| <html> | |
| <head> | |
| <title>GENE_FAMILY: Dof</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; } | |
| .container { border: 2px solid #ddd; margin: 20px; padding: 20px; } | |
| .header { padding: 0.6em; background-color: #ffdddd; font-weight: bold; } | |
| .title { padding: 0.6em; background-color: #80c4e6bd; font-weight: bold; } | |
| .section { padding: 10px; } | |
| .metrics { display: flex; } | |
| .metrics ul { list-style: none; padding: 0; } | |
| .metrics ul + ul { margin-left: 2em; } | |
| .confusion-matrix img, .learning-curve img { width: 100%; max-width: 565px; } | |
| .class_dist { width: 20em; } | |
| .mod_sum { margin-bottom: 2em; } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container title"> | |
| <h3>GENE_FAMILY: <a href='https://planttfdb.gao-lab.org/family.php?fam=Dof' target='_blank'>Dof</a></h3> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k2</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k2" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense (Dense) │ (None, 256) │ 113,152 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_1 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_1 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_2 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_2 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_3 (Dense) │ (None, 32) │ 2,080 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_3 (Dropout) │ (None, 32) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_4 (Dense) │ (None, 1) │ 33 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 469,253 (1.79 MB) | |
| Trainable params: 156,417 (611.00 KB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 312,836 (1.19 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img 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" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 5655 50.13 | |
| 0 5626 49.87</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 11281</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9860 0.9973 0.9916 1126 | |
| Class 1 0.9973 0.9859 0.9916 1131 | |
| accuracy 0.9916 2257 | |
| macro avg 0.9916 0.9916 0.9916 2257 | |
| weighted avg 0.9916 0.9916 0.9916 2257 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 1115</li> | |
| <li>True Negatives (TN): 1123</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 3</li> | |
| <li>False Negatives (FN): 16</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,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" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k3</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k3" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense_5 (Dense) │ (None, 256) │ 2,282,496 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_4 (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_6 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_5 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_7 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_6 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_8 (Dense) │ (None, 32) │ 2,080 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_7 (Dropout) │ (None, 32) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_9 (Dense) │ (None, 1) │ 33 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 6,977,285 (26.62 MB) | |
| Trainable params: 2,325,761 (8.87 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 4,651,524 (17.74 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img src="data:image/png;base64,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" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 5655 50.13 | |
| 0 5626 49.87</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 11281</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9965 1.0000 0.9982 1126 | |
| Class 1 1.0000 0.9965 0.9982 1131 | |
| accuracy 0.9982 2257 | |
| macro avg 0.9982 0.9982 0.9982 2257 | |
| weighted avg 0.9982 0.9982 0.9982 2257 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 1127</li> | |
| <li>True Negatives (TN): 1126</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 4</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6uElEQVR4nO3de1xUdf7H8fdwGwERwQsTXnEh0yQ1LUNdtbymSNS2arpmZa2ZWqwXiizBMlBr1TVKy0pay7XStLbMNC3NSPMeXspNUTNFtAgUEQzO7w/X+TWBLTIHBofXs8d5POSc75z5DM2u7z7f7znHYhiGIQAAAJjGw9UFAAAAuBsCFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvNydQFXkgctdVxdAlDp5ud/7+oSgMrnF1hpp3b274r5Rp5JlcCVCFgAAJiIqSFIBCwAAEzlYbG4ugRUAwRtAAAAk9HBAgDARHQuIBGwAAAwlQczhBABCwAAU9HBgkTAAgDAVCxyh0TAAgDAVHSwIPE9AAAAMB0dLAAATMQid0gELAAATMXUECQCFgAAprKwyB0iYAEAYCo6WJAIWAAAmIo1WJAI2gAAAKajgwUAgInoXEAiYAEAYCru5A6JgAUAgKnoYEEiYAEAYCoWuUMiYAEAYCo6WJD4HgAAAJiODhYAACbyEHOEIGABAGAq1mBBImABAGAq1t5AImABAGAqOliQCFgAAJiKNViQ6GQCAACYjg4WAAAmYooQEgELAABTMTUEie8BAACm8rA4t12ODRs2aODAgQoNDZXFYtGKFSscjhuGoaSkJIWGhsrX11c9evTQnj17HMYUFhZq3Lhxql+/vvz9/RUTE6OjR486jMnJydHw4cMVGBiowMBADR8+XD///HMFfjs1BwELAAATecji1HY58vPz1bZtW6WmppZ5fObMmZo1a5ZSU1O1ZcsW2Ww29e7dW6dPn7aPiYuL0/Lly7VkyRJt3LhRZ86cUXR0tIqLi+1jhg4dqp07d2rVqlVatWqVdu7cqeHDh1fsF1RDWAzDMFxdxJXiQUsdV5cAVLr5+d+7ugSg8vkFVtqpX63TwKnXj8w7WaHXWSwWLV++XLGxsZIudK9CQ0MVFxenRx99VNKFblVISIhmzJihUaNGKTc3Vw0aNNCiRYs0ePBgSdKxY8fUpEkTrVy5Un379tW+ffvUunVrbdq0SZ06dZIkbdq0SVFRUfrmm2/UsmVLpz6vu6KDBQCAG8rMzFRWVpb69Olj32e1WtW9e3elp6dLkrZt26bz5887jAkNDVWbNm3sY7788ksFBgbaw5Uk3XTTTQoMDLSPQWkscgcAwETOXkRYWFiowsJCh31Wq1VWq/WyzpOVlSVJCgkJcdgfEhKiw4cP28f4+PgoKCio