violence-detection-using-mobilenetv2-and-lstm (1).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":397693,"sourceType":"datasetVersion","datasetId":176381},{"sourceId":9218464,"sourceType":"datasetVersion","datasetId":5574676}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#IMPORT STATEMENTS\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport os","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-10-30T07:18:49.237771Z","iopub.execute_input":"2024-10-30T07:18:49.238848Z","iopub.status.idle":"2024-10-30T07:18:49.471227Z","shell.execute_reply.started":"2024-10-30T07:18:49.238804Z","shell.execute_reply":"2024-10-30T07:18:49.470358Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# **HYPERPARAMETERS**","metadata":{}},{"cell_type":"code","source":"#HyperParameters\nIMG_HEIGHT, IMG_WIDTH = 129,129\nSEQUENCE_LENGTH = 10  # Number of frames per video\nBATCH_SIZE=50\nEPOCHS = 50\nNUM_CLASSES = 2","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:18:49.472825Z","iopub.execute_input":"2024-10-30T07:18:49.473129Z","iopub.status.idle":"2024-10-30T07:18:49.478998Z","shell.execute_reply.started":"2024-10-30T07:18:49.473103Z","shell.execute_reply":"2024-10-30T07:18:49.478010Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# **Extract Frames**","metadata":{}},{"cell_type":"code","source":"# Extract frames from a video\ndef extract_frames(video_path):\n    cap = cv2.VideoCapture(video_path)\n    frames = []\n    while len(frames) < SEQUENCE_LENGTH:\n        ret, frame = cap.read()\n        if not ret:\n            break\n        frame = cv2.resize(frame, (IMG_HEIGHT, IMG_WIDTH))\n        frames.append(frame)\n    cap.release()\n    if len(frames) == SEQUENCE_LENGTH:\n        return np.array(frames)\n    return None","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:18:49.480181Z","iopub.execute_input":"2024-10-30T07:18:49.480469Z","iopub.status.idle":"2024-10-30T07:18:49.495462Z","shell.execute_reply.started":"2024-10-30T07:18:49.480445Z","shell.execute_reply":"2024-10-30T07:18:49.494566Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"# **Load Videos**","metadata":{}},{"cell_type":"code","source":"# Load videos from folders\ndef load_videos_from_folder(folder_path, label):\n    videos = []\n    labels = []\n    for filename in os.listdir(folder_path):\n        if filename.endswith(\".mp4\"):  # Assuming videos are in .mp4 format\n            video_path = os.path.join(folder_path, filename)\n            frames = extract_frames(video_path)\n            if frames is not None:\n                videos.append(frames)\n                labels.append(label)\n    return videos, labels","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:18:49.497431Z","iopub.execute_input":"2024-10-30T07:18:49.497755Z","iopub.status.idle":"2024-10-30T07:18:49.509276Z","shell.execute_reply.started":"2024-10-30T07:18:49.497730Z","shell.execute_reply":"2024-10-30T07:18:49.508378Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# Load datasets\nviolent_videos, violent_labels = load_videos_from_folder('/kaggle/input/real-life-violence-situations-dataset/Real Life Violence Dataset/Violence', label=1)\nnon_violent_videos, non_violent_labels = load_videos_from_folder('/kaggle/input/real-life-violence-situations-dataset/Real Life Violence Dataset/NonViolence', label=0)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:18:49.510269Z","iopub.execute_input":"2024-10-30T07:18:49.510524Z","iopub.status.idle":"2024-10-30T07:19:39.177224Z","shell.execute_reply.started":"2024-10-30T07:18:49.510502Z","shell.execute_reply":"2024-10-30T07:19:39.176413Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# Combine and Normalize\nX = np.array(violent_videos + non_violent_videos)/255.0\nY = np.array(violent_labels + non_violent_labels)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:19:39.178469Z","iopub.execute_input":"2024-10-30T07:19:39.178817Z","iopub.status.idle":"2024-10-30T07:19:41.847553Z","shell.execute_reply.started":"2024-10-30T07:19:39.178784Z","shell.execute_reply":"2024-10-30T07:19:41.846748Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"print(\"Shape of X:\",X.shape)\nprint(\"Shape of Y\",Y.shape)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:19:41.848623Z","iopub.execute_input":"2024-10-30T07:19:41.848882Z","iopub.status.idle":"2024-10-30T07:19:41.854329Z","shell.execute_reply.started":"2024-10-30T07:19:41.848859Z","shell.execute_reply":"2024-10-30T07:19:41.853287Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Shape of X: (1951, 10, 129, 129, 3)\nShape of Y (1951,)\n","output_type":"stream"}]},{"cell_type":"code","source":"#IMPORT STATEMENTS\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential,Model\nfrom tensorflow.keras.layers import Dense,LSTM,TimeDistributed, Dropout\nfrom tensorflow.keras.layers import GlobalAveragePooling2D, Input, Lambda\nfrom tensorflow.keras.layers import BatchNormalization","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:19:41.855497Z","iopub.execute_input":"2024-10-30T07:19:41.855768Z","iopub.status.idle":"2024-10-30T07:19:55.473319Z","shell.execute_reply.started":"2024-10-30T07:19:41.855744Z","shell.execute_reply":"2024-10-30T07:19:55.472342Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"2024-10-30 07:19:43.833796: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-10-30 07:19:43.833920: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-10-30 07:19:43.978498: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **SPLITING THE DATASET**","metadata":{}},{"cell_type":"code","source":"# Split the data into training and testing sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:19:55.474608Z","iopub.execute_input":"2024-10-30T07:19:55.475281Z","iopub.status.idle":"2024-10-30T07:19:58.135834Z","shell.execute_reply.started":"2024-10-30T07:19:55.475244Z","shell.execute_reply":"2024-10-30T07:19:58.134929Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"# **MobileNetV2**","metadata":{}},{"cell_type":"code","source":"from