeqypUrK4vFolJTU9XQoUND9yulT9368MMPqxo1aiiz2axSUlLUgAED1Pbt20PrZGVlqf79+yuHw6Hi4uLUyJEj1fr168udAvivn7+gjRs3qm7duqnIyEiVmJiobr75ZvX777+X+56tX79e9evXT8XGxiqbzabq1q2rHn300TLbdLvdKi4uTsXExCin03nU1+WvsrOz1Z133qmqVKmiTCaTSklJUTfccIPavHmzKiwsVPXq1VPR0dHlTpsbFPwcH+324YcfKqWUysjIUMOHD1eJiYnKYrGoxo0bl9nfjz/+WF122WUqOTk59DkdOXKkOnDgQGidp59+WrVs2VLFxsYqu92u6tWrp5555hnl8XiOu7+ffPKJ0jRN7d69O2x5cNrr8m433nijUkopr9erxo8fH/p8VKlSRT344IOhaX2Dli9frlq3bq3sdruqVKmSGjdunFqwYEG500e/8cYbqkaNGspqtaoWLVqo77//vszvmVL6lMHPP/+8atiwobJarSouLk41b95cjR8/XhUUFITWczqd6u6771YJCQkqIiJC9erVS+3Zs6fcKYDvv/9+VbVq1bAppYUQQpx/NKVO46hvQgghhDinvPrqq4wePZq9e/dSuXLls92c84bP56NSpUr06tWLd99992w355zl9/tp0KABgwYNOuGKmwuR2+2mevXqPPDAA4waNepsN0cIIcTfIGOSCCGEEBcIp9MZ9rPL5eLtt9+mdu3aEpCcpHnz5pGVlRU2GKwoy2g08uSTT/L666+XO+7Pv8WUKVMwm83ceuutZ7spQggh/iapJBFCCCEuEN27d6dq1ao0bdqUgoICpk2bxoYNG5g+fTrXXHPN2W7eeWHVqlWsXbuWp556isTERH799dez3SQhhBBC/INk4FYhhBDiAnH55ZczefJkpk+fHuoGMWvWrBOavlbo3nzzTaZNm0bTpk15//33z3ZzhBBCCPEPk0oSIYQQQgghhBBCCGRMEiGEEEIIIYQQQghAQhIhhBBCCCGEEEIIQEISIYQQQgghhBBCCEBCEiGEEEIIIYQQQghAQhIhhBBCCCGEEEIIQEISIYQQQgghhBBCCEBCEiGEEEIIIYQQQghAQhIhhBBCCCGEEEIIQEISIYQQQgghhBBCCEBCEiGEEEIIIYQQQghAQhIhhBBCCCGEEEIIQEISIYQQQgghhBBCCEBCEiGEEEIIIYQQQghAQhIhhBBCCCGEEEIIQEISIYQQQgghhBBCCABMZ7sBQvybKKVwOp0EAgHsdjtGozF0n9/vx+l0YjQasdlsaJoWus/r9eJ2u8s8RilFaWkpVqsVk8kU2obZbMZisYS24fP58Hg82O32sO0Gud1uvF5v2DKDwYDb7Wb+/Pn069eP6Ojo0/Y6+Hw+XC7XUe83mUxYrdZy23o8Ho+HuXPn0rZtW6pVq/Z3mnlMSik8Hg8+nw+Hw3FKbRVCCCHOhuB3mNfrxWq1Yjabw+4rLS0FwG63YzAcvqYaCARCxx1/fYzL5cJgMGCxWAAoLS1F07SwY49AIIDT6cRms4UdzwQd7fjAYrHw6aefcskll1CzZs3T8yIc0R6lVLn3GwyGox47HY9SikWLFhETE0OrVq3+blPL3b7b7UbTtLBjPiHE3yeVJEL8g3w+H0OHDqVFixYsW7Ys7Et56dKlNG/enJtuugmfzxdarpTirbfeonnz5ixYsCDsMYWFhfTs2ZPFixcDsGHDBi655BJGjBgRdpCxZMkS+vbtS1FRUbntev3112nRogWdOnUiPT2d9PR0hg8fzr59+3j22WfJzc09ra/DypUr6dy5M+np6XTs2JFmzZrRrl270HM//fTTp7xtt9vNhAkT+OOPP05ji8vy+/3cc889tG3bli1btpzR5xJCCCFOtwkTJtC0aVNee+21sGOLPXv2kJ6eTteuXcnKygotV0rxww8/0KJFC55//vmwxyilGD16NBMmTAD045Mrr7ySbt26sXfv3tC6+/bt47LLLjvq9+by5ctp3rw5HTt2DB0TdO/enR07dvDqq6+yYcOG0/oaZGZm0qtXL9LT0+nUqRMtWrSgVatWoee+/vrrcbvdp7z9mTNnsmjRotPY4sMCgQBjxozhxRdfPCPbF+LfTCpJhPgHKaXYt28fTqeT6dOn06FDB4xGI36/n2nTpuFyudi3b1/YY4qLi5k2bRpms5mpU6dy2WWXYTLpv7qBQIDdu3dTXFwM6AHBnj17yMjIYOHChfTu3RtN0ygpKWH37t0EAoFy25Wbm0tiYiLvv/9+6EqExWIhKSmJ//3vf1SqVKnMfgQCATRNC93+er/f78doNJZ7ZePiiy/mo48+QilFfn4+V155JWPGjOHSSy8FICoq6oRez0AggFIKg8EQep6IiAhmz55NcnJymTYFb0euf6xtHcuuXbv4+uuvMZlMfPLJJzz44INHfR2C2zzy/mPddzzl7cvxthd8z4AT3kchhBAXruzsbEpLS5k1axY33ngj0dHRKKX43//+R0ZGBn6/H7/fH1pfKcW0adMwGAx89NFH3HbbbSQkJITuP3jwIA6HAzh8fJKbm8vbb7/Nk08+iaZpeL1edu7cedTgobS0lIKCAmbMmEFsbCygf2dVqlSJGTNmkJiYGLb+8b7bj3e8kpiYyHvvvYff70cpxbBhw6hXrx4PPvggoB8LBStjjuVoxz3PPPNMWMVN0LGOOY53DHWkgwcPlrv9v+4/lP3uD75ugUAAg8EQVjEkxwzi305CEiHOgp49e7Js2TIOHDhAamoqe/bsYfXq1Vx55ZVs2rQptJ5Sip9++om8vDxeeuklRo0axY4dO6hdu/ZRtx0TE0OPHj2YMGECnTt3PuFuMna7nZo1a4Z9SWZkZDB58mTGjBlDQkICs2fPDnUJ+t///ofdbueWW26hdevWoRP1NWvW8MEHH7Bnzx4qV67MiBEjaNKkSdgXrMPhCJXL5uTkYDQaSUlJIS0tjY0bNzJ16lQeeughrFYrSik++eQTSkpKuP7668nKyuKVV16hV69ezJkzh927d9O6dWtuvfVWoqKi8Hg8fPjhhwwYMICGDRuycuVKvvvuO5o0acKsWbNwu90MHDiQPn36YDQaUUrx888/M3nyZAoKCujatSs2mw2TycSQIUPKPTBQSjF//nyqV6/OkCFDeOedd7jzzjtDr3UgEGDNmjV8+OGH7N69m5iYGAYPHhwKgdavX8/UqVP5888/iYqKol+/fvTu3Zvvv/+eX3/9lVGjRmEwGAgEArzzzjtUr16dyy+/nK1bt/Lhhx/SqVMnZs+ejcVi4fnnn2fJkiV8+eWXZGRkkJCQwJAhQ0hPT8dgMKCUIicnh2nTprFy5UqUUrRo0YLbbruNjz/+GIvFEtpPpRS///478+bNY9y4caGDXSGEEBemiy++mOzsbFatWkW3bt1wuVx8/PHHDBw4kDlz5oSte+DAAZYuXcpzzz3H448/znfffUe/fv2OegJtNBoZNmwYM2bM4Prrr6dOnTon1CaTyUS1atXCAhGPx8P06dPp2bMnTZs2ZfXq1Xz99de0bNmSGTNm4HQ66devH/3798dkMqGUYu/evUyZMoV169YRGRnJwIEDwy40BZ+revXqAKGu0LGxsaSlpZGXl8eLL77IHXfcQeXKlQH4/fff+fzzzxk7diyapjFhwgRatGjBzz//zOrVq6levTr33HMPqampAHz11VckJyfTq1cvDhw4wH/+8x969uzJrFmzOHDgAB06dODmm2/G4XCglGLPnj28/vrrbNu2jYsuuojWrVuzevVq7rvvvhMKa4KUUhQVFTF16lSWLVuG2Wyme/fu9O/fH5vNhs/nY/78+Xz22WcUFhaSmJjI9ddfT4cOHSgoKOC9995j5cqVBAIBatSowahRo0L7JMS/gXS3EeIsqFWrFrVq1eKrr75CKcWXX35JgwYNqFGjRth6gUCAGTNm0KlTJ7p27Uq1atWYP3/+UfvOAmiaxk033URubi6ffPLJMdc9ktvtZv/+/aFbcXExBQUFzJw5M9RN58cff+Txxx/n999/59prryU6Opobb7yRgwcPopRi1apVDB06lKSkJG688UYSExO54YYb2Lx58wm/Nnv37mXOnDlhY6SsWrWK7777DqUUhYWFvPvuuzzxxBM0bdqU/v37M2XKFN555x2UUni9XubMmcOePXsA2Lp1Ky+88AKzZs2ib9++XHzxxdx999388ssvKKXYtGkT1113HTExMVx33XVs3LiRRx55hGXLlh21jaWlpcyZM4cBAwbQo0cP8vPzWbFiReiqzI8//sigQYMwGo0MHz6cTp06sWfPHpRS/PrrrwwYMACXy8WwYcNCpcgAmzZt4osvvghdvQn2Z/79998B/QB10qRJvPrqq3Tt2pWePXuiaRrbt2+nXbt23HzzzdSvX58777yTn376KXSQNGLECL744gv69u3LkCFD8Hg8OJ1OIiMjef7558nLywP0z9sbb7zBgQMHsFqtJ/yeCSGEOD9FRkbSp08fZs2aRSAQYO3ateTn59OpU6ew9ZRSfP3118TGxnLppZdyxRVXMGPGjLBKk/Kkp6dz8cUXM3HixOOuG+T3+8nIyAgdj+Tm5uLz+Zg7dy47d+4E4M8//2TChAl88MEH9OrVi1atWjFmzBhWrFgB6BUW1157LQcPHuSGG26gTZs2PPDAA8c9hjpSSUkJM2bMCH1Hgl5FOnfuXHw+H36/n08++YT7778fm83GDTfcwPr16xkzZkyo2/SSJUv45ZdfAMjPz+ett97i6aefpnXr1vTp04fXXnuNqVOnho5vhg0bxvbt27nhhhuw2+3cd999fPLJJ2HdsE+Ez+dj7NixfPjhh/Tt25f09HSeeuop/vOf/xAIBPjhhx8YO3YsrVq1YuTIkbRq1SrUteqVV15h7ty5DBw4kOHDh5OSkkJ+fv5JPb8Q5zupJBHiLDCbzVx99dW89957DBgwgI8//pi77rqLP//8M2y9/fv3s3TpUt5++20cDgf9+/dn+vTpjBw5ksjIyKNuv0KFCtxxxx1MnDiRnj17nlCb1qxZEzrpBrjnnnto06ZNmfXS0tJ46qmnsNlstGnThq+++oqNGzeSnJzMf/7zHzp37syNN96Ipmk0a9aMNWvWMHfuXB5++OGw7j5/t3xz1KhRXHnllYDex3nhwoWMGjWq3HUjIyP5v//7PypWrIjP5+Obb75h2bJltGzZkpkzZ1K7dm2efvppLBYLXbp0YfXq1Ud9XqUUv/zyC/v27aN79+5UrFiR9PR0Zs6cSbdu3VBKMXHiRLp27cpzzz1XZqDd119/nebNmzNhwoRjlsgejcFg4IknnqB58+ahZXfccQe7du1i//79XHTRRaSlpfH555/TqlUrli5dyoYNG1iyZAlVq1YFoE+fPgB07dqV//u//2PJkiX079+fvXv3smTJEt5///2wiiIhhBAXrj59+jB48GD27dvHnDlz6NatG/Hx8WHruN1uZs6cSZ8+fYiKimLAgAEMGjSI7du3H7NCxGq1ct999zF48GBGjBhRZrvlycvLY/DgwaGKj+AJ/l/Z7XaeeeYZqlatit/v59tvv+W7776jffv2zJkzB03TeOCBB0Khf05ODu+9916oK3LQyXZ5PZJSip49e3LPPfegaRpxcXEMGzaMvLw8kpKSyqyvaRrjxo0jPT0dgG3btrFo0SJuvvlmli9fzp49e5g2bRoVK1YkEAhw4MCBY160OZpt27bxxRdfMGfOnNCxnN1uZ/z48dx4443s3r2bhIQE+vfvT1JSUtjAutu2baNVq1b06NEDu90eOtYS4t9EjoKFOEu6dOlCZmYmM2bMoKCggI4dO4bdH6wwsVgsVK9enczMTFq0aMHu3btDVRBHo2kagwcPxmKx8MEHH5xQe5o3b84333zD4sWLWbx4MYMGDSp3vTp16oRm33E4HERGRlJUVITb7Wb9+vV8+umndO/enSuuuILu3buzZs0a8vPzOXjwIEOGDKFXr1706dOHVatWnfiL9Rc2m406deqEDmxSUlIoKSk56lWqypUrk5CQgKZpGI1GkpKSKCgoAGDLli00bdo0NDK8w+GgcePGR31upRSzZs2iYcOG2Gw2srKy6NKlC0uWLGHv3r14PB42b95Mx44dQ/2JgzePx8PGjRvp2LEjZrM57L4TPUCLj4+nRo0aoce4XC7Gjh1Lnz59eOKJJ5gwYQIbNmwgIyMDpRTr16+nVq1aVKpUqczzxcbGMmjQIKZOnYrX62XevHmkpqbSvHlz6X8shBD/ErVq1aJ27dpMmzaNxYsXM3DgwLD7g98l69evp23btmRmZlKhQgUSExP57LPPjrv9Fi1acOmll/LSSy+VmUmvPPHx8Xz66aeh45Enn3yy3OC+YsWKoRN8o9FIYmJi6Lv9t99+Y/369fTu3ZsrrriCK664gg8++ACn04nT6WT06NH06tWLXr168f7775/YC1UOTdNo2LBh6MJPQkICgUDgqDP4RUZGUrNmzdD3cIUKFSgsLEQpxfbt26lSpQqJiYmhfWrRosUpfR/v3LkTu90edqzUvHlziouLycjIoEuXLsTHx9O9e3eGDx/O1KlTKSgowGAwMHz4cJYsWcLll1/OPffcwzfffHPSlSxCnO+kkkSIsyQhIYHOnTvz+OOPc9NNN5W5uuJyuZg1axaFhYUMHDgQTdMIBAKhqzl/DVX+KjY2lnvvvZfHH3+cO+6447jtMZlMxMfHH7eCoLz7lVJomobJZOL2229nxIgRYV/qwTE+7rzzTrxeL5qmkZaWdtTtB7utBP11gLejDUx6Mm0OstvtoYFvg9spKSk56ngcBw8e5KuvvsJgMNCjRw9AL2vNy8vjyy+/ZNiwYZjN5nIPkILT9DmdznK3HRwX5Mi2eDyesHWMRmPY/vz666/Mnz+fefPmUa9ePYxGI3feeWfoNbNarbjd7nJfH03TGDhwIO+99x4//fQTM2fO5KabbsJmsx3t5RJCCHGBMZlMDB48mNtvv50mTZrQqFGjUBeRoNmzZ+N2uxk9enTYeFdz5szhtttuIyIi4qjbNxqN3HPPPaHx2I7HYDAQGxsbdlxU3vfmXytSj/wONZvNpKen88Ybb4R9ZxqNRhwOB9dffz2FhYUAVKtWrdx2BI81juwCW96As0du/3iBxl+PX45ss81mw+VyhT1fcXHxCXcPOpLFYikz8K7H4wkdq1WuXJm5c+eyZcsWfvrpJ9544w2+++473nnnHbp27co333zD+vXr+e677xg5ciSPP/44N9xwg1xAEf8aUkkixFkSHDtkxIgRDBs2rMwXz9q1a9myZQtz5sxh0aJFLFy4kG+++YbXX3+dRYsWceDAgeNuv2fPnlSpUoV33333TO4KoJ+Md+jQgaVLl2I2m0lMTCQxMTEUNtjtdjp27EjXrl3p0qVLuWWooI80X1JSwr59+1BKkZubG+pjfCZ07NiRpUuXhmb/2bZt21EP4pRSLFy4ELvdHnpPFi5cyJIlS7j33nv56KOPCAQCtG/fnrlz54auDgUCAZxOJxaLhQ4dOvDZZ5+Rk5MTdp9SipSUFA4cOEBeXh5KKXbv3s26deuO2f7CwkLMZjMVK1bEbDazb98+vv3229D9bdq0Yfv27aHqo+BBXvDAqXr16nTo0IEHHniAvLw8evToIQdBQgjxL9OtWzduvfVWxo4dW2ZMqpycHD7//HOee+45vvnmGxYuXMiiRYv4/PPPycjI4KeffjrmtjVNo169egwaNIgJEyb8rSl1T1Tnzp1Zu3YteXl5oeORmJgYQA81LrnkErp27UrXrl2pVatWud97kZGROBwOtmzZErpo8c0335xSaHEiWrRowd69e1m1ahWBQID8/HzmzZt33McF23PkTD/BLlDffvstgUAAn8/H559/TqVKlahUqRJFRUUYjUaaNWvGyJEjufPOO/n5559xuVzk5eURGxtLp06dePjhh2nbti0///zzGdlnIc5VUkkixD8s+EWsaRr169fn+eefL7NOIBBg9uzZNGzYkBYtWoQdsFx66aWMHz+eBQsW0K9fv2M+l8PhYOzYsfTt25cqVaqcULtOlcFg4L777mPEiBGhQdRcLhfr1q3j/vvvD1VdHE+dOnVo1qwZw4cPp23btqFxWv565eV00DSNAQMGsGDBAnr37k2dOnXIz8+nUqVKYWOJBHk8HmbOnEn37t1D5bKgH5j079+f//73v6xbt457772XoUOH0rt3by655BJycnKoVq0ajz32GHfccQc///wzffr0oXXr1hQUFBAbG8uLL75ImzZtQgPINmzYkO3bt2O324+5D82aNQt7zPr168OmUG7ZsiVDhw5lxIgRdOrUCbvdzp49e3jttdeoWLEiBoOBoUOHcsUVVzBy5EgqVKhwWl5bIYQQ577g91hsbCxPPvlkaNmR37PffvstTqeTHj16hF3giIuLo1WrVsyaNavMQK9/ZTQaue222/joo4+OWk15umiaRu/evfnuu+8YNGgQHTp0wGq1smnTJtq2bcsjjzxyQtuJiopi4MCBPPzwwyxfvpzMzEyys7PPyPEIQOPGjbnxxhu5+eabadSoEUVFRdjt9uNOBbxgwQKuu+660M9Wq5XHH3+cMWPG8Mgjj7BkyRKcTicrVqxgwoQJREREMHnyZGbNmkWjRo0AWLp0Kf3798disXDXXXdRUFBArVq1yMrK4ueff2bSpEmnbT+FOB9o6kzFoUKIMgKBAN9++y1Vq1alVq1aZe7/448/2L9/P+3bt2fZsmUkJCTQuHHjsC/HQCDAihUrsNlsNG7cmMWLF9O4cWNSU1PJzc3lxx9/pEuXLqEKDo/Hw5IlSwB9HJTyppBbv349ubm5dOjQIey5ioqKWLZsGZ06dcLhcPD7778TCARo1qwZmqbh8/n49ttvqV+/PpUrVw6Nzr58+XK2bt2K3W6nYcOGNGvW7KjdV9xuN4sWLaJZs2ahbeTk5LBgwQLy8vJo3749oHdpad68OSUlJXz33Xd06tQpNHjtrl27+PPPP+nYsSOBQIClS5dy0UUXkZKSEnbfkVP+OhwOGjVqhFKK0tJSVq9eTUFBAQ0aNOC+++7jkksu4cEHHwx7PVwuV+j1rlKlSth9Xq+XxYsXU7t2bWrWrEleXh7fffcdu3btIjExkQ4dOoQGTi0oKGDZsmVs376duLg42rdvH5oSee/evSxatAiv10vnzp3Jzs4mPj6eunXrkp2dzerVq8Pex+CUgYsWLcLtdtO+ffvQLD/BwV19Ph+rV6/m119/BaBp06a0bNkyNCjexo0bufzyy/n0009p0aLFUT69QgghLhRKKdauXYvT6aRVq1ZlTsKD0wJ36dKFrVu3kpOTQ6dOncK6liil2LJlC3v37iU9PZ1Vq1YRGRnJRRddFPpObNq0KRUrVgytv3LlSg4ePEh6ejpxcXFl2nXw4EF++eUXLr300rALRH6/nyVLltCgQQMqV67M3r172bJlC+np6aHv9tWrV2M2m2nSpEmo8uOXX34JHbvUqlWLSy65hPj4+HJDB6UUy5YtIyYmhiZNmgB6N58lS5awbds2GjVqRFpaGlu3bqVz585omsa3335L3bp1Q9PjFhYW8sMPP5Ceno7dbuenn34iMjKShg0bUlhYyLJly+jcuXPomGj79u3s27eP9u3bh46r1q1bx549e6hZsyZffPEFy5cv57PPPitz8UYpxYoVK9i3b1/YcpPJROfOnYmMjGTNmjX89NNPmM1m2rdvT926dTEYDOTm5rJq1Sq2b98OQJMmTWjZsiUWi4UdO3aEBqiPjo6mbdu2occJ8W8hIYkQ4l/N6/Xy+eefU6NGDaxWKwsXLuTVV19l7ty5YTPIXIjcbjc7d+7k5ZdfJjMzk1mzZsnUv0IIIcRZoJRi8eLFOBwO4uPj2bBhAw899BDjxo0rM9abEOLMku42Qoh/vZ9//pmXX34Zt9sdmsq4WbNmZ7tZZ9zu3bu58cYbiYyM5KWXXiq3ykgIIYQQ/4w9e/bw4YcfUlRUREREBHfeeSfXXnutBCRC/MOkkkQI8a8XnDVIKYXZbMZkMv0rDkiCg8aazebQlMRCCCGEODuC3YR8Ph8mkwmLxSLfzUKcBRKSCCGEEEIIIYQQQiDdbUIkKxJCCCH+WRfyFVI5rhBCCCH+OafzmEJCkiPMnj2bVatWlTv1pxBCCCH+PqUUERERjBs3joiIiLPdnDNm27ZtvPHGG6FZpIQQQghxeimlMJlMjBs3jvj4+NO2XfnmPsKqVauwWq20atXqbDdFCCGEuCAVFxfz+uuvM3r06LPdlDMqMzOTtWvXcuedd17QFTNCCCHE2eLxeJgwYQJ33XXXad2uhCRHMBqNtGrVir59+57tpgghhBAXpNzcXN5//30MBsPZbsoZZTQaSU1NpU+fPhf8vgohhBBnQ2lpKZMnTz7t37PyrS2EEEIIIYQQQgiBhCRCCCGEEEIIIYQQgHS3OS6lFIFAAKfTidfrPdvNEX+DpmlYrVZsNpv0DxdCCCGEEEIIUYaEJMcRCATYv38/Xq8Xi8Vytpsj/galFNnZ2cTHxxMXFydBiRBCCCGEEEKIMBKSHEdpaSk+n4+qVavK1MAXgKKiInJycoiNjZWQRAghhBBCCCFEGAlJjsPn82E2mzEajXJSfQGwWq0EAgGUUme7KUIIIYQQQgghzjEycKsQQgghhBBCCCEEUklyXvF4PBQWFpapgtA0jaioKKxW6zEfr5Tiyy+/pFKlSjRr1uyknlsphdPpxO12S1cVIYQQQgghhBAXJAlJziObNm3iwQcfxO/3k5mZidfrpXLlyhgMBu6//37S09OPu43NmzcDnFJI8uijj7J06VK+/PJLUlJSTmUXhBBCCCGEEEKIc5aEJOeRRo0a8fHHH6OU4oUXXiAjI4MJEybgdrux2WwUFBRQWlpKhQoVcLvd5OTkYDQaSUpKCg06e+utt2IymUKVIWazmaKiIpxOJ8nJyZhMpnKrRLKysvj++++JiopiyZIlDBkyBE3TQlMk5+Tk4Ha7SUxMxGazAfp4LtnZ2QQCARITE7FYLDidTiwWS1gbLBYLRqOR0tJSLBYLeXl5mM1mYmJiKCoqoqCggMjIyFAFS/B5S0pKyM3NJSIigri4ONxuN0ajMTQLkd/vx+l0EhERIZUvQgghhBBCCCGOS0KSk6SU4kwN+Rk8jT/aCb3RaMThcKCUwmw2YzabcTgcTJgwgd27d5Ofn09SUhIPPvggDz30EIWFhZSWllK/fn2effZZIiIieO6557jooosYMGAA48aNw2QysXv3brKzs2nZsiX/93//V2aqY6UUS5YsIS0tjauuuopZs2YxcOBAzGYzhYWFPP7446xduxa73U5qaiqTJk0iNzeXBx54gL1792KxWLj44osZP348o0eP5pprrqFTp074fD5uu+027r77burXr891111HlSpV2L17N5dffjkNGzbkpZdeAvRZaa677jqGDx8OwJdffsnLL7+MzWYjEAjw6KOP8tNPPxEIBBg9ejQAK1as4J133uG///3vcbsiCSHEv83RBrAOKAgohT+gCAQUJqMBi0mGMBNCCCHOW8HvfE07/P/gz6IMCUlO0tq9Bbz13fYzEpS0S0vkutZVT/pxubm57N+/n6lTpxIVFYVSihdffJGIiAiKi4u56667WLp0KT179iQ3N5fi4mIAMjIyiImJYcqUKRQVFdG3b1+2bt1Kw4YNw7bv8/mYM2cOV199Nenp6bz44ots27aNevXq8d5777Fv3z4++ugjoqKiyMvLw2Aw8OKLLxIVFcXcuXOxWCwUFBSgaRpZWVk4nc7QtjMzM3G5XAQCAXbs2EGrVq147rnnMJlMlJaW8t5772E2m/nzzz+588476dGjBz6fj0cffZQXXniB9u3b43Q6QwHSqFGjuPHGG4mKimL69Om0atWqTOgjhBAXIqUU6lDAEVDgVwqvL4DbF8Dt8+P2BXB6/BS7ffrNpf9b5PJS6PJR6PRS5PJR4tZvxR4fpW4/N3esyZCWJ//dJIQQQoizSAXA64SCfXBwHdiioVY3ffmaGWC0QEpjiEkFSwRoBglNDpGQ5CQVuXxs2F/ImagnqZEYccqPvfTSS0lISEDTNIqKinjzzTdDlRXbtm2jQ4cOZR5jMBjo3r07MTExOBwOUlJSyM7OLrPen3/+ybZt22jatCkmk4nGjRszf/586tSpw9KlSxk+fDiJiYlomkbFihVxuVz8+OOPTJw4kejoaDRNw263H3faXbvdTvfu3UPVMpmZmTz77LPs27cPpRQ7duwgMzOTgwcPkpCQQKdOnTCbzaHuPQ0aNCAhIYEff/yRxo0bs3r1ah544AHpaiOEOO+oQ0GHxx+gyOXF6fHj9PopcftDIUZRMOBwHg46SoIByKF1XF49IPEcCkt8fhX6/lJKPxYyaBoWkwGryYDFaMBqNuKwGImwmkiOshFtuzAOFYLdQw0Gg3wvCCGEuLAcWSlSmgub/we7V8KBNZC/G7ylUPdKSOsMPjesngIHfgeTFaIqQYUGUKU1XHw9WKP0IAXtXxuaXBhHPv+gVjXj+eKu9mdk2+ZTLGcOhhDBsTrmzZvHunXreO2114iJieGxxx7D5/OV/5xmc2gbBoOBQCAQdr9Sivnz55Ofn8+dd96Jpmnk5OSwadMmbrvtNpRSZQ429auZCoOh7P4E2xhcz+v1hu4zGo2hbjGBQICnn36ahg0b8tRTTxEIBLjqqqvw+/2h7ZS3L4MHD2bmzJns3LmTiy66iNTU1BN9GYUQ4h8V7L7p8ytK3D7ySj3sL3CxJ6eUP7OL2ZVTyt48JznFbko8fnz+AAEFChU6FjJoGmajHnKYjXrQYT8i4IiwGomymYmymYg+9G+UzUyk1USkzUSk1USE1YjNZMRqNmA1GbGaDBgNGgZNO/TvWX2Z/jalFNnZ2UyaNImtW7fStGlTbr311lCIH7Rv3z4mTJiAx+MB9O+hypUrM3bsWPLz83nuuedC9zkcDu6//34SEhLOyj4JIYT4l1MKlB+c+ZC7Hfb/BtU76mFHcQYsfASMJkiopQcfVVpBxSagGcFkhwHvQcZG2PcL7PsV9v4CGRugydX6tjf/D3avgMotoEJDiK4EZse/ptpEQpKTZDYaMNvP7b7Zubm5pKSkkJqaSkZGBitXriQtLe2UtlVaWsr8+fN56623QtUopaWlXHXVVfz222906tSJ2bNn07FjRyIjIykoKCAuLo5WrVoxc+ZM6tWrFxocNiEhgZSUFNauXUvXrl3ZsGEDf/zxR7nPGwgEyMvLo1atWiQnJ7N06VL27t0LQP369cnOzmbFihW0adMGl8uFpmlERkbStWtXXn31VdasWcOrr75ablAjhBD/lGAo7A8onF4/hU4fBwtd7M0rZUd2CTuzS9idW0pmkZu8Ug8eXwClwGExEuuwUDHGRoNK0SRFWYk+IuCICgUcJmxmIzazAZtZDzhMBgMGAxg1DYNBo7xDmX9TJYXf7+fhhx/GZrNx7733MnHiRF566SXGjx8f9jrExMTQu3dvAoEASinefPNNvF4vJpOJgoIClixZwjPPPIPdbsdkMuFwOM7iXgkhhPhXObJSJHMTbPoc9v6s/78kU7//sqf1kCSuBgyZBbFVICJJ71YTfGzw37jqEFsN6naHgA9cBXoFii1WX2ffr/Drh7DqbTDZ9C45FRpB27ugUlO90iQQAIPxggxNJCQ5T0VFRYXG9oiOjiYi4nBXnR49evDZZ5/Rv39/bDYb9erVIzIyEiDUtQYgLi4u1FUFID4+vsz4HX/88Qc2m422bdsSFRUFQGRkJL169eLbb7/l7rvvZtu2bQwYMICoqChSUlKYNGkS48aNY8yYMVx11VXYbDYaN27MU089xYgRI7j77rtZvnw5SUlJ1K1bF4vFgqZpJCYmYjLpH0mTycSwYcN44YUXmDp1KomJiTRo0ACz2UxqaiqPPPIIjz76KDExMfj9fh566CHatWtHfHw8nTp1YsmSJbRs2fJfdSIghDg7gkFIQIHHF6DY7SOryM3+fCc7c0rYkV3CrpxSDhQ4ySn2UOr14w8orCYDUTYTFaJtNK4cQ43ECKonRlAt3kGFaBvxERbsFiOmv5RyyN+1k3PgwAFWrlzJ/PnzqVatGvfddx8jRozg3nvvJS4uLrReZGQk6enpABQUFLB7925uvfXW0OvtcDho3bo10dHRR50JTgghhPjbgoGIz6VXhWRshKxN0GIE2OP0cOSHlyEyRa8OqdIKUltAcgP9cWYbVGtz/OcJfo8ZzRCRqN+Cz99pLDS9BjI3wr7Vemiy5yd9jBOAzM2w6DG9yiS1BSTXh6iKYLZzIXTTkZDkPHXLLbeEusaMGzcuNMWvpmmkpaUxb948MjMzQ1PvBg/mHn744VAQ8dJLL4VCEaPRyKRJk8JCE9DH+Zg7d24oIAk+xz333IPX6yUyMpJJkyaRkZGBx+MhOTkZs9lMpUqV+OCDDzh48CBKKSpUqICmaVx88cV8/vnn5Ofnk5KSgs/nC12Vmzp1aijs0TSNfv360a5dO5xOJxUrVsTj8eBwOEL3denShezsbKKiokhMTEQphd/vp7CwkCFDhoQFR0II8Xcd2T2m1OMjt8TDwUIXu3IOVYXklLAvz0lWkZsilw+PP4DJoOGwGEmMtJIa56BdrUQ9DEmIoGKsjaRIKxFWExaT4bgznIlTs3//fiwWC8nJyWiaRuXKlfH5fGRlZYWFJEFKKVatWoWmaWFhe1ZWFtdffz1Go5GrrrqKa6+9NuzCglKK3NxcSkpKAELff0IIcUxKgd8NxVn64Jm2aL1LBJz3J5riBIV9VyjYuki/7f8Vcv8EdxFYoyGtqx6S1L0SUi6CuGr68tPdBUbT9K41ibX17jr1e0PAq1ebWKP1dUqzoWCP3iVnxWtgjtArV5peC61v0/fJU6JXoRjOv8+zhCTnoeAYJEF/DQM0TSM6Opro6Ogyjz1y3WB1SfAxRwYhQVartdzpc202WyhQCVZ3lPfYatWqlVkeHx9PfHx8meV/ba/BYCAlJSXsOY9sb2xsLLGxsaFlu3fv5rnnnmPt2rU89NBDcqIhxL/IsU5GFRAIHJ7xRf+//q8voPD4A3h8Abz+AB6f/rPXF8Dl8+Py+nF5A5R6/GQUukLdYw4Wuihw6gOqooHdbCTWYSYl2kaH2olUT4ygRkIEqfF2KkTZiLKbsZuNofE95O/TP8fpdGIymUIXE4JVIG63u9z1A4EAs2fPpmfPnqHvxZSUFGbNmkVKSgrbt2/nnnvuISYmhn79+oW9lx9++CELFiwAID8/nxo1apzhvRNCnNcK9sHaj2DrQsjaog+YmVQHLroaGg04FKB49Cv9F8DVeXGIUnp3FXcR5O/SxxPxOqHlzXrgsX4u/LFA7xLTsN/h8UTia+qPP7Lq40wLVZtY9K47QdXbw03f6J/hjPX62Cb71xC64uMpho+u0wO/ys31W3J9PUjRzv3hECQkEReM6Oho+vXrx/3330+lSpXOdnOEEH9TaDwPpSh1+8kscpFX6j0UXOjhRfD/zrD/H/rXc3gdty+Ax394ppfgbC9efwB/MEAJqMO3QwNQgz44qqaByaB3j0mOstKoUgzVEh3USIigaoKDijF24hxmIqwmjIfGAZEg5NwQFRWFx+PB6/VitVpxu90EAoGjVhsePHiQVatWMWrUqLBtNG/eHIBKlSpx1VVXsWjRIvr16xf22DvuuIPbbrsNgFWrVjF58uQztFdCiPNKMMj3FEPWHxBfHRwJ+oCby17WB8WsdyWU5uhhScG+w+t/eitYHFCpOVS8SD9Rtsfrwcn5+j3z1wsb5+t+nIjgvganlPOW6mN97P4RDq6HogPg90JKI2h2LViiIP1BSH8AolL0SoxzMSDTDHqol1wPkupCo/56oBfk90JEst5VZ/cK/b6UxjD0C7BGHn275wgJScQFIzY2lksvvfRsN0MIcQqCgYTXryhyeTlQ4GJbZjGbDhSy+WARO3NKyC524/IG9NIQjUNBxKHZuQjO0gVmgz7Ti9mohf1rMRlwWEzERxiwmYxhA57azEbsh362m43YLMGf9cFQrSa9UiQx0kqUzYTZaMCgSRByPqhSpQqBQIAdO3bQqFEjNm/eTFRUFMnJyfh8Ptxud6grp1KKRYsWUa1aNerWrRt6fwOBQNj/MzMzy1RfapoWmjEOCOvqKoT4FwqeHLsK4OA62LoAti/Vu090fwGaXafPHHLDPL1bgzVKf4y7CAyHTtG8Tv2292fYME9fZo+Hqq2h7xt6lwhnnn613hp5+Ar9ufC3569BiLtID4GKM6FgN+TthJIc6HifXhVRkg15u/TAyB6rT01LqPzyH2783xDsPlWaC7k7IGOdXmFRoxM0GayHB6vfB3chJNWDxgOhyiV6lYUlUt/X+POsCjH4/piO6H1gj4N+b+nve8EePRDyuQ6NWXLuk5BECCHEP+bIbjFuX4D8Ug9785z8kVHExgOF/JFRzJ7cUvJKPPgCCpvZSHKUlVpJkfRoXJGq8Q4cFiPWI8ILq8kQmr7WYjJgMmiYDBpGo4bJoP9sPHTT4FDAooVCliA5ob0wJSYmMmjQIO6//3569uzJ7NmzufHGG4mIiGDFihU8//zzfPTRR9hsNrxeL3PmzGHQoEFhgcfXX3/NihUrqFmzJps2beKHH35gypQp8pkRQoRTilCSD7DlK/jmMcjbrVd+JNeDdqP0kAP0CpHUFocfr6EHBEERSfosJc5cPVw5sFYfp8LsODRjiYIlT8Gf30JiXajcDCo207vsRFc+1E3nH6ACeqWAp0QPB4oOQP5uvf21L9Pv++QWvaLAo4/bhNGkVxq0GKGHJFsXwhej9TEvoivpY2FUaABpXaBi00PP4z93xmsJHs/4PeAqBHuM/p7sXqEPaJq3E1z56H1y4/TZYUAfc+bqD/VZZOyxeiB2tvflTNA0/b2yx+q3lMZlg7NzmIQkQgghzhil9K4sLq+fnGI3u3JL2XJQD0S2ZRazP99JodOHQhFpNZESbaN5tTjqV4ymXkoUNRIjSIqyYrcYMWqanJSKk6ZpGvfee2+oiuThhx8mPT0dTdOoWbMmt956a2hA80AgwMiRI+nQoUPYZ61JkyZkZmZy8OBB6tSpw6233irjjQghdMHxJUqy9IqPP7+F1rfr3WIikiA6FZpeD7U6Q0JtvfvEiX6XaRqYLHq3i6gUqNoGUBDw6yfXKgAVGkPBXn32kx3fgt+nn5Rf8xFUvvhQhcYOfbpXeywYTqGbjlJ6QOH36oGAMweKDurhhSNeD2/+N0Zf5irQKwY09AFGa1+qhzVVWkJCTYirCbFVIaay3uUoOLZGagvo+pg+k0v2H3p3lM1f6NUxFZvqFQlfjtUrZpIb6F08Yqvq2zA7/pmgQSm9cidvJ2RsgANr9PE4CvfDgCn6PpgdeptrX65PlVuhkT62iCNB34Zm0CuH/o3Oo2M4CUmEEEKcFkrp43mUePxkFbn4M6uEzQeL2HSgkO1ZxWQUuin1+PSBl+1mKsfa6Vw3mQaVoqlbIYqqCQ7iIyzYTMZQNxoh/i5N07DZbPTu3ZvevXuH3ZeSkkL37t1DPwfX+6vKlSszbNiwM91Uca4InvQGZ4zwOvXSeJMNjFZ9pgbNcG51bTjXBCsqgq+lCkDAp59Agh4oFOzVX1dXof6vM18fDLJiE/3q/Jav9JP66Mr6Sb816tBV99M8k8epUkrvOrLje/jjK9i9Uv/ZFq1XP8TX1EOKa2efWjBRHk0DNDAGP3tGaDEcmt+gv44Fe/ST9qw/9AEyAbYvgfl36d10EtL017fyxZB6CcRU0bcZvMIf8IHPrb8ffo8erAD88p5e6VGwV+8y4yrQ39MhM/V9Ndv197ZKK328lbga+vPH1SBUVdP+Ho45tkZiHT1EgkPVGQV6RUpwsFBviT6GS8528Hygb8sSqe/LoKn682f/oQc0URXBFnPqA94qpb8W7iJ9n135UK2d/tlb+n/w24d6UGWN1CtEqrY53I0kpTEMna9XlZwHA5SK8klIch4pKSmhsLCQlJSUsJOHwsJCXC4XSUlJ5Z5ULF68mJKSEnr16lXu/V6vlwMHDpCcnFxmCmAhhCiPUvrMMIVOffyQ7VnFbDxQyJaDRezMLiGr2I3bG8BsNBAfYaFqvIM2aYk0qBhF7QpRVI61E2s3YzEdPoCQUESIkxQqXQ4O1KMdvuKr1OH/B/z6v5ZI/QTfU6qX7/u9+slI8OZI1K/MKj/s/EE/GfK69JMOn1s/12l8NUQk6NtwF+lTlpoOBQfn4uCC5flryXdJln5yW7hPHxMh708ozYPLn9ZP0LYuhK8eALNNn+bSFqOX1tfrAU2u0a/c//GV/jrbYvTuArZo/bWJTD58xV8FgCNO8M/11yo04GQAUIe7OXhL9RNyn1P/DARvwelC3UWwfOKhk8tCcBcc6o4QB4On65/DX6fC9y8cDlFA/wxd+pR+El+aA1+N1a/aG616WBKRDJWa6e+Lya5v3+fUT/7/iQBFKX0a1JIsvS0GE6ybA988oe9b6iVQ9wo96ImpcnifMJ6Z9gRpGmgmvaLDEa9PDXukyhdDl0dh36+QuVF/7Ve9pQ8O2mGMHgL+/C5kb9VnWinar3eZiU+DGz7Tf7/zdur3R1fUQ4C4Gnp1RHID/TkSa8N1c/UuNEcdR+QE3pcjx7aITNZvQZEV9PaUHJp6NmuLvj8m2+GuR8snwvpPwBGnV/Ak1tG77DToq1euBP8mHtllJ/g51zT97922RbD3F30cmZxtUJKpByE3fqP/ftfqqm8rpbH+eY9I1v82BPfPYDw85a04b0lIch7ZunUrt99+O/PnzycxUS9NCwQCPPPMMzgcDh577LFyH/f777+Tm5tLr169ytynlOK7777j2muv5fnnn2fo0KFyoiLEv1hwzBClIKD06XD1KXB95Jd6yTs0hsimQ4HInrxScks8eP0Ku9lIUpSVGokRXNm4IvUqRlMrKYKUGDtRNhOmQ/Pfyt8YIf6GjI2waf7h4CL4b/UOhwcF/GI05GzVlwe8+km8yapf9Y1J1QeQ/GK0vq4KHL7q32IEXP5/+vKFj+rl5KCfPBiMYIvTy+cd8fpV/oUP6f3qI5L0q/0xlaFmZ/0kEaUPymg06+MuaMazU3mhAvoYCO6iI4KQnfqYBw36HhorYSTsWaHvt3bo6nRkil7ZEJGk/z+tix4sBSsfCvfpJ+ygvwdLn9Gv3muGwydJkRVg6OeHXvNF8NM7+kmWLVZ/De1xeheEyi30dubu0E8yzXY9GDBZ9AqE4JgFfo9+EhcMXII3i0Nvc8Cvt8vn0t/zwKGbpkGFhvqJZN4uyNp8KCDzQsBzaGaNxno44XXCb9P0kMJTol/NdxXoj+3xkh4ArZkB3z6nP08wgFMB6PyIXi0Q8MOWL/X3P9g2a5TeXSR4Qlqzkx4i2WIO36xR+okt6K/PgPf1cTjyduivTcEevbIg+Dn68T96W4IBSrByockQvSuG8uvvlcVxqJLjFAKU4BS8eTvhz+/gj6/1aoUhM/VuFPWu1KsWqrbU/9WMZz/8+uvzx6dBmzsApYebRQf0z0BiXX3dooOw8nX9c+BIgKhKemVESpPD2+o0DjqN1QMJg7nsc2kG/fN6RvfLoH9mLBEQV02v7IDDVV8oaHqtvr+ZG/WAY+tC2DgPKl2s/33K2ABLn9bXSW6gV4IcXK9/9uv10D/T3zwBRRn657VCo8OVN8EuPXWu0G9H7r+44EhIch6pW7cuNpuNH374gT59+qBpGnl5eSxatIi33nqLkpIS9u/fTyAQoEqVKqHR+o9FKcXs2bPp1KkTc+fOZciQIVit+sjEgUCArKwssrKyiIuLIyUlBaPRiN/v5+DBg+Tl5ZGUlERSUhJutxu3201MTAyaplFaWorX6yU6OpqSkhKUUjidTvLz86lZsyalpaUcOHAATdNITU3FbreHZhZwOp3s3bsXpRSpqamYTCZKSkqIi4sLrZOXl4fD4ZDKFyFOwJGDpSoFvoDC4/Pj8gUocvkoKPWQV+olq9hNVtHhW3axm3ynlyKnl2K3D48/gM+vbyvSaiIlxkbTKnHUrxhF/ZRoaiRGkBxtxWExYQhdSJIDCCFOq+w/4MdJ+smYwXDoX2P4FVef81DXBhsYo/UTXFvs4auncTWg6TWHTsRteoBisuknyqCvf8Whk2CTVV/PaNaXR1fW14muBLW66VOVlmTpoYynRF+nentwF8OMQXqZuiNBXz+6sn7ltclg/UTHU6wHOcFBKE82RAmGOwG/fnLvKoDiDP0ksGprPeDYtxrmj4Lig/oJot+jBxH1ekL93noAUbc7VG+nXxmPraKHG/Y4/eQe9CCjSsvDzxUMnoyHDqPNdujzhn7F2ZmnhyvO3EPl+IdmQXIX6a9V5mb9/fEfCifajdJDEk8xzL5BDwLMNv31MUfqV+77va2HBuvmwA+vHGqD7/C/be+GtnfqAc6Mq/UBM4PVH0rpn42bvtH3a8uXsOBh/b4gzaCfBFdsor8fP0/WqzQMRv19MTv0987v09dPqAUNeh8eeNIWq4ccwc+PLRqunat/ZoJVRgbToRlbDlUbVW6h3472Xpts+ntSrW3wzT4U6vj0zyNAne56aJP7p/667fsVdiyDGh31AVIzt+ivqTUKYqoe6gZSE1IaQuXmeluU/9Dn7ijt2PAJ/PyeXlXgLdU/xzU66pUsmnaooqLGuX2yHGqbpocC1kMVP0HRleD6efrrZI3WP3+aMfzxwc/xuSQU0ATbqumfl2ptAaV/lp35egiUVEdfpTQHsrfBzh/17jtK6Z+15jfogZc1Cvq8rv/+R1XUfw//+nfpXH6vxWkjIcmp+Lsj8x5Z2lXefUdhs9m46qqrmD17Nj179sRoNLJs2TJiY2OpWbMmY8aMIS8vD5fLBcDrr79OlSpVjtmUAwcO8Ntvv/Hf//6X2267jS1bttC4cWMCgQCTJ09m2rRppKSk4HK5eOqpp2jQoAHPP/8833zzDRUqVMDr9TJx4kR+++03Fi1axH/+8x80TWPevHmsWbOG559/njfffJPly5djsVhISEjgoYce4tFHH6W0tJTS0lLsdjuvv/46SUlJbN++ndGjR6OUwmKx0KRJEwYNGsTo0aOZNWsWsbGxFBYWcs011zBhwgQaNmx4ym+DEOe7I8OPgAKvP4DHF8Dp9VPg9FJQ6iWnxENWkSssAMkt8VDg9FLo9OH0+vH6A3j9AQyahsWkT5UbaTURYzeTGGGhXkoUSVFWkiKtJEVZqRhjo0r8ofFDzMbQoaUEIkL8A9K6wM1L9Ku5xiNupkP94Y1mfWpQODxuRqhf/KHf0YpN9NuRwqZ6MuonqEejlB5CVG2tn4z73PoJpDNPP0EOPnfNTnpJfHGGPgXmn9/pgzQ2ugqUA1Z/ACve0AOAyAp6xUVMZX3Aw5TG+rad+YeCGpNeReHM00+WEw+d5K18EzZ/qYcgpTl6O5TSZwSp1VU/gY+tAlVb6SFIXHX9eaIrH35dWt5c9jUo97U5NBaE0QxHThqiGfUrzUe+Pn/VsK/eFcPv1ffDU6zfIg6FW0aLPrZEwR59H0vz9IApeIUc9H2JrxX+vhvM+jgToJ/sNb1WD2SMlsPrWKPBcugkt/Zlh2c9CT7eaD48foU1Cq6epocHRusR3anMh8dcqJmu38q8PqEFerhzLCczcGlwmyYrcMQUo2md9VswQAl+BiMr6PcbLXoFQf5OfYDNP5fqYVrty+Dqqfo2Fz2h3x9XQ592NaqiflJ90dX6/hYd1MOviwbq1QOVLta7cpyPU9Me6ch2m6x65c2F4MjPi9mu3478LFZvr//9LMnSuxa5i/Xfn9gq+mMMhsMzD4l/NQlJTsXOZfogTX9V+1JIban/kf7pv/qX35FssXDJjfov7M4fYMd3h++LrgQXDw1Pbv9C0zSuvPJK3nnnHfbs2UOVKlWYM2cO/fr1Iy4ujueeew5N03C5XLzwwgt89NFH3HfffUfdnlKKRYsWUbt2bRo2bEjHjh359NNPady4MZs3b+bNN99k2rRp1K9fH5fLFQplvvzyS2bPnk2lSpUoLS3FarXi8XgoLj68v263m5ISfYovp9NJcXExc+bMITo6GqUUr7zyCpqm4XQ6eeSRR/jiiy8YOnQoTz/9NC1btmTs2LGhChK73U5ERATLli2jZ8+eLF++HJPJRK1atY62a0JccIKBiNPjZ3deKVszisksch2u/Ch2k1+qByNFbh9urx+PP4A/oDAZDJiNGjazkSibHn5UiXeEQo/gLSHCSqzDTIzdrE+ze2hKXU1DghAhzhW26MNBxNGYjlNl+Xd/j/8aqFgc+i04SwXoJ9ddH0c/gfUcqvQo1I+RrIfaH1tNDy8K9+snqvt/0++PTNFDkpJMmNoHfYDGCD0Ecebp3QSGfa6fxJdk62FCfJoeIAXHSgiGQAm1YPCMf7arT3nPYTAdrkyxl/MYsx0uuemIBUcMeBrs3lD3Sr3qJfzJDv/XZDvUreIY7UhIOxyqlMdgPH9m3vhrgGKy6hUAQQm14Kq3Dwcopbl66GE6VCkR8OmfqQNr9QDP59KX2WL1yqEKDfVj86bXHvqdO0/G3BFHZzAe/ht6rN8D8a8nIcmp2PuLfuXiryIrHA5JVr+vp5RHiqkCza7Tvwj/uo2KTaHZ9RxvcKeqVavSoEEDvv76a7p37866det46qmn8Hg8vPLKKyxfvhyj0cj+/fvp2LHjMbfl9XqZM2cOPXr0ICsrizZt2vDss88yevRofv/9d+rWrUv9+vUxGo1EREQA8NNPP9G+fXtSU1PRNI2oqBMrv2vfvj2xsbFomkZRURH/93//x6+//orRaGTXrl1UqlSJkpISNmzYwJgxY0LdaGJiYlBKMXjwYGbNmsVll13GrFmzGDhwIBbLGe77KMRZFAxFvH5FRqGL9fsK+HF7Dqt35bErpwS3L4DRoGE2GoiwGomy6eFGvYpRJB4ZfkRaiY+wEOuwEG0zYTMbsZoNmAwSfgghzqCwE1ibfjvyBFYpfQyAelce7i7jKdFPWqNSDg1GaYRq7fXuFO5C/aQ1troeoARDj45j9ZvJWn4QEtzO+SDs77BGmZkxgjObnNDjRej1OjJAOfLE2GCC3hP1z50zDwoP6F21Yqse7o5ijTwrTRdCnF0SkpyK5sOgQZ+yy4PzX9vj4IZ5+pf+kQymw336mg/V+3MGmayH+moem9FoZNCgQbz11lt4PB4aNmxI1apV+f777/nhhx+YMmUK8fHxvPPOO2zduvWY29qyZQvr1q3D5XLx+eef4/f72bdvH6tWrcJkMuH1ess85mjLDQYDgYA+OrlSCrfbHXa/1WoNnYQtWrSIzZs3M3XqVKKjo3nuuefw+/1omobBYMDn86GUCq2vaRpdunRh0qRJLF26lC1btvDCCy/ISZ24oARDkYCC/FIPWzKKWPVnLqt25PBHRjGFTi8Oi5G05Eiua12N5tXiSI62EecwE2ExYbMYsZoMGDRNxgMRQpz7yuvCYosOL42PSNIHCw1WVAQHfz3yb5vF8Y82W1xANE3vkmO36Mfu8TXPdouEEOcICUlORXCKraPRTHqp59/ZxtE2rWl07NiRJ598kkmTJvHiiy9iNBpxu92YTCYiIyPJz8/nf//7H7VrH71cUinFvHnz6NWrFxMmTAidTE2aNIk5c+Ywbtw4nn32WZYtW0bLli0pLCzE4XDQsWNH7rjjDtauXUvt2rXJy8sjPj6eSpUqsW3bNg4ePIjRaOTLL7886ngoLpcLi8VCZGQkmZmZLFq0iG7duuFwOGjdujVTpkzhiSeewGw2U1BQQOXKlYmPj6dLly6MGTOGyy67jOTk5HK3LcT5JqAUpW4/O3NKWL0rjx+357B+XwGZRS5MBgOV4+x0q59Mm7REmlaJoWKMHavJIAGIEOLCF6wE+GtFhRBCCHEGnbMhiVIqdDMYjn5CoJQKVTAca70LSUJCAn379uX777+nY8eOaJpGmzZtmDlzJgMHDiQ2NpZmzZoRGamXCMbFxWEwhB9guN1uNm7cyMiRI8NmiOnZsyfjxo0jLi6OJ554gqeffhqz2YzJZOLZZ5+lefPm3H777YwePRqr1UpERAQTJ06kadOmtGjRgmuuuYakpCTq1KlDdHR0qL0m0+GPWrdu3Zg3bx79+/cnLi6O1q1bk5SUhMFg4MEHH+SRRx4Jdafp1KkT48aNw2AwcNVVV/HWW29x9dVX/yveZ3FhUkrh8QU4UOhi7Z58ftyew6+789iT68QXCJAYaaVR5RhurFmDFtXjqJEYQYTVhIZUhgghhBBCCHGmaUr93alaTj+lFKtWrWLy5Mm43W4GDBgQms3lSF6vl7lz5/Lll1/i9/vp1KkT119/PXb74RGx8vPzee6552jSpAmDBw8+Ztgybtw42rVrR9++fUPL8/LyKCkpoXLlyufMCYpSCp/Ph9/vD3VjUUrh9XrJzc0lMjISq9UamiHG6/WilMJsNof2QSmFy+XCarWGBShHLg9O5VtUVERERASRkZGh5yoqKqK0tJTo6OjQ6+3z+cjLy8Nut2Oz2ULP6fPpU8aZzebQc3g8HvLy8oiKisJisYTWBfD7/eTl5aFpGjExMZhMJpRSLFiwgIkTJzJv3rxTnvrX7Xazd+9eqlevXubzJMSZoJTCH1DklnrYfKCIlX/m8NOOXLZmFlPi9hFlM1E7OZKWNRNoXSOBuilRxEWYMWraOfM3R4jTKTc3l0GDBvHJJ5+EwvQL0cqVK3nzzTeZMmVKmQsVQgghhPj7SktL6du3Lx988AEVKx5nVq2TcE5Wkuzdu5e77rqLe++9l8TERB588EGSkpJo27Zt2Hrff/89zz77LBMmTMBms3HffffhcDi47rrrAAgEArz99tt8+umnlJSUMHjw4LOxO6edpmmYzeZQqBBcZrFYSElJKbP+kesduf6RYdLRlkdERIQGbT1ynejo6DIHt2azudxuMH99fk3TsFqt5bYV9HFPkpKSQj/7fD6eeeYZvvzySx5++GGsVmu5jxPiXKCUQgHFLh9/Zpfwy85cVmzPYcP+QnJK3JiNBqrGO+h5UUXapCVwUWosKdFWzEb9JEqCESGEEEIIIc6ecy4kCU5LW6NGDQYOHIjRaGTFihXMnj2bNm3ahJ1A7Nixgzp16tC5c2cMBgOtWrVi+/btoe2sXr2a33//nX79+oWmoxXnH4PBwMCBA7nmmmuoWbOmnESKc0qwGM/lC7A/z8lve/RxRdbszmd/gZNAACpEW7m4Wixt0xJpXi2OqvEOHBa9kkk+z0IIIYQQQpw7zrmQBGDjxo3Uq1cv1B2iQYMGvPfee/h8vrCqhC5dujBr1qxQJcnmzZt56aWXACguLubll1/mnnvuYdGiReWGJH6/nwMHDuD3+1FKUVhY+M/soDgpBoOBBg0anO1miH+5YBiigEBA4fYFKHR62XigkBXbc/h5Zy5/Zpfg9PiJsZupmxJF32aVaVUjntoVooixmzFoEooIIYQQQghxLjsnQxKn00lCgj6dbrD7h8vl4sjhU5RSWK1WEhIS2Lp1KxaLBe1QH/5AIMD7779P/fr1adGiBYsWLSr3eYqKinjooYfIzs4G9Clxu3fvHrZOcAwOcWGQ91L81ZGfiYACjy+A2+en2O0jv9RLXqmHrCI3GYVuMotcZBa6ySp2k1/qIafYQ4HTi81spHqCg/4Xp9ImLYFGlWNIjrJiMhyexloIIYQQQghx7jsnQ5LY2FgKCgoA/QSmoKCA6OjoMgOf/fe//6VixYqhKWxfeuklXn75ZZ5//nneffddLrvsMl566SWWLVtGcXEx8+bNo0+fPqHtxMTEMHny5NBJ0oMPPlimLRaLBY/HQ2lpqYyFcZ4LDjhrNptlEL1/gb+GH15/AK8/QLHbR6HTS36pl8wit34rdJFZ5CaryE1eqYe8Ug+lbj8unx+vT6FpYDJqWE1Gomwmom1mkqNsNK0SS/NqcTSrGkdqnB27WbrQCCGEEEIIcT4750ISTdNo3rw5b7/9Nm63G4vFwqpVq2jWrBlGoxGn04nBYMBisZCXl0dERETohDciIoLi4mLsdjsPPfQQpaWlAERGRhIIBIiNjQ07eQkOdgr6CVV5s53YbDZiYmI4ePDgP7D34kwKvscVKlQ4200Rp0EwBPH6FV5/gBKPjyKnj3ynl+xi96HqD73yI7PIRW6Jh7xSL8VuHy6vH7cvgAYYDRpmo4FIm4lom4k4h4UaiREkRVmpEG2jQpSVxCgr8Q4LsQ4LDosRm8WIxWhA05CpeYUQQgghhLiAnHMhCUDnzp155513eOihh4iPj2flypVMmzYNTdMYP348lStX5s4776RPnz6MGjWK6OhorFYrH3zwAQ899BAREREMGjQotL29e/eSkZFBenr6SZ/MGAwGEhISiI2NJRAInO5dFf8wo9GIwWCQk9rzTDAQ8StFscvHvnwnmw4UsXZvPlsOFpFV5KbI7cPp0as/UGDQNExGjQiriSiriViHmfoVo0iMPBR+RFtJirQSH2klzmEmwmrCYTZiMRkwaBrBj4h8VoQQQgghhPj3OCdDktjYWKZMmcIXX3yB0+nkww8/pFatWgD07ds3NCVteno6U6dO5YcffsDn8/Hmm29yySWXlDmp6dOnDy6X65Tbo2kaJtM5+VIJccEKKIXL6yeryM22zGLW7Stg7d4CtmUWk1XkxusPEGE1USnWRrUEB0lRVpKjDoUfUTYSIi3ER1iItJqIsBixmIwYDRoGCT+EEEIIIYQQR3FOnvlrmkbFihW5+eaby9zXunXrsPWaNWtGs2bNjrm9xo0bn/Y2CiFOH6UUXr8i3+lhV04pG/cX8vuefDYdLGR/vosStw+z0UBytJU6FSLp3zyVxpVjqJUcSVKkFbNJw6jJIKlCCCGEEEKIv+ecDEmEEBcupRQBBSVuHwcKXGw5WMjvewvYsL+AHdkl5JV6UUoR67BQLcFB32aVaZIaQ72K0VSOtRNpNclUukIIIYQQQogzQkISIcQZo5RCoU+rm1PsZntWyaFuM/lszSjmYKELty+Aw2KkUoyNVjUSaJIaQ6PKMVRPjCDOYcFslAoRIYQQQgghxD9DQhIhxGmjlMIfUBS6vOzJdbLxgN5tZuOBQvbkllLk8mEyaCREWklLiqB74xQuqhxL7QqRVIi2YbcYZbYYIYQQQgghxFkjIYkQ4piCM8sAKEAp8PkD+tS7gQAur58ip48/s0tYuzefdfsK+DOrmJxiD76AItpmokq8gysapXBRaiwNKkZTJd5BtM2E0SBVIkIIIYQQQohzh4QkQvwLHBl0BAWCYUdA4fMHcHr9lLj9lHp8FLt9FJR6KXB6yXd6KSj1ku/06MtcXopdPko8fkrcPlxePx5fAJcvgM1koEK0jcaVY7koNYbGqTHUTIwkIdKC1WQAJBARQgghhBBCnLskJBHiPBcMQNw+varD6fFT6tHDjiK3j4JDIcdfA49Cpx6GFLt9lLh9uL0BPP4AXn8An1+BBgYNjAYNk8GA1WzAYTbisJiIsBpJjbMTYzcTYzcT57CQlhxJ3QpRVIy1EWGRwVWFEEIIIYQQ5x8JSYQ4Dx0ZjGzNKGbx5gy+3ZLJwUI3Hl8gdPMrhQYYNA2DAUwGAzazAbvFiMOshx1V4uxEHwo7Yh1mYu0WYh1mou1moqwmIm0moqwmbGYjFpMBi8mA2WjAoGloGgRjEAlEhBBCCCGEEOc7CUmEOI8opfD4A+zILmHp5iwWbcxg88FC/AFFWlIkLavHHwo6zMQ6LMQ4zMTYzETZTETZTEQcCjusJgNWkwGT0YCm6SGKhB1CCCGEEEKIfzsJSYQ4xyml8AUUu3NL+W5LFgs3HmT9vkI8/gDV4h1c26oalzWoQP1K0ThkdhghhBBCCCGEOGUSkghxDgpOpbs/38kP27JZsCGDNXvyKfX4SI1zcNXFlbm8YQqNK8cQZTNJKCKEEEIIIYQQp4GEJEKcI5RSBBRkFLpYsT2HrzccZPWuPAqdXlJibFzRKIXLG6bQrGossXYzIBUjQgghhBBCCHE6SUgixFkUDEZyit38tDOXr9cfZNWOXPJKPCRGWulYO5ErGqXQono88REW6UojhBBCCCGEEGeQhCRC/MOUUiggr8TDr7vz+Hr9QVZszyGzyE2sw8wl1eO5olEKrWokkBRllal0hRBCCCGEEOIfIiGJEP+A4JS9hU4va/YWsGD9QX7Yls3+fCdRNhPNqsZxT7cU2tZKICXGhlHTJBgRQgghhBBCiH+YhCRCnCHBYKTY7WP9vgIWbsjguz+y2JNXit1s5KLUWG7uUIMOtZOoHGfHZJBgRAghhBBCCCHOJglJhDiNgsGI0+Nn08FCFm3MYOnmLHbklGA2ajSsFMM1rarSqW4S1eIjMBslGBFCCCGEEEKIc4WEJEKcomAgAuDxBch3etmTW8oP27JZvCmTrZlFaGjUTYliVJfadK6XTM2kCKwmgwQjQgghhBBCCHEOkpBEiBMUnInG5fWTU+Jhd04JWzKK2HSgiG2ZxezLd1JQ6sWgQc3kSG7uUJNu9StQp0IkNrNRghEhhBBCCCGEOMdJSCJEOZRS+AOKErefzCIXf2aXsPlAIZsPFrE9q5jMQjclHh8aGjF2M5VibXSsnUj9itE0rRJLvYrRRFgkGBFCCCGEEEKI84mEJOJfLdhlxutXFDi9HChwsi2zmE0HCtlysIhdOaVkF7tx+wKYjQbiIyxUjXfQpmYC9StGU7tCFJVj7cQ6zFhNBkCm6xVCCCGEEEKI85WEJOJfQymFUuDy+ckr8bI7t5Q/MorYdKCQrRlF7M13kl/qxR9QOCxGkqKs1K4QSa+mlaiXEkVaUiQp0TYibSZMBj0IkUBECCGEEEIIIS4cEpKIC06wOiSgoMTtI6vYzc7sEjYfLGLzgUK2ZRVzsMBFsdsHQJTNTKUYG60PVYfUS4miWkIESZFW7BYjh/IQCUSEEEIIIYQQ4gInIYk4bwXDEF9A4fT4yXd6yShwsSevlK2ZxWw+UMjOnFKyitw4vX7MRo1Yh4UqcXaubFyR+hWjqVMhktQ4B3EOC1azAQ0JQ4QQQgghhBDi30pCEnFOCwYhfqVweQMUOr1kFrrYm+dkZ04pu3JK2J1bSkahi9xSD06Pn4ACu9lIYqSFagkRXNagAvUrRpOWFEmlWBtRNjNmo3SXEUIIIYQQQggRTkIScdYd2T3G7fNT5PKRVeRmX76TXTkl7MwpZXdOKQcLnOSUeCjx+PEHFBajgUiricQoC7WSI6maEEGNBAdVEyKoHGsnKcpKhNWIUZNARAghhBBCCCHE8UlIIv4RwSBEKfD4AxS7feQUu9mf72JXbik7s0vYlVvCgXwX2cVuit0+vH6FyaARYTWREGGhUpyd1mmJ1EhwUC0hgkqxNhIjrUTZTFhMMnaIEEIIIYQQQoi/R0IScVodOaVusdtHbomHAwVO9hwKQnbmlLI/30lWsZtCpw+vP4DBoOEwG4mLsFAxxkaTKrFUT4igeoKDynF6RUi0zYzVbJCqECGEEEIIIYQQZ4yEJOKUKaVQgNsbIK/Uw57cUv7IKNan1M0sYl++kwKnF7c3gKaBzWwkzmGhQrSVdmmJVE+MoFqCg9Q4BxWircTYzdjMRpleVwghhBBCCCHEWSEhiTghSikCSlHq8ZNd7GZHdilbDhax6UAh27OKOVDgosjlBfQpdVOibTSrEkeNRL0iJDXeQUq0jTiHBbvFiMmoyUwyQgghzjilFG63m4ULF7J582aaNWtGeno6ZrM5bL28vDy+/vpr/H5/aFlcXByXX345RqOR7OxsPv/8cwoKCrj00ktp2LChfIcJIYQQFyAJSUSYI6fVLXL5OFjg4s+sYjYdLGTzwSJ2ZJeQVahPqWs0aMQ5LKTG2bm8YYVDU+pGUSXOQXzE4Sl1QcIQIYQQZ4dSildeeYVly5bRs2dPnnnmGfbs2cPw4cPDvpu8Xi979uzB5/MBsGjRolBIUlJSwsiRI6lYsSLVqlVjxIgR/Pe//6VJkyZna7eEEEIIcYZISPIvFuwu4/EFKHB62ZtXytZD3WW2ZBSxJ9dJbokHjz+AzWwgIcJK9QQHXeslU79iNLWSI6kUaydaptQVQghxjsrJyWH27NlMnTqVRo0aUb9+fR5++GEGDRpEZGRkaL2kpCTGjh0LgMvlYvHixVx99dUYjUZWrlxJVlYWU6dOJSIigtLSUqZMmcIrr7wi33tCCCHEBUZCkn8JpRRKgdPrJ6fYw67ckkPdZYrYdmj8kEKXj0BAEWE1kRJto3FqDPVToqhXMZoaiRFUiLYRYTFilDFDhBBCnCd2796NpmnUqFEDTdOoX78+RUVFZGZmhoUkR36nrV+/nuzsbDp37gzAmjVraNiwIQ6HA4CWLVsyYcIEvF4vFosF0L9n/X5/qLuO1+sNVWcKIYQQ4vwhIckFSimF2xdgd24pO7JK9O4yB4r4M7uYjEI3JW4fBoNGtM1EapyD9LrJ1K8YRd0K0VRNcJAQYcFuNqLJtLpCCCHOY0VFRVgsltAYJFarFYPBQElJSbnrK6X45JNP6NSpE4mJiQDk5+cTHR2NpmlomkZ0dDRFRUVh45cAvPbaayxcuDD0mOrVq5+5HRNCCCHEGSEhyQUk2H0mo9DFkk2ZzFuzj00HinB6/ViMBuIjLFSNd9A2LZH6FaOolRxFapydGLsZi8kgA6kKIYS44Njtdnw+XyjQ8Pl8KKWwWq3lrp+Xl8fChQt59dVXQ9+JdrudwsLC0DoulwubzYbBYAh77PXXX0+/fv0A+O233/jkk0/OxC4JIYQQ4gySkOQCoJTC5Q2wbl8B89bsY8mmTLKL3VSJdzD4kio0qxpHzaQIUqJtRNpMMsWuEEKIf41KlSrh8XjIzMykWrVq7Nu3D6PRSFJSUlh3GE3TUEqxfPlyHA4HF198ceh7sn79+ixbtgyv14vZbGbTpk3UrFkzbIYcTdNISEggISEBgAMHDsj3rBBCCHEekpDkPBWqGilwsXizXjWyfl8BJoOBS6rH0e/i+rSvlUicwywHaUIIIf61KlasSKtWrXj55Ze5/vrrmThxIj179iQmJoZ169bx4Ycf8vTTT2O1WgkEAsyePZs+ffqExh8BaN++PS+99BJTp06lVq1azJgxgyeeeEK+X4UQQogL0DkbkgQCAfLz8/H7/cTHx2M0Gstdz+fzkZ+fTyAQIC4uDpPJhKZp+P1+SkpKKCkpwW63Ex0dXaYs9nx0rKqR4e1q0OuiitRKjsJs1OTgTQghxL+e0WjkmWeeYdKkSUyYMIGmTZty2223oWkaFouFuLi40Pely+WiVq1aDBw4MGwbSUlJvPHGG7z11lssXbqUe+65h65du8r3rBBCCHEBOidDEr/fz1tvvcWcOXPQNI1WrVrx2GOPhV3VASgoKODhhx9m48aNGAwGKlasyIsvvkhKSgpz585l8uTJmEwmSkpK6NWrF6NGjQorjT2fBAKKjMJDVSO/7WP9/gLMBgMtqsdx1cX1aSdVI0IIIUQZmqaRnJzMk08+id/vx2g0hr4r69Wrx0MPPRRaNyIigscff7zcbTRp0oQ33ngDpRQGg0G+b4UQQogL1DkZkqxbt453332X6dOnExcXx/XXX8///vc/BgwYEHZQ8vXXX7N27VrmzJmDxWJhxIgRzJgxg3vvvZf09HQ6d+5MZGQk27Zt4+qrr6Zbt240bdr07O3YSQqrGvltH0s2S9WIEEIIcSo0TcNkOvXDnuDMNkIIIYS4sJ1zIYlSiiVLltC8eXPq1q2Lpmn07t2bL7/8kv79+4cdoAQCAaKiooiOjsZoNBIfHx+6Pzk5WR+3QymSk5Ox2+0EAoGztVsnpbyqkeBYI0dWjYAMviqEEEIIIYQQQpwu51xIArBr1y6qVq0aumpTtWpVPv/8c/x+f9i4It27d+eLL75g4MCBoW4011xzTej+VatWMXnyZP744w+uuOIKGjVqFPY8LpeLhQsX4nQ6UUqxZcsW2rVr98/s5F8ca6yREe1q0POiStSuEInJIFeyhBBCCCGEEEKIM+GcDEkCgUBYGGIwGAgEAmFT9Sml+OOPP8jOzmb48OHYbDbeeOMNVqxYQZ8+fdA0jZo1azJ06FBWr17NJ598wu7du6lVq1ZoGz6fjz/++IOCggIAsrOz/7mdPLQPSsHBQheLN2Uwb81+Nhwaa+SSGvFc1awy7WolEitVI0IIIYQQQgghxBl3ToYkKSkpHDx4ENCDhIyMDJKSksrMcDNjxgzatm3L1VdfDeghx7Rp0+jVqxdGo5Hk5GSSk5Np27Yty5cvZ8mSJWEhSUREBGPGjAn97HK5/oG9O1w1snZffmiskZxij1SNCCGEEEIIIYQQZ9E5F5JomkaHDh0YN24cWVlZREZGsmDBAi699FIMBgPbtm3DarWSmppKZGQku3fvxuPxYDAY2LFjBzExMQDk5uYSFRWFyWQiNzeXAwcOkJSUVOa5go6sUjkTyq0a2VeA2ahXjfQ7VDUiM9QIIYQQQgghhBBnxzkXkgC0atWKdu3acc0112C323E4HKGZbV577TVSU1MZM2YMw4YNY+TIkQwaNAiTycTBgweZNGkSAM8//zxbt24lKiqK3bt306RJE7p16/aP7kcweDmyamTp5qzDY420r0HPJpWonRSJSWaoEUIIIYQQQgghzqpzMiSxWCw8++yzbNu2Da/XS61atXA4HAA88sgjoSn80tLS+PTTT9mxYweBQIDq1asTGxsLwMMPP8zevXspLi4mMTGRKlWq/K2p/07Vqh25vLRgC+uPGGtEqkaEEEIIIYQQQohzzzkZkmiahsVioUGDBmXuS0xMDPs5OjqaJk2alFkvOjq63Mf/0zQNit0+fayRJpWolRSJWapGhBBCCCGEEEKIc845GZJcKDRNo3nVOGaPbEOUzSTBiBBCCCGEEEIIcQ6TkOQMMxkNRNsNx19RCCGEEEIIIYQQZ5WcvQshhBBCCCGEEEIgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBCAhiRBCCCGEEEIIIQQgIYkQQgghhBBCCCEEICGJEEIIIYQQQgghBACms92Ao1FKhf2sadpJr3fkfUd7vBBCCCGEEEIIIQScoyGJUoqdO3cye/ZsnE4nffr0oWnTpmWCjkAgwM8//8zXX3+N3++nU6dOdOrUCaPRyKZNm1i8eDH79u2jRo0aXHXVVSQmJkpYIoQQQgghhBBCiHKdk91tcnJyGDFiBB6Ph+TkZG655RY2btxYZr3Vq1dz0003kZycTM2aNRkzZgwLFiwgEAgwc+ZMSkpKaNmyZWi90tLSs7A3QgghhBBCCCGEOB+ck5UkixcvJiIigvvvvx+TycSOHTuYOXMmTz31VFglyG+//Ub9+vW55ZZbMBgMrFmzhlWrVnHllVfy6KOPYjab0TSNdu3a0blzZ3bt2kWDBg3O4p4JIYQQ4p8WCATYsWMHe/fupWbNmqSmppZbWaqUIj8/n02bNgFQp04dEhIS8Pl87NixA7/fD4DBYKBGjRpYLJZ/dD+EEEIIceadcyGJUoo1a9Zw0UUXYTabAWjevDkzZszA7/djMh1ucsuWLZk6dSqff/45drudjRs38sgjj6BpWujARSnFgQMH0DSNmJiYsOcKBAI4nU6UUiil8Hq9/9yOCiGEEOKMU0oxd+5cXnnlFerWrcvmzZt55pln6Ny5c5lxzNatW8e4ceNITk7GZDLRqFEjRo8ezf79++nVqxcNGjTAZDJht9t5/vnnqVix4lncMyGEEEKcCWc0JPH7/fj9/lBFx4kqLCwkNTUV0AdcjYqKori4mEAgELZe9erVqVOnDq+99hpmsxmbzUbNmjVD9yulyM3N5dFHH2Xo0KFlDmYKCwu5/fbbycrKAmDbtm2kp6ef4t4KIYQQ4lxTVFTEyy+/zNNPP03nzp2ZPXs2L7zwAu3atcNqtYbWc7vdPPbYY9x888307dsXTdPw+XyAfjwRFxfH5MmTiYmJQdM0DIZzsseyEEIIIf6mM/oNv3PnTq699lqmT59ORkYGgUCgzGw05YmIiKCkpATQD0xKSkpwOBxhByRKKd577z2UUnz++ed89tlnNGzYkBdffDH0PIWFhdx7773UqlWLu+66q0xQEx0dzaRJk5gxYwYzZsygR48ep/cFEEIIIcRZtWvXLpxOJ82bN8dgMNCuXTv27t1LZmZm2Hp//vknu3btwmg0Mn78eKZMmRI2lllpaSlffPEFX3/9NdnZ2WWeJ1iVeuRNCCGEEOefMxqSpKamMnz4cBYsWEDfvn0ZN24cP//8My6X65gHD40bN2bDhg2hKzhr166lfv36GI1GAoFAqKJk+/bt1KhRA5vNhtlspnbt2uzZs4dAIEBJSQn3338/NpuNp556CpvNViYkMRgMJCQkkJSURGJiIna7/cy9GEIIIYT4x+Xl5WG1WkPf8RERERiNRgoKCsLWO3DgADt27ODjjz+mYcOGLFq0iLFjx+Lz+bBarbRu3To0816/fv34448/yjzXRx99xOjRoxk9ejQTJ04MHccIIYQQ4vxxRrvbWK1WunfvzmWXXcbOnTv56KOPGDJkCDVq1OC6666jT58+xMbGhoUXmqbRrVs33njjDSZPnkxiYiJfffUV77zzDpqmMWHCBCpUqMB1111H586deeaZZ2jcuDE2m413332Xa665Bk3TePbZZ1m6dCkPPvggCxcuxGg00qFDB+Lj48/kLgshhBDiHGI0GsMqO4LVpn/tLmMwGDCbzTz88MPUq1ePFi1a0KtXLw4cOECVKlV46623Ql1wRo0axXvvvcdzzz0XdgxTv359IiMjAdiyZQtr1qz5x/ZTCCGEEKfHGQ1JggclWVlZ/PDDD6xcuZKaNWty2WWXsWTJEubPn8/UqVNDBxRBKSkpvPPOO3zwwQds2LCBl156iWbNmgGQlpZGbGwsAH369MFkMvHNN9/g9/u5/fbb6d+/PwA1a9ZkwIABbNu2Td9Rk4mLL75YQhIhhBDiXyQ5ORmXy0VBQQE2m428vDyUUiQmJoatV6lSJeLi4oiKikLTNCIjIzEYDLjdbjRNC4UhJpOJOnXq8Ouvv4Y9XtM0mjRpQpMmTQBITExk7dq1/8xOCiGEEOK0OaMhSV5eHk8//TQrV66kcePG3HfffbRo0QKHw4HT6WTw4MHk5uaWCUk0TaNRo0a8+OKLKKXCDk769esXWs9sNtOnTx969+4delxwvRtvvPFM7poQQgghzgNVqlShatWqzJ07lyFDhjB9+nRatGhBfHw8u3fvZtmyZVx99dVUq1aNOnXq8MUXX/x/e/cdJ0V9/3H8Ndvb7V4v3B29Cdgbdn+KGktUxG7ssQQj9hJ71CAiUdRobDHWqEixG40FLFGUGFGk936da3vb5/fH3C2ciNGE44699/PxWI6bnZv5zpXZmfd+v58vp556Km+//Ta5ubmUlJSwatUq4vE4BQUFrFu3jmnTpnH66ad39qGJiIhIB+jQkKSlpYWBAwcyZswYysvLsdls6RDD6/Vy3333UVRU9INf27bef5oVZ9NgRERERGRTbrebsWPHcvPNNzNlyhTy8/MZN24cdrud9evX89ZbbzFq1Cjcbjfjxo3jlltuYcqUKXi9XiZMmIDf7+eLL77g7rvvBiAej3PooYdyxhln6PpDREQkA3VoSJKXl8fgwYMpLS3FbrcDsHbtWtavX8+uu+5Kv379OnL3IiIi0s0ZhsGOO+7IpEmTaG5uJhAI4HQ6MQyDPfbYg7/+9a/pz4cOHcoLL7xAc3Mzfr8fl8uFYRgcfPDBDB8+nJaWFtxu92Yz7omIiEjm6NBX+Hnz5vHEE0+0e6elubmZP/zhD8Tj8Y7ctYiIiAhgBSVut5vc3Nx08AFWsdZNP990Pbfb3W49n89HXl5eulaJiIiIZKYOfZWvq6sjGAyme5EAhEIhwuEwsVisI3ctIiIiIiIiIvKzdGhI0rt3b7799lvmzp1LPB6npaWFV199lby8PDweT0fuWkRERERERETkZ+nQmiS9evXi1FNP5Ve/+hUFBQVEIhEMw+DBBx9s17tERERERERERKSzdWhIYrfbufjiiznyyCNZunQpXq+XIUOGkJ2drYrwIiIiIiIiItKldOhwG9M0SaVSGIZBTk4ObrebJUuWsHDhQkzT7Mhdi4iIiIiIiIj8LB3akyQWi3HLLbcwY8YM1qxZQ0lJCcuXL+eEE07goYce0pAbERER+clM06SpqQmXy4XL5WL9+vXMmTOHYcOGUVxcrF6qIiIi8j/r0J4k8+fP57vvvuNPf/oTu+++O++++y5jx46lpKREFzIiIiLys6RSKa666ioWLlxIU1MTv/71rxk3bhznnnsuGzZs6OzmiYiISAbo0JCkpqaGvn37UlBQQDwex+Px8Mtf/pIvvviClpaWjty1iIiIZJhIJMLq1aspLi7mu+++w+fz8dprr1FUVMR3333X2c0TERGRDNChw2169OhBXV0dwWCQaDTKyy+/TE1NDXa7HafT2ZG7FhERkQzTNky3oqKC9957j3333Refz4ff7ycWi3Vy60RERCQTdGhI0rdvX37961/j9/u59dZb+dOf/oTD4eDWW29VSCIiIiI/i9vt5phjjuG8887D6XTy1FNPEQ6HWblyJeXl5Z3dPBEREckAHRqSLFu2jFWrVnHggQey//77s++++wJgs9lUk0RERER+FsMwuPDCCznyyCPx+/0UFBQQiUSYMGECffr06ezmiYiISAbo0JokDQ0NfPLJJ5imiWEY2O127Ha7AhIRERH52UzT5OOPP8bpdJKfn89LL73Eueeey1dffaVrCxEREdkqOjQk6d+/PzU1NTz99NN88803fPfdd3z33XcsX74c0zQ7ctciIiKSYWKxGPfddx+xWIw1a9bw4IMPcvjhh/Poo4+ybNmyzm6eiIiIZIAOHW6zYcMGqqur+fOf/4zH40m/y7PLLrtw7733pguwiYiIiPwn8XiceDxOVlYW//znP9l1110555xz+Pe//83y5cvp379/ZzdRREREtnMdGpL06tWLN998c7PlNpsNm61DO7GIiIhIhvF4PASDQV566SWmT5/Oqaeeimma1NfX4/f7O7t5IiIikgE6NCRpaWnhq6++IpVKtVuenZ3NTjvt1JG7FhERkQxjt9u58cYbuf/++xk6dCi/+MUvaG5uJjc3lwEDBnR280RERCQDdGhIUl1dzcSJE4nFYoAVmsybN48TTjiB+++/X0XWRERE5CczDIMdd9yRhx9+mOrqampqasjNzWXChAkawisiIiJbRYeGJD179uSll15Kf55KpfjHP/7BP//5TxVuFRERkZ/FNE1WrFjB9ddfz4IFC7DZbHg8Hq666iqOP/54vfkiIiIi/7MOLQxiGAYOhyP9cLlc7L777nz11VdEIpGO3LWIiIhkmFQqxZ133smQIUN47bXXePvtt7n11luZMGECK1as6OzmiYiISAbo0J4kjY2NfPLJJySTSQCSySTvvfceubm5uFyujty1iIiIZJhIJMLq1au58847KS4uBuCwww5j2rRpLF26lD59+nRyC0VERGR71+FTAD///PPE43HAmtVm4MCBXHvttTgcHbprERERyTAul4usrCymT5/O8ccfj91uZ+XKlSxYsCAdmoiIiIj8Lzo0qSgrK+Ppp59uN92vapGIiIjIf8PhcHDllVdy5ZVX8vDDD+PxeKiqquKkk05i0KBBnd08ERERyQAdGpJUVFRw//33c+utt+LxeACYM2cOb7/9NldffbUKrImIiMhPZhgGw4cP54033mD+/PlEIhF69uzJF198QXV1tXqTiIiIyP+sQwu3Lly4kLq6Otxud3pZQUEBM2bMUOFWERER+dkMwyAvL4/99tuPQw89lL59+/Lmm29SVVXV2U0TERGRDNChIYnT6aSxsTFduNU0TZqamkgmk+pFIiIiIiIiIiJdSocOt9lhhx2oqKjgnnvu4bDDDqOxsZGHHnqIAw44ID38RkREROTHmKZJKpX6wbpmyWRS9c5ERERkq+nQkCQUCvGnP/2JCRMm8Prrr+N2uzn66KO5+OKL1ZNEREREfpJkMslVV13FokWLNnvONE3mz5/fCa0SERGRTNTh8/D26dOHRx99lHg8js1mw2azpd8NUlAiIiIi/4nNZuO4446jrq5ui8/37NlzG7dKREREMlGHhiSrV69m7Nix3HfffenhNV9//TUvvPACY8eOxW63d+TuRUREJAPYbDYOOeSQzm6GiIiIdAMdWrh18eLFGIbRbnabHj168O2332p2GxERERERERHpUjo0JAkEAlRWVhKLxTBNE9M0qaysBFAvEhERERERERHpUjp8dptEIsEVV1zBEUccQX19PU899RQnnXRSu94lIiIiIiIiIiKdrUNDEr/fz8MPP8zjjz/Ok08+ic/n44ILLuDII4/syN2KiIiIiIiIiPxsHRqSGIZBjx49uOWWW2hubmb27NlMmTKF6dOn8/DDD//okBvTNInH45imicvl2uJMOKZpEovFAHC5XOn9mqaZfr7tazWbjoiIiIiIiIhsSYeFJKZpkkwmWbZsGW+88QbTpk1j7dq1nHnmmZx77rnYbFsuh5JKpXj77bd57LHHSCQSHHfccZx77rk4nc5260UiER555BHee+89TNNkzz335MorryQYDLJkyRKeeeYZvv32W44++mjOP//8jjpUEREREREREckAHRKShMNh3nzzTSZPnszy5cvZY489OO200/jggw+4+eabfzQgAViyZAk333wz99xzD3l5efzmN7+hT58+jBgxol1vkPfff5/nnnuOJ598ErfbzW9+8xt69OjBhRdeSE1NDW63G8MwmD9/fkccpoiIiIiIiIhkkA6Z3Wb+/PlceOGF5OXl8dxzz/HAAw9wwAEHYLPZ/uOQF9M0ee+99xgyZAgHH3wwO++8MyNHjmTq1KnpITRtKisr6dmzJ0OGDGHAgAEMGTKEqqoqAPbaay9uuOEGhg4d2hGHKCIiIiIiIiIZpkN6kgwYMIB77rmHV155hXPOOYeDDjqIwYMHp6cB/k9ByaJFixgwYEA6VOnfvz8ffPAByWSyXS+UI444gqlTp3LVVVfhdrtZtmwZV155JfDT6o/E43Hmz5+fnqK4oqLifztwEREREREREdludUhIkpWVxfnnn8+ZZ57JvHnzmDZtGg8++CB1dXVMnDiRY445hgEDBmwxyIjFYukirGAVZG0r4trGNE2am5ux2Wzk5OTgdruJxWKsX7+ePn36/KSQJBwO8+ijj1JbWwvA119/zQknnPA/Hr2IiIiIiIiIbI86rHCrYRi43W523nlndt55Z6688ko+//xzJk2axLfffssTTzyxxdlt8vLyqKmpAawwpLa2luzs7M3W/8tf/sIOO+zALbfcgmEYBAIBHnroIfbee+8fnTmnTTAY5IEHHkjv57rrrvsfj1pEREREREREtlcdOgUwbBz2EgqFOPzwwxkxYgRNTU1bLN5qGAZ7770348ePp7m5GY/Hw0cffcTw4cOx2WzU1dVht9vJysoikUiQSqXSPUyi0Wi7qX/bHm2fb9qetv+3ff5ThgGJiIiIiIiISObqkMKtW2IYBna7nVAo9KOBxIEHHkhRUREXXXQRY8aMYcmSJZx22mkYhsHvf/97Hn/8cQBOOeUUPvzwQy6//HKuvfZannvuOc4++2xsNhtLlizh8ssv55133uGDDz7g0ksvZe7cudvqUEVERERERERkO9PhPUn+G36/n8cee4xPP/2UaDTKLbfcQlFREQCjR4/G7XYDsMcee/DKK6/w1VdfkUwmueiii+jXrx+GYVBQUMCJJ57IqFGj0tstKSnplOMRERERERERka6vS4YkhmEQCoU46qijNntu4MCB7T7v1asXvXr12my9UCjEgQce2GFtFBEREREREZHMsk2H24iIiIiIiIiIdFUKSURERERERERE6KLDbURERES2FtM0aWlpoaGhgezsbNxu9xYLyCcSCerq6gDIzs7G4XBgGAamadLQ0EA0GiU3NxeHQ5dQIiIimUiv8CIiIpKxTNPkX//6FzfffDOJRAK/38/48eMZMGBAu6DENE0qKyu57bbbWLJkCTabjSOOOILLL7+cVCrFiy++yOOPP47NZmPQoEHcddddhEKhTjwyERER6QgabiMiIiIZKxqNctNNN3HSSScxdepUhg8fzu23304ymWy3XjKZ5NZbb6VXr15MmjSJl19+mTPOOAOAFStWcM899zBu3DgmTZpEVVUVzz77LKZpdsYhiYiISAdSSCIiIiIZa+XKlaxdu5ZjjjmGrKwsTjrpJGbPnk11dXW79VatWsWXX37JYYcdxj//+U8WLVpETk4OhmHw2Wef0bNnT3bffXdyc3M5+eSTefvtt0kkEp10VCIiItJRNNxGREREMlZlZSUej4dgMAhYdUZsNhu1tbUUFxen12sLU2677TYGDhzIzJkz+eUvf8k111zDihUrKCsrw263YxgG5eXlVFRUkEgkcDqdgDVc58svv2TZsmUALFq0aLPeKiIiItL1KSQRERGRjJVKpTAMI11/pO1jKpVqt148HqelpYUbbriB4cOH8+9//5szzzyTs88+m1Qqhc22sfOtzWYjlUptNtxmzZo1fPvtt+n/aziOiIjI9kchiYiIiGSs3NxcIpEI4XAYt9tNU1MTyWSS7OzsdusVFBSQl5dHr1690r1FbDYb9fX1lJSUMHv27HRYUllZSV5e3mYz3Bx//PEcf/zxAHz++ec88sgj2+goRUREZGtRTRIRERHJWL169SIQCPD555+TTCb58MMP6dWrFwUFBdTX1/PNN9+QSqXo06cPRUVFfPvtt6RSKebOnYvD4SA/P5+99tqLRYsWsXLlSuLxOG+99Rb7779/eqgNkO6tsulDREREtj/qSSIiIiIZKxAIcN1113HHHXfw9NNPs2rVKu6++25cLhczZ87k97//PW+88QZZWVnccMMN3HHHHTzzzDMsX76ca665htzcXEKhEKNGjeLcc88lNzeXaDTKjTfeqCBEREQkAykkERERkYxlGAbHHHMMu+22G+vWraO8vJyioiIMw2DPPffk2Wefxe12YxgGRx99NLvuuitr166lpKSEHj16YBgGDoeD66+/npNPPpmmpib69+9PVlZWZx+aiIiIdACFJCIiIpLRbDYbZWVllJWVtVvu9Xrxer3pzw3DoLS0lNLS0s224XA4GDhwYIe3VURERDqXapKIiIiIiIiIiKCQREREREREREQEUEgiIiIiIiIiIgIoJBERERERERERARSSiIiIiIiIiIgACklERERERERERACFJCIiIiIiIiIigEISERERERERERFAIYmIiIiIiIiICKCQREREREREREQEUEgiIiIiIiIiIgIoJBERERERERERARSSiIiIiIiIiIgACklERERERERERACFJCIiIiIiIiIigEISERERERERERFAIYmIiIiIiIiICKCQREREREREREQEUEgiIiIiIiIiIgIoJBERERERERERARSSiIiIiIiIiIgACklERERERERERACFJCIiIiIiIiIiADg6uwE/xDRNkskkq1evJh6P07NnT1wuF4ZhbLZeNBpl9erVpFIpSktL8fl8GIaRfm7VqlW4XC5KS0ux2+2bbUNEREREREREBLpoSJJIJLjzzjuZMWMGLpeLsrIyJk6cSDAYbLdedXU1l1xyCfX19TgcDlKpFA8//DB9+vRhw4YNjBkzhsrKSiKRCIcffjjXXXcdDkeXPGQRERERERER6WRdcrjNrFmzeOutt3j++eeZPHkyNTU1TJ06FdM0263397//nbq6OiZPnszkyZPJzc3lxRdfxDRNXnrpJSKRCFOmTOGZZ55h6tSpzJ49u5OOSERERERERES6ui4XkpimyYwZM9hrr73o0aMHWVlZHHXUUbz33nubhSR+vx/DMEgmk6RSKQBycnJIpVK8//77HHPMMfj9fsrLy9l11135+OOP223DNM30Q0RERERERES6ty459mTNmjX06NEDAMMwKCkpoaKigkQigcvlSq93+OGH8+abb3LUUUfhcDgoKiri5JNPJplMUlFRQUlJSboGSY8ePVi9enW7/bS0tPDMM8/Q2NgIwJdffsl+++23jY5SRERERERERLqSLteTBEgXXv2h5W1M02TmzJmsXr2au+66i/HjxxOJRHjttdd+cJumaW5WtNUwDLKysgiFQgSDQdxu99Y9EBERERERERHZbnTJniTl5eUsWbIkHZSsXr2akpIS7HZ7u/WmTZvGgQceyIEHHgjAqFGjeO211zj99NMpKSlh9erV6eE0q1evZtddd20XlHi9Xs444wzAClEWL168jY5QRERERERERLqaLteTxDAMDj74YL788ktWrFhBXV0db7zxBkcccQSGYTBr1izmzp0LQHFxMd988w0NDQ2Ew2G++uorSkpKcDgcHH744bz66qvU19ezZMkSZs+enQ5TRERERERERES+r0v2JNl11105+eSTOeecc3A4HAwePJjjjjsOwzCYNGkSZWVl7LDDDpx77rlcfvnljBo1CpvNhtPp5L777sMwDE466SRmzpzJiSeeSCwW41e/+hXDhg3r7EMTERERERERkS6qS4Ykdrudq6++mrPOOotEIkFxcTEOh9XU2267DZvN6gDTo0cPnnvuOaqqqkilUhQUFODxeNK1Rv70pz9RUVGB0+mksLBws5okIiIiIiIiIiJtumRIYhgGdrudkpKSzZ7z+XztPne73ZSVlf3gNlwuF+Xl5R3WThERERERERHJHF2uJomIiIiIiIiISGdQSCIiIiIiIiIigkISERERERERERFAIYmIiIjIdiGRTLGipplIPIlpmp3dHBERkYzUJQu3ioiIiEh7y6qbOfMvX1Ca4+XgQQUcNLCA/oUBvE67ZvATERHZShSSiIiIiGwH/G4Hx+xUwkeLqvjTB4v58/Ql9C8McPDAAg4eXMigoix8LgUmIiIi/wuFJCIiIiLbgZKQhxuP3oHLogOYt66RD+dXMmNRFY99vJTHPl5K3/wABw7M55DBhexQEiTgti7zFJqIiIj8dApJRERERLYDbWFHlsfJnr1z2LN3Dr89pD8LKxr5cEEV0xdU8sxnK/jrp8vpledj/wEFHDq4kGE9gmR5nRgoMBEREflPFJKIiIiIbGfawg6/28Eu5dnsUp7Nbw7qx5KqJmYsrOKD+ZVM+nIlz322grIcL/v2z+PQHYrYuSybbJ8CExERkS1RSCIiIiKyHWsLO7wuO8NKQwztEeT8/fuwvLqZjxZZgcmrX6/lpS9XURLysk/fPA4dUsiu5Tnk+V0YhgITERGRNgpJRERERDKIYRh4nHYGlwQZVJzF2fv2ZmVNmE8XV/P+/Ere+W49U75aTVHQw159cjlsSBG798ohP+DGpsBERES6OYUkIiIiIhnKMAzcDjsDirLoXxjg9L17sWZDC/9cUs378yqZsbCK12evJT/gZo/eOYzYoYi9+uRSGHRjNwwFJiIi0u0oJBEREZGMZpompmmSTCZxOH54xhfTNH/waw3D+NHntieGYeByGPTJ99M7z8fJe5Szrj7C50treH9eBTOX1vL3OevJ8bnYrVc2I3YoYnjfPEpCHuw2BSYiItI9KCQRERGRjGWaJuvWrWPChAksW7aMYcOGccUVV5CTk9Pupj8Wi3Hbbbexbt06DMPAZrNx6aWXsssuu1BdXc1NN91ELBYDwOv1cuutt1JUVNRZh/U/MwwDp92gZ66P8hwvJ+xaSmVjlC+W1fLevApmLa/j/XmVBL1OdinP5tAdCtmnbx4l2V7cDhs2BSYiIpKhFJKIiIhIxkomk9xwww0UFhZyxx13cN9993H33Xdz1113tQtJkskkH374IVdeeSU9e/bEMAx69eoFQHNzMzNnzmTChAkEAgHsdjuhUKizDmmrMwwDh92gR7aX43bpwS937kF1U5RZy+t4b14FXyyr5eNFVfhcDspzvexQHGTHshBDSoL0zPWR7XPhtKuniYiIZAaFJCIiIpKx1qxZw1dffcUbb7xBz549ueyyyzjrrLO47rrryM3NbbeuzWZj8ODBDBgwALfb3e6m3+PxMHToUHJycjZ7LpMYhoHdgKKgh6N2LObIYcXUNMf496o6PlpYzdy19Xy4oJJXvl6DYRjkeJ30zPMxtEeIHUtDDC7OokeOlyy3Q0N0RERku6SQRERERDLWunXrcLlcFBQUAFBSUkIqlaKqqmqzkATgyiuvBGC33XZLBymGYVBXV8c555yDaZoceeSRXHzxxXi93vTXmaZJRUUF9fX1AKxcuXKLtUy2F4ZhYBhQkOXmsB2KOGyHImLJFHXNcZbXNDN3bQPfrK5nQUUD0/69huc+X4HDbpAfcNOvIMDQHlaPkwGFWRQF3fhcDs2eIyIiXZ5CEhEREclY0WgUh8OBzWYDwG63YxhGur5IG7fbzeOPP05BQQGNjY387ne/46677mL8+PEUFBQwdepUioqKWLVqFaNHjyY3N5ezzjqr3Q3/yy+/zHvvvQdAXV0dZWVl2+5AO1jbcboddopDdoqCbvbuk4sJROJJqhqjLK1q5ts19Xy7pp7FlU3MWl5LJJHC67RTHPQwoCjAjqUhhpWG6JvvJy/gxu20YaDgREREug6FJCIiIpKxgsEg0WiUWCyG2+0mGo2STCYJBALt1rPb7QwdOhSAgoICLrzwQm655Rai0Sg+ny/9XF5eHieddBIffvghZ511VrttjB49mt/85jcAzJw5k0cffXQbHGHnaAs1DMDnctArz0HPXB8HDyogZUJzLMH6DREWVjby7ep65qytZ/bqDXw4v5JEyiTgcVCW7WVwSZAdS0MM6RGk1yb1TTbdh4iIyLakkEREREQyVnl5OYZhsHjxYnbZZRfmzJlDdnY2hYWFxONxwuEwwWAwPU1wW4+TlStXkpWVhd1uJ5lMtg49MUilUqxatWqzoTqGYWC329Oft/VY6U7ajtduQNDjJFjsZEBRgKN3LCGZMmmIxFlV28L89dYwnXnrGpi+oJJXv16DgUG2z0mvPB9DegTZqTSbQcVZlG5S32TTfYiIiHQUhSQiIiKSsXJzc/nVr37FNddcw+GHH85rr73GxRdfjM/n49NPP+UPf/gD06ZNY+XKlUyYMIEhQ4ZQU1PD66+/zu9//3ucTifvvPMOb7/9Nv3792fRokX885//5Mknn+zsQ9sutIUaDrtBrt9Nrt/NTmUhTt6jfPP6Jms2sGB9I6/8ey3Pf74Sp91GXsBFv4IAQ3oE6V8QoFeej6KghxyfC4/TpvBERES2OoUkIiIikrEMw+C3v/0tO+20EwsXLuTuu+9m7733xjAMBg0axHXXXYfT6aS0tJTjjz+eFStW0LdvX/72t78xePBgDMNg9913p7m5mfXr1zN8+HCuueYaysrKdGP+X/p+fZPikGez+iZLqpqZs6aeOWvqWVTZxKwVtUTjKQwD/G4HeX4XPbK99Mn30zffT+98P6XZXvIDbnxuOy67bbP9iYiI/BQKSURERCRjGYaBy+VixIgRjBgxot1zBQUFHHzwwQD4/X6OOuqoH9xGQUEBo0aN6uimdmuGYbSrb9Irz8//tdU3iSaoaoqypq6F5TXNLK1qZnlNM6vrWpizpp7mWJJUysTtsBHyOikOeSjP9dE330+fggC9cn0UZrkJ+Zy4HXZaO58oPBERkR+kkEREREREuhzDMKz6Jl4nQa+TfgUBDqQA0zRJpkyiiRQbwnHWN0RYVRtmaVUTS6ubWVkbZuayWt79roJ4MoXNZpDldpAXcFGe46N3vp9+BX565fnpke0l1+fE53LgUMFYERFBIYmIiIiIbEcMw8BhN3DYbfjdDkpzvOzeK6e1+C7Ekykaowmqm6KsrrV6nyypamJFTZglVU3MWlFHOJYAwOOwk+N3URLy0CvPR9/8AH3y/ZTleikIuAl6nTjtNmyGwhMRke5CIYmIiIiIbPesGYjAbbPjdtrJD7gZVJSVfj6RMonEk9Q2x1hXH2FF69CdZdXW0J0P51fxamQtyZSJw24Q9DgpyHJTluOlKOihOOihOOShIMtNnt9Nts+Jz2XH67LjsNk0jEdEJEMoJBERERGRjLRpYOG0GzjtNrI8Tnrl+dm7jzWNs2lCNJGiIRKnsjHKqtowy6qbWVrVxMraMHPXNfDFsloi8SSxpInNALvNwOO0E/Q4CXmtMKUo6E6HKYVBD3l+F7l+F363A6/TjsthwzDAQEGKiEhXppBERERERLqdtqDCMMDb2iOkKOhhWI9gep140up90hRNsCEcp7Y5SkVDlIrGCBX1EdY3RKhuirG8upnZqzYQiSeJJlKYgNNm4HLYCHgcZHtd5AVcFGZ5KAq6KQ55KAp6yA+4yfU7CXqceJx23A4bNptVxHbTNoqIyLajkEREREREpNWmwYTLYQUdQa+THtne9HLTNK2PQCJpEkskCceSbGiJUxeOUdUQZX1DhIqGCOsbolS2/n/B+kZaYlaQkkilcNhsuBw2fC472T4nOT4XhVmtPVJCHkpzvPTO81MU9JDlceCwqbisiEhHU0giIiIiIvIzpHuhsDFICXicFAY96XU2DVJSrbPxROJJ6lvi1IXjVDdFrRCl3gpQKhqi1DbHWFUXpimSIJZIEW+d2jjb56Qsx8eAwgCDirLoXxSgPMdHrt+F12nHUD0UEZGtRiGJiIiIiMhWtmmQYttkNp68gLvdeqZpYmLVRoklUrTEkzRHE9Q2x1i7oYUFFY0srGhkWVUzb327jpdnrSZlmgTcDgqDbnrn+RlYnMWgoiz65vspDnnI8jhxakpjEZH/ikISEREREZFOYhitNUg2qY2S63dRnutj5/JsfjGsmJQJ0USSunCc1XVhllQ2sWB9I4sqm5i3roFPFlcTS6Rw2A2yvS7Kcrz0KwgwqDiLAUUBynN95AfceJ12zcIjIvIfKCQREREREemiDMPAboDP5cDnclCa7WWv3tbMPImUSXM0QWVjlOXVzSysaGRBRRNLq5p4f34F0/69hpRp4nPbKQi09jopymJgcRb9CqxeJyGPs3XmHYUmIiKgkEREREREZLvSFmg47QbZPhfZPhcDi7I4bEiRNaVxMsWGsDVcZ0llM/PXN7K4spHFVU3MbJ3O2G4zyPY5KQl56VfgZ1BxkAFFAXq19Tpx2bG3zrSjAEVEuhOFJCIiIiIiGcAwDGtKY5sdb8hLScjL7r1yMU2TZMokHEtS1RhleU0zi1qH7CytauLjRdW8/s06UqaJx2GnIMtNSciaYack5KFHtpfioIfCLDc5fhcBtwOfy47dZtPwHRHJOF06JDFNE9M0W0/4P//E2/b1wH+9DRERERGR7ZlhGDjsBkGvNZ1x3wI/hwwuBCCaSFHfEmddfYQlVa21TioaWdv6eXM0SSSeBMBuM/A47YS8TnJ8zvRUxT2yvfQIeSgMesgPuAl5nfhcdtwOGzb1RhGR7UyXDElM02T9+vU88sgjrFy5kv33358zzjgDj8fTbr3Fixfz+OOPk0ql0l/Xv39/LrjgAkzT5O233+aNN97A6XRy5plnstdee+kELSIiIiLd2qbXwx6nHY/TTlHQw85lIQBSJsQSScKxJBta4tQ1x1jfEGFdfYS1G1pYtyFCZWOEBRWNfLm8lpZ4knjCxGYDZ+ssPtleJ/kBd7veKCUhK0TJC1i9UbwuOw71RhGRLqZLhiSxWIwrr7yS8vJyzj77bMaNG0c8Hueiiy5qd/LMzs7mwAMPxDRNUqkU9913Hw6HA5vNxttvv83tt9/O73//exobG7nssst49tlnGTBgQCcemYiIiIhI19R2nW03wOty4HW1TllcQLp3NoAJxJMpYokUjZEEdeEYVY1R1tdvEqTUR6hpjrK0uolwNEkkkSSVsuqouJw2Qh4nOX4XhVluSkLedJBSFPSQF3CR7XPibQ1w2mqjbNpGEZGO0iVDkiVLljBv3jzuv/9+CgoKiMVi/OEPf+Dss8/G6/Wm18vPz+foo48GoLKykpqaGkaNGgXAq6++ygknnMCRRx6JaZq89957vPLKK1x99dU6uYqIiEinME2TeDxOPB5vd9MpW49hGLhcLhwOh675tqJNv5cG4HbYcTvsZHmc9Mi2rs83/Z1um7Y4Ek+yIRynpjlGZUOEtfUR1rWGKBUNERZXNvGvFXVE4iliyVTrtm14XHayvU5y/S4KszbWRykOeSjM8pDrd5HtdeJ12XE5bDhsxg+2VUTk5+qSIcmyZcvIzc0lNzcXwzDo168ftbW11NfXtwtJ2pimyYcffkhBQQHDhg1LL2uXeJsm3377bbrGCUAymaS2tpZk0hpn2dzcvA2OTkRERLoj0zRpaGigqqoKu92uG7kO0tbDuLi4GJ/Pp+/zNrTp93rTaYtz/W76FljL267PTSCRNInEkzTHEtQ2x6hujLK+IcLaDRHWN0SoqI9Q1RTl61UbaFwUJ5pIkUiZGFjDhLytQUpewApS2kKU4qBVHyXH5yTUFqTYbdgVpIjIT9AlQ5Lm5mbcbjc2mw0Al8uFaZrEYrEfXD+RSDBp0iRGjRqVrltyzDHHcOuttzJ06FCampqYMWMGO++8c7vgpLGxkcsvv5yqqioAFi1axOGHH97BRyciIiLdkWma1NbWUlBQQFZWVmc3J6PV1tZSW1uLz+fr7KbI97SFEwbgchi4HFYx2ZLQxjdCTdPEBEzTGtbTEkvSFG0NUpqsIGVdW5DSEKG6KcrKmjANkQTxpBWk2AwrSPG57GT7XOT7XRQGvxekZLnJ9rkIeq2hPU67oSBFRLpmSBIMBgmHwyQSCVwuF5FIBMMwNivc2mbZsmXMnz+fCRMmpE9mRx55JMlkkr///e8UFRVxzDHHpLfTJhQK8cQTT6R7ndx4443b5PhERESk+0kmkySTSXw+X/qNIOkYPp+PhoaGdj2IZfthGK01SAyw26y6JDl+F+W5G0OvtiAllTKJJ03CsQRN0QQ1m/RIWVcfYX19hMoGq0fKkqpmGqNx4gmTlGla0yU77VahWZ+LgoCLgiw3RUEPRa0hSkGWmxyfiyyPA7/bgdNuw2HfWCOlrb0ikjm6ZEjSv39/6urqqKyspLS0lDlz5lBSUkIoFCKRSJBMJnG5XBiGgWmavP766+y6666Ul5ent+FwODj++OM5/vjjiUajnHbaaZx22mntx1MaRnr4jmmaOJ3ObX6sIiIiIrJ16aY187UFKTa7gcMOXpedvICbXnn+9Drtg5QUzdEkjdEENU1Rq9DspkFKo1UvZUFFI83RJLFEKh2kuBw2/C4HAY+DPL+LvICboiw3RSEPRVkeCrLc5AesHikBtwOP047DtrFXin4fRbYvXTIk6d27NwceeCDXX389hx56KE888QSXXXYZLpeLt956i2nTpvHII4/gcDgIh8O88sorXHvttdjt9vQ2Vq5cyZQpUygtLWXGjBn4fD6OOuoonaRERESk20ulUlscxuxwOHA4fvwSsa3Wm81mS9eD+ynaetM4nU5dk0mHax+k2PC6HORnuemTv3mQktwkSGmKWjP2VDduDFMqGiJUNUapborx3Zp6voglCUcTVo0UA2yGgddpJ+B2EPQ6yc9yURCweqUUBz0UBN3W9Md+a/pjn9uB227DZjOwGQpSRLqSLhmS2O12xo4dy9SpU1m+fDm33norhxxyCIZhMHDgQI4//vh0N9VkMskll1zCwQcfvNlQGr/fz3fffce+++7LL3/5S/x+/5Z2KSIiItJtrFixgssuu4xYLEY4HCYajZKTkwPARRddxMiRI//jNqZPn47T6fxZIcn06dOZNGkSDz300H8MYkS2hU2DFKfdhs/loCDLTR+s+4ZN6xkmTZNk0iSSSNEUSdAQiVu9UpqiVDZYYUplQ5TqpihrN0RYsL4xPf2xaVpBisNuEHA7yPI4yPFZw3sKW4f4FIc8VpAScJHtdeFz2/E57djtBrbWdipMEel4XfLVyTAMgsEgZ599drtlAAMGDGDAgAHp5cFgkFNPPXWzbWRnZ3PhhRdu9vUiIrKdMlNWFT/DgLbR4Jlwbk9fgJuAYR1TKgWxRkhEIRkDuwvcWdZHw5YZxy2dqqysjMcffxyA559/nunTp/Poo48CEAgEiMfjRCIR/H4/pmnS0tIC0G62mAsuuCC9vWQyiWEYxONxotEofr8fm8222fVXQ0MDy5Yt22z6Y9M0SSaThMNh3G53u2HVbcttNhterzf9Rlk0GiUSieDxeHC73brWkw6x6e+VwzBw2MDttBPyOill86mPwZq1J55KEY4maYjE2dASp6q1V0pFg1VwtqohSk1zjG/X1NMcTdASTxJPWgVn7TYDt8NO0NPaKyVgBSmFQQ/FQetjnt9Frt9FlseavcftsGE3jPTLg/4eRP57XTIkafO//nHr5CAisp0wTUjFId4CsTC01EJztfV5v/8Dhxu+nQJzJoM7CJ5g68cQ9D4AynaHZAIqvgXDDi4/OH3gcFnBgiuw6c5g05J7W/u1wjStfZgmpBJWyJGMWZ/78qx1quZD1QJoqWt91ELLBtjtbCjfE+pXwYunQ7j1e+D0gb8AyvaEX4wFuxtql0EiYi13BzYGKB1xTNIhvn9j1RG2dC3kdDopKioCrDecPB4PRUVFPP/888yZM4fKykri8Tjjx4/nj3/8I/Pnzycej7PHHntwww034Pf7eeGFF3A4HJx11lncf//9NDc3M3/+fNatW8f+++/PjTfeiNvt/o9tNE2TVatWceutt7JmzRpcLhdjxozhsMMOo6qqihtvvJEVK1Zgs9k4/PDDufzyy3n33Xd54IEHSCaTuN1uxo4d+7N6tIhsTd//O3M6DJxYvVLyszb+DWz6N59qnbkn2torpb4lTm1zlMrGjb1SrJl7YqzZ0MLcdQ20xJJEE0lSKbDbDZx2A7/LQcjrJNvnpDDLQ2HQTVGWh6KQm4KAh1y/i2yfE5/LKoDrtNswDNQrReRHdOmQREREtmNtF4OGAck4xMMQa4JwnXXz37ge8vpD2R4QbYRpF1vhQWQDxCOQjFoBwEUzIFBk9SSpWWyFBvEWK4BIJcBmt0KSyAZ4+RxoqrSW2Zzg9EJ2LzjtBStQmf8mzH3F+n9b2OIJQflwKNwBzCRULwK7szVk8YDNYX3uaJ1hLdpotT/aZPX2iDRYQUfZntbxhKvhH7dCwzqI1kOs2TruUBmc+Yq1na+egZmPWIGO3dn6cEO/Q6yQxOmz2mN3gjtkhSgbVkK0wfoaTPjoHpj3qnUcgUIIlkFuH9jjXKsdyThE6q3AyOEmo3rfZIg1G1p47vOVpFJbPyzJ8jo4f/8++Fw/71Jv7dq1vP322/ztb3+jR48eeL1ezjvvPIqKimhpaeG6667jjTfe4JRTTmH16tW4XC7Ammlw3rx56d4op59+OkcffTR77733f9xnMpnk5ptvpqysjPHjx/Pll19y/fXXM2TIEN577z2i0SjPP/88pmnS0NBALBbjj3/8I2PGjGH//fenrq6O7Ozsn/09EtnWNg0l7JvM3BPyOinNaT8FMpCulRJNpIjEktRH4mwIx6hqjFHZaIUoFQ1RKhsi1DbH+HrVBpqiCaIJq1eKATjtNtwOG1leB9leF3kBF0VtYUrQQ1FrrZTikIeQ14nHaU+/jaAQRborhSQiIvLzbPrut2FYn7eFBeHajQFIpB52Od0KIRa8Be/dZi2Lt1gBCAbscwmU7t4aSnghfwBk9YBgCQR7WMGCJ9va17CRMOhIKxhJRCHebPU6CVjvhuPywUHXQ1OFtZ9og9Ump9faPlg9NFZ8ZgU2qTikktbj8NutUCJcB387xQo9bA5wesDpt8KHE/9qBQ6fP2w9knHrYSat4OLoCVY4YZqw/lvrOXcAgqXgzYG8fqSDil3OgL4HWQGIOwCuLKv9npD1vD8fTnis7ZsMtPVKSVjtwoQdT4TsnlC3DDasgMq5sGom7PBLqx3rv7FCI28uhEohpw/k9oWSna3vOWwMmRSgdIqaphiTZq0inkxt9W0XBz2cObwXPtfP/9oRI0YwdOjQ9PCZb775hrfeeovm5maWLFlC7969OeWUUzb7umOPPZa+fftimiaDBg1i1apVPykkaWhoYPbs2dx8880UFBQwYsQIHnzwQWbPnk1ZWRkLFixg8uTJ7LvvvgwePBi73U7Pnj15+eWXMU2T3XffPV1PRSQTtIUTm9ZKCbh/vFdKLJkiGreKzm4Ix6lrjlHROrynsjVMqWqKsqo2zJw19dbwnoRJIpXC0br94qCH3vk+BhRmMbAoQO98P8VBDwGPA5fd1q5tIplMIYmIiLT3/RAkGbOGvoRrrQCica3VSyJvAAw93go8Jp8Pq2dBogUSMWvYhycE/UdYH335VggRKLbCj2APyCqG3H7WfhweGPlIa72NLQwZsbcOndkSpw92Oa19+2n7f+u2dj8XdjrZOqZY2OrlEW+2epsYhhWK7DO6NWhpDVmiDdYx2FpnUCvdHXY/xwo+vLnWR08ICgZZz/vz4ezXW3uguKyv+34dleJh1mNLNl2/rf3tjt+A/odaPU/aApRY2AqHAoWt31Mv9NoXapdDxXew7GNreM6Q46wAJpWCv19vBVo5fawgKKe3Ferk9rWGKm3aG0i2usElWbz22/3oiFE3dptBlsf5X31tMBhM3wh99tlnPPjgg9x5552UlJTw9NNPE4/HN/sawzDwer3pOiJ2u51kMvmT9pdIJDBNMz00p632SDQa5YgjjmDixIn84x//4Oqrr2bIkCHcc889TJgwgXfeeYe3336bsWPHMnbsWP7v//5PN3DSbXy/V4rXZsfrtJPtc1G2SWaY7pViWoVnI/EkkXiS+pYEdc0xqpujrK1rYVFlE0uqmpi9qp7pC6qIJlK47AYhr4se2R76FgQYWBRgQGEW5bk+CrLc+F12TXMsGUkhiYhId/JDd2OxptYeILWtAcgaa8jKHudZYcbyT2HqhVZvkWTM6jVhc8CwUTD0OGtYS+nurTfYrQFIoAj8hVZPB4Be+0DP4T9eM8P+393Qbcb4XriwKafHemyJOwv2vrh9MdV0UNBaMLX/COuxxQtCA7zZ/13bf662MMXuAq+r/X4Ld4Dj/7wxQGnZYP183VnW15gJKwRKBygtVq8YXwGc+5bV8yUR2TjMSLY6t8NOWY6vs5vxo1atWkXfvn058MADiUQizJs3r10B/f9GWzHWtpuqQCBAYWEhn332GaWlpaxevZply5YxcOBAwuEwu+22G8OHD2fOnDmcddZZbNiwAafTycknn8xJJ53EzTffzD/+8Q/+7//+b2scskhGSfdKMcCG1Ssly+OkIGvjOptOg9wYSVDVGGFFTZiFFY0sqmxiaVUzMxZW8frstSRSJl6nnVy/k7IcH/0LAwwsyqJfQYCyHC+5fhcepx2bCsjKdkwhiYhIJvl+L5BU0ho6EqmH5iqrB0jDaqv3wy5nWHU+3rgSFv/DupE2k1YY4ArAgMM3DnkZchxklUB2mfXRX2A92mZjOehafnTIhmHbLK/o0tLHYGze7u3lgu+HApTc3huft7vh+IetkKxlAzSus+qeNFdbvVHaCs9KxnM4HOleHE6nk0Ri4899n3324aGHHuLss88mkUgQj8fTdUicTmd6Gl+Xy4XTuTHodLvd2O32zfZlt9tZtGgRp59+evrm6bjjjuP666/n5ptv5s0332TlypWMHDmSwYMH8+yzzzJ16lRKSkpYsWIFhx9+OB6Ph4svvhjDMHC73SxcuJBx48Z12PdHJNNtOg1ybuusOYOKgxw2pMgaUZtMUd8SZ319hGXVzenwZEVNM3PXNdAcTWCaEHBb0yf3yrOG7AwoCtC3IEBJa70Tl8OmgrGyXVBIIiKyvTJN653+SL1VtLSxwrrRbVhjhRp5/a1CqC+eAeGa1p4CWIFF34Ngp1OsHiFle4IvF7LLreKfWSUQKLACErC2c9Q9/yEc0AXPdscwrN473hzrkdvHGp7TxjStGiyS8UaOHMnhhx+OYRicc8457Wod9OnTh6lTp7J48WJKSkrIyclJD6O58MIL0zc71157bTpoMQyD2267Db9/89+fQw45hHfffbfdspycHPLz8xkyZAhLly4lLy+P/v3743A4OPXUU9l7772prq4mPz+f/v3743Q6mThxIsuXLycej9O3b1+Ki4t14yWylRmtUwp7WgvMFgU97FyejWmapEyIxJPUhWOsrmthaVUTC9Y3sqSqmUUVTXy+tJZIPInNZhDyOCkKuemT3zZkx6p3UpjlJuB2YrcZ6nkiXYpCEhGRrspMWTOoRBut2U0a10PDWuu5Xc+whr3MuBu+fsGqm5GKWze2dpdVLySvv3Xz2+cAq3ZGdnlrLZASaziMrfUlYK8LrI9bujDRBUv3tFldFMlUoVCIUMgqGpybm9vuOcMwKC4upri4eLOvy8vLS/+/sLBws6/5IYFAgIEDB/7gcyUlJZSUlLRb5vV6GTJkyGbrFhYWttuniGw7hmFgN8DvduB3OyjL8TG8bx6maZJImYRjSaqboqysCbO4somFFY0srW5m1vJa/jF3PYmkidNuI8fvpDjYNk2xixyfk1y/m1y/9f+Q10nQ6yTgduBx2vE6bbgc1lAeW2uAo1BFOoJCEhGRzpKMWwFItNEaCtNUAfVrrNodJTtZociLp1tDIGLN1tAZTCgaCsNOsIbE5A+wCniGyq1hMW0FUdtqgWSVwC8faL9fXVCIiIjIVmYYBk67QchrI+R10jffz8GDCgCIJ1M0RhJUNERZVt3MospGFlY0sabOClLCsSTRhDVDTzxlTV9sMwxsNnDZbfhcDnwuOwGPg2yvk2yfi2yfMz08KNfvskIVjxWs+Fx23A4bHqcdh83AMNRbRX46hSQiIh2hbShMrNkaCtNUaYUesWZr6lanF756Gj59wHo+EbXqPxg2OOx2KyRx+qBwCPTYzQpA0iFIifUcwM6nW48233/h14WAiIiIdIJNwwiXw05ewE5ewM0OJVlAMSYQT6SIp6xZd5qjCcKxJA0tcerCMerC1sfaZuuxIRyjviXB+oYIiyqbiMSTxBIpYskUqRTYbFaw4rAZeFx2/C4HfredoMdJziahSk5rqJLjc5Hd2lsly+PA7bDhdtpx2g1sGOlLKIUq3Y9CEhHp2r5fiDQRgaYqq+dF4zrrYbb2rui5j1V4dOkMq3eG3WkNKbE5wOG2wgaXz5rFpW5Z6ywtrQ/DYRW29FvveBCusbZF64wmbVPTurOs9ZMJq8ZHPNw6NW6lNRQmrx+U72UNf5l8HlTOtdqSiFo9QYIlMOAwKyTJKoGSnVsDkHIIlUJWj43FNT0hOO5PtBvyoBdqERER2Y6lZ9wB3E47bqyir/kBd7v1zO/NyGeakEiZxJMp4skU4ViSpmiCpmiC+tZApa45Rm04Rm1znLrmGBtaYjRGEqyrj9AcTRBLpoglUiSSJhjgsFmhittpJ+B2EHA7CHmtMCUv4Gr96Ca/dUhQyOto7aniwOWw4bLbcNiMdnXeFaps/xSSiEjn+v6UtPGwNS1puNrqedE228ae54M/Hxa+C69fZq2XjFuBBQbsca4VkiRj8P7tsP6b9tv1ZMOv37NCjKUfwquXtD5hbKy9sNtZcMRYK4h5+WyonLcxSDHs4AnCaS9CTi9Y+gG8c5M1c0y82QpBwJo+tmxPK5TJKrFqgrT1Agm1FkX1tY75H3Sk9djSrDCqCSEiIiLd1PfDBsMAl83A5bABkP0DM6hvGqyYQCplEk9awUokYfVWaYxYj7pwjJqmGDXN0daPsXSwsqoubIUqiRTxpEkilcJmWFMoOx0GPqeDLI/1yPG5yA24yPO7yPO7yWv9f8jnstZpranibA1VbO3e+9J1XlekkEREth0zBfEWa8hJuNrqfbFhFfjyrLAgFYdXfwvLPmoNHmJWQOEJweCjrJAkrx/sfCoES626G4Eiq39lWw8Qu9uaiSVSb20vmWgdxmJY05oCFO8EI35vhSypuPV8MgE9drWeN+zQbwTkD7Kea9uO3QUOT+t+PJDT2+o10tYTJFgKeX03tuOX97cPOjYLQWwd+d0WERER6VY2DR3apjV22MGLnSBOyGq//g+FKm29TSLxFI2ROA2ROBvCcWqaY9Q0RalpjlHbGqpsCMdYVtPMt2vqaYkliaesXiop08Rht0IRt8OG3+0g6LF6oeT6XRQFPfTM9VGe66U46CU/4CLgceBuLUz7/WORbUshiYj8PN/v+dHuuZTVk6NtCEpzJTSsg977W8VE139rhSBNFRBrsnpsGDbofxgMPMIaFlM01ApFsntaj2APKwgJlVv7KBxi9faAHx56Ytit4OLH5A+wHltid8L+l7cd1Pd3YH3ocwD03m9jCLKltoiIdEGb3hhseiG+peVtampqaGhooHfv3j/r60REuqIfDlVs+FzWsuKQp936bec6E+uSOJlKWQVnEymaowkaWuLUt7T1UolS3RqotNVVqWmOsaImTGMkTiSRIpkycdlt+N128gNuioMeynN99MzzUZ7jozTbQ17ATcjrxO2wYd+kG4rOtR1HIYlIZ/ux0MEwWs/AcSuAIGV9nkpYz7uD1sfmSmvYRyrZur3W9bJ7WXU24mEroGjbTipl1dtwB6B0DyuoWP8N1C1vfT5pfTRT0GtfK6xo2QDzXreGlZjJjev582HHk6y2fnIfzHvNqhcSbbL2a9jhlOeskMSdZX0sHgahntawlbYeIW29Kg64ki32vNjSsq2t3T5+ZFpchSAisp1avnw5999/P7fffjvBoPVaYpom77//PrNmzeKaa67Bbt/8HPfuu+/y0Ucf8dBDD7W7QG9ubuaWW27h8ssvp2fPntvsOEREtqVN66lggN1mx+WwkwWb1VSBjaFKyrT+H0umiMZTNEUTVDREWFsfYWVNMytrw6yqa2FlXZjZqzcQjiVJpkzsNgO/20GOz0lxyENZto9eeT7Kc32U5ngpCLjJ9jlxO6yCs99vp/x3FJKI/LdMszVISFqhQypu9YxIxqw6FE6fFShULbCGmCQirY8ouPzQ92ArGFj0D1j7Ves6LRBvXW+vC6FsD6hfBa+N2TgDSiJq7atwCJz8jDUE5KMJ8O/nWoMU02qbYYMTHoMhx1lDWv52ilVAdFMlu8C5b1rb+NdT8NWz1vJ05SkbjHzMCjGaq+Aft1g9QNpWMrCGrgwdaW3DNK3j7rnvxp4goTKrYCpATh849QVreMyWel+oBoeISIfLz8/nyy+/5PPPP+ewww7DMAySySSPP/44Bx54IDabjWQyiWma2O329AW3aZokk8nNtheLxXjvvfc477zzNnvONE1SqRQANput3bZSqdQW92EYRrv1RUS2N23nL3trqtLWSyXH76I81yqqYpomJpBMmUTjSRoiCaoao6ytb2FVbZgVNWFW17Wwrr6FheubaIzGSSRNDAP8Lgchn5OioIfSbC+9WnuhlGZ7KQp6yPY58brsOO02DBSe/FQKSaT7Mk2sQCG1sXdFMmYV3HS4rTCicq5V2yLaaBUTjWywQoDdzrR6EXzxKMx7w3o+1twaksThlxNh8NFWwPHMsVawsGmHkZKdoNd+1n4W/wP+/TzYHRtnYrE7YejxrSsbVo8Nh9cqPurwWF+X1590oNDvEKsmh81pBRCGfePQFbCGqxxxlxXoGHYr/LDZwJtrfQ0G7HGeNetK2/OGzaoHUjjE2kaoDM6YZB2HrfV5w259P+xOK/DY/zLrYXPwgyGIYVjHKSIinSoQCHDMMcfw8ssvM2LECAzDYMWKFcydO5c//OEPPP3003zwwQdEIhH23ntvLrroIgKBwM/eTzKZ5I033uDll18mlUpx7LHHMmrUKOx2O++++y7PP/88kUiE3r17c9NNNwHwpz/9ia+//hqXy8Vxxx3HSSedpAt7EclYhmGkh/o47TYCHic9sr3sXJ4NtAbHrbVSGloS1DbHWFvfwuraMCtqw6yqDbN2Q4TPltTwznfriSVSGAZ4nHZCXicFWW5Ks72U5/roleujKOgh1Dr1sc9lx+O043HacNhs2FunUW5rV3elu5WOZprWFKVNldaNpN3ZeiNrt25Q3QHr5jyyYWP3fZt94w2qzblxO6TYfBjCjwxLyDTthqW0hRuty9qGfrR9j5MJaFpvDfmINVohRrTRunkfeIT1fZ4zGRa+Y4Uf0Qbr+XgLHHY77HCM9XN7bpS1vC1QMWxQvKNVONRhh1jYCkDcQWvYiCcIroA1gwmALx8Oucn6Oodn48OXZ7UT4ICrYM8LWkMS58Zpa9uG0oRK4VeTN6l9Ydu8GOjAX1iPLfGErDa3rf9Dine0Hlvi9FqztvwYu+vHnxcRkfaqF8OqmZsvLxpi9fZLJWDh363Xqk053FbBa5ff2sbqLza+Jtrs1mudN2eLuzUMg+OOO47TTjuN9evXU1JSwttvv82OO+5IWVkZJSUlXHXVVZimyb333suzzz7LxRdf/LMOzTRNPv30U+644w7uuusu3G431113HX6/n+HDh3PLLbdw22230adPH1atWoXNZmPy5MnMnj2bW265hUQiQUtLy8/ap4hIpjEMA0e6VoqD4pCHIT2C7YbyxJOp9Iw96+sjrKoLs7ImzMraMGvrW/hqZR3TF1QRiSetW04M7DYDr8uO12nH57IT9DrJ9jnJ9jrJ8VvTH+f6XOT4XVao4nGS5XGkQxWXw4bdMLAZRvr2IlOCFYUk28K/noJP7yf9zrphWDfBh90Bu58N4Sp4/iSrx0JbDwCb3RqqcOKT1jv1s1+A76ZaN6F2Z+tHl3Xj2/sAKwyY+Yg1DMPu3vi8LxeGHGv9v+I7qFm8sbdC27Sm+QOtG/FkDCrnY4UBm7TD4bGKZ9rsG3tMGG036zbABi6fdcFmpjZOhQpWcNFWP8MTsj62bIBwDSSjG4ePJKOQN8BqR7TJqn0RqW8dftL68IRg30utY/n6BZg7bePQlLYhKAf9DoYeZwUkfz3SGiKSSm2s55E3wBrm4vRB7VJY8U/r/+4s63vlyrL+D1aQceit1v48QWv/7izro8NtfQ/2GwP7XLLxe/n92Uo8IdjjfOv/WzppZBVbjy0xbNb+fsx/OiFlyAlLRCTjrPgU3rxi8+X7jrFCkmQU3r8Dahe3f96bBz2HWyHJin+2bqM1JLG74fx3fzQkAejfvz+9evXi/fff58QTT2TatGmMGTMGt9tNaWkpr7/+OpWVlWzYsIGPP/74Z4ckAK+//jrHHnssI0aMAODMM89k6tSp7LPPPhiGwerVqxkwYAAHHXQQLpcLl8vFhg0bqK6uZujQoeTn52fMRbeIyNa06VAeu83qEVKQ5WZgUVa7ArPxRIpwPEldc4zKxihVjVE2hDcWk60Nx6gPW7P4LK1qJhxLEI1bxWjjyZTVidwAu2HgdNjwOe343A78Ljshn5Mcn4tsn4tcnzVzT27r9Mdts/n4XQ7cTmuWn7Zgpauf1xWSbAv9DrVulpMxa0rTZOujYLD1vM1p1WxoqbWeT8WtIRvu4Mab7mgD1K9unbK0dUrSVNIKSMDqzfDVM1b40DZ8xExZU5QOPMK60Z8zxSqs2W4YhAFHjbeGWjRXwwunWrOStFUjMgzI7QvnvGUFBTMftcKYtp4ubT1fDr4Bdj7F2sbkc62AA6z2JuNW+HHqC9bF3MxH4LMHN6np0dpL48jxsMe5EK2H926zCpFuuo/cvrD3RdaxtNRB3UorPLC7rPDC7gKnx9qeJwQ7n24FPd4QuEPWskDBxsBh+CVW3Q+bc2MgZLOT7qHhCsBuZ7V+m7bwh2x3wY/V7uziJwAREelkA4+AvNc3Xx4qtT46vDDyEevNgk3ZHRunPh94OOS9tvG5ttfu/8DpdHLSSScxefJk+vfvz4YNG9h///1Zs2YN559/PieffDIjRowgJyeHf//73//V4dXW1jJgwID0BXFRUREbNmwgNzeXhx9+mL/97W9MnjyZnJwc7r//fk444QSSySR/+ctfWLFiBaeccgqjR4/GZtOU6SIiP9WmBWbdTjtup50cn4u+BdawSbNdD31ImSaJpEkiZRKJJ2mOJWmOJmiMJNgQjlEXjlEXjlO3SbDS0BKnpinG8upmWuLWtMmxRIpEKoXNsHqq2G0GHqcdv8sKVkbsUMjVhw/a1t+On00hSUczDOi5949PSerNgWPu2/j5prOT2Fp/RHueD7ueSfsaGkkrdDAMa1jHWa9a7zilEtZwk1TC6nXitIoCsfNpVjvanmt7lO5uPe8OwoHXWIFLOoxJtPacaB1GUbgDDD5m43Nt6/nzNx6vN3fjsdjdVnAR2mT2kp7DrfY7vVYvFafXWqe0dSiHrwBO+xtWyehNes04vdbxAux1Aex+zsbaGpvW0QAr4Dj4+vY/h+9z/4ex1Qo4RESko/3H3oR2KN3tf9vGljZtGBx66KFMmDCBCRMmcOihh5KTk8O8efMIBoP89re/xeFwMGvWrHTh1f/ENM12F98DBw7kq6++IpFIYBgG//rXvxgwYACmabLTTjux22670dTUxMknn8ynn37KL3/5S371q19xxhlnMHPmTH77299y1llnpWfgERGR/933e3LYDQO7DdyA3+0g73vrfz9UMaE1VEkRT5qEYwmao0maognqW6wwpS4cs0KVcIy65jh14Rhe5/YxM6RCkm3lx264f6i4Jd97x6QtKNgSuwNy+/x4G/IHWI8tcQesnhyw5WlpBx1lPayVNm209cGXDyc9tfG5dsNPWtfpe7D1gB/+vjhcG4ObLWkrrioiIiL/teLiYoYPH86LL77Itddei2EY9O3bl4aGBm688UbsdjszZ84kNzf3P24rHo8zYcIE8vKsy+uCggJGjhzJhRdeyFVXXYXL5eKTTz7hySefZOXKldxyyy0MHjyYSCRCbW0tgwcP5qmnnmL27Nn06dOHr776it133x2v19vR3wYREfkR3w9VDMDlMHC13rOGvM7NvmbTYMWkdRDBlu4xuxiFJPLDthTqtFv+A+u0FZ8VERGRLs8wDG688UZOO+00dt11VwzDoKSkhOeff55PP/2UgoICRo8eTU1NDQAHH3wwO+2002YXzFlZWTzyyCOEw+H0Mr/fT79+/XjxxRf5+OOPSSaTXHrppZSXlxOPxxk9ejQLFizA6XRy9tln069fP/Lz8ykpKaGiooIzzzyT/fffH4dDl6siItubTV8njPb/dHl61REREZGMt+m7V/9Nwbjvv/vV1YvO/VSGYdCzZ0969uzZbln//v3p379/elmvXr0A6NGjBz169NhsO06nk4MOOugH99GjRw9OOeWUdstcLhf77LMP++yzT7vlRUVF/PKXv/yvj0dEROR/pZBEREREMpZpmrS0tPDqq68yd+5cdt99d4488kjc7vZDNhOJBFOnTqW6uhqwgoIjjzyS3r17Y5omlZWVvPzyy9TU1HDEEUew1157qZioiIhIBtKru4iIiGQs0zS5++67mTRpEgMHDuShhx7iL3/5y2Y9Q2KxGPfccw/r1q0jmUySTCbT6zQ3N3PRRRexdOlSiouLufTSS5k5c+Z2M7ZaREREfjr1JBEREZGMVVVVxauvvsrf/vY3dthhB/r06cPVV1/NmWeeSVZWVrt1nU4nZ5xxBoMGWdMTtg2pmTVrFlVVVTz77LMEAgEaGxt58skn2XvvvTNm2I2IiIhYFJKIiIhIxlq1ahU2m42ePXuma200NzdTWVm5WUgSj8e54447CAaDHHLIIRx77LG43W6++eYbBg0ahN/vxzAMdt99d1555RVisRgejweweqzEYjESiQQAkUhks54mNpsNm81GS0sLdruKnHcU0zQJh8M4HA6FWCIi8rMpJBEREZGM1dTUhMvlwum0pid0u93YbLZ2s7AAOBwOrr32WoqLi6mpqeHuu+9m3bp1XHrppdTX15OVlZW+4Q4EAjQ1NZFKpdpt46GHHuLvf/87APX19fTr16/d8zabjdzcXKqqqqiurtYNfAcxTRPTNCkqKurspoiIyHZIIYmIiIhkLJ/PRzweJ5lMAlZvkVQqtVnhVqfTyYknnohhGJimiWEY3HvvvVx00UX4/X5WrlyZXt7S0oLX692scOv555/PGWecAcC//vUvXnzxxXbPG4ZBKBTC7/cTj8dV06SDGIaBy+XCbrcriBIRkZ9NIYmIiIhkrNLSUuLxOOvXr6dPnz6sWrUKl8tFYWFhusfBD91It4UlpmkyZMgQXnvtNaLRKB6Phzlz5tCvX79075S29UOhEKFQCIDc3NwtbtfpdLb7WhEREek6NLuNiIiIZKzi4mIOPPBA7rrrLj766CPGjRvH8ccfTzAY5Ouvv+bSSy8lFouxatUqnnjiCT777DPefPNNxo8fz8iRI3G73ekCrQ8//DDvvPMOzzzzDL/61a80BbCIiEgGUk8SERERyVg2m43bb7+dxx9/nKeeeoqDDjqIc845B8MwyMrKYuDAgdhsNrxeL2vWrOGzzz7D7XYzZswYjjvuOAzDIDs7m0cffZTHH3+c+fPnc/311zNixAgN5RAREclACkk2kUwmmTdvHvn5+Z3dFBERkYzU2Nj4gzO/dBTDMMjNzeXaa69ND61pCzf69+/PZZddBkBBQQG33nprul2brmcYBgMGDGD8+PGbbWNLUqkUVVVVfPrppwpTREREOkA0GqW5uXmrX1MoJNnE0KFDmTFjBosWLdpq2zRNk48++ohddtklPU65O9iwYQPffPMNBxxwQLe6OFy7di1r1qxhzz337OymbFOffvopgwcPJi8vr7Obss2kUimmT5/O8OHD8fl8nd2cbaayspIlS5awzz77dHZTtqmlS5fS3NzMjjvu2NlN2WZM02TGjBnstttuBIPBrbrdXXbZZZtPgftTgo0fW2fTwOSnCIVC5OXl8de//vXnNfQ/+Oyzz+jXrx+FhYVbdbtdWSqVYsaMGey5554EAoHObs42U1NTw7x589h///07uynb1MqVK6mpqWHXXXft7KZsM6Zp8vHHH7PTTjuRnZ3d2c3ZZhKJBDNmzGC//fZLT6feHXTX+4WFCxeSTCbZYYcdtup2hwwZgsvl2qrbNEyVVgc2The3tSWTSU455RRuv/12hgwZstW331V99913/P73v+fFF1/sVmO2//73v/P2228zceLEbhMOpVIpzj77bC677DL22GOPzm7ONhONRhk5ciSPPfYYZWVlnd2cbeaTTz7hqaee4vHHH+82v+MAzz77LCtWrOCmm27q7KZsM4lEghNPPJF77rmHAQMGbPXt/5TQYnvWEdcVpmlywQUXcM4553SrG+d4PM6oUaOYOHEiffv27ezmbDOzZs3i/vvv5+mnn+5W11JTpkzhX//6F2PHju3spmwzqVSKU089lVtuuYVhw4Z1dnO2mXA4zMiRI3n22We7VfD77rvv8sYbb3D//fdn9Ovg9z366KOEw2GuuOKKrb7trX1NoZ4krTrqYm3Tbrvd6QVu03fcuttxtx1zdzrpbXrc3UXbMeu4u4dNj7u7/G1315/11tIRvyupVKpb/ky68zUFWHV1uuNxd6fzbXe+X+jO57Tuer+wPfxtKyTZBgKBwDbvVtzZ7HZ7t+oS28bpdHaroRdt/H5/t/sdNwyDQCDQrV7UARwOB36/v7Obsc253e5u1RUYuu/veFfn9/txOLrX5Vtbkd3u9rvocDi65bWUy+XqltdS3fF+obv+bTudzm57LZVKpTq7GT+Jhtt0MNM0qaioIDc3d6uPlerKYrEYdXV1FBYWdvmkcGsKh8OEw2Hy8vK6zXGbpklVVRXBYLBb3USapsn69espKCjoVjcsLS0tNDU1kZ+f321+x8EqNppIJMjOzu42x91dX7+6MtM0qa6uJhAI4PV6O7s520zb72JeXh5Op7Ozm7PNRKNR6uvrKSgo6DbnHYCmpiai0Si5ubnd5rhN06SyspKcnJxudb5NpVJUVFRQWFjYrQKi7ni/ANDQ0EAqlSIUCnX541ZIIiIiIiIiIiICdK++TSIiIiIiIiIiW9B9+oh3AtM0CYfDVFZWEgqFyMnJ6fJdi/5XpmnS3NxMdXU1TqeTwsJCHA5Hxh93m7afeSqVIhAIdIvjNk2T+vp6ampqCAaD5OXlZfzYUtM02bBhA7W1tYRCIXJzczPymE3TJB6PEw6Hcbvd7br3m6ZJXV0d9fX1FBUV4fV6M+L3va1zZSwWo6WlBZ/Pl+76bJomTU1N1NTU4HK50t2DM+m4I5EIkUiErKyszYaRdcfzW1dimiaRSISKigoCgUC36KZtmiYtLS1UVVVhs9koKirC6XRm/HG3aTv+eDxOMBjsFsdtmiaNjY1UV1fj9/vJz8/P+GEYpmkSjUapqKjAbrdTWFiYkb/npmmSSqUIh8OYpklWVlb6GNvOb+vXrycrKyujzm+bXkt5PJ700PS219Tq6uqM+7m3XVNEo1FaWlrw+/2bDSNrO78lEol2vwtdhUKSDmKaJkuWLOGyyy4jlUrR3NzM7373O37xi190uV+CrenTTz/l1ltvxePx0NLSwsCBAxk3bly3mPO97abx9NNPx+Px8PLLL2f82OlUKsWkSZP4y1/+gtfrJZlMMnHixA6ZLrSrME2TadOmcd9995GdnU1tbS1nnnkmF154YcYFJe+//z4TJkxg6dKlXHLJJVx22WWA9T148803GT9+PD6fD4fDwQMPPECfPn0y4vz2t7/9jSeffJIVK1bwxz/+keOOOw6AGTNmcMcdd+D1egmHwwwePJi77rqLUCjUyS3+35mmyX333cerr77KunXreOmll9htt93arVNTU8Npp51GTk4Ozz//fMaf37oS0zRZt24dY8aMobGxkXA4zOjRozn11FMz4m9uS+bMmcNVV12Fw+EgFotRVFTEvffeS1FRUWc3bZsIh8Ocf/75rF+/nrfeeivji5mapsnbb7/N/fffj9vtJh6Pc8cdd7DHHnt0dtM6jGmarFy5kjFjxhCNRonH4/Ts2ZN7772XnJyczm7eVlVdXc1VV13FnDlzKCws5NVXX8XtdmOaJmvWrGHMmDGEw2Gam5sZM2YMJ554Ykac39566y3uv/9+li1bxnXXXcevf/1rAGbPns21116bPr/16NGDP/7xjxQUFHRyi7eOJ598kueee46VK1fyyCOPcNhhh7V7vqmpiXPOOYeGhgZeeeWVLlfINrOu6LuQVCrF3XffzV577cXUqVP53e9+xx133EF9fX1nN61DlZWV8ec//5mpU6cyadIklixZwiuvvJJOFDNZKpXikUcewe/3U1NT09nN6XCmaTJ79mweeeQR/vSnPzFlyhT+9re/0bNnz85uWodqaWlh/PjxXH755UyZMoVx48bxwAMPZOTPvKysjOuvv57hw4fT2NiYXl5XV8edd97JTTfdxLRp09htt90YP358xvydDx48mNtuu42ePXvS0tKSXt6zZ08eeeQRpkyZwksvvcS8efN4/fXXO7GlW9dee+3FnXfeidfrJRaLtXsumUzy8MMPk5WVRW1tbcb8rLcnDz30ECUlJUybNo177rmHe+65h3Xr1nV2szpUfn4+9957L1OnTmXKlCnE43H++te/dovfP9M0ee6554jH49TV1XWLY166dCl33XUXd911F5MnT2bSpEkMHTq0s5vV4Z599llCoRBTpkxh0qRJLF68mPfff7+zm7XVud1uTj/9dC6//PJ2ryOmafLAAw/Qq1cvpk6dyrhx4xg3bhwVFRWd3OKto1evXtx4443ssssuNDU1pZcXFBRw3333MXXqVCZPnkxTUxPPPvtsxvytDxs2jDvuuIOioqJ211Jg3TM988wzAF32/KaQpIM0Njby5ZdfMnLkSLxeLwcccADxeJwFCxZ0dtM6VO/evRk4cCBut5vc3FzKy8upqqrq7GZ1ONM0mTVrFt999x2nnXZaZzdnm5kyZQrDhw9nxYoVTJ48OT0MIZMZhoHL5cLtdmOz2dJTFWZid+DBgwdz0EEHbdZTYv78+ZimyX777YfX62XkyJHMnDmzXZCyvTIMg91335199tlns3dt+/bty4ABA3C73eTl5VFWVkZ1dXUntXTrstlsHHDAAey+++4/2CX2888/Z/HixZx88smd1MLuLRqNMn36dEaNGoXP52O33XYjJyeHf//7353dtA5VUlLCsGHD8Hg8BINB+vfvT2VlZWc3q8OZpsmCBQt49913M7KX4pa89dZbDBo0iPr6el5++WXWrFmD2+3u7GZ1OJfLhcvlwm6343A42g3JyCTBYJBf/OIX9O7du93ylpYWPvroo/T5bY899iArK4tvvvmmcxq6lQ0bNowDDjiArKysdstLS0sZOnQoHo+HUChE3759M+aeyTAM9t57b/bee+/NZmIzTZO5c+fy4Ycfcv7553fZ3kLd46zbCRobG4lEIuTn5wOkQ4P169d3csu2DdM0mTNnDrNmzWLEiBFd9g9ga2lsbOTee+/l8ssvJxAIdHZztpl58+bx1ltv8dprrzFr1ixOPvlkvvnmmy6ZCG8tHo+HG2+8kVtvvZWTTjqJ0aNHc+2112Zct9gfs27dOvLy8tI30wUFBbS0tNDc3NzJLds2TNPkm2++Yfbs2RxyyCGd3ZwO19DQwMSJE7n88sszvrt/V9XS0kJ9fT3FxcUAOBwOioqKWLNmTSe3bNswTZPly5fzzjvvcPTRR2f8NUU0GuWee+7hoosuIi8vr7Obs02Ypsm8efP45JNPeOGFF5gzZw6nn346n3zySUZfUxiGwVlnncX69es57rjjOPbYYxkwYEC3eG1p0zbEpm2YicvlIi8vj7Vr13Zyy7YN0zRZunQpH3zwAUceeWRnN6fDtbS0cM899zB69Ogufe2skGQby+QTfRvTNFm1ahVXXHEFo0ePZuedd+7sJnUo0zR58sknGTRoEDvuuCOJRCJdpCmTf96maZJMJunfvz/33nsv48eP56CDDuLZZ5/t7KZ1qGg0ypQpUzjqqKP43e9+x+mnn84zzzzDhg0bOrtp28z3f683LbyW6dpu1q644grGjBnDsGHDOrtJHSqVSvH444+z0047MWTIEBKJBKlUKn2ek23nh/7uusPPwDRNqqqquOyyyzjhhBM46KCDOrtJHco0TSZPnozX62W//fZLX0tk+jUFWMP6CgsLmThxImPHjuWUU07hiSeeyOjjNk2TGTNm4HQ6+d3vfsc111zD3LlzmTVrVkYft1hM06SyspLLLruMU045hX333TejQ2DTNJk0aRK5ubkMHz68S5/fVLi1gwQCAdxuNzU1NfTo0YNoNEpdXV36XaBM1VZc7je/+Q1HHnkkv/71rzO+m2gymWTGjBnU1NTw5ZdfUl1dzZIlSxgzZgwPPPBAxr7zahgGZWVlOJ3OdAHHvn378tlnn3VyyzrW2rVr+fjjj5k+fTpFRUXssMMOvPjii8ydO5f99tuvs5u3TRQXF1NbW0s8HsfpdFJdXY3H48n4XlRtxeV+85vfcOyxx3LOOedk/PktHo/zySefUFdXxz//+U8qKytZsWIFV1xxBRMnTtysG610jLbu2BUVFQwePJhEIkFFRQU9evTo7KZ1qLaC6GPGjGHo0KFcffXVGTm08fumT5/O3LlzOfHEE2loaGDp0qVcfPHF/PnPf+7S77z+r8rLy2lsbEyfV/r168eHH35IKpXK2HNtW22GX/3qV+kA8PPPP+fVV1/lgAMO6OTWbRs+nw+fz0d1dTWDBg0iFotRW1vbLe6Zamtr+e1vf8uuu+7K5ZdfvtmscpkmlUoxY8YMFi5cyKhRo9iwYQOLFi3ikksu4eGHH+5SE31k9k+iEwWDQXbbbTdef/11Bg0axOeff47NZmPgwIGd3bQOVVNTw+jRo9lxxx254IILiMVimKaZ0dMA2+12Hn/88XShw+nTp3P//ffz+9//PiPHlG7q8MMPZ+LEidTU1ODxePjkk082mxEj07RdvC1atIi8vDxWrlxJU1NTlzqxby3JZJJoNEoymSQej9PS0oLL5WLw4MGkUilmzpzJvvvuy2uvvcYee+yRMSFJIpGgpaWFVCqVngrY7XZTXV3NxRdfzB577MF5552XPr9lyiwvbceaSqWIRqNEIhFcLhdPPPFE+vz27rvv8pe//IVbbrmlW9QK6Co8Hg8HHHAAr776KnvvvTffffcdNTU17Lrrrp3dtA7V1NTEFVdcQSgU4uqrr06fizJlmswtGT9+PJFIBLBmwLjmmmsYO3YswWCwk1vWcQzD4JBDDuH1119n7dq15ObmMn36dHbccceMDUjAOu78/HzmzJlDLBYjFouxYMEC9txzz85u2lbXNtVxNBpNT/9qs9nwer3su+++vPrqq+yxxx7Mnj2b+vp6dtppp85u8laRSCQ2u5Zyu900NjYyZswYCgsLueKKK0gmk8RisYw5v7Ud6/evpf74xz+mz2//+te/uPnmm7nzzjs3q9nS2Qyzq/VtyRBtRbfGjBmD2+2mrq6Oa665hmOPPTYjfvG3ZNKkSVx55ZUMHDgwXa/gpJNO4rzzzsvo497UBx98wMSJE5kyZUrG3DxtSUtLCzfddBOzZ8/G4XCQm5vL/fffT35+fsb+vFOpFI899hhPP/00JSUlrF+/nqOOOorrrrsu437eX375JbfeeisrV67E4XBQVlbGuHHjGDp0KFOnTk1PgxyPx3nwwQcZMGBARvzcX3vtNR5++GGWLFmSLkD94IMP8v777/O73/2OAQMGpM9vp5xyCueee24nt3jr+POf/8y0adNYuHAh5eXl9O/fnwceeKDdhcs777zD448/zgsvvJBxv+9dmWmarF69mtGjR5NKpaivr+fXv/41Z511VkbfQH7wwQecddZZ9OvXLx1QH3rooVx11VUZfdyb+vrrr7nyyit5/fXXu9wUmVtbPB5n3LhxfPDBB/h8PpxOJw888ADl5eUZ8dryQ9pqXF1xxRX4fD6i0SiBQCA9m1UmHXdzczO//e1vWbRoEWvWrGHgwIGMHDmSiy66iBUrVnDJJZcAUF9fz8UXX8zpp5+eEX/nH3/8MWPHjmX58uV4vV7KysqYMGECS5cu5YILLqBv377p89sRRxzB5ZdfnhE/95deeoknn3ySxYsXU1hYSK9evXjwwQfbTXE8a9YsbrjhBqZNm9blzm8KSTqQaZo0NjamE/GCgoKM+KX/MS0tLZvNcOHz+fD7/Rl/7G2i0SjhcJjs7OyMP2bTNEkkEqxZs4ZkMklpaSlutzvjjzuVSlFdXU1NTQ2hUIiioiJsNlvGHXc0Gm03bblhGIRCIVwuF6lUiqqqKurq6igtLSUQCGTM8YfD4XbT9BmGQW5uLtFotN1ysM5vmdKDprGxsd00fXa7nZycnHYXqdFolJaWFkKhUMb8vLcXpmkSDodZvXo1wWAwfd7JZN8/B4HVqyYrK6vb/P7F43EaGxvJycnpFsecTCZZu3YtkUiE0tJSvF5vxh+3aZo0Nzezdu1a7HZ7xl5LpVIp6urqSCaT6WVerzf9Gtrc3MyaNWsy7vwWiURoaGhIf24YBtnZ2SSTyXbLIbPOb83Nze0K+ttsNnJyctoNmezK5zeFJCIiIiIiIiIiaHYbERERERERERFAIYmIiIiIiIiICKCQREREREREREQEUEgiIiIiIiIiIgIoJBGRTtY2Q04sFmv3SKVSW3U/DQ0NfPjhhyQSia26XREREek6tsU1RUtLC++//z7RaHSrbldEugbNbiMinco0Ta699lr+9a9/4fF4AHA4HNxxxx3svPPOW20/c+fO5eKLL+bvf/87Pp9vq21XREREuo5x48a1e603DIPrrruOAw88cKvtY82aNZxwwgm89dZb5OXlbbXtikjX4OjsBohI92aaJkuWLOGYY47h5JNPBqwLmvz8fEzTTD8ikQgejwebzYZhGOnlLS0tOBwOXC5Xeo510zRJJpNEIhFcLhdOpzO9PJVK0dzcjNPpxOl0drl52UVEROS/t2LFCvbdd19Gjx6dXpabm5u+bgCrJ4jb7cZut7e7pohEIthsNtxud7trilQqRUtLC06nE5fLld6uaZqEw2Hsdnu76xAR2b4pJBGRLiEnJ4fS0tJ2y7744gueffZZnE4n3333HeXl5dx5550UFxdTV1fHXXfdxTfffIPT6eTcc89l5MiRGIbBzJkzue+++6ivrycQCHDnnXcCEI1Guffee/n8889xuVyMHTuWHXbYQRc1IiIiGSQYDG52TbFw4UL++Mc/kp+fz1dffUVubi533HEHffv2pbm5mQkTJvDZZ59hs9k4+eSTOfPMM7Hb7cyZM4fx48dTVVWF2+3mpptuokePHiQSCR577DE+++wzUqkUt9xyC3vttZeuKUQygEISEekSJk2axNy5cwHwer2MGTOGuro6Jk2axBNPPME111zDuHHjGDt2LBMnTmTixIlUVFTwxBNPsGzZMsaMGcOgQYPIzc3l0ksv5aqrruKQQw6htraWvLw8qqqqWLVqFX369OGiiy7i8ccf59577+Wxxx7TBY2IiEgGefvtt6mpqQHA6XTym9/8hsbGRqZOncp9993HmDFjePTRR7npppt45plneOqpp/jqq6945JFHqKur48ILL6R///4MGzaMSy65hDPOOIPjjz+exsZGvF4vAFVVVQQCAZ544gmmTJnCXXfdxcsvv5zuvSoi2y+FJCLSJQwePJiDDjoIsC5o2i5Chg0bxi9+8QucTifnn38+F1xwAbW1tXz44YfcddddlJWVUVpaym677cYnn3xCSUkJhYWFjBo1CqfTSWFhIWBdzJSXl3PccccRDAY5/PDDueGGG0gmk9hsqmEtIiKSKfr27Zu+prDb7WRlZVFRUUGvXr0YOXIkgUCAc845h+OOO46qqireeecdzj//fHr37k3v3r057LDDeP/997Hb7aRSKc4880x8Ph9FRUWAVZMkLy+Pk046iaKiIkaMGMEzzzxDLBZTSCKSARSSiEiXsPPOO3PMMcdsttzr9abHDHu9XpLJJPF4nGg0SiAQSPcCCQQCtLS0EA6HCQQCPxh8uN1uHA7rtNd24aPa1SIiIpll8ODBHH300Zv1FPV4POkQo63uSCwWo6WlhaysrPR6WVlZbNiwgZaWFrxeb/raYVMulytdn8Rut7ereSIi2ze9fSoiXUIqlSKZTKYfbRcaCxYsYM2aNaRSKb744guKi4vJycmhf//+fPTRR6RSKTZs2MDXX3/NkCFDGDJkCIsWLWLVqlWYpkk8Hicej3fy0YmIiMi2kkql2l1XtE0BvHLlS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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل SUB\n","✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل SUB. سيتم تحميله بدون إعادة تدريب.\n","\u001b[1m3390/3390\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1026s\u001b[0m 302ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99      1446\n","           1       0.81      0.84      0.82      1447\n","           2       0.99      1.00      1.00      1447\n","           3       0.98      1.00      0.99      1446\n","           4       0.99      1.00      1.00      1446\n","           5       0.98      0.99      0.98      1446\n","           6       1.00      1.00      1.00      1446\n","           7       0.99      0.99      0.99      1446\n","           8       0.99      1.00      0.99      1446\n","           9       1.00      1.00      1.00      1446\n","          10       0.98      1.00      0.99      1446\n","          11       0.99      1.00      0.99      1447\n","          12       0.97      0.98      0.97      1446\n","          13       0.85      0.89      0.87      1446\n","          14       0.98      0.99      0.99      1447\n","          15       0.99      1.00      0.99      1447\n","          16       0.95      0.99      0.97      1446\n","          17       0.98      1.00      0.99      1446\n","          18       0.98      0.99      0.99      1446\n","          19       0.64      0.62      0.63      1446\n","          20       0.60      0.63      0.61      1447\n","          21       0.83      0.89      0.86      1447\n","          22       0.99      1.00      1.00      1446\n","          23       0.98      1.00      0.99      1447\n","          24       0.99      1.00      1.00      1447\n","          25       0.98      1.00      0.99      1446\n","          26       0.97      1.00      0.98      1447\n","          27       0.94      1.00      0.97      1447\n","          28       1.00      1.00      1.00      1446\n","          29       0.84      0.82      0.83      1447\n","          30       0.99      1.00      1.00      1446\n","          31       0.90      0.95      0.92      1446\n","          32       0.96      0.91      0.94      1447\n","          33       0.96      0.99      0.98      1446\n","          34       1.00      1.00      1.00      1446\n","          35       0.95      0.99      0.97      1446\n","          36       0.26      0.07      0.10      1446\n","          37       0.49      0.56      0.52      1447\n","          38       0.81      0.78      0.79      1446\n","          39       0.51      0.54      0.53      1446\n","          40       0.98      0.99      0.99      1446\n","          41       1.00      1.00      1.00      1447\n","          42       0.36      0.22      0.27      1446\n","          43       0.97      1.00      0.98      1447\n","          44       0.91      0.94      0.93      1446\n","          45       1.00      1.00      1.00      1446\n","          46       0.99      1.00      0.99      1447\n","          47       0.98      1.00      0.99      1446\n","          48       0.97      1.00      0.98      1447\n","          49       0.76      0.87      0.81      1446\n","          50       0.96      0.99      0.97      1446\n","          51       0.66      0.58      0.62      1447\n","          52       0.30      0.26      0.28      1447\n","          53       1.00      1.00      1.00      1446\n","          54       0.99      1.00      0.99      1446\n","          55       0.80      0.79      0.79      1447\n","          56       1.00      1.00      1.00      1446\n","          57       0.99      1.00      0.99      1447\n","          58       0.96      0.96      0.96      1446\n","          59       0.37      0.30      0.33      1447\n","          60       1.00      1.00      1.00      1447\n","          61       0.96      0.98      0.97      1446\n","          62       1.00      1.00      1.00      1446\n","          63       1.00      1.00      1.00      1446\n","          64       1.00      1.00      1.00      1446\n","          65       1.00      1.00      1.00      1447\n","          66       0.92      0.96      0.94      1446\n","          67       0.98      0.99      0.98      1447\n","          68       1.00      1.00      1.00      1447\n","          69       0.94      0.97      0.95      1446\n","          70       0.78      0.91      0.84      1447\n","          71       1.00      1.00      1.00      1447\n","          72       0.99      1.00      0.99      1446\n","          73       0.91      0.95      0.93      1447\n","          74       0.47      0.59      0.52      1446\n","\n","    accuracy                           0.90    108480\n","   macro avg       0.89      0.90      0.90    108480\n","weighted avg       0.89      0.90      0.90    108480\n","\n","\n","🔄 Summary for SUB:\n","✅ Accuracy: 0.9013\n","✅ Precision: 0.8918\n","✅ Recall: 0.9013\n","✅ F1-Score: 0.8951\n","✅ عرض رسم Accuracy & Loss المخزن: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/CNN + Bilstm_sub_finetuned_acc_loss1.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل RF\n","✅ تم تحميل النموذج الأساسي لموديل RF\n","Epoch 1/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 10ms/step - accuracy: 0.9631 - loss: 0.1100 - val_accuracy: 0.9397 - val_loss: 0.1702\n","Epoch 2/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9699 - loss: 0.0918 - val_accuracy: 0.9415 - val_loss: 0.1704\n","Epoch 3/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.9689 - loss: 0.0932 - val_accuracy: 0.9423 - val_loss: 0.1707\n","Epoch 4/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 10ms/step - accuracy: 0.9698 - loss: 0.0887 - val_accuracy: 0.9431 - val_loss: 0.1687\n","Epoch 5/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.9718 - loss: 0.0843 - val_accuracy: 0.9431 - val_loss: 0.1687\n","Epoch 6/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 8ms/step - accuracy: 0.9709 - loss: 0.0878 - val_accuracy: 0.9445 - val_loss: 0.1670\n","Epoch 7/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 10ms/step - accuracy: 0.9706 - loss: 0.0859 - val_accuracy: 0.9443 - val_loss: 0.1673\n","Epoch 8/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 10ms/step - accuracy: 0.9722 - loss: 0.0848 - val_accuracy: 0.9459 - val_loss: 0.1678\n","Epoch 9/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 14ms/step - accuracy: 0.9722 - loss: 0.0860 - val_accuracy: 0.9448 - val_loss: 0.1673\n","Epoch 10/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 11ms/step - accuracy: 0.9725 - loss: 0.0835 - val_accuracy: 0.9460 - val_loss: 0.1669\n","Epoch 11/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.9729 - loss: 0.0818 - val_accuracy: 0.9458 - val_loss: 0.1681\n","Epoch 12/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 10ms/step - accuracy: 0.9750 - loss: 0.0793 - val_accuracy: 0.9461 - val_loss: 0.1672\n","Epoch 13/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.9739 - loss: 0.0827 - val_accuracy: 0.9470 - val_loss: 0.1657\n","Epoch 14/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 8ms/step - accuracy: 0.9760 - loss: 0.0766 - val_accuracy: 0.9472 - val_loss: 0.1675\n","Epoch 15/15\n","\u001b[1m1106/1106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 10ms/step - accuracy: 0.9745 - loss: 0.0792 - val_accuracy: 0.9476 - val_loss: 0.1679\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["✅ تم حفظ النموذج الجديد بعد Fine-Tuning: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/CNN + Bilstm_rf_finetuned1.h5\n","\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.93      0.97      0.95      8846\n","           1       0.97      0.93      0.95      8846\n","\n","    accuracy                           0.95     17692\n","   macro avg       0.95      0.95      0.95     17692\n","weighted avg       0.95      0.95      0.95     17692\n","\n","\n","🔄 Summary for RF:\n","✅ Accuracy: 0.9476\n","✅ Precision: 0.9482\n","✅ Recall: 0.9476\n","✅ F1-Score: 0.9476\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# ✅ استيراد المكتبات\n","# اجرب ال fine tuning for CNN + Bilstm for the all catagories\n","# ✅ استيراد المكتبات\n","#!pip install tensorflow\n","\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","from tensorflow.keras.models import load_model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","\n","# ✅ المسارات\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/\"\n","models_info = {\n","    \"main\": {\"classes\": 22, \"data_file\": \"train_test_data_main-deep-new.pkl\", \"model_file\": \"cnn+bilstm_main_with_attention.h5\"},\n","    \"sub\":  {\"classes\": 75, \"data_file\": \"train_test_data_sub-deep-new.pkl\",  \"model_file\": \"cnn+bilstm_sub.h5\"},\n","    \"rf\":   {\"classes\": 2,  \"data_file\": \"train_test_data_rf-deep-new.pkl\",   \"model_file\": \"cnn+bilstm_rf.h5\"}\n","}\n","\n","# ✅ دالة الفاين تيونينغ\n","def fine_tune_model(name, classes, data_file, model_file):\n","    print(f\"\\n🚀 بدء Fine-Tuning لموديل {name.upper()}\")\n","\n","    # المسارات\n","    model_path = os.path.join(base_path, model_file)\n","    data_path = os.path.join(base_path, data_file)\n","    fine_tuned_model_path = os.path.join(base_path, f\"CNN + Bilstm_{name}_finetuned1.h5\")\n","    acc_loss_plot_path = os.path.join(base_path, f\"CNN + Bilstm_{name}_finetuned_acc_loss1.png\")\n","    conf_matrix_path = os.path.join(base_path, f\"CNN + Bilstm_{name}_finetuned_conf_matrix1.png\")\n","\n","    # تحميل البيانات\n","    with open(data_path, \"rb\") as f:\n","        X_train, X_test, y_train, y_test = pickle.load(f)\n","\n","    if os.path.exists(fine_tuned_model_path):\n","        print(f\"✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل {name.upper()}. سيتم تحميله بدون إعادة تدريب.\")\n","        model = load_model(fine_tuned_model_path, compile=False)\n","        model.compile(\n","            optimizer=Adam(learning_rate=0.00005),\n","            loss=\"sparse_categorical_crossentropy\",\n","            metrics=[\"accuracy\"]\n","        )\n","        history = None\n","    else:\n","        model = load_model(model_path, compile=False)\n","        print(f\"✅ تم تحميل النموذج الأساسي لموديل {name.upper()}\")\n","\n","        model.compile(\n","            optimizer=Adam(learning_rate=0.00005),\n","            loss=\"sparse_categorical_crossentropy\",\n","            metrics=[\"accuracy\"]\n","        )\n","\n","        history = model.fit(\n","            X_train, y_train,\n","            validation_data=(X_test, y_test),\n","            epochs=15,\n","            batch_size=64,\n","            callbacks=[ ]\n","        )\n","\n","        model.save(fine_tuned_model_path)\n","        print(f\"✅ تم حفظ النموذج الجديد بعد Fine-Tuning: {fine_tuned_model_path}\")\n","\n","    # التقييم\n","    preds = model.predict(X_test)\n","    preds = np.argmax(preds, axis=1)\n","\n","    print(\"\\n📊 Classification Report:\")\n","    print(classification_report(y_test, preds))\n","\n","    accuracy = accuracy_score(y_test, preds)\n","    precision = precision_score(y_test, preds, average='macro', zero_division=0)\n","    recall = recall_score(y_test, preds, average='macro', zero_division=0)\n","    f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","    print(f\"\\n🔄 Summary for {name.upper()}:\")\n","    print(f\"✅ Accuracy: {accuracy:.4f}\")\n","    print(f\"✅ Precision: {precision:.4f}\")\n","    print(f\"✅ Recall: {recall:.4f}\")\n","    print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","    # رسم Accuracy & Loss\n","    if history:\n","        plt.figure(figsize=(14, 5))\n","        plt.subplot(1, 2, 1)\n","        plt.plot(history.history['accuracy'], label='Train Accuracy')\n","        plt.plot(history.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","        plt.title(f\"{name.upper()} Fine-Tuning Accuracy\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Accuracy\")\n","        plt.legend()\n","\n","        plt.subplot(1, 2, 2)\n","        plt.plot(history.history['loss'], label='Train Loss')\n","        plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","        plt.title(f\"{name.upper()} Fine-Tuning Loss\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Loss\")\n","        plt.legend()\n","\n","        plt.tight_layout()\n","        plt.savefig(acc_loss_plot_path, dpi=300)\n","        plt.show()\n","\n","    elif os.path.exists(acc_loss_plot_path):\n","        from PIL import Image\n","        print(f\"✅ عرض رسم Accuracy & Loss المخزن: {acc_loss_plot_path}\")\n","        img = Image.open(acc_loss_plot_path)\n","        plt.figure(figsize=(14, 5))\n","        plt.imshow(img)\n","        plt.axis('off')\n","        plt.title(f\"{name.upper()} Fine-Tuning Accuracy & Loss (Loaded)\")\n","        plt.show()\n","    else:\n","        print(\"⚠️ لا يوجد ملف رسم محفوظ لعرضه.\")\n","\n","    # رسم Confusion Matrix\n","    plt.figure(figsize=(24, 20))\n","    cm = confusion_matrix(y_test, preds)\n","    sns.heatmap(cm, annot=False, fmt=\"d\", cmap=\"Blues\")\n","    plt.title(f\"Confusion Matrix - {name.upper()} Fine-Tuned\")\n","    plt.xlabel(\"Predicted\")\n","    plt.ylabel(\"Actual\")\n","    plt.tight_layout()\n","    plt.savefig(conf_matrix_path, dpi=300)\n","    plt.show()\n","\n","# ✅ تنفيذ Fine-Tuning أو تحميل النماذج الثلاثة\n","for name, info in models_info.items():\n","    fine_tune_model(name, info[\"classes\"], info[\"data_file\"], info[\"model_file\"])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":94027,"status":"ok","timestamp":1745841137459,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"ohhFoHl01c85","outputId":"85856239-1d56-4dae-c8c2-247f991c2b2a"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل MAIN\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل MAIN. سيتم تحميله بدون إعادة تدريب.