ResNet_CNNBiGRUAttention.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyO8DmvcJJBteMpyy6Crj2QF"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Mount Drive**"],"metadata":{"id":"4-qoIC1ocdR4"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u2_3p2pgPYdt","executionInfo":{"status":"ok","timestamp":1729063299418,"user_tz":-210,"elapsed":24719,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"4788761c-a512-4c0a-c3c6-e1017a96e642"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["# **Libraries**"],"metadata":{"id":"z-5UeF-wTn15"}},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from keras.models import Sequential\n","# from keras.layers import Conv1D, BatchNormalization, Activation, MaxPooling1D, Flatten, Dense, Dropout\n","import tensorflow as tf\n","from tensorflow.keras.metrics import RootMeanSquaredError\n","from tensorflow.keras.models import Model\n","from keras.models import Sequential\n","from tensorflow.keras.metrics import RootMeanSquaredError\n","from keras.callbacks import EarlyStopping\n","from keras.callbacks import LearningRateScheduler\n","from tensorflow.keras.losses import Huber\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.regularizers import l2\n","import tensorflow.keras.backend as K\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Input, Conv1D, Activation, Add, Flatten, BatchNormalization, ReLU, MaxPooling1D, Bidirectional, GRU, Dense\n","from sklearn.metrics import r2_score\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import accuracy_score\n","from sklearn.linear_model import HuberRegressor"],"metadata":{"id":"D9J3zGr3PYa_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Load data**"],"metadata":{"id":"V97JztCqTuSW"}},{"cell_type":"code","source":["# train_data [signal, label, index]\n","# hint: change the addresses if you wanna use other folds\n","train_data = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_trainArray2.npy')\n","val_data = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_valArray2.npy')\n","test_data = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_testArray2.npy')\n","test_labels = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_testAHI2.npy')\n","\n","test_data = test_data[(test_data[:,-1] !=304.) & (test_data[:,-1] !=371.) & (test_data[:,-1] !=353.)]\n","test_labels = np.delete(test_labels, [108,113], axis=0)  # fold2: [108,113], fold1: [90]\n","\n","# test_labels = np.delete(test_labels, [90], axis=0)      # if using fold1\n","\n","val_data = np.random.permutation(val_data)\n","train_data = np.random.permutation(train_data)\n","\n","X = train_data[:,:-2]\n","y = train_data[:,-2]\n","\n","X_val = val_data[:,:-2]\n","y_val = val_data[:,-2]\n","\n","X_test = test_data[:,:-2]\n","y_test = test_data[:,-2]\n","\n","print(\"train data shape: \", X.shape, \"train label shape: \",y.shape)\n","print(\"test data shape: \", X_test.shape, \"test label shape: \", y_test.shape)\n","\n","\n","# balancing data by repeating non-zero labelled data\n","\n","zero_X = X[y == 0]\n","zero_y = y[y == 0]\n","nonzero_X = X[y != 0]\n","nonzero_y = y[y != 0]\n","print(\"zero labelled data shape: \", zero_X.shape, \"zero labelled label shape: \", zero_y.shape)\n","print(\"non-zero labelled data shape: \", nonzero_X.shape, \"non-zero labelled label shape: \", nonzero_y.shape)\n","\n","nonzero_data = np.hstack((nonzero_X, nonzero_y.reshape((-1, 1))))\n","zero_data = np.hstack((zero_X, zero_y.reshape((-1, 1))))\n","nonzero_data = np.repeat(nonzero_data, repeats=2, axis=0)\n","nonzero_data = np.random.permutation(nonzero_data)\n","zero_data = np.random.permutation(zero_data)\n","\n","data = np.concatenate((zero_data, nonzero_data), axis=0)\n","new_train_data = np.random.permutation(data)\n","\n","X_train = new_train_data[:,:-1]\n","y_train = new_train_data[:,-1]\n","\n","\n","print(\"balanced train data shape: \", X_train.shape, \"balanced train label shape: \", y_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"riZhovlyPYXv","executionInfo":{"status":"ok","timestamp":1729069497167,"user_tz":-210,"elapsed":776,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"0d5c0eb8-8258-4efa-e32c-9a4851be5ceb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train data shape:  (14672, 1200) train label shape:  (14672,)\n","test data shape:  (7349, 1200) test label shape:  (7349,)\n","zero labelled data shape:  (10000, 1200) zero labelled label shape:  (10000,)\n","non-zero labelled data shape:  (4672, 1200) non-zero labelled label shape:  (4672,)\n","balanced train data shape:  (19344, 1200) balanced train label shape:  (19344,)\n"]}]},{"cell_type":"markdown","source":["# **Huber Regressor**"],"metadata":{"id":"CyZsoLOQVzCY"}},{"cell_type":"code","source":["# hint: change the addresses if you wanna use other folds\n","val_data = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_valArray2.npy')\n","val_labels = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_valAHI2.npy')\n","\n","X = val_labels[:, 0]/((20./60.) * np.unique(val_data[:,-1], return_counts=True)[1])\n","y = val_labels[:, 0]/(val_labels[:, 1] * (1/60))\n","X = X.reshape(-1,1)\n","\n","\n","# Create a Huber Regressor\n","huber = HuberRegressor(epsilon=9)\n","\n","# Train the model\n","huber.fit(X, y)\n","\n","# Make predictions\n","y_pred = huber.predict(X)\n","\n","# Evaluate the performance\n","mse = mean_squared_error(y, y_pred)\n","print(f'Mean Squared Error: {mse}')\n","\n","# Plot the results\n","plt.scatter(X, y, color='black', label='Actual')\n","plt.plot(X, y_pred, color='blue', linewidth=2, label='Huber Regression')\n","plt.xlabel('X')\n","plt.ylabel('y')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"s-4iyXBaVvVl","executionInfo":{"status":"ok","timestamp":1729067296440,"user_tz":-210,"elapsed":1053,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"56f795fe-a4f9-4269-aee9-a09467596547"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error: 0.671558334324548\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABHsUlEQVR4nO3de1yUdd7/8fc4CuIBCEUBZxIrT9ualqlrRkGap600ZCvL1jZ/tbdZgma13R3MDrdbbRt0dGtd3XbLXHG0u5O7aaJkpllr1m55q2EhAiqbIB5Qh+v3BzIxzAwMMMyBeT0fj3k8nO91cc0HcZi339NlMgzDEAAAQAhqF+gCAAAAmosgAwAAQhZBBgAAhCyCDAAACFkEGQAAELIIMgAAIGQRZAAAQMhqH+gCWlt1dbX279+vrl27ymQyBbocAADgBcMwdOTIESUlJaldO8/9Lm0+yOzfv19WqzXQZQAAgGYoLCyUxWLxeLzNB5muXbtKqvmLiI6ODnA1AADAGxUVFbJarY7PcU/afJCpHU6Kjo4myAAAEGIamxbCZF8AABCyCDIAACBkEWQAAEDIavNzZLxlt9t16tSpQJcBuNWhQweZzeZAlwEAQSfsg4xhGCopKdHhw4cDXQrQoNjYWCUkJLAfEgDUEfZBpjbE9OjRQ506deJDAkHHMAwdO3ZMBw4ckCQlJiYGuCIACB5hHWTsdrsjxHTr1i3Q5QAeRUVFSZIOHDigHj16MMwEAGeE9WTf2jkxnTp1CnAlQONq/50ylwsAfhTWQaYWw0kIBfw7BQBXBBkAABCyCDIAACBkEWTgUyaTSatXrw50GQCAMEGQ8QG73a68vDwtW7ZMeXl5stvtfnndzZs3y2w26+c//3mTvi45OVnZ2dmtUxQAIGy89540YYIUyK3Ywnr5tS/YbDZlZmZq3759jjaLxaKcnBylp6e36msvXrxYd911lxYvXqz9+/crKSmpVV8PAABJqq6W+vWT9uypeX7WWZJhBKYWemRawGazKSMjwynESFJRUZEyMjJks9la7bUrKyu1fPlyzZw5Uz//+c+1dOlSp+Nvv/22hg0bpo4dO6p79+669tprJUmpqan67rvvNGfOHJlMJsdKmEceeURDhgxxukZ2draSk5Mdzz/99FNdeeWV6t69u2JiYnT55Zfr888/b7XvEQAQfPbskczmH0NMoBFkmslutyszM1OGmwha25aVldVqw0x/+9vfNGDAAPXv31/Tpk3Tn/70J8frvvvuu7r22ms1ceJE/fOf/9S6des0fPhwSTXhy2Kx6NFHH1VxcbGKi4u9fs0jR45o+vTp+uijj/TJJ5+ob9++mjhxoo4cOdIq3yMAILhkZ0vnnefaXlnp91IcGFpqpvz8fJeemLoMw1BhYaHy8/OVmprq89dfvHixpk2bJkkaP368ysvLtWHDBqWmpuqJJ57QDTfcoAULFjjOHzx4sCQpLi5OZrNZXbt2VUJCQpNe84orrnB6/sorryg2NlYbNmzQVVdd1cLvCAAQrOx2KSlJOnOnFIc77pBefDEwNdWiR6aZvO3JaEqPh7d27typrVu3aurUqZKk9u3b6/rrr9fixYslSdu3b9fo0aN9/rqlpaW67bbb1LdvX8XExCg6OlqVlZX6/vvvff5aAIDg8M03Uvv2riFm8+bAhxiJHplm8/bGfa1xg7/Fixfr9OnTTpN7DcNQZGSkXnjhBcd9eZqiXbt2LsNk9bfCnz59usrKypSTk6PevXsrMjJSI0eO1MmTJ5v3jQAAgtoTT0gPPujafvy41LGj/+txhx6ZZkpJSZHFYvG4bbzJZJLValVKSopPX/f06dN67bXX9Mwzz2j79u2OxxdffKGkpCQtW7ZMF1xwgdatW+fxGhERES5zd+Lj41VSUuIUZrZv3+50zqZNmzR79mxNnDhR559/viIjI3Xo0CGffn8AgMA7dUqKinINMfPm1axOCpYQI9Ej02xms1k5OTnKyMiQyWRyCgC14SY7O9vndyl+55139MMPP2jGjBmKiYlxOjZlyhQtXrxYTz/9tEaPHq1zzz1XN9xwg06fPq333ntP9913n6SafWQ2btyoG264QZGRkerevbtSU1N18OBBPfXUU8rIyNCaNWv0/vvvKzo62nH9vn376i9/+YsuvvhiVVRU6J577mlW7w8AIHjt2CGdmVbp5PPPpQsv9H89jaFHpgXS09OVm5urXr16ObVbLBbl5ua2yj4yixcv1pgxY1xCjFQTZLZt26a4uDitWLFC//u//6shQ4boiiuu0NatWx3nPfroo9q7d6/OPfdcxcfHS5IGDhyol156SS+++KIGDx6srVu3at68eS6v/cMPP+iiiy7SzTffrNmzZ6tHjx4+/x4BAIHxwAOuIcZslqqqgjPESJLJcLd+uA2pqKhQTEyMysvLnXoXJOnEiRMqKChQnz591LEF/WR2u135+fkqLi5WYmKiUlJSfN4TA/jq3ysA1FdV5X64aP586ZFH/F6OpIY/v+tiaMkHzGZzqyyxBgCgtX36qXRmqzEnX30lnX++/+tpKoaWAAAIU1lZriEmNrZmsm8ohBiJHhkAAMLO8eNSp06u7U89Jd1zj//raQmCDAAAYeSjjyR3O4Ps3FlzI8hQw9ASAABh4v/9P9cQY7XW3IIgFEOMRI8MAABtXmWl1LWra/sLL0izZvm/Hl8iyAAA0IatXStdeaVre0GBlJzs93J8jqElAADaqBtucA0xP/mJVF3dNkKMRJAJe7fccosmT54c6DJC3iOPPKIhQ4YEugwAkCQdPiyZTNLy5c7tixdL//pXzbG2giATgjyFj7y8PJlMJh0+fNjvNXnLZDI5HtHR0Ro2bJjeeuutQJfVYvPmzWvwRp0A4C/vviuddZZre1GRdOut/q+ntRFk4HOGYej06dMejy9ZskTFxcXatm2bRo0apYyMDH355ZetWtPJkydb9fpdunRRt27dWvU1AKAxV11V86hrxIiaoaSkpMDU1NoIMm2Yu+GO7OxsJbsZGF2wYIHi4+MVHR2t//qv/3L64K+urtbChQvVp08fRUVFafDgwcrNzXUcr+0Jev/99zV06FBFRkbqo48+8lhXbGysEhIS1K9fPz322GM6ffq01q9f7zheWFio6667TrGxsYqLi9OkSZO0d+9ex/HTp09r9uzZio2NVbdu3XTfffdp+vTpTr1UqampuvPOO5WVlaXu3btr3LhxkqSvvvpKEyZMUJcuXdSzZ0/dfPPNOnTokOPrcnNzNWjQIEVFRalbt24aM2aMjh496vg+hw8frs6dOys2NlajRo3Sd9995/bvurq6Wo8++qgsFosiIyM1ZMgQrVmzxnF87969MplMstlsSktLU6dOnTR48GBt3rzZ498bAHhy6FDNcNG77zq3v/GG9MknbWsoqT6CDLRu3Tp9/fXXysvL07Jly2Sz2bRgwQLH8YULF+q1117TokWL9K9//Utz5szRtGnTtGHDBqfr/OY3v9Fvf/tbff3117rgggsafd3Tp09r8eLFkqSIiAhJ0qlTpzRu3Dh17dpV+fn52rRpk7p06aLx48c7wtWTTz6p119/XUuWLNGmTZtUUVGh1atXu1z/z3/+syIiIrRp0yYtWrRIhw8f1hVXXKELL7xQ27Zt05o1a1RaWqrrrrtOklRcXKypU6fq1ltvdfx9pKenO3qYJk+erMsvv1w7duzQ5s2bdfvtt8vk4bdDTk6OnnnmGf3ud7/Tjh07NG7cOF1zzTXatWuX03kPPPCA5s2bp+3bt6tfv36aOnVqg71ZAFBfbq4UH+/aXloqTZ3q/3r8zmjjysvLDUlGeXm5y7Hjx48b//73v43jx4872oYONYxevfz/GDrU++9p+vTphtlsNjp37uz06NixoyHJ+OGHHwzDMIz58+cbgwcPdvraZ5991ujdu7fTteLi4oyjR4862l5++WWjS5cuht1uN06cOGF06tTJ+Pjjj52uM2PGDGPq1KmGYRjG+vXrDUnG6tWrG61dktGxY0ejc+fORrt27QxJRnJyslFWVmYYhmH85S9/Mfr3729UV1c7vqaqqsqIiooy/v73vxuGYRg9e/Y0nn76acfx06dPG2effbYxadIkR9vll19uXHjhhU6v/dhjjxljx451aissLDQkGTt37jQ+++wzQ5Kxd+9el7rLysoMSUZeXp7b76v+33VSUpLxxBNPOJ0zbNgw44477jAMwzAKCgoMScYf//hHx/F//etfhiTj66+/dvsa7v69Aghf1dWGcdllhiE5P0aPDnRlvtHQ53dd7CNTT0lJzYSoYJeWlqaXX37ZqW3Lli2aNm1ak681ePBgdapz042RI0eqsrJShYWFqqys1LFjx3RlvfV7J0+e1IUXXujUdvHFF3v1es8++6zGjBmjb7/9VnPmzNFzzz2nuLg4SdIXX3yh3bt3q2u9nZtOnDihPXv2qLy8XKWlpRpe5y5nZrNZQ4cOVXV1tdPXDB061On5F198ofXr16tLly4uNe3Zs0djx47V6NGjNWjQII0bN05jx45VRkaGzjrrLMXFxemWW27RuHHjdOWVV2rMmDG67rrrlJiY6HKtiooK7d+/X6NGjXJqHzVqlL744guntro9V7XXOnDggAYMGODx7w8ASkokN79+tGqVFG4LUQMaZBYuXCibzaZvvvlGUVFRuuSSS/Tkk0+qf//+jnNSU1NdhjB+/etfa9GiRa1SU0JCq1zW56/buXNnnXfeeU5t+/btc3rerl07GYbh1Hbq1KkmvU5lZaUk6d1331WvXr2cjkVGRrrU5I2EhASdd955Ou+887RkyRJNnDhR//73v9WjRw9VVlZq6NChev31112+Lt5d32kD6tdTWVmpq6++Wk8++aTLuYmJiTKbzfrggw/08ccf6x//+Ieef/55PfDAA9qyZYv69OmjJUuWaPbs2VqzZo2WL1+uBx98UB988IF+9rOfNamuujp06OD4c+0wVf1ABgB1/eUv0i9/6dpeViad+T9hWAlokNmwYYNmzZqlYcOG6fTp0/rv//5vjR07Vv/+97+dPoRuu+02Pfroo47nndzdstNHtm1rtUv7XXx8vEpKSmQYhuNDcvv27S7nffHFFzp+/LiioqIkSZ988om6dOkiq9WquLg4RUZG6vvvv9fll1/u8xqHDx+uoUOH6oknnlBOTo4uuugiLV++XD169FB0dLTbr+nZs6c+/fRTXXbZZZIku92uzz//vNF9XC666CKtXLlSycnJat/e/T99k8mkUaNGadSoUXr44YfVu3dvrVq1SnPnzpUkXXjhhbrwwgt1//33a+TIkXrjjTdcgkx0dLSSkpK0adMmp7+zTZs2OfUkAUBTGIY0dKj0z386t0+eXNMTE64CGmTqruKQpKVLl6pHjx767LPPHB9SUk1wSfCyy6KqqkpVVVWO5xUVFb4pNgSlpqbq4MGDeuqpp5SRkaE1a9bo/fffdwkIJ0+e1IwZM/Tggw9q7969mj9/vu688061a9dOXbt21bx58zRnzhxVV1fr0ksvVXl5uTZt2qTo6GhNnz69xXVmZWXp2muv1b333qubbrpJTz/9tCZNmuRY9fPdd9/JZrPp3nvvlcVi0V133aWFCxfqvPPO04ABA/T888/rhx9+8DjxttasWbP06quvaurUqbr33nsVFxen3bt3680339Qf//hHbdu2TevWrdPYsWPVo0cPbdmyRQcPHtTAgQNVUFCgV155Rddcc42SkpK0c+dO7dq1S790998iSffcc4/mz5+vc889V0OGDNGSJUu0fft2tz1NANCYfftqbu5Y3/vvS+PH+7+eYBJUq5bKy8slyTFfotbrr7+u7t2766c//anuv/9+HTt2zOM1Fi5cqJiYGMfD6u4nHyYGDhyol156SS+++KIGDx6srVu3at68eS7njR49Wn379tVll12m66+/Xtdcc40eeeQRx/HHHntMDz30kBYuXKiBAwdq/Pjxevfdd9WnTx+f1Dl+/Hj16dNHTzzxhDp16qSNGzfq7LPPVnp6ugYOHKgZM2boxIkTjgB23333aerUqfrlL3+pkSNHqkuXLho3bpw6duzY4OvU9pLY7XaNHTtWgwYNUlZWlmJjY9WuXTtFR0dr48aNmjhxovr166cHH3xQzzzzjCZMmKBOnTrpm2++0ZQpU9SvXz/dfvvtmjVrln7961+7fa3Zs2dr7ty5uvvuuzVo0CCtWbNG//u//6u+ffv65O8MQPh45RX3Iaa8PLAhxm63O1a75uXlyW63B6QOk1F/EkWAVFdX65prrtHhw4ed9iB55ZVX1Lt3byUlJWnHjh267777NHz4cNlsNrfXcdcjY7VaVV5e7tITceLECRUUFKhPnz6NfggieFVXV2vgwIG67rrr9NhjjwW6nFbDv1cgvFRXS/36SXv2OLdPm1YzTyaQbDabMjMzneZmWiwW5eTkKD093SevUVFRoZiYGLef33UFzaqlWbNm6auvvnLZSO322293/HnQoEFKTEzU6NGjtWfPHp177rku14mMjHSZhIq25bvvvtM//vEPXX755aqqqtILL7yggoIC3XjjjYEuDQB84ttvJTcfcfrwQyktzf/11GWz2ZSRkeGymKSoqEgZGRnKzc31WZjxRlAMLd1555165513tH79elkslgbPHTFihCRp9+7d/igNQahdu3ZaunSphg0bplGjRunLL7/U2rVrNXDgwECXBgAtlp3tPsRUVgY+xNjtdmVmZrqEGEmOtqysLL8OMwW0R8YwDN11111atWqV8vLyvJpzUbvqxt3+HQgPVqtVmzZtCnQZAOBTdrvUq1fNjrx1/dd/SfW2DQuY/Px8l60+6jIMQ4WFhcrPz1dqaqpfagpokJk1a5beeOMNvfXWW+ratatKSkokSTExMYqKitKePXv0xhtvaOLEierWrZt27NihOXPm6LLLLvNqC3wAAELBN99I7jqVP/5YGjnS//V4Ulxc7NPzfCGgQ0svv/yyysvLlZqaqsTERMdj+fLlkmruv7N27VqNHTtWAwYM0N13360pU6bo7bff9mkdQTLfGWgQ/06Btul//sd9iDl+PLhCjOT9aIg/R00CPrTUEKvV6rKrry/V7qp67Ngxx2ZwQLCq3Xag7m7AAELX6dNSTIxUf0eRefOkp58OTE2NSUlJkcViUVFRkdvPcJPJJIvFopSUFL/VFDSrlgLBbDYrNjZWBw4ckFSz8V5jm6oB/mYYho4dO6YDBw4oNjZWZrM50CUBaKEdO6TBg13bP/tMuugi/9fjLbPZrJycHGVkZMhkMjmFmdrPz+zsbL/+ngrrICPJsWNwbZgBglVsbKzXO1wDCF4PPFAznFSXySSdOCFFRASmpqZIT09Xbm6u231ksrOz/br0WgqiDfFai7cb6tjt9ibfUBHwlw4dOtATA4S4kycld9ucPfywtGCB/+tpKbvdrvz8fBUXFysxMVEpKSk+/T0VchviBZrZbOaDAgDQKrZtk4YNc23/6ivp/PP9X48vmM1mvy2xbkhQbIgHAEBblZXlGmJiYqRTp0I3xAQTemQAAGgFx49LnTq5tv/2t9J99/m/nraKIAMAgI9t2iRdeqlr+86dNTeChO8wtAQAgA/ddptriLFYam5BQIjxPXpkAADwgcpKqWtX1/bnn5fuvNP/9YQLggwAAC20bp00Zoxre0GBlJzs93LCCkNLAAC0wNSpriFm4ECpupoQ4w/0yAAA0Azl5VJsrGv74sXSrbf6vZywRZABAKCJ3n1Xuuoq1/Z9+6RevfxfTzhjaAkAgCa46irXEDN8eM1QEiHG/+iRAQDAC2VlUvfuru2vvy7deKP/60ENggwAAI1YuVLKyHBtLy2VevTwfz34EUNLAAB4YBjS5Ze7hpgrrqg5RogJPHpkAABwo6RESkx0bbfZpGuv9X89cI8eGQAA6vnrX92HmLIyQkywIcgAAHCGYUgXXSTdfLNz+6RJNcfi4gJTFzxjaAkAANXsAWO1ura/9540YYL/64F36JEBAIS9V191H2IOHybEBDt6ZAAgBNntduXn56u4uFiJiYlKSUmR2WwOdFkhxzCk/v2lXbuc22+6qWaeDIIfQQYAQozNZlNmZqb27dvnaLNYLMrJyVF6enoAKwst334rnXuua/uHH0ppaf6vB83D0BIAhBCbzaaMjAynECNJRUVFysjIkM1mC1BloSUnx32IqawkxIQaggwAhAi73a7MzEwZhuFyrLYtKytLdrvd36WFDLtdSkiQsrKc22+/vWaYqXPngJSFFiDIAECIyM/Pd+mJqcswDBUWFio/P9+PVYWOnTul9u1rbitQ16ZN0h/+EJia0HIEGQAIEcXFxT49L5wsXCgNGODafvy4dMkl/q8HvsNkXwAIEYnutpptwXnh4PRpKSZGOnbMuX3uXOmZZwJTE3yLHhkACBEpKSmyWCwymUxuj5tMJlmtVqWkpPi5suC0Y4fUoYNriNm2jRDTlhBkACBEmM1m5eTkSJJLmKl9np2dzX4ykh56SBo82LnNZJKqqqShQwNTE1oHQQYAQkh6erpyc3PVq1cvp3aLxaLc3Nyw30fm5MmawPL4487tDz0kVVdLERGBqQutx2S4W8fXhlRUVCgmJkbl5eWKjo4OdDkA4BPs7Otq2zZp2DDX9i+/lH76U//Xg5bx9vObyb4AEILMZrNSU1MDXUbQmDNHys52buvaVfrPf2qWXKPt4scLAAhZx49LnTq5tv/2t9J99/m/HvgfQQYAEJI2bZIuvdS1fedOqV8//9eDwGCyLwAg5Nx2m2uI6dWr5hYEhJjwQo8MACBkVFbWzH2pLydHmj3b//Ug8AgyAICQ8OGH0ujRru3ffiv16eP/ehAcGFoCAAS9G290DTEDBtTsDUOICW/0yAAAglZ5uRQb69r+6qvS//t/fi8HQYggAwAISu+9J/38567thYWSxeL/ehCcGFoCAASda65xDTHDhtUMJRFiUBc9MgCAoFFWJnXv7tr++us182SA+ggyAICgsHKllJHh2l5SIvXs6f96EBoYWgIABFxammuISUuTDIMQg4bRIwMACJjSUikhwbV95UopPd3/9SD00CMDAAiI1193H2LKyggx8B5BBgDgV4YhDR0qTZvm3H7NNTXH4uICUxdCE0NLAAC/2bdPslpd2999V5o40f/1IPQFtEdm4cKFGjZsmLp27aoePXpo8uTJ2rlzp9M5J06c0KxZs9StWzd16dJFU6ZMUWlpaYAqBgA01x//6D7EHD5MiEHzBTTIbNiwQbNmzdInn3yiDz74QKdOndLYsWN19OhRxzlz5szR22+/rRUrVmjDhg3av3+/0hk8BYCQYRhSv37Sbbc5t994Y82xmJjA1IW2wWQYhhHoImodPHhQPXr00IYNG3TZZZepvLxc8fHxeuONN5RxZl3eN998o4EDB2rz5s362c9+1ug1KyoqFBMTo/LyckVHR7f2twAAqKOgQDrnHNf2deukK67wfz0IHd5+fgfVZN/y8nJJUtyZmV6fffaZTp06pTFjxjjOGTBggM4++2xt3rzZ7TWqqqpUUVHh9AAA+N9zz7kPMUeOEGLgO0ETZKqrq5WVlaVRo0bppz/9qSSppKREERERiq1369OePXuqpKTE7XUWLlyomJgYx8PqbkAWANBq7HYpKUnKzHRuv/32mqGkLl0CUxfapqAJMrNmzdJXX32lN998s0XXuf/++1VeXu54FBYW+qhCAEBjdu6U2reXioud2zdtkv7wh8DUhLYtKJZf33nnnXrnnXe0ceNGWerc1jQhIUEnT57U4cOHnXplSktLleBuFyVJkZGRioyMbO2SAQD1LFwo/fd/u7YfOyZFRfm/HoSHgPbIGIahO++8U6tWrdKHH36oPn36OB0fOnSoOnTooHXr1jnadu7cqe+//14jR470d7kAADdOn64ZLqofYubMqRlKIsSgNQW0R2bWrFl644039NZbb6lr166OeS8xMTGKiopSTEyMZsyYoblz5youLk7R0dG66667NHLkSK9WLAEAWteXX0oXXODavm1bze69QGsL6PJrk8nktn3JkiW65ZZbJNVsiHf33Xdr2bJlqqqq0rhx4/TSSy95HFqqj+XXANA6HnpIevxx1/aqKikiwv/1oG3x9vM7qPaRaQ0EGQDwrZMnJXdTER98UHrsMf/Xg7bJ28/voJjsCwAIDdu2ScOGubbv2CENGuT/eoCgWX4NAAhud9/tGmK6dpVOnSLEIHDokQEANOj4calTJ9f2hQul3/zG//UAdRFkAAAeffyxNGqUa/s330j9+/u/HqA+hpYAAG79+teuISYxsWbfGEIMggU9MgAAJ0ePur8fUk6ONHu2/+sBGkKQAQA4fPihNHq0a/u330r1Nl8HggJDSwAASdKNN7qGmP79pepqQgyCFz0yABDmysulOvfldXjlFem22/xeDtAkBBkACGPvvy9NnOjaXlgoWSz+rwdoKoaWACBMXXONa4i5+OKaoSRCDEIFPTIAEGbKyqTu3V3b//pX6aab/F8P0BIEGQAIIzabNGWKa3tJidSzp//rAVqKoSUACBNXXOEaYtLSJMMgxCB00SMDAG1caamUkODanpvrvncGCCX0yABAG/b66+5DzKFDhBi0DQQZAGiDDEMaNkyaNs25/aqrao516xaYugBfY2gJANqYoiL3y6fffdf9njFAKKNHBgDakD/+0X2IOXyYEIO2iSADAG2AYdTcF6n+LQWmTq05FhMTmLqA1sbQEgCEuIIC6ZxzXNvXrnV/J2ugLaFHBgBC2PPPuw8xR44QYhAe6JEBgBBkt0vt3fwGv+22mrtWA+GCHhkACDHvvOM+xHz0ESEG4YceGQAIIWedVbMCqb5jx6SoKL+XAwQcQQYAQkBVldSxo/tjhuHfWoBgwtASAAS5N990H2IWLSLEAPTIAEAQM5nctx85InXp4t9agGBEkAGAIHT0qOegQi8M8COGlgAgyLz6qvsQ89e/EmKA+uiRAYAg4mko6fhxz5N9gXBGjwwABIHDh92HmOjoml4YQgzgHkEGAALsmWdq9oep7623pPJy/9cDhBKGlgAggDwNJZ08KXXo4N9agFBEjwwABMCBA+5DTJ8+NUNJhBjAOwQZAPCzhx+WevZ0bV+7Vvr2W//XA4QyhpYAwI88DSWdPi2Zzf6tBWgL6JEBAD8oLHQfYi6+uGYoiRADNA9BBgBa2ezZ0tlnu7Zv3ix9+qn/6wHaEoaWAKAVeRpKqq72fAyA9+iRAYBWsHu3+6AyblzNUBIhBvANggwA+Nj06VLfvq7t27dLa9b4vRygTWNoCQB8iKEkwL/okQEAH/jyS/dBZepUhpKA1kSPDAC00FVXSe++69q+c6fUr5//6wHCCUEGAJrJMKR2Hvq1DcO/tQDhiqElAGiGrVvdh5iZMwkxgD/RIwMATTRqlPTxx67t333nfuM7AK2HIAMAXqqu9nwrAXphgMBgaAkAvJCX5z7E3H8/IQYIJHpkAKAR/ftL//d/ru0lJVLPnv6vB8CPAtojs3HjRl199dVKSkqSyWTS6tWrnY7fcsstMplMTo/x48cHplgAYef06Zr9X9yFGMMgxADBIKA9MkePHtXgwYN16623Kj093e0548eP15IlSxzPIyMj/VUegBBnt9uVn5+voqIiHTx4UPHx8erVq5dSUlJk9jTZ5Yx33pGuvtq1/be/le67r5UKBtBkAQ0yEyZM0IQJExo8JzIyUgkJCX6qCEBbYbPZlJmZqX379rkcs1gsysnJ8fgfqO7dpbIy1/ayMikuzteVAmiJoJ/sm5eXpx49eqh///6aOXOmytz9dqmjqqpKFRUVTg8A4cVmsykjI8NtiJGkffv2KSMjQzabzam9qqpmKMndrxnDIMQAwSiog8z48eP12muvad26dXryySe1YcMGTZgwQXa73ePXLFy4UDExMY6H1Wr1Y8UAAs1utyszM1NGI0uJDMNQVlaW4/fJ8uVSx46u5738MquSgGBmMhp7t/uJyWTSqlWrNHnyZI/nfPvttzr33HO1du1ajR492u05VVVVqqqqcjyvqKiQ1WpVeXm5oqOjfV02gCCTl5entLQ0r89fv3690tJS3R6rqJC6dvVJWQCaqKKiQjExMY1+fgd1j0x955xzjrp3767du3d7PCcyMlLR0dFODwDho7i4uAlnd/IYYgyDEAOEgpAKMvv27VNZWZkSExMDXQqAIGS321VaWurl2TMkHXVp/ctfGEoCQklAVy1VVlY69a4UFBRo+/btiouLU1xcnBYsWKApU6YoISFBe/bs0b333qvzzjtP48aNC2DVAIJRQ6uUXLlPKsePu58nAyB4BTTIbNu2zWkse+7cuZKk6dOn6+WXX9aOHTv05z//WYcPH1ZSUpLGjh2rxx57jL1kADipXaXU+JS/GEmHXVo7d5YqK1ujMgCtLWgm+7YWbycLAQhNdrtdycnJXvTEzJH0e5fW1aulSZNaozIALeHt5zf3WgIQcmp37C0uLlZpaakXIcb9/9dOnpQ6dPB9fQD8hyADIKQ0bS7MTyT9y6U1OVkqKPB1ZQACgSADIGR4PxdG8tQL88EH0pgxvq0LQOAQZACEBG937K3h/pxTp6T2/NYD2pSQ2kcGQPjKz8/3YjhppDyFGMMgxABtEUEGQEhofMdeQ9LHLq3Tp+9ggzugDeP/JwBCQsM7entalWRXhw4XtE5BAIICPTIAQkJKSoosFotMJlOd1hvU0FBShw5mv9QGIHCaHGSmT5+ujRs3tkYtAOCR2WxWTk6OJJ0JM4akZS7n/fa33CsJCCdNDjLl5eUaM2aM+vbtq//5n/9RUVFRa9QFAC7S09OVm5srw6h2e7y6WrrvPj8XBSCgmhxkVq9eraKiIs2cOVPLly9XcnKyJkyYoNzcXJ06dao1agQASdJDD0lTpqS7PWYYktOoE4Cw0Kw5MvHx8Zo7d66++OILbdmyReedd55uvvlmJSUlac6cOdq1a5ev6wQQ5kwm6fHHXdsXL2YoCQhnLZrsW1xcrA8++EAffPCBzGazJk6cqC+//FI/+clP9Oyzz/qqRgBhrKGeFsOQbr3Vv/UACC5NDjKnTp3SypUrddVVV6l3795asWKFsrKytH//fv35z3/W2rVr9be//U2PPvpoa9QLIIz86ldSOw+/peiFASA1Yx+ZxMREVVdXa+rUqdq6dauGDBnick5aWppiY2N9UB6AcOWpF+a116Sbb/ZvLQCCV5ODzLPPPqtf/OIX6tixo8dzYmNjVcCtZQE0w6lTUkSE+2P0wgCor8lDSzfffHODIQYAmuuKKwgxAJqGnX0BBAWTSVq/3rX9/fcJMQA8415LAALq6FGpSxf3xwgwABpDjwyAgOnThxADoGUIMgACwmSS9u51bd+yhRADwHsMLQHwq0OHpPh498cIMACaiiADwG8auhcSIQZAczC0BMAvPIWYnTsJMQCajx4ZAK1q796aSb3uEGAAtBRBBkCrYSgJQGtjaAlAq/AUYoqKCDEAfIcgA8CnduzwHGIMQ0pK8m89ANo2hpYA+IynABMTIx0+7NdSAIQJemQAtIjdbldeXp7HEHP4MCEGQOshyABoNpvNpsTE65WWlur2uGHU9MYAQGthaAlAs9hsNk2Zki4p3c3Rz7Vy5V4PxwDAd0yG0bbXD1RUVCgmJkbl5eWKjo4OdDlAm2C329W+vdnD0UiZTKdksVhUUFAgs9nTeQDgmbef3wwtAWGmdk7LsmXLlJeXJ7vd3qSvX7VKDYQYk6STMgxDhYWFys/Pb3G9ANAQhpaAMGKz2ZSZmal9+/Y52iwWi3JycpSe3vgwkOcN7mySpri0FhcXN69QAPASPTJAmLDZbMrIyHAKMZJUVFSkjIwM2Wy2Br/ec4gxy12IkaTExMSmFwoATUCQAcKA3W5XZmam3E2Jq23LyspyO8y0aJHnEGMytZNU7abdJKvVqpSUlEbraskwFwAQZIAwkJ+f79ITU5enOS0mkzRzpuv5mZnSypW2M+eY6n1NzfPs7OwGJ/rabDYlJycrLS1NN954o9LS0pScnNxozxAA1EWQAcKAt3NV6p7nqRemulrKzpbS09OVm5urXr16OR23WCzKzc1tcM5NS4e5AKAWy6+BMJCXl6e0tLRGz1u/fr3Wr0/Vo4+6P+7ut4Xdbld+fr6Ki4uVmJiolJSUBnti7Ha7kpOTPfYQmUwmlm4D8PrzmyADhIHa8FBUVOR2nkxteCgs/N7t1z/5pHTvvb6ppSmhKjU11TcvCiDksI8MAAez2aycnBxJ7ue0GIY8hhjD8F2IkZo3zAUAnhBkgDDhaU5Lp06vy93KI8n9UFJLebskm6XbALzB0BIQZurOabnxxqluz1m6VJo+vfVe35thLubIAOHN289vdvYFwozZbNaoUamKiHB/vKn/tWnqZN/aYa6MjIwzw1o/vqC3S7cBoBZDS0CY6dNHPgsxzd0LpiVLtwGgLoaWgDDiaW+Yd9+VJk5s2rVq94Kp/yuktlfFm0DS1N4cAOGD5ddnEGQAqaJCiolxf6w5vwHYCwZAa2P5NQBJNb0wvgwxUvNveQAAvkaQAdowT0NJ773XsqXV7AUDIFiwaglog4qLpaQk98d8MZjMXjAAgkVAe2Q2btyoq6++WklJSTKZTFq9erXTccMw9PDDDysxMVFRUVEaM2aMdu3aFZhigRBhMrVuiJGklJQUWSwWl12Cf6zBJKvVqpSUFN+8IAB4ENAgc/ToUQ0ePFgvvvii2+NPPfWUnnvuOS1atEhbtmxR586dNW7cOJ04ccLPlQKhwdNQ0rZtvt2lt7FbHkjsBQPAP4Jm1ZLJZNKqVas0efJkSTW9MUlJSbr77rs1b948SVJ5ebl69uyppUuX6oYbbnB7naqqKlVVVTmeV1RUyGq1smoJbdo330gDB7o/1prvcJvNpszMTKeJv1arVdnZ2ewFA6BFQn5n34KCApWUlGjMmDGOtpiYGI0YMUKbN2/2GGQWLlyoBQsW+KtMhLFg2QPFUy+M1LohRqrZ2G7SpElB8fcAIDwFbZApKSmRJPXs2dOpvWfPno5j7tx///2aO3eu43ltjwzgS+56IiwWi3JycvzaE+EpxOzeLZ17rn9qMJvNSk1N9c+LAUA9bW75dWRkpKKjo50egC/V7mhbfx+VoqIiZWRkNLo9vy9s2uQ5xBiG/0IMAARa0AaZhIQESVJpaalTe2lpqeMY4G92u12ZmZlu79pc25aVlSW73d5qNZhM0qWXuj8WHDPeAMB/gjbI9OnTRwkJCVq3bp2jraKiQlu2bNHIkSMDWBnCWaB3tPXUC3PgACEGQHgK6ByZyspK7d692/G8oKBA27dvV1xcnM4++2xlZWXp8ccfV9++fdWnTx899NBDSkpKcqxsAvwtUDvarloleZp6Q4ABEM4CGmS2bdumtLQ0x/PaSbrTp0/X0qVLde+99+ro0aO6/fbbdfjwYV166aVas2aNOnbsGKiSEeYCsaNtIFclAUCwC5p9ZFoLd7+GLzV212dJio+P17PPPqtevXq1eCmypxBTWSl17tzsywJA0OPu10ArMJvNmjp1aoPnHDx4UNOmTVNaWpqSk5ObtYrppZcaXpVEiAGAGgQZoAnsdruWLVvm9fnNWZJtMkmzZrm2x8czlAQA9RFkgCZobNVSfU1dku2pF+bUqZqVSQAAZwQZoAmasxrJmyXZ99/f8FBS+6DdgxsAAotfj0ATtGQ1kqcQ5CnAXHJJzQ6+vhQs94cCAF8hyABNkJKSIovFoqKiIre7+zbEXQjyFGKqqxtedt0cwXJ/KADwJYaWgCYwm83KycmRJJm8TBomk0lWq1UpKSmOtuuvb3goqTVCTKDvDwUArYEgAzRRenq6cnNz1atXr0bPrQ072dnZjiEck0n6299cz50+vXVWJQXD/aEAoLWwIR7QTPXnmxw6dEhz5sxx6vWwWq3Kzs5Wenq6DENq5+G/Dq35LszLy3PaQduT9evXKzU1tfUKAYAm8PbzmzkyQDOZzWaXD/5rr73W7WTaoUOlzz93f53W/q9EoO4PBQD+QJABfMhduPE032X+fOmRR1q9pIDcHwoA/IUgA7SSU6ekiAj3x/w5oNvYSiuTySSLxeI0GRkAQgWTfYEG2O125eXladmyZcrLy/N6QmyXLkZQhBip4ZVW7iYjA0AoIcgAHthsNiUnJystLU033nij1zeBNJmko0ddx5Nmzvw8YPdK8rTSymKxKDc3l31kAIQsVi0BbtTuu1L/7VHbg+Huw//wYemss9xfz2Rq5/Hr/ImdfQGECm8/vwkyQD12u13Jyckebw5ZO6ekoKDAaW8Yz0wevw4A4J63n98MLQH1NHaH6/o3gfQcYjJUG2LcfR0AoOUIMkA93u6n8uWX5Q2EGJOklS26PgCgcSy/Burxbj8VQ7NnezrW8I2S2K8FAHyHHhmgntp9VzzfFNLTtLLRaizEdOvWjf1aAMCHCDJAPZ73XblUnkLM+vV5kj5s9NqzZ89moi8A+BBBBnDDdd8VQ5L7SbqG4U0vTk1vzAMPPOD7YgEgjDFHBkHL33ue1H+9SZMmadKkSWrf3v1rPvro35WSEim7PcXRi5ORkSGTyeT2VgCvvPIKvTEA4GtGG1deXm5IMsrLywNdCppg5cqVhsViMVTTFWJIMiwWi7Fy5Uq/vd5ZZ91t1PS3uHu4r8vddaxWa6vVDQBtlbef32yIh6DTnF11ff96Db0t3N+vqLYuds8FgJZjZ98zCDKhpTm76vr+9dy/JXr2vFylpRv9UhcAhDt29kVIauquur59vSflKcTccsuvPIaY1qgLAOAdJvsiqHi7662vdsf98ToNDyUtXdrU6wEA/IEeGQQVb3e99dXuuDXX8RRiuquxDe7cXw8A4C8EGQSVxvZjMZlMslqtPtkdd8wYKS0t1cNRk6SyJl3PV3UBALxHkEFQ8byr7o/Ps7OzWzyh1mSS1q3zeNTt6zfGF3UBAJqGIIOg47qrbg2LxeKTpdee8kn37hbVHUqyWCyaP3++V9dcsGCBT5eEAwC8w/JrBC1v92Px9rzYWKm83NOr1QSY7t27a9q0aZo0aZIOHjyouXPnNriKSpLi4uJ04MABemMAwIe8/fxm1RKCltlsVmpqaoPn2Gw2ZWZmOoUNi8WinJwcpx6ShkeJfjxYVlamnJwcdejQQb/73e/c3mrA5aubOAQFAPAdhpYQsmp35K3fY1JUVKSMjAzZbDZJDYUYk+qvSjIMQ4Zh6Pe//71XIUaqCT/sHwMAgUGQQUiy2+3KzMx0GzZq26ZMSW8kxDR8/aZg/xgACAyCDEJS4zsAV3s89sYby3xeD/vHAEBgEGQQkhruAXE/JFR732pfhg5f7msDAGg6ggxCkvswYqihEFOrsU33pJqJxt5O4mX/GAAIHIIMQpJrGHEfYLp1M1R/Gk1jm+6ZTCbNnTvX7fG6rFarT/a1AQA0H0EGIak2jBhGpDyFmJUrbTp0yH0QaWzTvaeeesrt8fj4eGVlZWn9+vUqKCggxABAgLEhHkJWQyM/K1favAoZjW2m5+1mewAA3/L285sgg5DkKcRcfPF/9MknMYQNAAhx7OyLNqm0VEpIcH+sJpLH+bMcAECAEWQQMhoaSmrb/YoAAE+Y7IuQ4CnEzJxJiAGAcEaPDILaV19Jgwa5P0aAAQAQZBC0GEoCADSGoSUEJU8h5oUXCDEAgB/RI4Og8s470tVXuz9GgAEA1EeQQdBgKAkA0FRBPbT0yCOPOO59U/sYMGBAoMtCK/AUYt5+mxADAPAs6Htkzj//fK1du9bxvH37oC8ZTZCdLc2Z4/4YAQYA0JigTwXt27dXgqetXN2oqqpSVVWV43lFRUVrlIVmqnvvohtvnOrxPEIMAMAbQT20JEm7du1SUlKSzjnnHN100036/vvvGzx/4cKFiomJcTysVqufKkVjbDabkpOTlZaW5jHEbN9OiAEAeC+obxr5/vvvq7KyUv3791dxcbEWLFigoqIiffXVV+ratavbr3HXI2O1WrlpZIDZbDZlZGTIMF6VNMPtOcH7LxEA4G9t8u7Xhw8fVu/evfX73/9eM2a4/zCsj7tfB57dbldycrL27Sv0eI7VerYKCgq4azUAQJL3n99BP7RUV2xsrPr166fdu3cHupQ2z263Ky8vT8uWLVNeXp7sdnuzr5Wfn99AiEmUZFJhYaHy8/Ob/RoAgPAU9JN966qsrNSePXt08803B7qUNs1msykzM1P79u1ztFksFuXk5Cg9Pd3RVnfibmJiolJSUlx6VAYPlnbsSPXwSs5rrouLi331LQAAwkRQ98jMmzdPGzZs0N69e/Xxxx/r2muvldls1tSpnle7oGVq57LUDTGSVFRUpIyMDNlsNsd5P07cvVFpaWlKTk52HJdq9obZscPTK7luHNOjRw9ffRsAgDAR1D0y+/bt09SpU1VWVqb4+Hhdeuml+uSTTxQfHx/o0toku92uzMxMuZs2ZRiGTCaTsrKyVF1dreuuu87lvNqwk5ubqylT0l2uUSNK0gnfFw8ACEtBHWTefPPNQJcQVmrmsuzzeNwwDBUWFuqOO+7wGHYkQ1OmeLpCA/cgkHTgwAHviwUAQEE+tAT/8naOysGDBz0caWgBXMMhRpISExO9en0AAGoRZODQsiDhPsTY7dLp03ZZLBaZPNxQyWQyyWq1KiUlpQWvDwAIRwQZOKSkpDQYONwz5CnEGIbUrp1kNpuVk5MjSS7Xrn2enZ3NHjIAgCYjyMChbuDwjuehpNOnnfedSU9PV25urnr16uXUbrFYlJub67SsGwAAb4XUzr7Nwc6+TXfvvffq97//fSOb4Ln/Z2MytWswmHiz9wwAAN5+fgf1qiX4n81m0+9+9zu3q5JqeM69VuvZys5uuHfFbDYrNTW1ZUUCAHAGQQYODe0jU8NziJk27WYtXcq9kgAA/sUcGTh43kcmQp5DjEmSScnJyYQYAIDf0SMTJryZm+J+Hxnv9oYhxAAAAoEemTbG3V2rV6xYoYSEhAbviyS520fGU4j5QPU3uHv00UddrgcAQGtj1VIb4u6u1V26dFFlZaXb800mk9MKI7vdroSEBB06ZJf0Hw+v4nlTO4vFooIC5skAAFrO289vemTaCE93rfYUYqSaeyNlZWU5llmbzWYdOnRQTQ0xtdcqLCxUfn5+U0sHAKDZCDJtQOOrjTyrGz48b+i7SN7cK0ny/n5NAAD4AkGmDWjsrtWNWbHinw2EGJOkmV5fixs/AgD8iVVLbUDLekEMvfSSp2Pe33Opdo4MN34EAPgTPTJtQPN7QTwNRf1KDYUYbvwIAAgWBJkQVXeZtd1uV69evZpw1+oRaniDu6Uev7Jbt25KSkpyauPGjwCAQGFoKQS5W2bdrVs3GYYhk8nUyKRf7za486SsrExr166V2Wzmxo8AgIAjyISY2mXW9cPKf/5Ts2Q6Li5OZWVljvZu3bpJ0pk2TyHmUkmbvK7hwIEDmjp1alPKBgCgVRBkQkhDy6xre2OioqK0du1alZSU6ODBg4qPj9eKFVF66y1Pwz7eT+itxcokAECwIMiEkMaWWRuGoX379unpp5/W1q1b9cMPP6ilQ0lOZ7MyCQAQZAgyIcTbZdZ///vfz/zJU4g5X9K/m/TarEwCAAQjVi2FEO+HdO5Ww6uSmhZiJFYmAQCCEz0yISQlJUUWi0VFRUUNrEzy3VCSJGVlZWnSpEmsTAIABCWCTAgxm83KyclRRkaGh2XWnkJMvKRDTX6tN998UxkZGc0pFQAAv2BoKcSkp6crNze33qZ0b6jhoaSmhRhJWrZsGSEGABD0CDIhpHY33/z8fFVVVZ1pNSR52tOl8aGkuLg4p+dWq1UrV67UL37xixbVCgCAPzC0FCLc7ebruRemo6QqD8dqWK1WZWdna9KkScrPz2eXXgBASCLIBCG73e4ULg4ePKjrr7++zpyYf0sa6OGrPffCREdH66WXXlKvXr2cAktqaqovywcAwG8IMkHGXc9Lu3bt6oSY5q1KMplMWrJkCcunAQBtCnNkgsiKFSs0ZcoUl917q6urz/zJU4hpp7ohJioqyumo1WplDxgAQJtEj0yQyM3N1Q033ODhaNN6Yd5++23uTg0ACAsEmSCwYsUKXXfddR6ONi3EdOvWTampqQQXAEBYIMgEWPN6Ypq+Qy8AAG0Rc2QCyGaz6Re/+EWdOTC1DDU3xJSVlSk/P98X5QEAEPQIMgFit9uVmZnp5oinAFMkb3tivL1LNgAAoY6hpQDJz8+vtzopQp43sWvaUJL3d8kGACC0EWQC5JlnnqnzzDd3rDaZTLJYLEpJSWl2XQAAhBKGlgIgNzdX77zzzplnnkLMLjU1xEhSdnY2K5YAAGGDIONndrtdt956q6QuanhCb78mXddisbDpHQAg7DC05Ed2u11XXnmljhypaOCsxnth4uPj9eKLLyo+Pp5N7wAAYY0g08pqbwD51ltv6U9/+pMqKso9nPkbSU82er34+Hjt27dPERERPq0TAIBQRJBpRStWrNAdd9yhQ4cOSeouyVOIabwXpnYOzKJFiwgxAACcwRyZVmC323XDDTfouuuuOxNiDEkHPZzt3YRe5sAAAOCKHhkfs9lsuu222/Sf//znTIunCb1XSFrf4LXi4+P17LPPqlevXsyBAQDADYKMD9lsNk2ZMuXMs3hJBzyc6d1Q0qJFi+iBAQCgAQwt+YDdbtc//vEP3XLLLWda/qmWhBiGkQAA8A5BpoWWLVumzp07a9y4cTpy5IhqhpKGuDkzWd6EmAULFmjv3r2EGAAAvMDQUgsMHz5cn3766ZlniZL2eziz8QDTrVs3vfLKKwQYAACagB6ZZjh+/Lj69OlTJ8R8KfchZoUaCjHR0dHKyMjQ2rVrVVpaSogBAKCJQiLIvPjii0pOTlbHjh01YsQIbd26NWC1TJ48WZ06ddLevXvPtBiSfurmzK6SrvN4nb///e/6z3/+oxUrVmj06NGsSAIAoBmCPsgsX75cc+fO1fz58/X5559r8ODBGjdunA4c8DSZtvVMnjxZb7311plnZ6nheyVVerzOPffco7FjxxJeAABoIZNhGJ4+jYPCiBEjNGzYML3wwguSpOrqalmtVt111136zW9+0+jXV1RUKCYmRuXl5YqOjm52HcePH1enTp3qtLj7a3te0myP1zCbzZo7d66eeuqpZtcBAEA48PbzO6gn+548eVKfffaZ7r//fkdbu3btNGbMGG3evNnt11RVVamqqsrxvKKioRs0eu+ee+5p5Iz2kuwurZ07d9Yvf/lL9evXT3fccQe3FwAAwIeCOsgcOnRIdrtdPXv2dGrv2bOnvvnmG7dfs3DhQi1YsMDntezatateyzRJfz3zuNnj17322mtM4gUAoJUE/RyZprr//vtVXl7ueBQWFvrkun379q3X8rpq5sK4DzERERFauXIlIQYAgFYU1EGme/fuMpvNKi0tdWovLS1VQkKC26+JjIxUdHS008MXnn76aa/PveSSS3Ts2DFCDAAArSyog0xERISGDh2qdevWOdqqq6u1bt06jRw50q+1REVFadKkSQ2ek5ycrGPHjmnTpk2sSAIAwA+COshI0ty5c/Xqq6/qz3/+s77++mvNnDlTR48e1a9+9Su/17J69WqPYWbSpEkqKChQVFSUn6sCACB8BfVkX0m6/vrrdfDgQT388MMqKSnRkCFDtGbNGpcJwP6yevVqHT9+XPfcc4927dqlvn376umnnybAAAAQAEG/j0xL+WofGQAA4D/efn4H/dASAACAJwQZAAAQsggyAAAgZBFkAABAyCLIAACAkEWQAQAAIYsgAwAAQhZBBgAAhCyCDAAACFlBf4uClqrduLiioiLAlQAAAG/Vfm43dgOCNh9kjhw5IkmyWq0BrgQAADTVkSNHFBMT4/F4m7/XUnV1tfbv36+uXbvKZDL57LoVFRWyWq0qLCzkHk5Bhp9NcOLnErz42QSncP+5GIahI0eOKCkpSe3aeZ4J0+Z7ZNq1ayeLxdJq14+Ojg7Lf2ChgJ9NcOLnErz42QSncP65NNQTU4vJvgAAIGQRZAAAQMgiyDRTZGSk5s+fr8jIyECXgnr42QQnfi7Bi59NcOLn4p02P9kXAAC0XfTIAACAkEWQAQAAIYsgAwAAQhZBBgAAhCyCTDO9+OKLSk5OVseOHTVixAht3bo10CWFvUceeUQmk8npMWDAgECXFXY2btyoq6++WklJSTKZTFq9erXTccMw9PDDDysxMVFRUVEaM2aMdu3aFZhiw0xjP5tbbrnF5T00fvz4wBQbRhYuXKhhw4apa9eu6tGjhyZPnqydO3c6nXPixAnNmjVL3bp1U5cuXTRlyhSVlpYGqOLgQpBphuXLl2vu3LmaP3++Pv/8cw0ePFjjxo3TgQMHAl1a2Dv//PNVXFzseHz00UeBLinsHD16VIMHD9aLL77o9vhTTz2l5557TosWLdKWLVvUuXNnjRs3TidOnPBzpeGnsZ+NJI0fP97pPbRs2TI/VhieNmzYoFmzZumTTz7RBx98oFOnTmns2LE6evSo45w5c+bo7bff1ooVK7Rhwwbt379f6enpAaw6iBhosuHDhxuzZs1yPLfb7UZSUpKxcOHCAFaF+fPnG4MHDw50GahDkrFq1SrH8+rqaiMhIcF4+umnHW2HDx82IiMjjWXLlgWgwvBV/2djGIYxffp0Y9KkSQGpBz86cOCAIcnYsGGDYRg175EOHToYK1ascJzz9ddfG5KMzZs3B6rMoEGPTBOdPHlSn332mcaMGeNoa9euncaMGaPNmzcHsDJI0q5du5SUlKRzzjlHN910k77//vtAl4Q6CgoKVFJS4vT+iYmJ0YgRI3j/BIm8vDz16NFD/fv318yZM1VWVhboksJOeXm5JCkuLk6S9Nlnn+nUqVNO75sBAwbo7LPP5n0jhpaa7NChQ7Lb7erZs6dTe8+ePVVSUhKgqiBJI0aM0NKlS7VmzRq9/PLLKigoUEpKio4cORLo0nBG7XuE909wGj9+vF577TWtW7dOTz75pDZs2KAJEybIbrcHurSwUV1draysLI0aNUo//elPJdW8byIiIhQbG+t0Lu+bGm3+7tcIHxMmTHD8+YILLtCIESPUu3dv/e1vf9OMGTMCWBkQGm644QbHnwcNGqQLLrhA5557rvLy8jR69OgAVhY+Zs2apa+++or5fU1Aj0wTde/eXWaz2WW2eGlpqRISEgJUFdyJjY1Vv379tHv37kCXgjNq3yO8f0LDOeeco+7du/Me8pM777xT77zzjtavXy+LxeJoT0hI0MmTJ3X48GGn83nf1CDINFFERISGDh2qdevWOdqqq6u1bt06jRw5MoCVob7Kykrt2bNHiYmJgS4FZ/Tp00cJCQlO75+Kigpt2bKF908Q2rdvn8rKyngPtTLDMHTnnXdq1apV+vDDD9WnTx+n40OHDlWHDh2c3jc7d+7U999/z/tGDC01y9y5czV9+nRdfPHFGj58uLKzs3X06FH96le/CnRpYW3evHm6+uqr1bt3b+3fv1/z58+X2WzW1KlTA11aWKmsrHT6H3xBQYG2b9+uuLg4nX322crKytLjjz+uvn37qk+fPnrooYeUlJSkyZMnB67oMNHQzyYuLk4LFizQlClTlJCQoD179ujee+/Veeedp3HjxgWw6rZv1qxZeuONN/TWW2+pa9eujnkvMTExioqKUkxMjGbMmKG5c+cqLi5O0dHRuuuuuzRy5Ej97Gc/C3D1QSDQy6ZC1fPPP2+cffbZRkREhDF8+HDjk08+CXRJYe/66683EhMTjYiICKNXr17G9ddfb+zevTvQZYWd9evXG5JcHtOnTzcMo2YJ9kMPPWT07NnTiIyMNEaPHm3s3LkzsEWHiYZ+NseOHTPGjh1rxMfHGx06dDB69+5t3HbbbUZJSUmgy27z3P1MJBlLlixxnHP8+HHjjjvuMM466yyjU6dOxrXXXmsUFxcHruggYjIMw/B/fAIAAGg55sgAAICQRZABAAAhiyADAABCFkEGAACELIIMAAAIWQQZAAAQsggyAAAgZBFkAABAyCLIAACAkEWQARBS7Ha7LrnkEqWnpzu1l5eXy2q16oEHHghQZQACgVsUAAg5//d//6chQ4bo1Vdf1U033SRJ+uUvf6kvvvhCn376qSIiIgJcIQB/IcgACEnPPfecHnnkEf3rX//S1q1b9Ytf/EKffvqpBg8eHOjSAPgRQQZASDIMQ1dccYXMZrO+/PJL3XXXXXrwwQcDXRYAPyPIAAhZ33zzjQYOHKhBgwbp888/V/v27QNdEgA/Y7IvgJD1pz/9SZ06dVJBQYH27dsX6HIABAA9MgBC0scff6zLL79c//jHP/T4449LktauXSuTyRTgygD4Ez0yAELOsWPHdMstt2jmzJlKS0vT4sWLtXXrVi1atCjQpQHwM3pkAISczMxMvffee/riiy/UqVMnSdIf/vAHzZs3T19++aWSk5MDWyAAvyHIAAgpGzZs0OjRo5WXl6dLL73U6di4ceN0+vRphpiAMEKQAQAAIYs5MgAAIGQRZAAAQMgiyAAAgJBFkAEAACGLIAMAAEIWQQYAAIQsggwAAAhZBBkAABCyCDIAACBkEWQAAEDIIsgAAICQ9f8BIAiC+09HyUEAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **Define Base Model - Load Weights**"],"metadata":{"id":"FP83NkNVULVA"}},{"cell_type":"markdown","source":["## **Tensorflow**"],"metadata":{"id":"_euC33qSUVOW"}},{"cell_type":"markdown","source":["### **CNN-BiGRU-Attention**"],"metadata":{"id":"7Wqa_sXRft-i"}},{"cell_type":"markdown","source":[],"metadata":{"id":"rlG3TJk6LGTn"}},{"cell_type":"code","source":["class AttentionLayer(Layer):\n","    def __init__(self, **kwargs):\n","        super(AttentionLayer, self).__init__(**kwargs)\n","\n","    def build(self, input_shape):\n","        # Create a trainable weight variable for this layer.