keras.applications.mobilenet_v2 import MobileNetV2\nmobilenet= MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3))\n\n#Fine-Tuning to make the last 40 layer trainable\nmobilenet.trainable=True\n\nfor layer in mobilenet.layers[:-44]:\n  layer.trainable=False\n\nx = mobilenet.output\nx = GlobalAveragePooling2D()(x)\ncnn_model = Model(inputs=mobilenet.input, outputs=x)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:19:58.139470Z","iopub.execute_input":"2024-10-30T07:19:58.139774Z","iopub.status.idle":"2024-10-30T07:20:00.375484Z","shell.execute_reply.started":"2024-10-30T07:19:58.139747Z","shell.execute_reply":"2024-10-30T07:20:00.374688Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_34/2177351994.py:2: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n  mobilenet= MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3))\n","output_type":"stream"},{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **COMBINE CNN AND LSTM**","metadata":{}},{"cell_type":"code","source":"model = Sequential()\nmodel.add(TimeDistributed(cnn_model, input_shape=(SEQUENCE_LENGTH, IMG_HEIGHT, IMG_WIDTH, 3)))\nmodel.add(LSTM(32, return_sequences=False)) \n\n# Add Batch Normalization after LSTM\nmodel.add(BatchNormalization())\n\nmodel.add(Dropout(0.6))\nmodel.add(Dense(32, activation='relu'))  \n\n# Add Batch Normalization before final Dense layer\nmodel.add(BatchNormalization())\n\nmodel.add(Dropout(0.6))\nmodel.add(Dense(1, activation='sigmoid'))","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:00.376659Z","iopub.execute_input":"2024-10-30T07:20:00.377030Z","iopub.status.idle":"2024-10-30T07:20:00.981331Z","shell.execute_reply.started":"2024-10-30T07:20:00.376996Z","shell.execute_reply":"2024-10-30T07:20:00.980303Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/layers/core/wrapper.py:27: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n  super().__init__(**kwargs)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **SUMMARY**","metadata":{}},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:00.982582Z","iopub.execute_input":"2024-10-30T07:20:00.982928Z","iopub.status.idle":"2024-10-30T07:20:01.017701Z","shell.execute_reply.started":"2024-10-30T07:20:00.982897Z","shell.execute_reply":"2024-10-30T07:20:01.016870Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential\"\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: \"sequential\"</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┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m1280\u001b[0m)       │     \u001b[38;5;34m2,257,984\u001b[0m │\n│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │       \u001b[38;5;34m168,064\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m1,056\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m33\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>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ time_distributed                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,257,984</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ lstm (<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\">32</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">168,064</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</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\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (<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\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</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\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (<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\">33</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,427,393\u001b[0m (9.26 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\">2,427,393</span> (9.26 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,912,449\u001b[0m (7.30 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\">1,912,449</span> (7.30 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m514,944\u001b[0m (1.96 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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">514,944</span> (1.96 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"from tensorflow.keras.utils import plot_model\n# Visualize and save the model architecture\nplot_model(model, to_file=\"model.jpg\", show_shapes=True)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:01.019005Z","iopub.execute_input":"2024-10-30T07:20:01.019364Z","iopub.status.idle":"2024-10-30T07:20:01.344534Z","shell.execute_reply.started":"2024-10-30T07:20:01.019332Z","shell.execute_reply":"2024-10-30T07:20:01.343590Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"image/jpeg":"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ImdgoNk65PqeB+pFY/wM/5JNpX/XSf/wBGvQB49oPhvxdc/GDWtKtPE/2fW4YWa41Haf3q5jyMfiv5V7l4H8OeLNDubx/Enib+2I5UUQptI8sgnJ59eK4Pwh/yc14p/wCvWT+cNe4UAFeS/FzxTr0OvaD4P8PXn2C51Zh5l2DhlDNtUBuo5ySRzwMe/rVcR8Q/hvZ+PILWX7ZLYanZEm2u4xnGcHBGRkZAIIIINAHKp8E9WhUTw/ETWkvxz52Wxn6b8/rXo3hOz1yw8PQ2viK+jvtRiZ1a5jGBIm47SeBztxn+vWvL7i3+M/gu0e5TUbDxFY26l2SRd0m0dSchXJx6Mxrvvh345t/H3hv+0o7c21xFIYbiDduCuADkHuCCP1HagDxcXOvRftAeJbPw2I11K/D2qXEoytsp8tmkI74CHHuR16V0+vfCbxRp+l3Gs6f491e61e3QzbXd0EuBkqpDnHsOR2pngpQf2l/FhIyVtZSPb5oa9umGYJAf7p/lQBw3wj8ZXPjTwSl3fkNf2srW1w4GPMIAIfHbIYZ9waXxl4V8W+J9Zjh0/wAUvomhrAu9bZT50ku5t3IwQMbf4vXiuO/ZsP8AxS+sjt9tX/0AVa8T+N/F/iH4gXPgvwMba0ezTddX04BxjG7qCAAWC8AkmgClrPwq8VeGdOm1jw1431Se8tUMrW87HEoAyR94gnjoQQa7/wCGfjFvHHgu31SdES8jdre6VBhfMXByPYgqfbOK4m48DfFCOzmnuviJGqJGzSAIduAMnsKj/ZtYnwlrC54F8CP+/a/4UAYbeLvHOo/FjxB4W0XUnJnnlggebBSyjVgWkAx1CggfX1xW1q/wj8WadZSarovjvVrvWIVMmyV2UTEckA7zjPYHIPSqnw9khT9ozxgshAkdLgR57nzUJA/Afoa93oA4D4S+PJfHPhh3vgo1SxcQ3W0YD5GVfHbODkeoPaud8beLvEfiTx2PAPgy5FnJEu6/vweYxgEgEcgDI5HJYgcd8v4DETeL/G1xbf8AHm067COhzJKV/TNcvo2jeItW+NPi+00XXho2oC4uZDIykmSLzuFH5qaAO3n+DHiK0tmu9M+IOrNqyDcrSu6pI3oSHJAP4/Suh+Evjm/8Vadf6briBNc0mXybk4C+YMkBiBwGyrA444HrWL/wr/4o/wDRRv8AyG3+Fafw4+G2r+D/ABJqusarrEOoS6hGRIUQhi5fcWOfx/OgCPUvGHim3+NVp4dggzoMmzfJ9mJ6xlj8/wBa9QoooAK+Xf2h9We88e2+nBj5VhaKNvo7ksT+Wz8q+oq+SfjvA8PxWv3cHbNDA6fTywv81NAH0d8N9MTSPhxoFoq7T9jSVx/tSDe36sa4r9oz/knVn/2FI/8A0XLXovhOZLnwdok8ZyklhAw+hjWvOv2jP+SdWf8A2FI//RctAHzrYeHrrUvDer6zbjdHpckAnQDkJLvG78CoH/Avavo/4VeNP+Ep+Gd5Y3Uu7UtLtmglyeXj2HY/5DB91z3rj/2c7K31Ky8YWN3EJba4itopUboysJgR+VcSr3/wh+JV9ZSb3ttj27/9N7aQfK31HB+qkUAZnwu/5Kf4d/6/F/rX1h428WW3gvwrd61cJ5jR4SGHOPMkPCrn9T7A18n/AAu/5Kf4d/6/F/rXt37R8czeBdOdMmJdRXfjsfLfB/n+dAHlEPiD4jfFHX3tbC/vHcgyGC2mMEEKZ74IGOcZOSfejVLj4lfC7U7f7bqV9b+blos3PnwS46jBJUkZ6EZ5ruP2aJrYS+I4SVF0ywOoPUoN4OPYEjP1FbX7SM1sPB+kwsV+1Nf74x32CNg34ZZP0oA7f4a+OY/HvhZdQaNYb2F/Ju4l+6HxnK99pByPxHOK8C8d/EbxhpvjzXLKz8QXkNtBeSJFGjDCqDwBxXZfszxzCDxLIQfILWyr6FgJM/oR+leS/En/AJKV4j/6/wCX/wBCoA73xAnxW8c6TL4njS5tNFEZmt7OC58smIDO7aCC5xzk8nsMYpPgv8SNah8XWugapqE97p98THH9okLtDJglSpPODjGOnOa+jlgjh0YW8aBYkt9iqOgULgCvjX4bf8lL8Of9f8X/AKFQB9FfF/4lyeBdNt7PTFRtXvQTGzjKwIOC5Hc54A6cHPTB8W0XR/if8RYpdUtdQ1CeBXK+dNemJC3cIMgfkMCtL9oqOZfiJavJny206Pyz2wHkyPz/AJ1J4J0L4qX/AITs5/DOtJFpJ3iGJbpV2HcdwIxwc5P40AUdA+JPjP4c+J20vxDLd3VvDIEurK7k8xkX1jck44ORg7T+tfU9pdQX1nBd20gkgnjWWNx0ZWGQfyNfL/iD4SfETU55dX1+6sZHRB5lzc3qqFUdMnAAAr6D8BWF1pfgPRLG8eN54LVULRvuUj+HB7jGKAOjoqims6XJKsSalZtIzbQgnUkn0xnrV6gAqjq2pWmiaVeateZWC1hMkrIuW2rk/j3q9VHVdNtNd0i80u7y1tcxtDMEbBwRyM9jg0AeP+Mfjv4WvvC2padplveXdzeW8kCiWEJGu5SuWJOeM5wBz7Vw3wR8Capq3iyx8RSQNDpNi5k85uPNcAgKo78nJPTj1r0XXP2ffCjaVcSafdXtjcRxs6SSTB4wQM/MCOn0IrxT4YeJNQ8O+PNKNnM4hu7qO3uIQfllR2C8juRnI96APsW7sbS/SNLy2iuEjkEqLKgYK46Ng9xVimu6RRtJIyoigszMcAAdSTXzl8VfjNLrDy+HfCkrizY+XPeR53XHbZH3C+/Vvp1AOI+LupadrHxN1W50opJDuSIyR9JHVQrEevIxnvjNfTvw40m60T4d6Hp96pS5jtw0iN1QsS20+43Y/CvNvhJ8Gjpj2/iLxPADeDElrYuP9SezuP73oO3fnp7lQB4z+0dpiXHg3TdSC5ltL3y8+iSKc/qi079nPV3u/BuoaZIxb7Dd7k/2UkGcf99Kx/Gr/wC0HOkXw0CMRmW+iRfrhm/kprnP2aIHWw8R3BB8uSWBAfdQ5P8A6EKAPeKKKKACvHv2j/8AkQNO/wCwon/oqWvYa8e/aP8A+RA07/sKJ/6KloA84/Z6/wCSlP8A9eEv/oSV9UV8r/s9f8lKf/rwl/8AQkr6ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK429/4/7j/rq38zXZVxt7/wAf9x/11b+ZoA7KiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDw/9pT/kXtD/AOvt/wD0CuV/Zv8A+R41T/sGt/6Mjrqv2lP+Re0P/r7f/wBArlf2b/8AkeNU/wCwa3/oyOgD6aooooAK4D4yaNqOvfDy4sNKtJLu6aeJhFGOSA3Jrv6KAOa+H1hdaX8P9Esb6B4LqC1VJIn6qfQ1xOieGtZt/wBoXWtdm06ZNLmtysd0R8jHZGMD8Qfyr1uigAryT4GeGtZ8OWuvrrGnTWbT3KNEJRjeAGyR+Yr1uigD55+KOinxD8etH0pLl7WS4sUEc6dY3HmsrfgQK6O48RfGbTrc6YfC9je3QHlpqMTAq/beRuAB+oA9qseIvDWs3X7QGga5Bp00mmQW6rLcgfIhxLwf++h+det0AeafCv4dX3heW+17xDOtx4g1InzSG3eUpO4jd3YnBOOOBipvi34AvPGukWVxpMqx6vpshkt9zbQ4OMrnscqpB9vfNei0UAeO2XiT4y3kCaafCtjb3eNj6jcMAi9t+AxBPfjI9u1UPA3gLXvDfxpu727juruyNsxfU5ANs8zojOf++y35V7jRQBw3xY8G3XjfwW2n2DIL2CdbmBXbaHYBlK57ZDH8cVymjeLfippsFjpl74GWUQhIHuVfqowNxwxGcc8cV7JRQAVjeK49Zl8L36eHnCavsBtmJUAMGB/i46Z61s0UAeNw+MPjFYx+RfeB7W7lXjzYnAz7na5H5Yo8N+CfF/ib4h2vjXxtFb2P2JQLWyhYE8Z2jgnABYtySSewFeyUUAeTfHjw3rPiTQdJh0bT5r2SK5Z5FiGSo24zXq6DCKD1wKdRQB5J4p8NazefHzw5rdtp00mmW1sizXKj5EIMuQf++h+dbnxa8B3PjrwzDDp8qJqNlL50AkOFfIwy57E8EH1Hvmu/ooA8asPFPxlW3TTpfB1pLdqAn2yZwqn/AGmw+0n6H8Kx7L4f+K7D4y6FrepCbUmkAuNRv0A8pJSHGxenyqoQDj8ule+0UAeI/tKf8i3on/X4/wD6BVyE/E7wBbDR9K0m28R6PFxYzs+JYo+yONwJx06fj2Fn48eG9Z8SaDpMOjafNeyRXLPIsQyVG3Ga9XQYRQeuBQB474O8BeJ9c8cr438eCOK5gx9jsUIOwj7pwCQqrkkDJJPJ9/RPGnhKy8a+GrjR71jGHIeGZRkxSDowHfqQR3BNdBRQB4hpUnxd8A2iaOui2viDToBstp1kyyoOij5g2B6FeOgOBW3pd/8AFzxDq1k13punaBpUc6PcZYNLLGGBZByxGQCOi9eteqUUAcf8Tf8AhI/+ELm/4RX7T/annR7Ps+N+3PzdfatDwP8A2v8A8IXpf9ved/anlf6R53392T1/DFdBRQAVj+K9DTxL4U1PRnIH2u3ZEY9Ffqp/BgD+FbFFAHyn8ENXfw18UP7MvgYftqPYyI/GyUHKg++5Sv8AwKvqyvMPE/wV0vxB4sfxFb6pdabdu6SsIEUjzV/jGeh4B+vPevTYwyxqHbc4ADNjGT64oA+TdM+G3jKHxzZ3knh68W2TUklaQqMBBKCT19K+p9X0q01zR7vS76PfbXUTRSL7EdR7jqD6irtFAHx1qXwh8a2Wp3NrDod1dRRSsiTxKNsqg8MOe45r6W+F2mXujfDfRtP1G2e2u4UkEkT9VzIxGfwIrr6KAPAfiX8C76/1i51rwr5Un2lzJNYu4QhzyShPGCecEjHb0HIT+FfjFqWnLolzBrEtlgJ5Mt0uwgdASWwQPQnFfVtFAHjvwn+DkvhK/Gva9JFJqaqVt4IjuWDIwWLd2wSOOBk8nPDvj34Y1vxLYaImjabPetBLKZBEAdoIXGfyNewUUAeX/Arw/q3hzwdf2msWE1nPJqDSLHKMEr5cYz+YP5VxXxw8E+JfEXjm3vNI0e5vLZbCOMyRAEBg7kjr6EfnX0LRQBxnw+0GWD4V6boet2RRjbSQ3NtKOdrM2QfqDXi3iT4F+KvD+rm+8KSte26PvgaKcRXEPpnJGSPVTz6CvpyigD5gTQvjfrCiylm1mOI/KzS3ixDHudwJ/WvXPhP8O7zwDpt//aF/Fc3l+6PIsQO2Pbu43Hlidx5wK9DooA+Y/wBo+aN/HOnRKwLx6cu8DtmR8V7H8HtPutM+FeiW95C0MxSSXY3Xa8jOp/FWB/GsDVvg9L4i+KD+J9Z1CCbTfMRhZKhyyooCqSeMZGT65PrXrAGBgdKACvOvjH4X13xT4ZsYPD0fmXttercYEwiYAI4yGJHOWHevRaKAPl1dN+ONnF9nD68V6f8AH0sh/wC+tx/nUGlfBHx14j1Q3Ouf6Esrbprq8nEsrepwCST9SPrX1TRQBmeHtCs/DWgWWjWCkW1pHsUt1Y9Sx9ySSfrXyv4k+HXjzVPE2p3r6FqNz511Iyythiy7jt5J6Yxj2r67ooA+VtP074z6TYQ2On22sW1rCu2OKJUVVH0r0D4V/wDCy/8AhLn/AOEt/tP+zfsr4+042+ZlcdO+M17TRQBy/wARNfXw14C1fUi+2UQNFBzyZH+VcfQnP0Brwr9nnw0+o+MJ9dkQ/Z9MiKoxHWVwVA/Bd35ivbPiB4BTx/Y2llc6rcWdtbyGUxwoG8x8YBOfQE/ma0fBnhGw8E+HItHsCzqrNJJM4AaVz1Y49sD6AUAdBRRRQAUUUUAfAT/fb6mvvHSP+QLYf9e8f/oIr4Of77fU1946R/yBbD/r3j/9BFAFyiiigArynx/8LL7UPEEfi7wderp+vRkM6E7VmIGNwOOGI4IPDd8c59WooA8bTxh8Y7eL7LN4HtJroDaJ1f5CfU4kx+RFN8PfC/xB4h8Vx+KviLcxSzQkGDToyGVcHKhsfKFB52jOT1PXPs1FABXhEXhbx38L/FepXXhHTItX0PUH3/ZywynJKgjIIK5IyMgjrz093ooA8J8X6F8R/H3hW8uNasItPitgr2ekWjBpLiUsBuc5PAUsQMj6V6X8NNNvNI+HWi2GoW7293DCyyRP1U72PP511dFAHiGq+GvG/gj4m6r4o8LaTDq9jqgYyxFwGUsQzAjIOdwJBGRg12Wg6/4n13w7r58SeHf7HaG2IgG4nzco+7r6YH513tU9WiefRr6KJS0j28iqo6klSAKAPm34SaV4ttPD114n8HtBc3KXbWt3ptwcJcRhEZSDkYYFm7jr9Qen1yx+KfxO8rRdR0eDQNH8xWuXL53YOefmJbHUAADOMnoR1HwL8P6t4c8GX1prFjLZ3D6g8qxyjBKmOMZ/MH8q9PoAztC0W08O6FZaRYqVtrSIRpnqfUn3JyT7mvH7/wAJeNvh9481LxB4MsYtU0zU2LzWjMMqSdxBGQeCTtIzwcH39xooA8R8R6X8SfiB4avl1nTodIsIIWlh062bdNeTAfIGOThQeccdBx3HbfCLSNQ0P4badp+p2slrdxvMXikHIzIxH6EV3FFAHi3ijwt4x8M/FK48a+E9Nh1SG8iCT27OAV+VVYYyCclQwIzz1Hr2vgfxH4s1y5vE8SeGf7HjiRTC+4nzCScjn04rtKKACvO/HVz8SNP8Q2954Ps7e/0wWwWe2mKf6zc2SMsrdCvQ/hXolFAHjF54o+MGs2cunWvgy3sJZlMbXLyD5AeCV3PjP5/Sux+F3gRvAXhY2NxOk99cSme4ePOwNgAKueoAHX1JrtqKAPJPCfhrWbL49eJdaudOmi025t5FhuWHyuS0RAH/AHyfyr1mUExOB1KmnUUAeT/Afw3rHhvw/qsGs6fNZSy3SuiyjBYbAM1m+JvB/jLwr8R7rxn4LtodQjvlIubRyMjONwIJGQSoYEHIPbHX2qigDyCT/hZ3j22bS9U0u28OaPKuLyVH3TzJ3jQZOM9Og+p6Gx8CPDmseG/DmqW+s6fNZSy3YdElGCy7AM16vRQB8zWOhatrPxt8Wz6BfLaaxp00t1as/wByQiQKUb2KsRXW6vrfxk1yyk0SLwtbadJOpimvYpRgKeCVYuQPwyfTmtHwL4a1nTvjT4t1e806aHT7tZRBcMPlkzKpGPwBr1ugDj/hv4Fh8BeGF0/zFmvZn867mUcM+MYXPO0DgfieM1zfj74b6vc+J4fGfgy7S112IDzYnICz4GMgnjOPlIPBHp39UooA8eTxn8YFUWz+ArRroceb5mIyfX/WY/8AHq7PwND418u+u/Gc9mJZyn2a0tQNtuBu3AkdScju3TrXXUUAeX6l/wAJ1/wuq0+y/bv+EV+TzduPJ/1Zznv96vUKKKACvBP2j/DTyQ6X4lhTIizZ3JA6Aksh+md4/EV73Wdr2i2fiLQrzSL9N1tdxmN8dR6MPcHBHuKAOH+BuvrrXw2tLZnzcaa7Wsg77QdyH6bSB/wE0z45aBqviPwPa2ej2Mt5cLqEcjRxDJCiOQE/mR+dXPh/8LYPh9qF3cWWs3VzFdRhJYJY1Ckg5VuO4yR+Jr0CgDxf4BeFdd8Mt4g/trTJ7L7QLfyvNAG/b5ucfTcPzrR+N/w+uPFmj22qaRamfVrJtnlp96aJjyPqp5HsWr1eigD5V+Hvw68X6X8QNEvr7QLyC1guleSV1GFHqea+lPE3h2x8V+HrvRtRUm3uFxuX7yMDlWHuCAa1qKAPle8+EHxD8Haybvw/5lyEJEV5YTiN9p7MpIP1HI9zTD8MPid411SObXI51IG37TqNwMRr6BQSfwAr6rooA5zwP4OsfA/huLSLNjK24yTzsMGWQ4y2Ow4AA7AD6186eO/hz4w1Lx5rl7Z+H7ya2nvJHikRRhlJ4I5r6uooAhZWNkUx83l4x74r5X8CfDnxhpvjzQ7288P3kNtBeRvLI6jCqDyTzX1dRQBw3xM+HFt8QNIiQTLbala5NtcEZXnqjf7JwPcdfUHw218B/FrwbPLFo8V9HE7ZLWF0rRye+3P8wDX1XRQB8z23ws+J3jW4j/4SfUZ7a0Bzm+uvNI91jUkZ+u2vo6zt00zSre3MmY7WBYzI3HCrjJ/KrVZniPT7rVvDepadZTpb3N1bvAkrgkJuGM8exNAHx/4Es7jV/ifoy2MTSkalHcHA6RpIHZj6AKCa+0686+F3wth+H8d3cXNxFeancnZ56IQEiHO0Z55PJ+g9K9FoAK+fPH/gr4jr8Q9V17wul2lrdNGVezvVjZsRqp3LuBPIPY19B0UAfK99oHxo123awvo9amgf5XjkuFRHHo3zAEfWux+GXwOvtG1y213xNJCslqwkt7KJt5Eg6M7Djg8gDPOOex93ooA8/wDjLpmtax4AksNCguJ7mW4jEkUBwWj5yD6jOK+etO+HnxF0m/ivrHw/fw3UJ3RyBFJU+oz3r7FooA+X/wDi+f8A1HP/AB2vePh3/bv/AAgmm/8ACS+f/a/737R5+N/+tfbnH+ztrqKKAPnj9pHX1lvdI8PxPkwq13OAehb5UH1wGP8AwIV6T8HPDT+GvhzYxzoUur0m8mUjkFwNo/74C/jmszUvgpputeNZPEmq6tdXbSXAme2aNQjKuNseeu0AAfQV6j0oAKKKKACvHv2j/wDkQNO/7Cif+ipa9hrx79o//kQNO/7Cif8AoqWgDzj9nr/kpT/9eEv/AKElfVFfK/7PX/JSn/68Jf8A0JK+qKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuNvf+P8AuP8Arq38zXZVxt7/AMf9x/11b+ZoA7KiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDw/8AaU/5F7Q/+vt//QK5X9m//keNU/7Brf8AoyOuq/aU/wCRe0P/AK+3/wDQK5X9m/8A5HjVP+wa3/oyOgD6aooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+An++31NfeOkf8gWw/694//QRXwc/32+pr7x0j/kC2H/XvH/6CKALlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV49+0f/wAiBp3/AGFE/wDRUtew149+0f8A8iBp3/YUT/0VLQB5x+z1/wAlKf8A68Jf/Qkr6or5X/Z6/wCSlP8A9eEv/oSV9UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcbe/wDH/cf9dW/ma7KuNvf+P+4/66t/M0AdlRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4f+0p/yL2h/wDX2/8A6BXK/s3/API8ap/2DW/9GR11X7Sn/IvaH/19v/6BXK/s3/8AI8ap/wBg1v8A0ZHQB9NUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF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64oA9toryPwv8AtAeHtcvo7LU7SbSJJTtSWSQSQ59C2AV+pGPUivXOtABRRRQAVieJfF+heELSO51zUI7VJSREpBZ5CMZ2qAScZH0yK2647xx8OtN8fXOltqlzcR29h5p8qDAMhfZ1Yg4A2dh36igDlpP2ifBiTFFtdYkX/notumP1cH9K63wp8SfC/jKU2+lah/pYG42s6GOTHqAeG/Amufv/AIFeApNMljjsprKQIT9rW6kJTj7xDMVx+FfNHhWS6tfGukNpzs1yl9EIjH/Ed4H5H+RoA+3L6/tNMsZr2+uI7e1hXdJLI2FUe5rzjUfj74HsZSkM19fY43W1tx/4+VrsvGWhS+J/COo6LBMkMl3GIxI4JC/MCTgdeBXEaP8AADwZYWoTUY7rU58fNJLM0Qz/ALKoRgfUmgC1ovx08E6xdJbvdXOnu5wpvogik+7KWA+pIFekqwZQykFSMgjoa+Qfi74GsfA3imG20ySRrK7g8+OORtzRncQVz3HHB9/bNe6/ArU7rUvhjbLdOzm0nkto2bqUGCB+G7H0FAFXx58Yp/AfigaTd+GjcW7xrLFdLe7d6Hgnb5Z5BBGM9h616ZYX1tqenW1/ZyiW2uY1licfxKwyDXkv7ROgrfeDLTWUT99p1wFZv+mcnyn/AMeCfrSfs7+I31HwleaJM+6TTJgYsn/llJkgfgwf8xQB7HRRRQAV578b/wDkkWtfWD/0fHXoVee/G/8A5JFrX1g/9Hx0AfPPwg/5KtoH/XZ//RbV9kV8b/CD/kq2gf8AXZ//AEW1fZFABRRRQAVyPiv4meFvB0v2fVNQ3XmM/ZbdfMkA9wOF/Ein/EfxS/g/wNqGrQ4+1BRFbAjI8xjgHHfHLfhXA/Bj4fWc+jL4w1+Fb/VNQdpYTcjf5a5I3YPVmOTn0xjHNAGpZ/tB+Cbq4EUo1O0UnHmz2wKj/vhmP6V6Xp2pWWr2EV9p11FdWsoyksTBlP41X1bQNJ12wey1TT7e6t2GNkiA4+h6g+4rw7wfLdfC34zTeDmuJJdF1Nh5Ac5wWGY2/wB7I2H169hQB9B0Vz/jHxhp/gjQxq2pQ3UtuZlh22yqzZYHHDMBjj1rW02/i1TS7TUIFdYbqBJ0VwAwVlDDOM84NAFqorq6t7K1kurueK3t4l3SSyuERB6kngCua8TePtK8K67o+kX1veSXGqyCOBoEUopLKvzEsCOWHQGue+NXi6w8P+Dp9Ju4bl7jWbeaK3aJVKqV253ksCB8w6A96APQrHULLVLRLvT7u3u7Z87ZreQSI2Dg4YEg8girNfOvw4+NHhzwf4HsdE1Cy1WW5t2kLPbxRsh3SMwwS4PQjtXq2sfE3RdE8E6b4rubW/ew1B0SKOONDKpZWYbgXA6KehPagDtKK8u1X40W63ZtPDPhzU/EM8aK0/2ZCEiJGdpKq3I6HjGe5rQ8D/FrSfGOpPpE1pPpWsJn/RLg5346hTgcjuCAfyNAHoNFYvivxPZeD/D1xreoRXEttAyKy26qzncwUYBIHU+tWtD1e31/Q7LV7VJUt7yJZo1lADAHpkAkZ/GgDQpk00VtBJPPKkUMSl5JJGCqigZJJPAAHeuY8ZePtK8Dyaamp295KdQkaOL7MittI253bmH94dM1nfFjxdYeF/Bt3Bew3MjapBPZwGBVIV2jIBbLDA57Z+lAHYafqen6va/adNvra9t9xXzbaZZEyOoypIzVuvmv4W/F/wAP+CPB/wDZGpWepy3H2l5d1tFGyYbGOWcHPHpXv3hnxDaeK/DtprdjHPHbXQYok6gONrFTkAkdVPegDWoorznxh8XtM8M63/YVhpt3rOrjG+3teAhIztJwSTjnABoA9Gqhrmpf2L4f1LVfJ877Fay3Plbtu/YhbGcHGcYzg15lD8cJLKZP+En8Fa1ols5Ci4dGdRnudyJx9Mn2rvfHH/JP/En/AGC7r/0U1AFX4f8AjH/hOvDC6z9g+w5meLyfO837uOc7V9fSupr59+GXxD0/wh8NrSxWzutT1e5upmh0+zTdIV4+ZvQcHsT7cGup0n442769DpPiXw5f+HpZyFje5JIBJwCwZVKj3wR+HNAHrNFFeVah8a0e+ntfDXhPWNeMDmOSSGNlTIPOMKx/MCgD1WivMPDfxp07Vddj0PW9HvdA1GZgsSXXKsx6KSQpBPbIx716Jquow6Po97qdwrtBZ28lxIsYBYqiliBkgZwPWgC3RXl0nxz0GbSba40rTNTv9QundYdOSNfOIXqzbS2FPbqTg8VX0r45Wh1uLS/E/h6+8OyTEBHuiSoz0LblUqPfBHr60Aes0UA5GR0rzjxX8YtJ0DWW0TTLC71zVkO17ezHCMOqlsElvYA470Aej0V5LZfHKGDUobPxV4X1Pw8JzhJrgMyj3IKqce4Br1cTRtAJ1dWiK7w6nIIxnIx1oAfRWB4Z8aeH/GKXLaDqH2sWxUTHyZI9u7OPvqM9D0rfoAK4L4lfFC0+HkVkhsf7Qvboki3E3lbYx1Yna3fAAxzz6V3tfH/jC+m+Ifxhkt45CYri+SwtiOQsQbYCPbq34mgD6h8F+IbrxV4XtNautM/s77Vl4oPO807M4DE7V69cY6Y9ap+IPiV4R8LaodN1nVvst2EEhj+zSv8AKehyqkfrXTWttDZWkNrboI4IY1jjQdFUDAH5Cvlf9oD/AJKc/wD15w/1oA+k/Dfi3QvF9pNdaFfrdwwv5chEboVbGeQwB/HGOvpT/EfifR/CenJqGt3n2W1eUQrJ5TvlyCQMKCein8q+WPhF4xbwZ42hF25j03UAsFyG4Cg8pJ+BPX0Jr2L9ov8A5Jzaf9hOL/0XJQB3nhnxv4d8Ym6Gg6j9sNrt879zJHt3Z2/fUZztPT0roK+ff2Zv9Z4n+lr/AO1a9q8S+KNI8JaS2paxdCCAHaoxlpG7Ko7mgDYorwm7/aWsUnK2Xhm4mhzw812sbEf7oVv511Pg343eHPFd/Fp08c2l38pCxJOQ0cjHoquO/sQM9qAPTaKoa1q0GhaJe6tdJI8FnC00ixAFiqjJwCQM/jXn2j/Hnwfq93JCY9RskjheZ57uKNUAUZx8rsST0AA5NAHqFFeGXf7Sumx3xS08O3U1oDjzpLlY3I9dm0j/AMer1bwl4s0vxnoUeraVIxiLFJI5Bh4nHVWHryPzoA3aK47xv8S/D/gREj1GWSa9kXdHaW4DSEf3jkgKPc/hnFebj9pi18/B8LTCHP3xejdj/d2Y/WgD3miuX8GePtD8dWLz6TMwmix51rMAskefUZ5HuMiuooAKKKKACuW1b4k+DtDuJbfUPEFnHPCxSSJCZHRh1BCAkH2rqa8c1v4CaXquuatrd9rd4TdTy3PkwRKmzcS23J3Z+uKAOt034ueBtX1K206x1zzbu5kEUUf2SddzHgDJQAfjXbV8UfDf/kpXhz/sIRf+hCvtegDz34lfEu6+Hctix0D+0LS7DATi78ra46qRsbsQQc88+ldP4S8TWni/wzZ61ZjalwnzxFsmJxwyk+x79xg96wPjBoK698M9VTZmazT7ZEfQx8t+abh+NeXfs4eI3h1XU/Dkr/up4/tcAJ4DrhWA+oIP/AKAPouiiigArB8cf8iB4k/7Bd1/6Kat6sHxx/yIHiT/ALBd1/6KagD4x8N/8jTpH/X7D/6GK+7K+E/Df/I06R/1+w/+hivuygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK429/wCP+4/66t/M12Vcbe/8f9x/11b+ZoA7KiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+TPj1/wAlUvP+veH/ANAr1z9nr/kmr/8AX/L/AOgpXkfx6/5Kpef9e8P/AKBXrn7PX/JNX/6/5f8A0FKAPV6KKKACvBPjD/yWTwR/v2//AKUV73Xgnxh/5LJ4I/37f/0ooA9t1v8A5AOo/wDXrL/6Ca8a/Zu0W0GiatrjRK141z9lVyOURUViB6ZLjP0Fey63/wAgHUf+vWX/ANBNeV/s4f8AIg6j/wBhR/8A0VFQBrfHmwt7r4XXlzLGpls5oZImxypLhD+jGul+HVzJd/Djw7NKxZzYRKSepwuP6Vh/HD/kkes/70H/AKOStb4Yf8kx8O/9eSUAeT/BPQ7W/wDiV4o1W4jWSTT5WWDcM7Gkkf5h74Qj8TXt/ibQ7XxJ4cv9JvI1eK4hZRuH3Wx8rD3BwR9K+ePhn4vg8IfEHxDNqQePSLu6a3nugpKwS73MZbHYgOP/ANVeseNfi14Y0bwzdy6drNnf6hLEyW0NrKJDvIwC237oHU5x0oAwP2c9UmuvBmoafKxZLO7zFn+FXUHA/EMfxqn8Of8Ak4Lxv/uTf+jkroPgT4ZufD/gM3N7E0VxqU32gIwwVjwAmR74J+jCuRttVtvh7+0PrU2tsbew1aJjHcsDtG8q4Y+25WUnsfagD3TU/wDkE3n/AFwf/wBBNeSfs3/8iRqn/YSb/wBFx10fjD4m+H7fQbm00XUrbVtWvImhtLaxkExLsCAWK5AA6nPpXOfs3/8AIkap/wBhJv8A0XHQBU1z/k6fQv8Ar1/9pS17hXhHji8t/Dv7Rvh/WNTkEFg1quZ2+6uVkTk+xIz6A17Dp3ijw/q9z9m0zXdMvbjaW8q2u45HwOp2gk4oA1q8s+O/hnVPEXg22fSreS5ksbnzpLeMZZkKkEgdyMjgdia9Trn/ABP400Twe1j/AG3cPbRXjskcojLKpUA/NjJHX0oA4fQPjz4Rnt4bbVIrnRrmNRG8TwFo0I4wpXJx9QK9J0m/0rVrY6lpNxa3MU+N09uQ28jsSO49D0rk9Z8SfDDW7B5dW1Pw7exbf+WskbyAew++D9Oa4L4Axt/wkfiuXSxOPDhl223mZ5O9tnX+IJ1+oz2oAreMdHtde/aY0nTr2MSWzxRvJGwyHCI74PsduDXv5jRojEUUxldpQjgj0x6V4jqv/J1mjf8AXqf/AERLXuFAHhvwPQab468c6PBkWkFztjTsuyWRR+n8q9Q8VeLPDnhCKHUNduYoZSGSABN8rDjcFAGccDPbpmvMfg//AMlY+IH/AF9yf+j3rO+Ikthp/wAf9HvfFcXmaD9nXZ5iF4wAHHI7gSEEj0oA6C5/aG8EyxyW8um6xcQupR1NtEVdTwQQZORWT+zzqMMmqeK7KyEiaaZUuLWOT7yKWcDPJ527Aee1eg6j8TfBGhaSZ7bV7C5IX91aWDrJJI3ZQq9M++BXnPwEmurjxv4xnvojDdyMHnjPVHMjlh+ByKAK/jbRbbxD+0ppOmXiB7aSKJ5EPR1RGcqfY7cfjX0F5aeV5WxfLxt244x6Y9K+c/iPq1xoX7QVjqttbyXDWlvHM8UYyzRKjGTH/ANxr10fFTwQdI/tL/hI7Hytm7yvM/ffTy/vZ/CgDzjwJCvhT9obxD4dshs0+5iZxCv3V+VZVx/uhmUexrI+K/iLT1+NmnxeIIZ7nRdJjRntoVDF2Zd/3WIBySgOT0Wtv4SwXfi/4meIPH81u8Nk+6G23jqTtAHvtRQD7tTvija3vgv4n6P8RLa1e408BYbwIOVOChz6ZRuO2R9KANIftG+DlUKum62ABgAQQ8f+Ra5H4feJtM1H4/y3Xh63uLTTdXhkElvMiqQwj3scKSPvIT17mvYtP+JvgrUbJLqLxLp0SsM7LmdYXHsVYg1JovxC8NeIvED6Lo+oC9uY4Gnd4kPlqoKj7x4J+YdM0AV/FHw30fxb4h03Wr+5vo7nT9vlLA6BG2vvG4FSevoRXYVx/ij4kaP4S8Q6bot/bX0lzqG3ymgRCi7n2DcSwPX0BrsKACvmj9pGZ28Y6TCSfLTT94+pkYH/ANBFfS9eA/tKaO5XQ9bRSUXfaSt6E/Mn8n/KgD0f4QRJD8KdAVOhhdj9TIxP6mofjT/ySPXf92H/ANHx1Q+A+qJqHwvtLcNmSwnlt39eW3j9HH5Vf+NP/JI9d/3Yf/R8dAHhHwV0K08TeItc0a9GYLrRpUJxkofNiKsPcEAj6Va+Geu3fw0+J1xoernyra4l+x3YJ+VXB/dyfTJ6/wB1iatfs5f8lEvv+wVJ/wCjYq6X9obwX5kNv4us4vmj2299tHVeiOfoflP1X0oA8cuv+SjTf9hZv/R1fU/xd1i40T4Y6xc2rlJ5EW3VwcFQ7BWP/fJNfI2kO0niCwd2LO11GWZjkk7xya+y/iB4bfxZ4G1TRoSBcTRhocnA8xSGUZ7ZK4/GgD5r+CvhPT/FvjhodViE9nZ2zXLQk8SMGVVB9vmz+Fem/Gz4e+HbTwLNrel6Xa6fdWMkeTaxCNZEZwhBUcHlgc9eK8f8C+Krz4Z+NnuruwlJVWtby1b5HCkgnGehBUH3x75rsfih8aLPxl4cGh6NY3UEEsivcy3W0MQpyFAUnuAc57dKALX7N2sXEfiPVtFLk201r9qCk8B0ZVyPqH/QVzXx2/5KvqH/AFxg/wDRa1337O/hC8slvvFF5C0MdzCLa0DDBkTcGZvplVAPfBrgfjt/yVfUP+uMH/otaAPX/hl8MfDA+H2nXOpaPaX93qNuLiWW4jDkBxlQpP3cKR0wc8187Sw/2B8QXgtGZf7P1UpE2eR5cuB/Kvr34f8A/JOvDX/YMt//AEWtfI/iL/kpmrf9hib/ANHGgD2T9pPWLiGw0TR4nKwXDyTzAH7xTaFH0+Zj+VedfDnxF4A8O21zN4p0O71XUJJMRD7PFLDHHgfwu4yxOc5HQDHevXvj74PvPEHhyz1bToWnn0tnMkSDLNE4G4gd8FQcehPpXkPw78ceGfDlrPY+JvCdlq0LyeZHcm1ikmTIAK/OOV4yORjn14AMjx/qnhPWdcjvfCem3OnW7x4nt5o0Rd+eqBWYAEY446V9K/BnWZ9a+GOmSXTmSa332xcnkhGwv/ju0fhXlGs/FjwAtuy6H8ONMknI+V76xgRVPrtQEn6ZFe2fDkMfAWlTyadb2E1xGZpYLe2W3QMxPOwAY4xQB1NFfPHiz48eKNB8XatpNrYaO8FndSQxtLDKWKqcDJEgGfwr6FQ7kVj1IzQA6orpZGtJliz5hjYJg4Occc1LXlnj34wHwF4yi0m50kXlnJaJP5kcuyRSWcHgggj5R6UAeRaz4K+MMmnSjVE1u8tcZeI6kLjI/wBwSMT+VUvhP4r0Pwp4qhk1rSI5Gd/LS/Zm32hPBO0/Lj1OAQM9eleq3P7SPhxbVmtdG1WS4x8qSiNEz7sGJ/SvBrLT9T8deMXisrbdealdPKyxr8ke9ssx9FGaAPuGsDxd4w0nwXor6lqs20ciGFeZJm/uqP69B3p/ifW08I+EL3V2ha4WxgBEYbBc8KBntyRXx34i8Vah4y8Rf2lr93JtZgu2FNwgjz92NCQOPcjJ6nvQBvznxH8afH7SQwAO4Cgc+VaQA8ZP4k+pJOBzivqnwt4cs/Cfhuz0WxyYrZMF2HMjE5Zj9SSf0rxrwn8YPhz4M0VNN0nRtcVesszQQmSZv7zHzOT+g7V6z4K8a6b480abVNLgu4YIrhrdlukVWLBVbI2swxhx39aAM/4txJN8K/ECv0FuG/EOpH6gV41+zdM6+NtUgBPlvpxc/USIB/6Ea9Q+Ouqpp3wuvYC2JL6aK3T/AL63n/x1D+dcP+zXo779c1t1ITCWkTep++4/D5PzoA+gaKKKACvPfjf/AMki1r6wf+j469Crz343/wDJIta+sH/o+OgD55+EH/JVtA/67P8A+i2r7Ir43+EH/JVtA/67P/6LavsigAooooA8j/aLWRvh1aFM7V1OMv8ATy5B/Miu3+Hjxv8ADjw2Ysbf7NgBx6hAD+uak8b+GI/GHhDUNFdgjzpmGQ9EkU5Un2yOfYmvJvhh8RI/BMcngjxqsmmzWUjC3mlUlQpOdrEdskkN0IPbAyAe9V8/fFj978e/ByQczj7HnHb/AEliP8a9Q1b4qeCtIsHun8QWVzgZWGzlWaRz6AKTj8cCvNfhzpOp/EH4lXHxE1a2a306Fj9ijbo5A2qF9Qo5J7t+OADpf2hP+SaL/wBf8X8mrvPB/wDyJOgf9g63/wDRa1ynxv0i41b4YX32aNpJLSRLoqoySqnDH8ASfwqH4ffEzwrceBdKjvdbsrG7s7WO3nhupljbcihcgH7wOM8Z60Ac18Zf+Sm+AP8Ar7X/ANHR16J8Tv8AkmXiL/ryevFfHXi2Dxd8V/CdzpySNpNvexQW9yyFVncTIZCuecDKj8DXuHxDtZbz4deIYIFLyNYSlVHU4UnA/KgDB+Bv/JJdJ/35/wD0c9Yv7Rv/ACTux/7Ckf8A6KlqP4LeNPDVj8OLPTr/AFzT7K7tpZQ8d1cLEfmcsCNxGRhu1N/aDure9+GWm3NpPFcW8upxNHLE4dHHlS8gjgigDvfh5odr4f8AAej2drGqFraOaZgOXkdQzMfXk/kAO1eW/Hq1TQPEHhrxdYKIr9JysjrwZNhVkz6/xD6YHaur+HvxL0NvC1lpmvajb6Vq2nwJbzw3sgi3BVAV1LYB3Lg8evpgnh/HusW/xb+IOheGPDzG60+zdnurpAdmCV3sPZVXAPctgdsgHf8AxyIb4SamR0LwH/yKtbvw0/5Jp4c/68I/5VU+LWkXGtfDDWrS0jMk6xpMqKMk+W6uQB3OFNc58KviR4ZPgDTrDUNYs7C9sIvIkiupliyFPylS2N2Rjp3zQBj/ALQv/H14O/6+5f5xV6p40/5ETxD/ANgy5/8ARTV4H8X/ABja+LfEegjSd82lWNw0YvNpCTTEoWC56hQF5/2voT9BeKLWW98I61aQKWlnsJ40UdSzRsAPzNAHA/s+/wDJMv8At+l/kteqV4f8CvGHh7S/A0um6nrNjYXUd5I+y7nWLcrBcEFiAe4