\n","\u001b[1m1287/1287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 11ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99      1871\n","           1       0.99      1.00      1.00      1871\n","           2       0.81      0.75      0.78      1872\n","           3       0.93      0.97      0.95      1872\n","           4       0.68      0.72      0.70      1872\n","           5       0.49      0.44      0.46      1871\n","           6       0.83      0.81      0.82      1872\n","           7       0.40      0.35      0.38      1872\n","           8       0.96      0.99      0.98      1871\n","           9       0.94      0.98      0.96      1872\n","          10       0.72      0.74      0.73      1872\n","          11       0.63      0.54      0.58      1871\n","          12       0.40      0.43      0.41      1872\n","          13       0.64      0.65      0.64      1871\n","          14       0.48      0.50      0.49      1872\n","          15       0.73      0.77      0.75      1871\n","          16       0.49      0.47      0.48      1872\n","          17       0.93      0.95      0.94      1872\n","          18       0.99      1.00      1.00      1872\n","          19       0.95      0.99      0.97      1872\n","          20       0.85      0.88      0.87      1871\n","          21       0.95      0.95      0.95      1872\n","\n","    accuracy                           0.77     41176\n","   macro avg       0.76      0.77      0.76     41176\n","weighted avg       0.76      0.77      0.76     41176\n","\n","\n","🔄 Summary for MAIN:\n","✅ Accuracy: 0.7670\n","✅ Precision: 0.7632\n","✅ Recall: 0.7670\n","✅ F1-Score: 0.7646\n","✅ عرض رسم Accuracy & Loss المخزن: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/main_finetuned_acc_loss.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل SUB\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل SUB. سيتم تحميله بدون إعادة تدريب.\n","\u001b[1m3390/3390\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 17ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.99      1.00      1.00      1446\n","           1       0.80      0.85      0.82      1447\n","           2       1.00      1.00      1.00      1447\n","           3       0.99      0.99      0.99      1446\n","           4       0.99      1.00      0.99      1446\n","           5       0.97      0.99      0.98      1446\n","           6       1.00      1.00      1.00      1446\n","           7       0.98      0.99      0.99      1446\n","           8       0.99      1.00      0.99      1446\n","           9       0.99      1.00      1.00      1446\n","          10       1.00      1.00      1.00      1446\n","          11       0.99      1.00      0.99      1447\n","          12       0.97      0.98      0.98      1446\n","          13       0.89      0.94      0.91      1446\n","          14       0.98      0.99      0.99      1447\n","          15       0.99      1.00      1.00      1447\n","          16       0.96      0.98      0.97      1446\n","          17       0.97      1.00      0.98      1446\n","          18       0.98      0.99      0.99      1446\n","          19       0.65      0.67      0.66      1446\n","          20       0.63      0.67      0.65      1447\n","          21       0.86      0.87      0.86      1447\n","          22       1.00      1.00      1.00      1446\n","          23       0.99      1.00      0.99      1447\n","          24       1.00      1.00      1.00      1447\n","          25       0.98      0.99      0.99      1446\n","          26       0.99      0.99      0.99      1447\n","          27       0.98      1.00      0.99      1447\n","          28       1.00      1.00      1.00      1446\n","          29       0.81      0.85      0.83      1447\n","          30       0.99      1.00      0.99      1446\n","          31       0.89      0.93      0.91      1446\n","          32       0.93      0.90      0.91      1447\n","          33       0.98      0.99      0.98      1446\n","          34       1.00      1.00      1.00      1446\n","          35       0.96      0.99      0.97      1446\n","          36       0.24      0.09      0.13      1446\n","          37       0.54      0.54      0.54      1447\n","          38       0.79      0.78      0.78      1446\n","          39       0.49      0.57      0.53      1446\n","          40       0.98      0.99      0.99      1446\n","          41       1.00      1.00      1.00      1447\n","          42       0.35      0.29      0.32      1446\n","          43       0.98      0.99      0.99      1447\n","          44       0.93      0.95      0.94      1446\n","          45       1.00      1.00      1.00      1446\n","          46       0.98      0.99      0.98      1447\n","          47       0.98      1.00      0.99      1446\n","          48       0.98      0.99      0.99      1447\n","          49       0.82      0.88      0.85      1446\n","          50       0.97      0.99      0.98      1446\n","          51       0.66      0.60      0.63      1447\n","          52       0.32      0.27      0.30      1447\n","          53       1.00      1.00      1.00      1446\n","          54       0.99      1.00      0.99      1446\n","          55       0.82      0.81      0.82      1447\n","          56       0.99      1.00      1.00      1446\n","          57       0.99      1.00      1.00      1447\n","          58       0.97      0.98      0.98      1446\n","          59       0.37      0.30      0.34      1447\n","          60       1.00      1.00      1.00      1447\n","          61       0.98      0.99      0.98      1446\n","          62       1.00      1.00      1.00      1446\n","          63       1.00      1.00      1.00      1446\n","          64       0.99      1.00      1.00      1446\n","          65       1.00      1.00      1.00      1447\n","          66       0.92      0.96      0.94      1446\n","          67       0.98      0.99      0.98      1447\n","          68       1.00      1.00      1.00      1447\n","          69       0.95      0.97      0.96      1446\n","          70       0.85      0.91      0.88      1447\n","          71       0.99      1.00      1.00      1447\n","          72       0.99      1.00      0.99      1446\n","          73       0.92      0.95      0.94      1447\n","          74       0.54      0.59      0.56      1446\n","\n","    accuracy                           0.91    108480\n","   macro avg       0.90      0.91      0.90    108480\n","weighted avg       0.90      0.91      0.90    108480\n","\n","\n","🔄 Summary for SUB:\n","✅ Accuracy: 0.9055\n","✅ Precision: 0.8976\n","✅ Recall: 0.9055\n","✅ F1-Score: 0.9008\n","✅ عرض رسم Accuracy & Loss المخزن: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/sub_finetuned_acc_loss.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","🚀 بدء Fine-Tuning لموديل RF\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stdout","output_type":"stream","text":["✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل RF. سيتم تحميله بدون إعادة تدريب.\n","\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n","\n","📊 Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.92      0.96      0.94      8846\n","           1       0.96      0.92      0.94      8846\n","\n","    accuracy                           0.94     17692\n","   macro avg       0.94      0.94      0.94     17692\n","weighted avg       0.94      0.94      0.94     17692\n","\n","\n","🔄 Summary for RF:\n","✅ Accuracy: 0.9431\n","✅ Precision: 0.9439\n","✅ Recall: 0.9431\n","✅ F1-Score: 0.9431\n","✅ عرض رسم Accuracy & Loss المخزن: /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/rf_finetuned_acc_loss.png\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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size 2400x2000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# ✅ استيراد المكتبات\n","# اجرب ال fine tuning for CNN + BiGRU for the all catagories\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","from tensorflow.keras.models import load_model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","\n","# ✅ المسارات\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/\"\n","models_info = {\n","    \"main\": {\"classes\": 22, \"data_file\": \"train_test_data_main-deep-new.pkl\", \"model_file\": \"CNN + BiGRU_model_all_main.h5\"},\n","    \"sub\":  {\"classes\": 75, \"data_file\": \"train_test_data_sub-deep-new.pkl\",  \"model_file\": \"CNN + BiGRU_model_all_sub.h5\"},\n","    \"rf\":   {\"classes\": 2,  \"data_file\": \"train_test_data_rf-deep-new.pkl\",   \"model_file\": \"CNN + BiGRU_model_all_rf.h5\"}\n","}\n","\n","# ✅ دالة الفاين تيونينغ\n","def fine_tune_model(name, classes, data_file, model_file):\n","    print(f\"\\n🚀 بدء Fine-Tuning لموديل {name.upper()}\")\n","\n","    model_path = os.path.join(base_path, model_file)\n","    data_path = os.path.join(base_path, data_file)\n","    fine_tuned_model_path = os.path.join(base_path, f\"CNN + BiGRU_{name}_finetuned.h5\")\n","    acc_loss_plot_path = os.path.join(base_path, f\"CNN + BiGRU_{name}_finetuned_acc_loss.png\")\n","    conf_matrix_path = os.path.join(base_path, f\"CNN + BiGRU_{name}_finetuned_conf_matrix.png\")\n","\n","    # تحميل البيانات\n","    with open(data_path, \"rb\") as f:\n","        X_train, X_test, y_train, y_test = pickle.load(f)\n","\n","    if os.path.exists(fine_tuned_model_path):\n","        print(f\"✅ [LOADED] تم العثور على نموذج Fine-Tuned لموديل {name.upper()}. سيتم تحميله بدون إعادة تدريب.\")\n","        model = load_model(fine_tuned_model_path)\n","        history = None\n","    else:\n","        # تحميل النموذج الأساسي\n","        model = load_model(model_path)\n","        print(f\"✅ تم تحميل النموذج الأساسي لموديل {name.upper()}\")\n","\n","        # تعديل الoptimizer\n","        model.compile(\n","            optimizer=Adam(learning_rate=0.00005),\n","            loss=\"sparse_categorical_crossentropy\",\n","            metrics=[\"accuracy\"]\n","        )\n","\n","        # Fine-Tuning\n","        history = model.fit(\n","            X_train, y_train,\n","            validation_data=(X_test, y_test),\n","            epochs=15,\n","            batch_size=64,\n","            callbacks=[\n","                ModelCheckpoint(checkpoint_path, monitor=\"val_loss\", save_best_only=True, verbose=1),\n","                EarlyStopping(patience=2, restore_best_weights=True),\n","                ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=1, min_lr=1e-6, verbose=1)\n","            ]\n","        )\n","\n","        # حفظ النموذج الجديد\n","        model.save(fine_tuned_model_path)\n","        print(f\"✅ تم حفظ النموذج الجديد بعد Fine-Tuning: {fine_tuned_model_path}\")\n","\n","    # التقييم\n","    preds = model.predict(X_test)\n","    preds = np.argmax(preds, axis=1)\n","\n","    print(\"\\n📊 Classification Report:\")\n","    print(classification_report(y_test, preds))\n","\n","    accuracy = accuracy_score(y_test, preds)\n","    precision = precision_score(y_test, preds, average='macro', zero_division=0)\n","    recall = recall_score(y_test, preds, average='macro', zero_division=0)\n","    f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","    print(f\"\\n🔄 Summary for {name.upper()}:\")\n","    print(f\"✅ Accuracy: {accuracy:.4f}\")\n","    print(f\"✅ Precision: {precision:.4f}\")\n","    print(f\"✅ Recall: {recall:.4f}\")\n","    print(f\"✅ F1-Score: {f1:.4f}\")\n","\n","    # رسم Accuracy & Loss إذا history موجود\n","    if history:\n","        plt.figure(figsize=(14, 5))\n","        plt.subplot(1, 2, 1)\n","        plt.plot(history.history['accuracy'], label='Train Accuracy')\n","        plt.plot(history.history['val_accuracy'], label='Val Accuracy', linestyle='--')\n","        plt.title(f\"{name.upper()} Fine-Tuning Accuracy\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Accuracy\")\n","        plt.legend()\n","\n","        plt.subplot(1, 2, 2)\n","        plt.plot(history.history['loss'], label='Train Loss')\n","        plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","        plt.title(f\"{name.upper()} Fine-Tuning Loss\")\n","        plt.xlabel(\"Epoch\")\n","        plt.ylabel(\"Loss\")\n","        plt.legend()\n","\n","        plt.tight_layout()\n","        plt.savefig(acc_loss_plot_path, dpi=300)\n","        plt.show()\n","\n","    else:\n","        # إذا ما فيه history لكن في ملف موجود\n","        if os.path.exists(acc_loss_plot_path):\n","            from PIL import Image\n","            print(f\"✅ عرض رسم Accuracy & Loss المخزن: {acc_loss_plot_path}\")\n","            img = Image.open(acc_loss_plot_path)\n","            plt.figure(figsize=(14, 5))\n","            plt.imshow(img)\n","            plt.axis('off')\n","            plt.title(f\"{name.upper()} Fine-Tuning Accuracy & Loss (Loaded)\")\n","            plt.show()\n","        else:\n","            print(\"⚠️ لا يوجد ملف رسم محفوظ لعرضه.\")\n","\n","\n","\n","    # رسم Confusion Matrix\n","    plt.figure(figsize=(24, 20))\n","    cm = confusion_matrix(y_test, preds)\n","    sns.heatmap(cm, annot=False, fmt=\"d\", cmap=\"Blues\")\n","    plt.title(f\"Confusion Matrix - {name.upper()} Fine-Tuned\")\n","    plt.xlabel(\"Predicted\")\n","    plt.ylabel(\"Actual\")\n","    plt.tight_layout()\n","    plt.savefig(conf_matrix_path, dpi=300)\n","    plt.show()\n","\n","# ✅ تنفيذ Fine-Tuning أو تحميل النماذج الثلاثة\n","for name, info in models_info.items():\n","    fine_tune_model(name, info[\"classes\"], info[\"data_file\"], info[\"model_file\"])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":1222927,"status":"ok","timestamp":1747035579522,"user":{"displayName":"noor alqudah","userId":"09593972035402767777"},"user_tz":-180},"id":"AmewsmDB3xSi","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"d5dd38bf-84f3-4909-c911-bb958b87ed58"},"outputs":[{"output_type":"stream","name":"stdout","text":["🚀 [TRAINING] MAIN model not found. Building and training...\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"functional_2\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_7 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │    \u001b[38;5;34m114,560\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_5 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ reshape_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │    \u001b[38;5;34m131,968\u001b[0m │ layer_normalizat… │\n","│ (\u001b[38;5;33mMultiHeadAttentio…\u001b[0m │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_10 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ multi_head_atten… │\n","│                     │                   │            │ reshape_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m512\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_8 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │     \u001b[38;5;34m16,512\u001b[0m │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_14          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_11 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│                     │                   │            │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_9 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m8,256\u001b[0m │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_10 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m)        │      \u001b[38;5;34m1,430\u001b[0m │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │    <span style=\"color: #00af00; text-decoration-color: #00af00\">114,560</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ reshape_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,968</span> │ layer_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttentio…</span> │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_atten… │\n","│                     │                   │            │ reshape_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_14          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│                     │                   │            │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_15          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_average_p… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,430</span> │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m273,750\u001b[0m (1.04 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">273,750</span> (1.04 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m273,494\u001b[0m (1.04 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">273,494</span> (1.04 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.4889 - loss: 1.8028\n","Epoch 1: val_loss improved from inf to 0.92777, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.4892 - loss: 1.8015 - val_accuracy: 0.7158 - val_loss: 0.9278 - learning_rate: 5.0000e-04\n","Epoch 2/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.7178 - loss: 0.9608\n","Epoch 2: val_loss improved from 0.92777 to 0.77756, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - accuracy: 0.7178 - loss: 0.9606 - val_accuracy: 0.7611 - val_loss: 0.7776 - learning_rate: 5.0000e-04\n","Epoch 3/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.7603 - loss: 0.7999\n","Epoch 3: val_loss improved from 0.77756 to 0.75973, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - accuracy: 0.7603 - loss: 0.7998 - val_accuracy: 0.7659 - val_loss: 0.7597 - learning_rate: 5.0000e-04\n","Epoch 4/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.7823 - loss: 0.7162\n","Epoch 4: val_loss improved from 0.75973 to 