\n","        self.W = self.add_weight(name='attention_weight',\n","                                 shape=(input_shape[-1], 1),\n","                                 initializer='random_normal',\n","                                 trainable=True)\n","        self.b = self.add_weight(name='attention_bias',\n","                                 shape=(input_shape[1], 1),\n","                                 initializer='zeros',\n","                                 trainable=True)\n","        super(AttentionLayer, self).build(input_shape)\n","\n","    def call(self, x):\n","        # e = K.tanh(K.dot(x, self.W) + self.b)\n","        e = K.relu(K.dot(x, self.W) + self.b)\n","        a = K.softmax(e, axis=1)\n","        output = x * a\n","\n","        # Sum over the time steps\n","        return K.sum(output, axis=1)\n","\n","    def compute_output_shape(self, input_shape):\n","        return (input_shape[0], input_shape[-1])\n","\n","\n","\n","# Define input layer\n","input_signal = Input(shape=(1200, 1))\n","\n","# CNN layer\n","x = Conv1D(filters=64, kernel_size=3, padding='same')(input_signal)\n","x = BatchNormalization()(x)\n","x = ReLU()(x)\n","x = MaxPooling1D(pool_size=3)(x)\n","\n","# Bi-GRU layer\n","bigru = Bidirectional(GRU(units=64, return_sequences=True))(x)\n","\n","# Attention layer (custom implementation required)\n","attention_output = AttentionLayer()(bigru)\n","# multi_attention_layer(bigru)\n","\n","# Output layer\n","output = Dense(units=1)(attention_output)  # Predicting a single value\n","\n","# Build model\n","model = Model(inputs=input_signal, outputs=output)\n","\n","model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=Huber(delta=1.5), metrics=[RootMeanSquaredError(),'mae'])\n","\n","# def lr_scheduler(epoch, lr):\n","#     if epoch % 10 == 0 and epoch > 0:\n","#         lr = lr * 0.5\n","#     return lr\n","\n","# # Create the learning rate scheduler callback\n","# lr_callback = LearningRateScheduler(lr_scheduler)\n","\n","# # Define early stopping to prevent overfitting\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True, mode='min')\n","# # Train the model with early stopping\n","# history = model.fit(X_train, y_train, epochs=300, batch_size=64, validation_data=(X_val, y_val), callbacks=[early_stopping,lr_callback])\n","\n","model.load_weights(\"/content/attention_model_fold_2_weights.h5\")\n","model.summary()\n","\n","print(\"test-segments-metrics\")\n","print(\"Loss      -      RMSE           -      MAE     \")\n","model.evaluate(X_test, y_test, verbose=0)"],"metadata":{"id":"HxvPU8VyUKIP","colab":{"base_uri":"https://localhost:8080/","height":453},"executionInfo":{"status":"ok","timestamp":1729068491868,"user_tz":-210,"elapsed":6278,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"17719c91-3d62-4727-dfc0-4bbcb5296bba"},"execution_count":null,"outputs":[{"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┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m1\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv1d_35 (\u001b[38;5;33mConv1D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │             \u001b[38;5;34m256\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_31               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │             \u001b[38;5;34m256\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling1d_3 (\u001b[38;5;33mMaxPooling1D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │          \u001b[38;5;34m49,920\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ attention_layer_1 (\u001b[38;5;33mAttentionLayer\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │             \u001b[38;5;34m528\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\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","│ input_layer_3 (<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\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv1d_35 (<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\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_31               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling1d_3 (<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\">400</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ bidirectional_1 (<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\">400</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">49,920</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ attention_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AttentionLayer</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\">528</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_5 (<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\">129</span> │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m51,089\u001b[0m (199.57 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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">51,089</span> (199.57 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,961\u001b[0m (199.07 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">50,961</span> (199.07 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m128\u001b[0m (512.00 B)\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\">128</span> (512.00 B)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["test-segments-metrics\n","Loss      -      RMSE           -      MAE     \n"]},{"output_type":"execute_result","data":{"text/plain":["[0.39019572734832764, 1.2221266031265259, 0.5247697830200195]"]},"metadata":{},"execution_count":77}]},{"cell_type":"markdown","source":["#### **Metrics**"],"metadata":{"id":"JLnAbx7Cf-am"}},{"cell_type":"code","source":["y_pred = model.predict(X_test.reshape(X_test.shape[0], X_test.shape[1], 1))\n","\n","# Assuming y_test and y_pred are your actual and predicted target values for the test set\n","r_squared = r2_score(y_test, y_pred)\n","print(\"R-squared value:\", r_squared)\n","\n","unique_indices = np.unique(test_data[:, -1])\n","y_pred = y_pred.ravel()\n","# Iterate over unique indices and calculate the sum of pred labels from same index\n","per_subject_sums = []\n","for index in unique_indices:\n","    subset = y_pred[test_data[:, -1] == index]\n","    per_segment_sum = np.sum(subset)\n","    per_subject_sums.append(per_segment_sum)\n","\n","sleep_durations = test_labels[:, 1] * (1/60)\n","AHI = test_labels[:, 0] / sleep_durations\n","total_records_times = (20./60.) * np.unique(test_data[:,-1], return_counts=True)[1]\n","pred_AHI = np.array(per_subject_sums) / total_records_times\n","\n","# Calculate RMSE\n","Y_pred = huber.predict(pred_AHI.reshape(-1,1))\n","# Print RMSE\n","rmse = np.sqrt(mean_squared_error(AHI, Y_pred))\n","print(f\"RMSE: {rmse}\")\n","\n","predicted_classes = np.where(Y_pred < 1, 1, np.where(Y_pred < 5, 2, np.where(Y_pred < 10, 3, 4)))\n","LABELS = np.where(AHI < 1, 1, np.where(AHI < 5, 2, np.where(AHI < 10, 3, 4)))\n","\n","# Calculate the accuracy\n","accuracy = accuracy_score(LABELS, predicted_classes)\n","\n","# Print the accuracy\n","print(\"4-class Accuracy:\", accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JdviLeTJf_DO","executionInfo":{"status":"ok","timestamp":1729068281867,"user_tz":-210,"elapsed":4189,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"7fe95b49-8fc1-44af-adb9-1024be28512b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m230/230\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step\n","R-squared value: 0.8167936092649283\n","RMSE: 1.6875472016606554\n","4-class Accuracy: 0.7768924302788844\n"]}]},{"cell_type":"markdown","source":["### **ResNet**"],"metadata":{"id":"IxArYOJYfDu4"}},{"cell_type":"code","source":["# def lr_scheduler(epoch, lr):\n","#     if epoch % 10 == 0 and epoch > 0:\n","#         lr = lr * 0.5\n","#     return lr\n","\n","# # Create the learning rate scheduler callback\n","# lr_callback = LearningRateScheduler(lr_scheduler)\n","\n","# from tensorflow.keras.callbacks import ReduceLROnPlateau\n","# lr_reduction = ReduceLROnPlateau(min_delta=0.01, monitor='val_loss', patience=5, factor=0.5, min_lr=0.00000001, verbose=1)\n","\n","def residual_block(input_tensor, filters, kernel_size, strides):\n","    x = Conv1D(filters, kernel_size=kernel_size, strides=strides, padding='same', kernel_regularizer=l2(0.01), kernel_initializer='he_normal')(input_tensor)\n","    x = BatchNormalization()(x)\n","    x = Activation('relu')(x)\n","\n","    x = Conv1D(filters, kernel_size=kernel_size, strides=1, padding='same')(x)\n","    x = BatchNormalization()(x)\n","\n","    if strides != 1:\n","        input_tensor = Conv1D(filters, kernel_size=1, strides=strides, padding='same')(input_tensor)\n","    x = Add()([x, input_tensor])\n","    x = Activation('relu')(x)\n","    return x\n","\n","def build_resnet(input_shape):\n","\n","    spo2_input = Input(shape=input_shape)\n","\n","    x = Conv1D(64, kernel_size=7, strides=2, padding='same', kernel_regularizer=l2(0.01), kernel_initializer='he_normal')(spo2_input)\n","    x = BatchNormalization()(x)\n","    x = Activation('relu')(x)\n","    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n","\n","    x = residual_block(x, filters=64, kernel_size=10, strides=1)\n","    x = residual_block(x, filters=64, kernel_size=10, strides=1)\n","    x = residual_block(x, filters=64, kernel_size=10, strides=1)\n","\n","    x = residual_block(x, filters=128, kernel_size=10, strides=2)\n","    x = residual_block(x, filters=128, kernel_size=10, strides=1)\n","\n","\n","    x = residual_block(x, filters=256, kernel_size=10, strides=2)\n","    x = residual_block(x, filters=256, kernel_size=10, strides=1)\n","\n","\n","    x = Flatten()(x)\n","    x = Dense(1000, activation='relu', kernel_regularizer=l2(0.01), kernel_initializer='he_normal')(x)\n","    x = Dense(1, activation='linear')(x)\n","\n","    model = Model(inputs=spo2_input, outputs=x)\n","    return model\n","\n","# Define the input shape\n","input_shape = (1200, 1)\n","\n","# Build the ResNet model\n","model = build_resnet(input_shape)\n","\n","model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=Huber(delta=1.5), metrics=[RootMeanSquaredError(),'mae'])  #Huber(delta=1.5)\n","\n","# # Define early stopping to prevent overfitting\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True, mode='min')\n","# # Train the model with early stopping\n","# history = model.fit(X_train, y_train, epochs=300, batch_size=64, validation_data=(X_val, y_val), callbacks=[early_stopping,lr_callback])\n","\n","model.load_weights(\"/content/resnet_model_fold_2_weights.h5\")\n","model.summary()\n","print(\"test-segments-metrics\")\n","print(\"Loss      -      RMSE           -      MAE     \")\n","model.evaluate(X_test, y_test, verbose=0)"],"metadata":{"id":"GR7mcz9PfEtR","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1729068471643,"user_tz":-210,"elapsed":8000,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"a7452574-3511-4249-a659-56e7bd362651"},"execution_count":null,"outputs":[{"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_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m1\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ -                      │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_18 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m600\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m512\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_16    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m600\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_15             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m600\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ max_pooling1d_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","│ (\u001b[38;5;33mMaxPooling1D\u001b[0m)            │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_19 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ max_pooling1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_17    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_16             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_20 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_18    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_7 (\u001b[38;5;33mAdd\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n","│                           │                        │                │ max_pooling1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_17             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_21 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_19    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_18             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_22 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ activation_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_20    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_8 (\u001b[38;5;33mAdd\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│                           │                        │                │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_19             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_23 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_21    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_20             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_24 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │         \u001b[38;5;34m41,024\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_22    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │            \u001b[38;5;34m256\u001b[0m │ conv1d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_9 (\u001b[38;5;33mAdd\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│                           │                        │                │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_21             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_25 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m82,048\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_23    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │            \u001b[38;5;34m512\u001b[0m │ conv1d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_22             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_26 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m163,968\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_24    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │            \u001b[38;5;34m512\u001b[0m │ conv1d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_27 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m8,320\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_10 (\u001b[38;5;33mAdd\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│                           │                        │                │ conv1d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_23             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_28 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m163,968\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_25    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │            \u001b[38;5;34m512\u001b[0m │ conv1d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_24             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_29 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m163,968\u001b[0m │ activation_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_26    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │            \u001b[38;5;34m512\u001b[0m │ conv1d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_11 (\u001b[38;5;33mAdd\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│                           │                        │                │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_25             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\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;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_30 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │        \u001b[38;5;34m327,936\u001b[0m │ activation_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_27    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │          \u001b[38;5;34m1,024\u001b[0m │ conv1d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_26             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_31 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │        \u001b[38;5;34m655,616\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_28    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │          \u001b[38;5;34m1,024\u001b[0m │ conv1d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_32 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │         \u001b[38;5;34m33,024\u001b[0m │ activation_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_12 (\u001b[38;5;33mAdd\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│                           │                        │                │ conv1d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_27             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_33 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │        \u001b[38;5;34m655,616\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_29    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │          \u001b[38;5;34m1,024\u001b[0m │ conv1d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_28             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n","│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_34 (\u001b[38;5;33mConv1D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │        \u001b[38;5;34m655,616\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_30    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │          \u001b[38;5;34m1,024\u001b[0m │ conv1d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_13 (\u001b[38;5;33mAdd\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │              \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n","│                           │                        │                │ activation_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_29             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\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;33mActivation\u001b[0m)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m19200\u001b[0m)          │              \u001b[38;5;34m0\u001b[0m │ activation_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ dense_3 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │     \u001b[38;5;34m19,201,000\u001b[0m │ flatten_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ dense_4 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │          \u001b[38;5;34m1,001\u001b[0m │ dense_3[\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_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_18 (<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\">600</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ input_layer_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_16    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">600</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_18[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_15             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">600</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ max_pooling1d_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_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\">MaxPooling1D</span>)            │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_19 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ max_pooling1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_17    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_19[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_16             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_20 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_18    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_20[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_7 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n","│                           │                        │                │ max_pooling1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_17             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_21 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_19    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_21[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_18             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_22 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ activation_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_20    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_22[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_8 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│                           │                        │                │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_19             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_23 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_21    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_23[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_20             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_24 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">41,024</span> │ activation_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_22    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv1d_24[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ add_9 (<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\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│                           │                        │                │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_21             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_25 (<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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">82,048</span> │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_23    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv1d_25[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_22             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_26 (<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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">163,968</span> │ activation_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_24    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv1d_26[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_27 (<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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│                           │                        │                │ conv1d_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_23             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_28 (<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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">163,968</span> │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_25    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv1d_28[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_24             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_29 (<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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">163,968</span> │ activation_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_26    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv1d_29[<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\">BatchNormalization</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\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│                           │                        │                │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_25             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_30 (<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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">327,936</span> │ activation_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_27    │ (<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\">256</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv1d_30[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_26             │ (<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\">256</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_31 (<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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">655,616</span> │ activation_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_28    │ (<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\">256</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv1d_31[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_32 (<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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │ activation_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│                           │                        │                │ conv1d_32[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_27             │ (<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\">256</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_33 (<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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">655,616</span> │ activation_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_29    │ (<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\">256</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv1d_33[<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\">BatchNormalization</span>)      │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_28             │ (<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\">256</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ conv1d_34 (<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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">655,616</span> │ activation_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ batch_normalization_30    │ (<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\">256</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv1d_34[<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\">BatchNormalization</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\">75</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n","│                           │                        │                │ activation_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ activation_29             │ (<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\">256</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\">Activation</span>)              │                        │                │                        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">19200</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ dense_3 (<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\">1000</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">19,201,000</span> │ flatten_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n","├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n","│ dense_4 (<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\">1,001</span> │ dense_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"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m22,366,673\u001b[0m (85.32 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\">22,366,673</span> (85.32 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m22,362,705\u001b[0m (85.31 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\">22,362,705</span> (85.31 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,968\u001b[0m (15.50 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\">3,968</span> (15.50 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["test-segments-metrics\n","Loss      -      RMSE           -      MAE     \n"]},{"output_type":"execute_result","data":{"text/plain":["[0.5794086456298828, 1.3304579257965088, 0.612430214881897]"]},"metadata":{},"execution_count":76}]},{"cell_type":"markdown","source":["#### **Metrics**"],"metadata":{"id":"IWkwj2EsV7f2"}},{"cell_type":"code","source":["y_pred = model.predict(X_test.reshape(X_test.shape[0], X_test.shape[1], 1))\n","\n","# Assuming y_test and y_pred are your actual and predicted target values for the test set\n","r_squared = r2_score(y_test, y_pred)\n","print(\"R-squared value:\", r_squared)\n","\n","unique_indices = np.unique(test_data[:, -1])\n","y_pred = y_pred.ravel()\n","# Iterate over unique indices and calculate the sum of pred labels from same index\n","per_subject_sums = []\n","for index in unique_indices:\n","    subset = y_pred[test_data[:, -1] == index]\n","    per_segment_sum = np.sum(subset)\n","    per_subject_sums.append(per_segment_sum)\n","\n","sleep_durations = test_labels[:, 1] * (1/60)\n","AHI = test_labels[:, 0] / sleep_durations\n","total_records_times = (20./60.) * np.unique(test_data[:,-1], return_counts=True)[1]\n","pred_AHI = np.array(per_subject_sums) / total_records_times\n","\n","# Calculate RMSE\n","Y_pred = huber.predict(pred_AHI.reshape(-1,1))\n","# Print RMSE\n","rmse = np.sqrt(mean_squared_error(AHI, Y_pred))\n","print(f\"RMSE: {rmse}\")\n","\n","predicted_classes = np.where(Y_pred < 1, 1, np.where(Y_pred < 5, 2, np.where(Y_pred < 10, 3, 4)))\n","LABELS = np.where(AHI < 1, 1, np.where(AHI < 5, 2, np.where(AHI < 10, 3, 4)))\n","\n","# Calculate the accuracy\n","accuracy = accuracy_score(LABELS, predicted_classes)\n","\n","# Print the accuracy\n","print(\"4-class Accuracy:\", accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fhsle2O2V7P2","executionInfo":{"status":"ok","timestamp":1729068315427,"user_tz":-210,"elapsed":10763,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"4e66a9b3-07bb-45f4-8d00-a720579c8279"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m230/230\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step\n","R-squared value: 0.7828746592137246\n","RMSE: 1.7155333808711228\n","4-class Accuracy: 0.7290836653386454\n"]}]},{"cell_type":"markdown","source":["## **Pytorch**"],"metadata":{"id":"J1SZb1s6f6_q"}},{"cell_type":"markdown","source":["### **Initiate Tensors**"],"metadata":{"id":"EnGa1fbqWmpO"}},{"cell_type":"code","source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","X_train = torch.from_numpy(X_train).float().unsqueeze(1).to(device)\n","y_train = torch.from_numpy(y_train).float().unsqueeze(1).to(device)\n","X_val = torch.from_numpy(X_val).float().unsqueeze(1).to(device)\n","y_val = torch.from_numpy(y_val).float().unsqueeze(1).to(device)\n","X_test = torch.from_numpy(X_test).float().unsqueeze(1).to(device)\n","y_test = torch.from_numpy(y_test).float().unsqueeze(1).to(device)"],"metadata":{"id":"DW6MCS_dWYG3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **Dataset and DataLoader**"],"metadata":{"id":"iTyHjvSCYx1a"}},{"cell_type":"code","source":["# Dataset and DataLoader\n","train_dataset = TensorDataset(X_train, y_train)\n","val_dataset = TensorDataset(X_val, y_val)\n","test_dataset = TensorDataset(X_test, y_test)\n","\n","train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=64)\n","test_loader = DataLoader(test_dataset, batch_size=64)"],"metadata":{"id":"qd7-l8VWYvMb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **CNN-BiGRU-Attention**"],"metadata":{"id":"xYztnEoSgCHg"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import DataLoader, TensorDataset, random_split\n","\n","\n","\n","class AttentionLayer(nn.Module):\n","    def __init__(self, input_dim):\n","        super(AttentionLayer, self).