/CvabK+tNStI7uxuoLq2kzsmgkDo2Dg4YcHkEUAT18+PqVz8Jvi5r+ta3o91daVqzu0N7AgOxXffgE8cdCpIPAPpn6DrjIPih4Lu7280251i3tLi2me3mhvf3QyrFTy3ykcetAEei/FDwR4tI0+HU4TLONhtL2Mx78/w/MNrZ9ATWx44/5J/4k/7Bd1/6KavFPjTP8PbrSID4fbTZdea4Xa2l7TuTndvKfKe2O+fbNeragl9H8E7xNT3fb18PSC43/e8z7Od2ffOc0Ach+ztotpbeDLrVxEpvLu6aNpSORGgGFHtkk/l6VP8AtEWFvP8AD+3vHjXz7a9QRvjkBgwYfQ8H8BVr9n//AJJhH/1+Tf0pP2gf+SYt/wBfsP8A7NQB3nhW5kvfB+iXUzFpZrCCR2PUkxqT/OuM1j40eBfDE7adDLLdNCSrR6dACiHPIBJVT+BNatrHeTfBK3j07d9tfw6iwbPvbzbjbj3z0rzf4Ha/4I0rw3LDqVzp9jrQnYyy3pWNnTjbtduMDpjPXPHNAHO/Fb4n+FvHeiWkem6fqUGqWtwHiuLiKNcJg7lyrk9dp6dq9v1i9k1H4NX99KcyXPh+SZj7tbkn+deV/G/x7pviLQE0bQJft0FvcJNeXkIzFHwwRA3Qkkk8f3frj0qb/kg0n/YsH/0loA5L9nTQ7W38JXutGNTeXVy0PmEcrGgXCj0+Ykn149K6L426Ha6t8NNQuZY1Nzp+24gkxyp3AMM+hUnj6eleffA3x5p/h3RpdF1+YWNvczNcWV3NxE54V0LdAQQDz6n2zufGH4j6PqHhd/DHh69i1TUNSdIj9jbzVRdwONy5BZiAABnqfbIBueFPE92n7Pkeuly15aabOqMefmiLohPr91a8t+FfxL8KeBdJu21Ow1O41i7mLS3MMUb/ACcYXczg9ck8dT7V7Z4e8Fmz+EkPhG6YJLLYSQzEc7JJdxbHrhnP5V5x8KfGtt4CF94J8YMdMuLa5Z4JpQfLIbGVJ7DI3BuhB69MgFfx38ZvBfjDwff6QdL1Y3EkZa2klgiAjlHKtkSEjng4HQmvQPgvfS6r8J9NS5dmaHzbbdnnarEKPwUgfhWxe/EzwTYW5mm8T6Y6gZxBOJm/75TJ/StjRtds9e0CDWrLzDaToZI964YqCR0/CgDE8DfDvSPh/HfJpVzfTC8KGT7U6Njbuxjaq/3j6111cj4G+ImkfECO+fSra+hFmUEn2pEXO7djG1m/un0rrqAK99K0Gn3MqffSJmX6gE18g/B6NZ/izoIk5HmyPz6iJyP1FfYrKHUqwypGCD3r430BW8D/ABjs4bolFsNU8iRm/wCeZbYW+m05oA+ya+Uv2gP+SnP/ANecP9a+ra+Uv2gP+SnP/wBecP8AWgCfxh4L+0/CDwv4ts4v3tvarBehR1jLHY5+hOD/ALw9KPEPjT/hKvgNY2l1Lu1LTNThgmyeXTypdj/iBg+6k969t+HNjbap8HdHsLyIS21zYmKVD/ErFgRXyv4w8OXfg/xPqGh3DOVikzG3QSxnlG/I/gcigD1/9mb/AFnif6Wv/tWua/aC1i4vfiENOZz9n0+2RUTPG5xvZvqcqP8AgIrpf2Zv9Z4n+lr/AO1ap/tC+ELyLW4fFNtC0llPEsNyyjPlSLwpPoCMDPqPcUAdr8K/ht4Z/wCEA06+1HR7O/vL+Lz5ZbqISbQ33VXP3cDHTvmvDfin4btPCPxCvtP00GO0wk8CbiTGGAO3PXg5x7Yrvfh78dbDw14St9F1rTryZ7NSkE1rtO9MkgMGIxjOMjPGK861/U9U+J3xAlubOxZrq+kWO3tkO7YgAABPsBknp1PFAH0VdatPrv7PdzqVyxa4n0RzKx/icIQx/Egn8a+ePhj4atfFnj/TtLvgTZktLOoOCyopbbn3IA+hr6U8QaMvh34H6jo6uH+x6M8TOP4mEfzH8Tk14T8Bf+Sp2n/XtN/6DQB3vx18E+HdJ8EW2paVpNpY3MN0kRa2iEe9GVshsdeQDk81T/ZnuJN/iO2LExYt5AvYH94D/T8q639oL/kmX/b9F/Jq4z9mj/j/APEf/XK3/m9AHkfiXWG8QeM9Q1PUZJSlxdsXKAFlj3YAUH0UAD6V6fN42+DUugtpK+D9RjUx7FuVtIfPU44bzPN3E9+uK4r4h+Fb3wP47uDJaq9lJcG5s3lj3RSpu3BSDwcfdIP8iK76z+K/wxaxRr74d2sd3t+ZINOtnjz7McHH4UAee/CvWZ9F+JehywOVW5uUtJVzwySEKQfxIP1Ar7Nr568GeMdJ8XfEzSLHR/Aei6dYRu8ssy2EbzLtjZkbeFAj+cL05zjmvWPiV4ovfB3gm71rToreW5hkjVVuFZkIZwDkAg9/WgDraK8k+EfxR1vx9rGoWeq2unwx21uJUNrG6kktjnc7cV63QAVDef8AHjcf9c2/lU1eV+JPjn4Y0XVNS0S5sdXe5tneB2jhjKFhxkEyA4/CgD58+G//ACUrw5/2EIv/AEIV9r18M+EtWt9B8XaTq10kj29ndJNIsQBYqpycAkDP419UeDPi7oHjnW20nTLPUoZ1hactcxRqu0EA8q5OfmHagDstXiSfRb+KTlHt5FbPoVINfJXwWmeH4taJsJ+czIw9QYX/AP1/hX0/481VNF8Ba5fs20x2cioT/fYbV/8AHiK+ef2fdHe/+Iv9obT5WnWzyFu25xsA/JmP4UAfVFFFFABWD44/5EDxJ/2C7r/0U1b1YPjj/kQPEn/YLuv/AEU1AHxj4b/5GnSP+v2H/wBDFfdlfCfhv/kadI/6/Yf/AEMV92UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXG3v8Ax/3H/XVv5muyrjb3/j/uP+urfzNAHZUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHyZ8ev8Akql5/wBe8P8A6BXrn7PX/JNX/wCv+X/0FK8j+PX/ACVS8/694f8A0CvXP2ev+Sav/wBf8v8A6ClAHq9FFFABWBrHgvw/r+s2Or6np/n31iVNvL50i7NrbhwrAHnnkGt+igCOeGO5t5IJV3RyKUdc4yCMEVl+G/Cui+EdPksdDsvsltJKZnTzXky5ABOXJPRR+VbFFAGdrmhab4k0ibStWtvtFlMVMkW9kztYMOVIPUDvU2l6ZZ6Nplvpunw+TaWyCOKPcW2qO2SST+Jq3RQB4N8FNPs9U1z4gWV/aw3NtLcxB4pkDKw3zdQa9HsPhT4G02/W9tvDtsJ1O5TI7yKD6hWYr+la2g+D9B8MXV/c6PY/Zpr9w9y3nO/mEFiDhmOOWbpjrW5QAVj6/wCFdD8U2yW+t6ZBeIhJQuCGTPXawwR+BrYooA5rQ/h94U8NmVtJ0WC3klQxtISzvtIwQGYlgD7GrnhvwponhGxlstCsvslvLJ5rp5ryZbAGcuSegFbNFAGTr3hjRPFFqltrWmwXsaElPMHzIT12sMEfgay/D/w28JeFtU/tLRdJ+y3ewx+Z9olf5T1GGYjsO1dVRQAVl634c0bxHbpBrOm297GhJQTJkoT1IPUfhWpRQBwi/Br4frMJR4di3DsbiYr+W/FdlYafZaVZR2en2sNrbRjCRQoEVfwFWaKAMOXwfoM/iuHxPJY7tZhTZHc+c4wNpXG3dt6MR071uUUUAYej+D9C0DVtQ1TTLHyL3UHL3UnnO3mEsWJwzEDkk8AVY1zw7o/iWyFprOnQXsAOVEq8qfVSOQfoa1KKAOT0X4Z+DfD16l5pug28VyhyksjPKyH1XeTg+4rQ0bwfoXh/VNQ1PS7HyLzUHL3UnnO3mEsWJwzEDknoBW5RQB4fqwB/ar0cEZH2Q/8AoiWu9uvhN4Evb43k3hy184tuPls8aE/7isF/SteXwfoM/iuHxPJY7tZhTZHc+c4wNpXG3dt6MR071uUAQWdna6faRWllbxW9tEu2OKJAqqPQAdKfPBFcwPBPEksUgKvHIoZWHoQeoqSigDh7n4P+Abq4M8nhyBXJziKWSNf++VYD9K6TRfDeieHIDFo+l2tkrfeMMYDN/vN1P4mtSigDn9b8EeHfEWrWeqarp/2i9s8eRL50ibMNuHCsAeeeQa6CiigArnvHHhiLxh4Qv9GkKrJMm6CQ/wAEq8qfpng+xNdDRQB89/Ay28T+FfFF7pOq6Bq1vYXy/wCuks5BHHKmcHdjGCNwznk7a911rRdP8RaRPpWq2/2iyuNvmxb2Tdhgw5UgjkDoav0UAcv4c+HXhXwlqMl/oelfZLqSIws/2iWTKEgkYdiOqj8q39R0+01bTrjT76BZ7S4jMcsbZwykYI45H1FWaKAODh+DHw/t5454tA2yRsHQ/bJzgg5H8dd5RRQBg694K8N+J2D6zo1rdyAYErLtkx6b1w2PxrK0/wCE3gTTLhZ7bw5bGRTkee7zAH6OxFdnRQAiqFUKoAUDAA6CuS174Y+D/E2rSapq+kfab2QKryfaZkyAMDhXA6D0rrqKAK2n2FtpenW2n2UflWttEsMMe4ttRRgDJyTwO9cjc/CLwLd6nLqM+h77uaYzvJ9rnGXLbicB8de2MV29FABXJ6v8MvBeu3LXF/4ftHmc5aSLdCWPqShGT9a6yigDl9G+HPg/w/Os+m6BaRTKcrLIDK6n1DOSR+FdLNNHbwSTysEjjUu7HoABkmn0UAfKHgDwrL8UPiRf6vfxN/Za3TXl3noxZiyxfj39gfavq8DAwKKKACsLXfBfhvxNKJtZ0a1u5guwSuuHC8nG4YOOT371u0UAcGPgx8PhJvHh1M+91Nj8t+K6nRvDujeHoDDo+mWtkjfe8mMKW/3j1P41p0UAUtX0ix17SrjS9Sg8+zuF2yx72XcMg9VII5A6GuM/4Ul8PP8AoXv/ACduP/jlegUUAef/APCkvh5/0L3/AJO3H/xyuo8N+FtG8I6dJYaHZ/ZLWSUzMnmvJlyACcuSeij8q2KKAPn3462/ibxR4lstI0rQNWubCxTPnRWchjklfGSGxggDAznglq9f8C+F4vB3g+w0ZNrSxJvuHH8crcsfpngewFdHRQAUUUUAFee/G/8A5JFrX1g/9Hx16FXnvxv/AOSRa19YP/R8dAHzz8IP+SraB/12f/0W1fZFfG/wg/5KtoH/AF2f/wBFtX2RQAUUUUAFZGu+FtC8TRLHrOlW16FGEaVPmX6MOR+BrXooA4qz+EfgKwuBPD4btmcHIEzySr/3y7EfpXZxxpFGscaKiKAFVRgAegFOooACAQQRkHqK4u5+EvgS6vjeS+HLbzi24hHdEz/uKwX9K7SigDnb/wACeGdSudLnuNJi36UQbIRO8Sw4YMMKhAPIB5BroqKKAOJv/hF4D1K6e4uPDsAkc5byZZIQT/uowH6Vxnx60y00b4UaTpthD5NpbajFHFHuLbVEUuBkkk/ia9prH8R+F9G8W6cmn65Z/a7VJRMqea8eHAIByhB6MfzoAw/+ED8L+KvD+kTa1o1vdTLZQqJssj42DgspBI9s1uaB4V0LwtbvBommQWaP98oCWf0yxyT+JrUt7eK0toraBdkUSCNFyThQMAZPtUlABXG3/wAKfA2p37Xt14dtjO7bmMbvGrH1KqwB/KuyooA5rUvh/wCFdWtNOtbvR4fs+nMWtIoXeFYicEnCEZzgdc10tFFAHG6p8KfA+s3sl3e+H4DPIxZ2ikkh3E9SQjAZro9F0bT/AA9pEGlaXb/Z7K3BEUW9n25JY8sSTyT1NX6KACuS1T4YeCtZupLm+8PWrTysXkkjLRFmJySdhGST3rraKAOV0T4beDvDt2t3pmg20VwhykrlpWQ+qlySD7iuivrK31LT7mwu4/MtrmJoZk3EbkYEMMjkcE9KsUUAZfh/w5pPhbSxpujWn2W0DmQR+Y7/ADHqcsSf1pPEHhzSfFOmHTdatPtVoXEnl+Y6fMOhypB7+tatFAEFlZ2+nWFtY2kfl21tEsMSZJ2ooAAyeT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","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **COMPILE THE MODEL**","metadata":{}},{"cell_type":"code","source":"# Compile the model\nmodel.