0.68981, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 30ms/step - accuracy: 0.7823 - loss: 0.7161 - val_accuracy: 0.7870 - val_loss: 0.6898 - learning_rate: 5.0000e-04\n","Epoch 5/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8026 - loss: 0.6481\n","Epoch 5: val_loss improved from 0.68981 to 0.66533, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - accuracy: 0.8026 - loss: 0.6481 - val_accuracy: 0.7908 - val_loss: 0.6653 - learning_rate: 5.0000e-04\n","Epoch 6/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8163 - loss: 0.6005\n","Epoch 6: val_loss improved from 0.66533 to 0.64465, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 30ms/step - accuracy: 0.8163 - loss: 0.6005 - val_accuracy: 0.8029 - val_loss: 0.6447 - learning_rate: 5.0000e-04\n","Epoch 7/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8267 - loss: 0.5625\n","Epoch 7: val_loss improved from 0.64465 to 0.61664, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 30ms/step - accuracy: 0.8267 - loss: 0.5625 - val_accuracy: 0.8080 - val_loss: 0.6166 - learning_rate: 5.0000e-04\n","Epoch 8/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8361 - loss: 0.5312\n","Epoch 8: val_loss improved from 0.61664 to 0.57193, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - accuracy: 0.8361 - loss: 0.5312 - val_accuracy: 0.8257 - val_loss: 0.5719 - learning_rate: 5.0000e-04\n","Epoch 9/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8457 - loss: 0.5002\n","Epoch 9: val_loss did not improve from 0.57193\n","\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 29ms/step - accuracy: 0.8457 - loss: 0.5002 - val_accuracy: 0.8251 - val_loss: 0.5897 - learning_rate: 5.0000e-04\n","Epoch 10/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8534 - loss: 0.4755\n","Epoch 10: val_loss improved from 0.57193 to 0.52484, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 30ms/step - accuracy: 0.8534 - loss: 0.4755 - val_accuracy: 0.8411 - val_loss: 0.5248 - learning_rate: 5.0000e-04\n","Epoch 11/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8597 - loss: 0.4534\n","Epoch 11: val_loss did not improve from 0.52484\n","\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 29ms/step - accuracy: 0.8597 - loss: 0.4534 - val_accuracy: 0.8338 - val_loss: 0.5475 - learning_rate: 5.0000e-04\n","Epoch 12/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8670 - loss: 0.4304\n","Epoch 12: val_loss improved from 0.52484 to 0.50048, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 31ms/step - accuracy: 0.8670 - loss: 0.4304 - val_accuracy: 0.8455 - val_loss: 0.5005 - learning_rate: 5.0000e-04\n","Epoch 13/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8705 - loss: 0.4176\n","Epoch 13: val_loss improved from 0.50048 to 0.49710, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 30ms/step - accuracy: 0.8705 - loss: 0.4177 - val_accuracy: 0.8552 - val_loss: 0.4971 - learning_rate: 5.0000e-04\n","Epoch 14/15\n","\u001b[1m643/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8744 - loss: 0.4045\n","Epoch 14: val_loss improved from 0.49710 to 0.49236, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_main.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 30ms/step - accuracy: 0.8744 - loss: 0.4045 - val_accuracy: 0.8561 - val_loss: 0.4924 - learning_rate: 5.0000e-04\n","Epoch 15/15\n","\u001b[1m642/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8800 - loss: 0.3901\n","Epoch 15: val_loss did not improve from 0.49236\n","\u001b[1m644/644\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 30ms/step - accuracy: 0.8800 - loss: 0.3901 - val_accuracy: 0.8489 - val_loss: 0.5089 - learning_rate: 5.0000e-04\n","\u001b[1m1287/1287\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n","\n","📊 Report - MAIN:\n","              precision    recall  f1-score   support\n","\n","           0       0.96      1.00      0.98      1871\n","           1       0.99      1.00      0.99      1871\n","           2       0.91      0.76      0.83      1872\n","           3       0.99      0.90      0.94      1872\n","           4       0.93      0.76      0.83      1872\n","           5       0.79      0.67      0.72      1871\n","           6       0.96      0.81      0.88      1872\n","           7       0.67      0.64      0.66      1872\n","           8       0.98      0.98      0.98      1871\n","           9       0.96      0.98      0.97      1872\n","          10       0.89      0.89      0.89      1872\n","          11       0.79      0.73      0.76      1871\n","          12       0.53      0.57      0.55      1872\n","          13       0.83      0.81      0.82      1871\n","          14       0.61      0.55      0.58      1872\n","          15       0.76      0.81      0.78      1871\n","          16       0.55      0.90      0.68      1872\n","          17       0.96      0.99      0.97      1872\n","          18       0.98      1.00      0.99      1872\n","          19       0.99      0.97      0.98      1872\n","          20       0.88      0.97      0.93      1871\n","          21       0.98      0.96      0.97      1872\n","\n","    accuracy                           0.85     41176\n","   macro avg       0.86      0.85      0.85     41176\n","weighted avg       0.86      0.85      0.85     41176\n","\n","✅ Accuracy: 0.8489 | Precision: 0.8590 | Recall: 0.8489 | F1: 0.8503\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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[TRAINING] SUB model not found. Building and training...\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"functional_3\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_11 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │    \u001b[38;5;34m114,560\u001b[0m │ input_layer_5[\u001b[38;5;34m0\u001b[0m]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_6 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ reshape_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │    \u001b[38;5;34m131,968\u001b[0m │ layer_normalizat… │\n","│ (\u001b[38;5;33mMultiHeadAttentio…\u001b[0m │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_12 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ multi_head_atten… │\n","│                     │                   │            │ reshape_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m512\u001b[0m │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_12 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │     \u001b[38;5;34m16,512\u001b[0m │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_18          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_13 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│                     │                   │            │ dropout_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ add_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_19          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_13 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m8,256\u001b[0m │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_20          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_14 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m)        │      \u001b[38;5;34m4,875\u001b[0m │ dropout_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │    <span style=\"color: #00af00; text-decoration-color: #00af00\">114,560</span> │ input_layer_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ reshape_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,968</span> │ layer_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttentio…</span> │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_atten… │\n","│                     │                   │            │ reshape_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ add_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_18          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│                     │                   │            │ dropout_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_19          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_average_p… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_20          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">75</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,875</span> │ dropout_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m277,195\u001b[0m (1.06 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">277,195</span> (1.06 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m276,939\u001b[0m (1.06 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">276,939</span> (1.06 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.5140 - loss: 2.0055\n","Epoch 1: val_loss improved from inf to 0.54294, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 31ms/step - accuracy: 0.5142 - loss: 2.0047 - val_accuracy: 0.8476 - val_loss: 0.5429 - learning_rate: 5.0000e-04\n","Epoch 2/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8076 - loss: 0.6962\n","Epoch 2: val_loss improved from 0.54294 to 0.42559, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 30ms/step - accuracy: 0.8076 - loss: 0.6961 - val_accuracy: 0.8775 - val_loss: 0.4256 - learning_rate: 5.0000e-04\n","Epoch 3/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.8482 - loss: 0.5375\n","Epoch 3: val_loss improved from 0.42559 to 0.38367, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 29ms/step - accuracy: 0.8482 - loss: 0.5374 - val_accuracy: 0.8880 - val_loss: 0.3837 - learning_rate: 5.0000e-04\n","Epoch 4/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8679 - loss: 0.4627\n","Epoch 4: val_loss improved from 0.38367 to 0.33179, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 30ms/step - accuracy: 0.8679 - loss: 0.4627 - val_accuracy: 0.9013 - val_loss: 0.3318 - learning_rate: 5.0000e-04\n","Epoch 5/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8804 - loss: 0.4121\n","Epoch 5: val_loss improved from 0.33179 to 0.31399, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 30ms/step - accuracy: 0.8804 - loss: 0.4121 - val_accuracy: 0.9070 - val_loss: 0.3140 - learning_rate: 5.0000e-04\n","Epoch 6/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8898 - loss: 0.3775\n","Epoch 6: val_loss improved from 0.31399 to 0.28772, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 30ms/step - accuracy: 0.8898 - loss: 0.3775 - val_accuracy: 0.9134 - val_loss: 0.2877 - learning_rate: 5.0000e-04\n","Epoch 7/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.8956 - loss: 0.3540\n","Epoch 7: val_loss improved from 0.28772 to 0.28387, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 29ms/step - accuracy: 0.8956 - loss: 0.3540 - val_accuracy: 0.9158 - val_loss: 0.2839 - learning_rate: 5.0000e-04\n","Epoch 8/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9012 - loss: 0.3369\n","Epoch 8: val_loss improved from 0.28387 to 0.26945, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 30ms/step - accuracy: 0.9012 - loss: 0.3369 - val_accuracy: 0.9200 - val_loss: 0.2694 - learning_rate: 5.0000e-04\n","Epoch 9/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.9059 - loss: 0.3161\n","Epoch 9: val_loss improved from 0.26945 to 0.26327, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 29ms/step - accuracy: 0.9059 - loss: 0.3161 - val_accuracy: 0.9201 - val_loss: 0.2633 - learning_rate: 5.0000e-04\n","Epoch 10/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9092 - loss: 0.3037\n","Epoch 10: val_loss improved from 0.26327 to 0.25507, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 30ms/step - accuracy: 0.9092 - loss: 0.3037 - val_accuracy: 0.9240 - val_loss: 0.2551 - learning_rate: 5.0000e-04\n","Epoch 11/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9120 - loss: 0.2928\n","Epoch 11: val_loss improved from 0.25507 to 0.24097, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 30ms/step - accuracy: 0.9120 - loss: 0.2928 - val_accuracy: 0.9267 - val_loss: 0.2410 - learning_rate: 5.0000e-04\n","Epoch 12/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9142 - loss: 0.2836\n","Epoch 12: val_loss improved from 0.24097 to 0.23567, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 29ms/step - accuracy: 0.9142 - loss: 0.2836 - val_accuracy: 0.9295 - val_loss: 0.2357 - learning_rate: 5.0000e-04\n","Epoch 13/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9179 - loss: 0.2743\n","Epoch 13: val_loss did not improve from 0.23567\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 30ms/step - accuracy: 0.9179 - loss: 0.2743 - val_accuracy: 0.9285 - val_loss: 0.2400 - learning_rate: 5.0000e-04\n","Epoch 14/15\n","\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9190 - loss: 0.2674\n","Epoch 14: val_loss improved from 0.23567 to 0.22445, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\b\r\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 31ms/step - accuracy: 0.9190 - loss: 0.2674 - val_accuracy: 0.9331 - val_loss: 0.2245 - learning_rate: 5.0000e-04\n","Epoch 15/15\n","\u001b[1m1694/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9210 - loss: 0.2608\n","Epoch 15: val_loss improved from 0.22445 to 0.22270, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_sub.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1695/1695\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 29ms/step - accuracy: 0.9210 - loss: 0.2608 - val_accuracy: 0.9332 - val_loss: 0.2227 - learning_rate: 5.0000e-04\n","\u001b[1m3390/3390\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step\n","\n","📊 Report - SUB:\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00      1446\n","           1       0.92      0.94      0.93      1447\n","           2       1.00      1.00      1.00      1447\n","           3       0.99      1.00      1.00      1446\n","           4       1.00      1.00      1.00      1446\n","           5       0.99      1.00      0.99      1446\n","           6       1.00      1.00      1.00      1446\n","           7       0.99      1.00      0.99      1446\n","           8       0.99      1.00      0.99      1446\n","           9       1.00      1.00      1.00      1446\n","          10       1.00      1.00      1.00      1446\n","          11       1.00      1.00      1.00      1447\n","          12       0.96      1.00      0.98      1446\n","          13       0.94      0.97      0.95      1446\n","          14       0.99      1.00      0.99      1447\n","          15       1.00      1.00      1.00      1447\n","          16       0.95      1.00      0.97      1446\n","          17       0.98      1.00      0.99      1446\n","          18       0.96      1.00      0.98      1446\n","          19       0.83      0.85      0.84      1446\n","          20       0.56      0.78      0.65      1447\n","          21       0.93      0.98      0.95      1447\n","          22       0.99      1.00      1.00      1446\n","          23       1.00      0.99      0.99      1447\n","          24       0.99      1.00      0.99      1447\n","          25       1.00      1.00      1.00      1446\n","          26       0.99      1.00      0.99      1447\n","          27       0.98      1.00      0.99      1447\n","          28       1.00      1.00      1.00      1446\n","          29       0.90      0.94      0.92      1447\n","          30       0.97      1.00      0.98      1446\n","          31       0.94      0.98      0.96      1446\n","          32       0.98      0.98      0.98      1447\n","          33       1.00      0.98      0.99      1446\n","          34       0.99      1.00      0.99      1446\n","          35       0.98      0.99      0.99      1446\n","          36       0.51      0.23      0.32      1446\n","          37       0.64      0.63      0.63      1447\n","          38       0.88      0.80      0.84      1446\n","          39       0.66      0.49      0.57      1446\n","          40       0.99      1.00      0.99      1446\n","          41       0.99      1.00      1.00      1447\n","          42       0.60      0.29      0.39      1446\n","          43       0.98      0.99      0.99      1447\n","          44       0.97      0.97      0.97      1446\n","          45       1.00      1.00      1.00      1446\n","          46       0.98      0.99      0.98      1447\n","          47       0.99      1.00      1.00      1446\n","          48       0.98      1.00      0.99      1447\n","          49       0.94      0.93      0.93      1446\n","          50       0.98      1.00      0.99      1446\n","          51       0.86      0.83      0.84      1447\n","          52       0.40      0.44      0.42      1447\n","          53       1.00      1.00      1.00      1446\n","          54       0.99      1.00      0.99      1446\n","          55       0.96      0.91      0.94      1447\n","          56       1.00      1.00      1.00      1446\n","          57       0.99      1.00      1.00      1447\n","          58       0.98      0.99      0.99      1446\n","          59       0.47      0.49      0.48      1447\n","          60       1.00      1.00      1.00      1447\n","          61       1.00      0.99      0.99      1446\n","          62       1.00      1.00      1.00      1446\n","          63       1.00      1.00      1.00      1446\n","          64       1.00      1.00      1.00      1446\n","          65       1.00      1.00      1.00      1447\n","          66       0.96      0.99      0.97      1446\n","          67       0.99      1.00      0.99      1447\n","          68       1.00      1.00      1.00      1447\n","          69       0.96      1.00      0.98      1446\n","          70       0.95      0.98      0.96      1447\n","          71       1.00      1.00      1.00      1447\n","          72       0.99      1.00      0.99      1446\n","          73       0.94      0.99      0.97      1447\n","          74       0.61      0.75      0.67      1446\n","\n","    accuracy                           0.93    108480\n","   macro avg       0.93      0.93      0.93    108480\n","weighted avg       0.93      0.93      0.93    108480\n","\n","✅ Accuracy: 0.9332 | Precision: 0.9305 | Recall: 0.9332 | F1: 0.9298\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x500 with 2 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🚀 [TRAINING] RF model not found. Building and training...