__init__()\n","        self.W = nn.Parameter(torch.randn(input_dim, 1))\n","        self.b = nn.Parameter(torch.zeros(1, 400, 1))\n","        self.alpha_vectors = []\n","\n","    def forward(self, x):\n","        e = torch.relu(torch.matmul(x, self.W) + self.b)\n","        alpha_vector = torch.softmax(e, dim=1)\n","        # print(\"alpha: \",a.size())\n","        self.alpha_vectors.append(alpha_vector)\n","        output = x * alpha_vector\n","        return torch.sum(output, dim=1), alpha_vector\n","\n","class CNNBiGRUAttentionModel(nn.Module):\n","    def __init__(self):\n","        super(CNNBiGRUAttentionModel, self).__init__()\n","        self.conv1 = nn.Conv1d(in_channels=1, out_channels=64, kernel_size=3, padding='same')\n","        self.batch_norm1 = nn.BatchNorm1d(64)\n","        self.relu = nn.ReLU()\n","        self.max_pool = nn.MaxPool1d(kernel_size=3)\n","        self.bigru = nn.GRU(input_size=64, hidden_size=64, num_layers=1, bidirectional=True, batch_first=True)\n","        self.attention = AttentionLayer(input_dim=128)  # 128 because of bidirectional GRU (64*2)\n","        self.fc = nn.Linear(in_features=128, out_features=1)\n","\n","    def forward(self, x):\n","        # print(x.size())\n","        x = self.conv1(x)\n","        x = self.batch_norm1(x)\n","        x = self.relu(x)\n","        x = self.max_pool(x)\n","        # print(x.size())\n","        x = x.permute(0, 2, 1)\n","        x, _ = self.bigru(x)\n","        # print(x.size())\n","        x, alpha_vector = self.attention(x)\n","        x = self.fc(x)\n","        return x, alpha_vector\n","\n","# Learning rate scheduler\n","def lr_scheduler(optimizer, epoch):\n","    if epoch % 10 == 0 and epoch > 0:\n","        for param_group in optimizer.param_groups:\n","            param_group['lr'] *= 0.5\n","\n","\n","\n","# Model, optimizer, loss function\n","model = CNNBiGRUAttentionModel().to(device)\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","criterion = nn.HuberLoss(delta=1.5)"],"metadata":{"id":"xVFAx0wCUf1F","colab":{"base_uri":"https://localhost:8080/"},"outputId":"4b453193-7338-40ec-9ddc-46c74c031031","executionInfo":{"status":"ok","timestamp":1729066109722,"user_tz":-210,"elapsed":245503,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["epoch:  0\n","Epoch 1/300, Loss: 2.090539313975734, Val Loss: 1.4390727752133419\n","epoch:  1\n","Epoch 2/300, Loss: 1.9111769559359786, Val Loss: 6.300161662854646\n","epoch:  2\n","Epoch 3/300, Loss: 1.379194553634121, Val Loss: 10.230070917229904\n","epoch:  3\n","Epoch 4/300, Loss: 1.1518147854325007, Val Loss: 5.577223238192107\n","epoch:  4\n","Epoch 5/300, Loss: 1.0542388371508506, Val Loss: 0.8503640529356504\n","epoch:  5\n","Epoch 6/300, Loss: 0.9935569280248271, Val Loss: 0.9352506187401319\n","epoch:  6\n","Epoch 7/300, Loss: 0.9247890294778465, Val Loss: 1.4954389098443484\n","epoch:  7\n","Epoch 8/300, Loss: 0.8883932457111849, Val Loss: 3.8264739513397217\n","epoch:  8\n","Epoch 9/300, Loss: 0.8627260367665747, Val Loss: 0.5591743423750526\n","epoch:  9\n","Epoch 10/300, Loss: 0.8434885069482004, Val Loss: 0.6692459167618501\n","epoch:  10\n","Epoch 11/300, Loss: 0.8310115413844782, Val Loss: 1.3784316911509162\n","epoch:  11\n","Epoch 12/300, Loss: 0.7795107359736666, Val Loss: 0.6959129752297151\n","epoch:  12\n","Epoch 13/300, Loss: 0.7891678338987206, Val Loss: 0.5495476957998777\n","epoch:  13\n","Epoch 14/300, Loss: 0.7719744910107981, Val Loss: 0.8470557786916432\n","epoch:  14\n","Epoch 15/300, Loss: 0.7643581641094126, Val Loss: 0.6936347743398265\n","epoch:  15\n","Epoch 16/300, Loss: 0.7588517302432076, Val Loss: 0.5352032866917158\n","epoch:  16\n","Epoch 17/300, Loss: 0.7464010167141558, Val Loss: 1.004055921968661\n","epoch:  17\n","Epoch 18/300, Loss: 0.7499782416490045, Val Loss: 10.708977812214902\n","epoch:  18\n","Epoch 19/300, Loss: 0.7426282571192229, Val Loss: 1.2051288046334918\n","epoch:  19\n","Epoch 20/300, Loss: 0.7363532211619241, Val Loss: 1.0369720662895001\n","epoch:  20\n","Epoch 21/300, Loss: 0.7463738413238683, Val Loss: 16.235008641293174\n","epoch:  21\n","Epoch 22/300, Loss: 0.7148946677104081, Val Loss: 0.5798970413835425\n","epoch:  22\n","Epoch 23/300, Loss: 0.7147633870049278, Val Loss: 1.3194397797710018\n","epoch:  23\n","Epoch 24/300, Loss: 0.7162074552904262, Val Loss: 0.5399588385694906\n","epoch:  24\n","Epoch 25/300, Loss: 0.7200049528784485, Val Loss: 0.7445975808720839\n","epoch:  25\n","Epoch 26/300, Loss: 0.7116835104553613, Val Loss: 0.7124889587101183\n","epoch:  26\n","Epoch 27/300, Loss: 0.7053629341888743, Val Loss: 0.7891968292625327\n","epoch:  27\n","Epoch 28/300, Loss: 0.705136661974117, Val Loss: 0.5449305659061984\n","epoch:  28\n","Epoch 29/300, Loss: 0.7047792980478148, Val Loss: 0.5061034462169597\n","epoch:  29\n","Epoch 30/300, Loss: 0.6966731421919939, Val Loss: 0.6142091845211229\n","epoch:  30\n","Epoch 31/300, Loss: 0.7057774963453658, Val Loss: 0.8309870001516844\n","epoch:  31\n","Epoch 32/300, Loss: 0.688602138421323, Val Loss: 1.4225116497591923\n","epoch:  32\n","Epoch 33/300, Loss: 0.689156166397699, Val Loss: 0.5012164194332925\n","epoch:  33\n","Epoch 34/300, Loss: 0.6816330269323324, Val Loss: 0.5766265745225706\n","epoch:  34\n","Epoch 35/300, Loss: 0.6821105049880031, Val Loss: 1.3982459777279903\n","epoch:  35\n","Epoch 36/300, Loss: 0.6844938926079093, Val Loss: 1.9782147344790006\n","epoch:  36\n","Epoch 37/300, Loss: 0.6848844065032776, Val Loss: 0.5936493532438027\n","epoch:  37\n","Epoch 38/300, Loss: 0.679080206470521, Val Loss: 0.4888591887919526\n","epoch:  38\n","Epoch 39/300, Loss: 0.6792936318188217, Val Loss: 1.4018470095960718\n","epoch:  39\n","Epoch 40/300, Loss: 0.6794314667730048, Val Loss: 0.495718777179718\n","epoch:  40\n","Epoch 41/300, Loss: 0.6794639370425699, Val Loss: 1.0799237403430437\n","epoch:  41\n","Epoch 42/300, Loss: 0.669999688126073, Val Loss: 0.5165480794875246\n","epoch:  42\n","Epoch 43/300, Loss: 0.6702276325166816, Val Loss: 0.5601085565592113\n","epoch:  43\n","Epoch 44/300, Loss: 0.6696083317003628, Val Loss: 0.5899747402260178\n","epoch:  44\n","Epoch 45/300, Loss: 0.6700440022123135, Val Loss: 0.5449772728116888\n","epoch:  45\n","Epoch 46/300, Loss: 0.6681769811379241, Val Loss: 0.5120483252562975\n","epoch:  46\n","Epoch 47/300, Loss: 0.6709410586569569, Val Loss: 1.3989435402970565\n","epoch:  47\n","Epoch 48/300, Loss: 0.6687868665842334, Val Loss: 0.9141992479562759\n","epoch:  48\n","Epoch 49/300, Loss: 0.6673526654247404, Val Loss: 0.5669361701921413\n","epoch:  49\n","Epoch 50/300, Loss: 0.6657573447959257, Val Loss: 0.5679523403707304\n","epoch:  50\n","Epoch 51/300, Loss: 0.6674143936079328, Val Loss: 0.5009158437973574\n","epoch:  51\n","Epoch 52/300, Loss: 0.6619781501124008, Val Loss: 0.48143375782590164\n","epoch:  52\n","Epoch 53/300, Loss: 0.6651230440674836, Val Loss: 0.49798747348157985\n","epoch:  53\n","Epoch 54/300, Loss: 0.6623160267623738, Val Loss: 0.6565921455621719\n","epoch:  54\n","Epoch 55/300, Loss: 0.6645116613249574, Val Loss: 0.5228308392198462\n","epoch:  55\n","Epoch 56/300, Loss: 0.6604905069956292, Val Loss: 0.4984216650849895\n","epoch:  56\n","Epoch 57/300, Loss: 0.6605824002436679, Val Loss: 0.48536615975593267\n","epoch:  57\n","Epoch 58/300, Loss: 0.6613802625106113, Val Loss: 0.4863458122862013\n","epoch:  58\n","Epoch 59/300, Loss: 0.6603536039787551, Val Loss: 0.4822759157732913\n","epoch:  59\n","Epoch 60/300, Loss: 0.6599672694902609, Val Loss: 0.5177733525633812\n","epoch:  60\n","Epoch 61/300, Loss: 0.6602773518353799, Val Loss: 0.5096977302118352\n","epoch:  61\n","Epoch 62/300, Loss: 0.6588100485085654, Val Loss: 0.5200971443402139\n","epoch:  62\n","Epoch 63/300, Loss: 0.6556527560496881, Val Loss: 0.5312114471667692\n","epoch:  63\n","Epoch 64/300, Loss: 0.6611831900310201, Val Loss: 0.49361873378879145\n","epoch:  64\n","Epoch 65/300, Loss: 0.657707887169945, Val Loss: 0.5068778403495487\n","epoch:  65\n","Epoch 66/300, Loss: 0.6579403234000253, Val Loss: 0.4873535797784203\n","epoch:  66\n","Epoch 67/300, Loss: 0.6580658739451135, Val Loss: 0.5277112334182388\n","epoch:  67\n","Epoch 68/300, Loss: 0.6559165288316141, Val Loss: 0.4996375884664686\n","epoch:  68\n","Epoch 69/300, Loss: 0.6586139916685154, Val Loss: 0.49456622569184555\n","epoch:  69\n","Epoch 70/300, Loss: 0.6561035942716567, Val Loss: 0.5107332864090016\n","epoch:  70\n","Epoch 71/300, Loss: 0.6581194167188292, Val Loss: 0.4843621885305957\n","epoch:  71\n","Epoch 72/300, Loss: 0.6550863408314513, Val Loss: 0.48963898655615357\n","epoch:  72\n","Epoch 73/300, Loss: 0.6558435785986803, Val Loss: 0.5272803063455381\n","epoch:  73\n","Epoch 74/300, Loss: 0.6572056592691062, Val Loss: 0.48791118240670156\n","epoch:  74\n","Epoch 75/300, Loss: 0.6600318238286689, Val Loss: 0.4935991038617335\n","epoch:  75\n","Epoch 76/300, Loss: 0.6567822692811293, Val Loss: 0.49941518275361313\n","epoch:  76\n","Epoch 77/300, Loss: 0.6602646667178315, Val Loss: 0.519797148673158\n","epoch:  77\n","Epoch 78/300, Loss: 0.6577963163160255, Val Loss: 0.4927289238885829\n","epoch:  78\n","Epoch 79/300, Loss: 0.6587312432995723, Val Loss: 0.5006251766493446\n","epoch:  79\n","Epoch 80/300, Loss: 0.6524722167465946, Val Loss: 0.5037082637611189\n","epoch:  80\n","Epoch 81/300, Loss: 0.6525407989721487, Val Loss: 0.5253224431684143\n","epoch:  81\n","Epoch 82/300, Loss: 0.6539903111485365, Val Loss: 0.49289378445399434\n","Early stopping\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-49-6f918bdda4f0>:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  model.load_state_dict(torch.load('best_model.pth'))\n"]},{"output_type":"stream","name":"stdout","text":["Test Loss: 0.39865885233992465\n"]}]},{"cell_type":"markdown","source":["#### **Training the model**"],"metadata":{"id":"SGuJibqcY_4b"}},{"cell_type":"code","source":["# Training the model\n","num_epochs = 300\n","best_val_loss = float('inf')\n","patience = 30\n","counter = 0\n","\n","for epoch in range(num_epochs):\n","    