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:01.345658Z","iopub.execute_input":"2024-10-30T07:20:01.345969Z","iopub.status.idle":"2024-10-30T07:20:01.360785Z","shell.execute_reply.started":"2024-10-30T07:20:01.345921Z","shell.execute_reply":"2024-10-30T07:20:01.359917Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"# **CALLBACKS**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping\n\nearly_stopping = EarlyStopping(\n                monitor='val_loss',  \n                patience=10, \n                verbose=1)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:01.362013Z","iopub.execute_input":"2024-10-30T07:20:01.362365Z","iopub.status.idle":"2024-10-30T07:20:01.368916Z","shell.execute_reply.started":"2024-10-30T07:20:01.362332Z","shell.execute_reply":"2024-10-30T07:20:01.368089Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ReduceLROnPlateau\n\nreduce_lr = ReduceLROnPlateau(\n    monitor='val_loss',  \n    factor=0.6,          # Factor by which the learning rate will be reduced\n    patience=5,          # Number of epochs with no improvement after which learning rate will be reduced\n    verbose=1,           # Verbosity mode\n    min_lr=0.0005        # Lower bound on the learning rate\n)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:01.369931Z","iopub.execute_input":"2024-10-30T07:20:01.370237Z","iopub.status.idle":"2024-10-30T07:20:01.377457Z","shell.execute_reply.started":"2024-10-30T07:20:01.370213Z","shell.execute_reply":"2024-10-30T07:20:01.376743Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"# **Train the Model**","metadata":{}},{"cell_type":"code","source":"# Train the model\nhistory=model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=0.2,callbacks=[early_stopping,reduce_lr])","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:20:01.378526Z","iopub.execute_input":"2024-10-30T07:20:01.378792Z","iopub.status.idle":"2024-10-30T07:27:13.771684Z","shell.execute_reply.started":"2024-10-30T07:20:01.378769Z","shell.execute_reply":"2024-10-30T07:27:13.770865Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 1s/step - accuracy: 0.5254 - loss: 1.0567 - val_accuracy: 0.6795 - val_loss: 0.5905 - learning_rate: 0.0100\nEpoch 2/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 562ms/step - accuracy: 0.6874 - loss: 0.6365 - val_accuracy: 0.7372 - val_loss: 0.5297 - learning_rate: 0.0100\nEpoch 3/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 564ms/step - accuracy: 0.7664 - loss: 0.4938 - val_accuracy: 0.7244 - val_loss: 0.5060 - learning_rate: 0.0100\nEpoch 4/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 569ms/step - accuracy: 0.7900 - loss: 0.4283 - val_accuracy: 0.7692 - val_loss: 0.4623 - learning_rate: 0.0100\nEpoch 5/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 576ms/step - accuracy: 0.8441 - loss: 0.3408 - val_accuracy: 0.8205 - val_loss: 0.3751 - learning_rate: 0.0100\nEpoch 6/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 581ms/step - accuracy: 0.8924 - loss: 0.2674 - val_accuracy: 0.8494 - val_loss: 0.3338 - learning_rate: 0.0100\nEpoch 7/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 590ms/step - accuracy: 0.9137 - loss: 0.2488 - val_accuracy: 0.8622 - val_loss: 0.3255 - learning_rate: 0.0100\nEpoch 8/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 592ms/step - accuracy: 0.9180 - loss: 0.2170 - val_accuracy: 0.8654 - val_loss: 0.2812 - learning_rate: 0.0100\nEpoch 9/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 600ms/step - accuracy: 0.9191 - loss: 0.1987 - val_accuracy: 0.8654 - val_loss: 0.2927 - learning_rate: 0.0100\nEpoch 10/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 605ms/step - accuracy: 0.9449 - loss: 0.1708 - val_accuracy: 0.8974 - val_loss: 0.2645 - learning_rate: 0.0100\nEpoch 11/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 610ms/step - accuracy: 0.9554 - loss: 0.1487 - val_accuracy: 0.9006 - val_loss: 0.2384 - learning_rate: 0.0100\nEpoch 12/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 605ms/step - accuracy: 0.9416 - loss: 0.1597 - val_accuracy: 0.9006 - val_loss: 0.2261 - learning_rate: 0.0100\nEpoch 13/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 603ms/step - accuracy: 0.9618 - loss: 0.1312 - val_accuracy: 0.8974 - val_loss: 0.2158 - learning_rate: 0.0100\nEpoch 14/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 600ms/step - accuracy: 0.9533 - loss: 0.1387 - val_accuracy: 0.9038 - val_loss: 0.2207 - learning_rate: 0.0100\nEpoch 15/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 605ms/step - accuracy: 0.9635 - loss: 0.1017 - val_accuracy: 0.9006 - val_loss: 0.2275 - learning_rate: 0.0100\nEpoch 16/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 604ms/step - accuracy: 0.9749 - loss: 0.1106 - val_accuracy: 0.9038 - val_loss: 0.2302 - learning_rate: 0.0100\nEpoch 17/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 605ms/step - accuracy: 0.9758 - loss: 0.0997 - val_accuracy: 0.9038 - val_loss: 0.2314 - learning_rate: 0.0100\nEpoch 18/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 516ms/step - accuracy: 0.9727 - loss: 0.1031\nEpoch 18: ReduceLROnPlateau reducing learning rate to 0.005999999865889549.