\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"functional_4\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_4\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m894\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_15 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │    \u001b[38;5;34m114,560\u001b[0m │ input_layer_6[\u001b[38;5;34m0\u001b[0m]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_7 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ reshape_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │    \u001b[38;5;34m131,968\u001b[0m │ layer_normalizat… │\n","│ (\u001b[38;5;33mMultiHeadAttentio…\u001b[0m │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_14 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ multi_head_atten… │\n","│                     │                   │            │ reshape_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m512\u001b[0m │ add_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m256\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_16 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │     \u001b[38;5;34m16,512\u001b[0m │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_22          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ dense_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_15 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│                     │                   │            │ dropout_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ add_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n","│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_23          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_17 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m8,256\u001b[0m │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_24          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_18 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │        \u001b[38;5;34m130\u001b[0m │ dropout_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">894</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │    <span style=\"color: #00af00; text-decoration-color: #00af00\">114,560</span> │ input_layer_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ reshape_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ reshape_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multi_head_attenti… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,968</span> │ layer_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MultiHeadAttentio…</span> │                   │            │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multi_head_atten… │\n","│                     │                   │            │ reshape_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ add_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ layer_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ layer_normalizat… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_22          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ add_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│                     │                   │            │ dropout_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_23          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_average_p… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dropout_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dropout_24          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">130</span> │ dropout_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m272,450\u001b[0m (1.04 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">272,450</span> (1.04 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m272,194\u001b[0m (1.04 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">272,194</span> (1.04 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.7516 - loss: 0.5862\n","Epoch 1: val_loss improved from inf to 1.62450, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 34ms/step - accuracy: 0.7521 - loss: 0.5849 - val_accuracy: 0.5792 - val_loss: 1.6245 - learning_rate: 5.0000e-04\n","Epoch 2/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.8485 - loss: 0.3434\n","Epoch 2: val_loss improved from 1.62450 to 1.31896, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 35ms/step - accuracy: 0.8486 - loss: 0.3432 - val_accuracy: 0.6979 - val_loss: 1.3190 - learning_rate: 5.0000e-04\n","Epoch 3/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.8856 - loss: 0.2692\n","Epoch 3: val_loss improved from 1.31896 to 0.43427, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 34ms/step - accuracy: 0.8856 - loss: 0.2691 - val_accuracy: 0.8314 - val_loss: 0.4343 - learning_rate: 5.0000e-04\n","Epoch 4/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9066 - loss: 0.2271\n","Epoch 4: val_loss improved from 0.43427 to 0.29924, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 32ms/step - accuracy: 0.9066 - loss: 0.2270 - val_accuracy: 0.8767 - val_loss: 0.2992 - learning_rate: 5.0000e-04\n","Epoch 5/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9222 - loss: 0.1894\n","Epoch 5: val_loss did not improve from 0.29924\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9222 - loss: 0.1895 - val_accuracy: 0.8750 - val_loss: 0.3170 - learning_rate: 5.0000e-04\n","Epoch 6/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9339 - loss: 0.1615\n","Epoch 6: val_loss improved from 0.29924 to 0.24109, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 32ms/step - accuracy: 0.9339 - loss: 0.1615 - val_accuracy: 0.9010 - val_loss: 0.2411 - learning_rate: 5.0000e-04\n","Epoch 7/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9435 - loss: 0.1408\n","Epoch 7: val_loss did not improve from 0.24109\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9434 - loss: 0.1409 - val_accuracy: 0.8806 - val_loss: 0.3017 - learning_rate: 5.0000e-04\n","Epoch 8/15\n","\u001b[1m275/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9479 - loss: 0.1276\n","Epoch 8: val_loss did not improve from 0.24109\n","\n","Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9479 - loss: 0.1276 - val_accuracy: 0.8942 - val_loss: 0.4007 - learning_rate: 5.0000e-04\n","Epoch 9/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9677 - loss: 0.0851\n","Epoch 9: val_loss did not improve from 0.24109\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9677 - loss: 0.0851 - val_accuracy: 0.9216 - val_loss: 0.2542 - learning_rate: 2.5000e-04\n","Epoch 10/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9723 - loss: 0.0696\n","Epoch 10: val_loss did not improve from 0.24109\n","\n","Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9723 - loss: 0.0696 - val_accuracy: 0.9118 - val_loss: 0.2588 - learning_rate: 2.5000e-04\n","Epoch 11/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9822 - loss: 0.0482\n","Epoch 11: val_loss improved from 0.24109 to 0.22189, saving model to /content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/transformer_model_rf.h5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \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\r\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 32ms/step - accuracy: 0.9822 - loss: 0.0482 - val_accuracy: 0.9323 - val_loss: 0.2219 - learning_rate: 1.2500e-04\n","Epoch 12/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9848 - loss: 0.0394\n","Epoch 12: val_loss did not improve from 0.22189\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 32ms/step - accuracy: 0.9848 - loss: 0.0394 - val_accuracy: 0.9270 - val_loss: 0.3076 - learning_rate: 1.2500e-04\n","Epoch 13/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9871 - loss: 0.0352\n","Epoch 13: val_loss did not improve from 0.22189\n","\n","Epoch 13: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9871 - loss: 0.0352 - val_accuracy: 0.9330 - val_loss: 0.2889 - learning_rate: 1.2500e-04\n","Epoch 14/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9904 - loss: 0.0259\n","Epoch 14: val_loss did not improve from 0.22189\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9904 - loss: 0.0259 - val_accuracy: 0.9249 - val_loss: 0.4160 - learning_rate: 6.2500e-05\n","Epoch 15/15\n","\u001b[1m276/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9920 - loss: 0.0208\n","Epoch 15: val_loss did not improve from 0.22189\n","\n","Epoch 15: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n","\u001b[1m277/277\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 31ms/step - accuracy: 0.9920 - loss: 0.0208 - val_accuracy: 0.9352 - val_loss: 0.3448 - learning_rate: 6.2500e-05\n","\u001b[1m553/553\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step\n","\n","📊 Report - RF:\n","              precision    recall  f1-score   support\n","\n","           0       0.91      0.96      0.94      8846\n","           1       0.96      0.91      0.93      8846\n","\n","    accuracy                           0.94     17692\n","   macro avg       0.94      0.94      0.94     17692\n","weighted avg       0.94      0.94      0.94     17692\n","\n","✅ Accuracy: 0.9352 | Precision: 0.9364 | Recall: 0.9352 | F1: 0.9351\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x500 with 2 Axes>"],"image/png":"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✅ نسخة محسّنة من كود Transformer Encoder\n","import os\n","import pickle\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import seaborn as sns\n","from sklearn.utils import shuffle, class_weight\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n","\n","import tensorflow as tf\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization, MultiHeadAttention, Add, Reshape, GlobalAveragePooling1D, Conv1D, BatchNormalization\n","from tensorflow.keras.optimizers import RMSprop\n","from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, CSVLogger\n","\n","# إعداد المسارات\n","base_path = \"/content/drive/MyDrive/PHD Dream/THESIS/dataset/1-csv/DEEP L files/\"\n","comparison_path = os.path.join(base_path, \"transformer_comparison.csv\")\n","\n","models_info = {\n","    \"main\": {\"classes\": 22, \"data_file\": \"train_test_data_main-deep-new.pkl\"},\n","    \"sub\":  {\"classes\": 75, \"data_file\": \"train_test_data_sub-deep-new.pkl\"},\n","    \"rf\":   {\"classes\": 2,  \"data_file\": \"train_test_data_rf-deep-new.pkl\"}\n","}\n","\n","# Transformer Encoder Block\n","\n","def transformer_encoder(inputs, num_heads=2, ff_dim=128, dropout=0.1):\n","    x = LayerNormalization(epsilon=1e-6)(inputs)\n","    attention_output = MultiHeadAttention(num_heads=num_heads, key_dim=inputs.shape[-1])(x, x)\n","    x = Add()([attention_output, inputs])\n","    x = BatchNormalization()(x)\n","    x_norm = LayerNormalization(epsilon=1e-6)(x)\n","    ff_output = Dense(ff_dim, activation=\"relu\")(x_norm)\n","    ff_output = Dropout(dropout)(ff_output)\n","    return Add()([x, ff_output])\n","\n","\n","def train_or_resume_transformer(name, X_train, y_train, X_test, y_test, num_classes, total_epochs=15):\n","    model_path = os.path.join(base_path, f\"transformer_model_{name}.h5\")\n","    acc_loss_plot_path = os.path.join(base_path, f\"transformer_acc_loss_{name}.png\")\n","    conf_matrix_path = os.path.join(base_path, f\"transformer_conf_matrix_{name}.png\")\n","    log_csv_path = os.path.join(base_path, f\"transformer_log_{name}.csv\")\n","\n","    X_train, y_train = shuffle(X_train, y_train, random_state=42)\n","    class_weights = class_weight.compute_class_weight(class_weight=\"balanced\", classes=np.unique(y_train), y=y_train)\n","    class_weight_dict = dict(enumerate(class_weights))\n","\n","    initial_epoch = 0\n","    if os.path.exists(log_csv_path):\n","        prev_log = pd.read_csv(log_csv_path)\n","        initial_epoch = len(prev_log)\n","\n","    if os.path.exists(model_path):\n","        print(f\"✅ [LOADED] {name.upper()} model loaded\")\n","        model = load_model(model_path)\n","        model.compile(optimizer=RMSprop(0.0005), loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n","    else:\n","        print(f\"🚀 [TRAINING] {name.upper()} model not found. Building and training...\")\n","        input_layer = Input(shape=(894,))\n","        x = Dense(128, activation=\"relu\")(input_layer)\n","        x = Reshape((1, 128))(x)\n","        x = transformer_encoder(x)\n","        x = GlobalAveragePooling1D()(x)\n","        x = Dropout(0.3)(x)\n","        x = Dense(64, activation=\"relu\")(x)\n","        x = Dropout(0.3)(x)\n","        output = Dense(num_classes, activation=\"softmax\")(x)\n","\n","        model = Model(inputs=input_layer, outputs=output)\n","        model.compile(optimizer=RMSprop(0.0005), loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n","        model.summary()\n","\n","    callbacks = [\n","        ModelCheckpoint(filepath=model_path, save_best_only=True, monitor=\"val_loss\", verbose=1),\n","        ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=1e-6, verbose=1),\n","        CSVLogger(log_csv_path, append=True)\n","    ]\n","\n","    history = model.fit(\n","        X_train, y_train,\n","        validation_data=(X_test, y_test),\n","        epochs=total_epochs,\n","        initial_epoch=initial_epoch,\n","        batch_size=256,\n","        class_weight=class_weight_dict,\n","        callbacks=callbacks\n","    )\n","\n","    preds = model.predict(X_test)\n","    preds = np.argmax(preds, axis=1)\n","\n","    print(f\"\\n📊 Report - {name.upper()}:\")\n","    print(classification_report(y_test, preds))\n","\n","    acc = accuracy_score(y_test, preds)\n","    prec = precision_score(y_test, preds, average='macro', zero_division=0)\n","    rec = recall_score(y_test, preds, average='macro', zero_division=0)\n","    f1 = f1_score(y_test, preds, average='macro', zero_division=0)\n","\n","    print(f\"✅ Accuracy: {acc:.4f} | Precision: {prec:.4f} | Recall: {rec:.4f} | F1: {f1:.4f}\")\n","\n","    if history:\n","        plt.figure(figsize=(14, 5))\n","        plt.subplot(1, 2, 1)\n","        plt.plot(history.history['accuracy'], label='Train Acc')\n","        plt.plot(history.history['val_accuracy'], label='Val Acc', linestyle='--')\n","        plt.title(f\"{name.upper()} Accuracy\")\n","        plt.legend()\n","\n","        plt.subplot(1, 2, 2)\n","        plt.plot(history.history['loss'], label='Train Loss')\n","        plt.plot(history.history['val_loss'], label='Val Loss', linestyle='--')\n","        plt.title(f\"{name.upper()} Loss\")\n","        plt.legend()\n","        plt.tight_layout()\n","        plt.savefig(acc_loss_plot_path)\n","        plt.show()\n","\n","    cm = confusion_matrix(y_test, preds)\n","    plt.figure(figsize=(24, 20))\n","    sns.heatmap(cm, cmap=\"Blues\", fmt=\"d\")\n","    plt.title(f\"Confusion Matrix - {name.upper()}\")\n","    plt.savefig(conf_matrix_path)\n","    plt.show()\n","\n","    result = pd.DataFrame({\n","        \"Model\": [f\"Transformer_{name}\"],\n","        \"Accuracy\": [acc],\n","        \"Precision\": [prec],\n","        \"Recall\": [rec],\n","        \"F1_Score\": [f1]\n","    })\n","\n","    if os.path.exists(comparison_path):\n","        result.to_csv(comparison_path, mode='a', header=False, index=False)\n","    else:\n","        result.to_csv(comparison_path, index=False)\n","\n","# تنفيذ التدريب\n","for name, info in models_info.items():\n","    try:\n","        with open(os.path.join(base_path, info[\"data_file\"]), \"rb\") as f:\n","            X_train, X_test, y_train, y_test = pickle.load(f)\n","        train_or_resume_transformer(name, X_train, y_train, X_test, y_test, info[\"classes\"])\n","    except Exception as e:\n","        print(f\"⚠️ خطأ أثناء معالجة {name.upper()}: {e}\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"41RSNs9ROp9W"},"outputs":[],"source":[]}],"metadata":{"accelerator":"TPU","colab":{"gpuType":"V28","machine_shape":"hm","provenance":[],"authorship_tag":"ABX9TyO/WnCLN7kOIgwtGmJC6LMy"},"kernelspec":{"display_name":"Python 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