print(\"epoch: \",epoch)\n","    model.train()\n","    running_loss = 0.0\n","    for inputs, targets in train_loader:\n","        inputs, targets = inputs.to(device), targets.to(device)\n","        optimizer.zero_grad()\n","        outputs, a = model(inputs)\n","        loss = criterion(outputs, targets)\n","        loss.backward()\n","        optimizer.step()\n","        running_loss += loss.item()\n","\n","    lr_scheduler(optimizer, epoch)\n","\n","    model.eval()\n","    val_loss = 0.0\n","    with torch.no_grad():\n","        for inputs, targets in val_loader:\n","            outputs, a = model(inputs)\n","            loss = criterion(outputs, targets)\n","            val_loss += loss.item()\n","\n","    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}, Val Loss: {val_loss/len(val_loader)}\")\n","\n","    if val_loss < best_val_loss:\n","        best_val_loss = val_loss\n","        torch.save(model.state_dict(), 'best_model.pth')\n","        counter = 0\n","    else:\n","        counter += 1\n","        if counter >= patience:\n","            print(\"Early stopping\")\n","            break"],"metadata":{"id":"QSW3aMdLY9t0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### **Load Weights**"],"metadata":{"id":"9pwgJjo6X_3s"}},{"cell_type":"code","source":["# Load the best model and evaluate on the test set\n","model.load_state_dict(torch.load('attention_model_pytorch_fold_2.pth'))\n","model.eval()\n","test_loss = 0.0\n","with torch.no_grad():\n","    for inputs, targets in test_loader:\n","        outputs, a = model(inputs)\n","        loss = criterion(outputs, targets)\n","        test_loss += loss.item()\n","\n","print(f\"Test Loss: {test_loss/len(test_loader)}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6W0aoUkaXKs6","executionInfo":{"status":"ok","timestamp":1729066234736,"user_tz":-210,"elapsed":956,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"c94e14e6-2a10-45ad-a1bc-f5f367dbed65"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-54-54cb49b3ab98>:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  model.load_state_dict(torch.load('best_model.pth'))\n"]},{"output_type":"stream","name":"stdout","text":["Test Loss: 0.41283661227511326\n"]}]},{"cell_type":"markdown","source":["#### **Attention weights**"],"metadata":{"id":"X1QwNTb5QZMX"}},{"cell_type":"code","source":["results_list = []\n","alpha_vec_list = []\n","with torch.no_grad():\n","    for inputs, targets in test_loader:\n","        outputs, alpha_vec = model(inputs)\n","        results_list.append(outputs.cpu().numpy())\n","        alpha_vec_list.append(alpha_vec.cpu().numpy())"],"metadata":{"id":"2a8gm3a8VQld"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["alpha_arr = np.vstack(alpha_vec_list)\n","predicts = np.vstack(results_list)"],"metadata":{"id":"8gST3Q79Vga7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["alpha_arr.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZDxJYwlYoyiz","executionInfo":{"status":"ok","timestamp":1729066139462,"user_tz":-210,"elapsed":4,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"4508e73d-333a-4506-9cab-ac028325284d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(7349, 400, 1)"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["np.unique(y_test.cpu().numpy(), return_counts=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CRUmG1UXQ6XU","executionInfo":{"status":"ok","timestamp":1729065321218,"user_tz":-210,"elapsed":2,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"e5f340b3-56b5-4f06-8fb9-dc35c08a4fc8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n","        13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n","        26., 27., 29., 30., 31., 32., 33., 34., 35., 37.], dtype=float32),\n"," array([5229,  975,  398,  201,  126,   87,   62,   34,   35,   35,   22,\n","          20,   16,   20,   13,   11,   11,    5,   10,    5,    2,    4,\n","           1,    3,    3,    4,    4,    1,    2,    2,    2,    1,    1,\n","           1,    2,    1]))"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["torch.where(y_test == 37)[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ca_vmNJiQ33a","executionInfo":{"status":"ok","timestamp":1729065323101,"user_tz":-210,"elapsed":10,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"613533ba-1971-479f-8e3c-0e6cc536ed1f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([6984], device='cuda:0')"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["torch.where(y_test == 35)[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4GO3z1a5U-k6","executionInfo":{"status":"ok","timestamp":1729065395707,"user_tz":-210,"elapsed":406,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"d80dd3c5-a9c8-40b8-ab54-236c876df6a6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([2898, 5396], device='cuda:0')"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.colors import LogNorm\n","\n","# Create a figure with two subplots (one for SpO2 signal, one for attention heatmap)\n","fig, axis = plt.subplots(2, 1, figsize=(15, 5))\n","\n","# Time axis for the SpO2 signal\n","t = np.arange(0, 1200)\n","\n","# Plot the SpO2 signal (assuming X_test is a PyTorch tensor)\n","axis[0].plot(t, X_test[5396].cpu().squeeze(0))\n","axis[0].set_title(\"20-min SpO2 Signal\", fontsize=14)\n","axis[0].set_xlabel(\"Time (s)\", fontsize=12)\n","axis[0].set_ylabel(\"SpO2 (%)\", fontsize=12)\n","# axis[0].grid(True)\n","\n","# Plot the attention heatmap (assuming alpha_arr is already loaded and in proper shape)\n","heatmap_data = alpha_arr[5396].reshape(-1, 400)\n","\n","im = axis[1].imshow(heatmap_data, cmap='viridis', aspect='auto', norm=LogNorm())\n","axis[1].set_title(\"Attention Heatmap\", fontsize=14)\n","axis[1].set_xlabel(\"Time Steps\", fontsize=12)\n","axis[1].set_ylabel(\"Attention Weights\", fontsize=12)\n","axis[1].set_yticks([])  # Remove y-ticks for clarity\n","\n","# Add color bar to the heatmap\n","cbar = fig.colorbar(im, ax=axis[1])\n","cbar.set_label('Attention Weight Intensity', fontsize=12)\n","\n","# Show the plot\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":492},"id":"67uKgnivblxl","executionInfo":{"status":"ok","timestamp":1729067246501,"user_tz":-210,"elapsed":2047,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"7a45e8f3-ae2f-4282-f4db-ad34de9a5461"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 3 Axes>"],"image/png":"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numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.colors import LogNorm\n","\n","# Create a figure with two subplots (one for SpO2 signal, one for attention heatmap)\n","fig, axis = plt.subplots(2, 1, figsize=(15, 5))\n","\n","# Time axis for the SpO2 signal\n","t = np.arange(0, 1200)\n","\n","# Plot the SpO2 signal (assuming X_test is a PyTorch tensor)\n","axis[0].plot(t, X_test[867].cpu().squeeze(0))\n","axis[0].set_title(\"20-min SpO2 Signal\", fontsize=14)\n","axis[0].set_xlabel(\"Time (s)\", fontsize=12)\n","axis[0].set_ylabel(\"SpO2 (%)\", fontsize=12)\n","# axis[0].grid(True)\n","\n","# Plot the attention heatmap (assuming alpha_arr is already loaded and in proper shape)\n","heatmap_data = alpha_arr[867].reshape(-1, 400)\n","\n","im = axis[1].imshow(heatmap_data, cmap='viridis', aspect='auto', norm=LogNorm())\n","axis[1].set_title(\"Attention Heatmap\", fontsize=14)\n","axis[1].set_xlabel(\"Time Steps\", fontsize=12)\n","axis[1].set_ylabel(\"Attention Weights\", fontsize=12)\n","axis[1].set_yticks([])  # Remove y-ticks for clarity\n","\n","# Add color bar to the heatmap\n","cbar = fig.colorbar(im, ax=axis[1])\n","cbar.set_label('Attention Weight Intensity', fontsize=12)\n","\n","# Show the plot\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":496},"id":"CE9Wg9_xYbTT","executionInfo":{"status":"ok","timestamp":1729067021760,"user_tz":-210,"elapsed":2185,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"a3353aac-f358-4a9f-b7ca-3ac3d08a95f4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 3 Axes>"],"image/png":"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**Metrics**"],"metadata":{"id":"HQQrKqBNcMTI"}},{"cell_type":"code","source":["from sklearn.metrics import r2_score\n","from sklearn.metrics import mean_squared_error\n","test_data = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_testArray2.npy')\n","test_labels = np.load('/content/drive/MyDrive/Folds/fold_2/OCM_testAHI2.npy')\n","test_data = test_data[(test_data[:,-1] !=304.) & (test_data[:,-1] !=371.) & (test_data[:,-1] !=353.)]\n","X_test = test_data[:,:-2]\n","y_test = test_data[:,-2]\n","test_labels = np.delete(test_labels, [108,113], axis=0)  #[108,113] [90]\n","# y_pred = model.predict(X_test.reshape(X_test.shape[0], X_test.shape[1], 1))\n","# Assuming y_test and y_pred are your actual and predicted target values for the test set\n","r_squared = r2_score(y_test, predicts)\n","print(\"R-squared value:\", r_squared)\n","\n","unique_values = np.unique(test_data[:, -1])\n","y_pred = predicts.ravel()\n","# Iterate over unique values and calculate the sum of column 1\n","sums = []\n","for value in unique_values:\n","    subset = y_pred[test_data[:, -1] == value]\n","    column1_sum = np.sum(subset)\n","    sums.append(column1_sum)\n","\n","sleep_dur = test_labels[:, 1] * (1/60)\n","AHI = test_labels[:, 0] / sleep_dur\n","pred_AHI = np.array(sums) / ((20./60.) * np.unique(test_data[:,-1], return_counts=True)[1])\n","\n","# Calculate RMSE\n","Y_pred = huber.predict(pred_AHI.reshape(-1,1))\n","# Print RMSE\n","rmse = np.sqrt(mean_squared_error(AHI, Y_pred))\n","print(f\"RMSE: {rmse}\")\n","\n","predicted_classes = np.where(Y_pred < 1, 1, np.where(Y_pred < 5, 2, np.where(Y_pred < 10, 3, 4)))\n","LABELS = np.where(AHI < 1, 1, np.where(AHI < 5, 2, np.where(AHI < 10, 3, 4)))\n","from sklearn.metrics import accuracy_score\n","\n","# Calculate the accuracy\n","accuracy = accuracy_score(LABELS, predicted_classes)\n","\n","# Print the accuracy\n","print(\"4-class Accuracy:\", accuracy)"],"metadata":{"id":"gVY1MvH4VzxL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1729067300912,"user_tz":-210,"elapsed":423,"user":{"displayName":"Erfan Mtzv","userId":"14753547186249884613"}},"outputId":"7453e394-09b3-40f9-ff4b-2e462ff4349c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["R-squared value: 0.8067992517860801\n","RMSE: 1.717052782059418\n","4-class Accuracy: 0.7330677290836654\n"]}]}]}