\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 603ms/step - accuracy: 0.9729 - loss: 0.1026 - val_accuracy: 0.8910 - val_loss: 0.2412 - learning_rate: 0.0100\nEpoch 19/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 602ms/step - accuracy: 0.9813 - loss: 0.0689 - val_accuracy: 0.9006 - val_loss: 0.2429 - learning_rate: 0.0060\nEpoch 20/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 603ms/step - accuracy: 0.9839 - loss: 0.0731 - val_accuracy: 0.9006 - val_loss: 0.2458 - learning_rate: 0.0060\nEpoch 21/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 604ms/step - accuracy: 0.9836 - loss: 0.0778 - val_accuracy: 0.8942 - val_loss: 0.2321 - learning_rate: 0.0060\nEpoch 22/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 604ms/step - accuracy: 0.9826 - loss: 0.0710 - val_accuracy: 0.8974 - val_loss: 0.2301 - learning_rate: 0.0060\nEpoch 23/50\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 516ms/step - accuracy: 0.9832 - loss: 0.0706\nEpoch 23: ReduceLROnPlateau reducing learning rate to 0.003600000031292438.\n\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 606ms/step - accuracy: 0.9831 - loss: 0.0708 - val_accuracy: 0.9071 - val_loss: 0.2359 - learning_rate: 0.0060\nEpoch 23: early stopping\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Accuracy Curve**","metadata":{}},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.title('Model Accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper left')\nplt.savefig(\"accuracy_curve.jpg\")  # Save the figure before showing it\nplt.show()  # Display the plot","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:13.773585Z","iopub.execute_input":"2024-10-30T07:27:13.773879Z","iopub.status.idle":"2024-10-30T07:27:14.227488Z","shell.execute_reply.started":"2024-10-30T07:27:13.773837Z","shell.execute_reply":"2024-10-30T07:27:14.226473Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Loss Curve**","metadata":{}},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'val'], loc='upper left')\nplt.savefig(\"loss_curve.jpg\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:14.228768Z","iopub.execute_input":"2024-10-30T07:27:14.229100Z","iopub.status.idle":"2024-10-30T07:27:14.586465Z","shell.execute_reply.started":"2024-10-30T07:27:14.229072Z","shell.execute_reply":"2024-10-30T07:27:14.585580Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Evaluate the model\ntest_loss, test_accuracy = model.evaluate(X_test, y_test)\nprint(f'Test Accuracy: {test_accuracy:.2f}')","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:14.587578Z","iopub.execute_input":"2024-10-30T07:27:14.587847Z","iopub.status.idle":"2024-10-30T07:27:21.053740Z","shell.execute_reply.started":"2024-10-30T07:27:14.587824Z","shell.execute_reply":"2024-10-30T07:27:21.052803Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 256ms/step - accuracy: 0.9051 - loss: 0.2542\nTest Accuracy: 0.91\n","output_type":"stream"}]},{"cell_type":"code","source":"pred=model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:21.054834Z","iopub.execute_input":"2024-10-30T07:27:21.055122Z","iopub.status.idle":"2024-10-30T07:27:47.858722Z","shell.execute_reply.started":"2024-10-30T07:27:21.055097Z","shell.execute_reply":"2024-10-30T07:27:47.857747Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 1s/step \n","output_type":"stream"}]},{"cell_type":"code","source":"pred.shape","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:47.860507Z","iopub.execute_input":"2024-10-30T07:27:47.860819Z","iopub.status.idle":"2024-10-30T07:27:47.867052Z","shell.execute_reply.started":"2024-10-30T07:27:47.860793Z","shell.execute_reply":"2024-10-30T07:27:47.866053Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"(391, 1)"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Confusion Matrix**","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nimport seaborn as sns\n\n# Convert predicted probabilities to binary values (0 or 1)\npred_binary = (pred > 0.5).astype(int)\n\ncm = confusion_matrix(y_test, pred_binary)\nsns.heatmap(cm, annot=True, fmt='d',cmap=\"Blues\")\nplt.savefig(\"confusion_matrix.jpg\")","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:47.868270Z","iopub.execute_input":"2024-10-30T07:27:47.868557Z","iopub.status.idle":"2024-10-30T07:27:48.416111Z","shell.execute_reply.started":"2024-10-30T07:27:47.868534Z","shell.execute_reply":"2024-10-30T07:27:48.415206Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_test, pred_binary))","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:48.417055Z","iopub.execute_input":"2024-10-30T07:27:48.417321Z","iopub.status.idle":"2024-10-30T07:27:48.433774Z","shell.execute_reply.started":"2024-10-30T07:27:48.417297Z","shell.execute_reply":"2024-10-30T07:27:48.432858Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.91      0.91      0.91       187\n           1       0.92      0.92      0.92       204\n\n    accuracy                           0.91       391\n   macro avg       0.91      0.91      0.91       391\nweighted avg       0.91      0.91      0.91       391\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\n\nprint(\"Precision:\",precision_score(y_test,pred_binary))\nprint(\"Recall:\",recall_score(y_test,pred_binary))\nprint(\"F1-Score:\",f1_score(y_test,pred_binary))","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:48.434857Z","iopub.execute_input":"2024-10-30T07:27:48.435179Z","iopub.status.idle":"2024-10-30T07:27:48.448252Z","shell.execute_reply.started":"2024-10-30T07:27:48.435154Z","shell.execute_reply":"2024-10-30T07:27:48.447254Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Precision: 0.9166666666666666\nRecall: 0.9166666666666666\nF1-Score: 0.9166666666666666\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#Save the model","metadata":{}},{"cell_type":"code","source":"model.save('violence_detection_MobileNet_Lstm_model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-10-30T07:27:48.449383Z","iopub.execute_input":"2024-10-30T07:27:48.449664Z","iopub.status.idle":"2024-10-30T07:27:48.778690Z","shell.execute_reply.started":"2024-10-30T07:27:48.449641Z","shell.execute_reply":"2024-10-30T07:27:48.777601Z"},"trusted":true},"execution_count":26,"outputs":[]}]}