1JiLr8/KylLDhg1Lnb9hw4b2MSiNDhYAACZydpF7SkqKfTH5xS0lJaXC9VgsjpHPMIxS+37rt2PKGl+e89RkBCwAAEzk7CL3hIQE5ebmOmwJCQmXXYfNZpOkUl2m7Oxse1fLZrOpqKhIOTk5vzvmxIkTpc5/8uTJUt0x/D8CFgAAJnK2g2W1WlWnTh2H7XKnByUpLCxMNptNa9asse8rKirS+vXr1blzZ0lShw4d5O3t7TDm+PHj2r17t31MVFSUcnNz9dVXX9nHbN68Wbm5ufYxKI01WAAAXKHOnDmj7777zv5zZmamdu7cqeDgYDVt2lRxcXFKTk5WRESEIiIilJycLD8/Pw0dOlSSFBgYqJEjR2rChAmqV6+egoODNXHiREVGRqpXr16SpFatWqlfv3564IEH9NJLL0mS/vrXvyo6OporCH8HAQsAABNV5dTQ1q1bdfPNN9t/Hj9+vCRpxIgRSktLU3x8vAoKCvTQQw8pJydHnTp10urVqxUQEGB/zezZs+Xl5aVBgwapoKBAPXv2VFpamjw9Pe1j3nzzTT388MP2qw1jYmIuee8tXMB9sC4D98FCTcB9sFAjVOJ9sP5Vt/QVd5fjrp+zTaoErkQHCwAAE3lwZR1EwAIAwFTEK0gELAAATEXAgsRtGgAAAExHBwsAABPRwYJEwAIAwFQ8PgYSAQsAAFMRryARsAAAMBWLmyERsAAAMBUzhJAI2gAAAKajgwUAgIksrMKCCFgAAJiKeAWJgAUAgKkIWJDcMGDl5+dr8eLFSk9PV1ZWliwWi0JCQtSlSxfddddd8vf3d3WJAAA35kHCgtxskfvevXt19dVXKz4+Xjk5OWratKkaN26snJwcTZo0SS1bttTevXtdXSYAwI1ZnPwH7sGtOlhjxoxRt27d9Prrr8vHx8fhWFFRke655x6NGTNGn376qYsqBAAANYFbBazNmzdr69atpcKVJPn4+Ojxxx/XjTfe6ILKAAA1BT0oSG42RRgUFKT//Oc/lzz+3XffKSgoqAorAgDUNBaLcxvcg1t1sB544AGNGDFCTzzxhHr37q2QkBBZLBZlZWVpzZo1Sk5OVlxcnKvLBAC4MTISJDcLWElJSfL19dWsWbMUHx9vf6K5YRiy2Wx67LHHFB8f7+IqAQDuzIOIBUkWwzAMVxdRGTIzM5WVlSVJstlsCgsLc/qcD1rqOH0OoLqbn/+9q0sAKp9fYKWdem2DRk69vufJH0yqBK7kVh2sXwsLCzMlVAEAAFwutw1YAAC4AgvVIRGwAAAwFfkKEgELAABTcTd2SAQsAABMxbMIIbnZjUYvWrVqlTZu3Gj/+YUXXlC7du00dOhQ5eTkuLAyAIC7szi5wT24ZcCaNGmS8vLyJEkZGRmaMGGC+vfvr4MHD2r8+PEurg4AALg7t5wizMzMVOvWrSVJy5YtU3R0tJKTk7V9+3b179/fxdUBANwZXShIbtrB8vHx0dmzZyVJn3zyifr06SNJCg4Otne2AACoDBYn/4F7cMuA1bVrV40fP15PP/20vvrqKw0YMECStH//fjVu3NjF1dUs4X/srIfef0vTf/hW8408tb1tgMPxdrcP1LhVy/XcyUzNN/LUuG2kw3G/oCANnvuskr7Zprn5WUo+vEeD/jFTteqUvqt+m/599eimdZp79oSeO5mpUcveqNTPBjjrzbeX6pYBtymyU1fdMfRubd2+w9UlwQQ87BmSmwas1NRUeXl5aenSpZo3b54aNbrw2IKPPvpI/fr1c3F1NYvV319Hd+3WkrETL3n8wBebtPyxxDKP1w21KTDUpmUTJ+upyCi9fs9oXduvl+5+NdVhXPs7YnTvopeVvvANTWvbRc926a0ti98x/fMAZln58RqlPDtLo0feqxX/WqQO7dvpgbFxOnY8y9WlwUkeTm5wD277LMLKwLMInTPfyNO82Lu0670PSx2r16ypnjm0W9PaddHRXRm/e57r74zVvW8s0CP+NpUUF8vD01PPHNqtfycmK/21RZVVfo3Bswirxp+H36vW17TU1MmP2ffdescg9erRXRMeHuPCymqISnwW4WZbE6de3ymL/w26A7cMy9u3b1dGxv//Jf3ee+8pNjZWjz/+uIqKilxYGczgG1hH5/JOq6S4WJLU9Pp2CmrcSEZJiR7f/rlmHNuvsSuX6arW17i4UqBsRefPa8++b9Q1qpPD/i43ddKOXV+7qCoAZnLLgDVq1Cjt379fknTw4EENGTJEfn5+eueddxQfH+/i6uAM/+Bg9X8yXp+/tNC+r36L5pKk6KQEfTTtWb0QPUhnc37WhPUfyS8oyEWVApeWk/OziouLVS+4nsP++vWCdfLHH11UFcxisVic2uAe3DJg7d+/X+3atZMkvfPOO+rWrZsWL16stLQ0LVu2rFznKCwsVF5ensNWLGZTXalWQIDGfPiOju/9Vh9MTbHvt3hc+Bp/9Mxz2vHu+zqyfaf+ee9oGYahDn+OdVG1wP/2279LDcPgL1g3wI1GIblpwDIMQyUlJZIu3Kbh4r2vmjRpolOnTpXrHCkpKQoMDHTYdojpRVex1q6tcaveVeGZM5p/+1CV/PKL/VjufxcFH9/7jX3fL0VFOnXwkIKbctUoqp+goLry9PTUqd90q378KUf1g4NdVBXMQsCC5KYBq2PHjpo2bZoWLVqk9evX22/TkJmZqZCQkHKdIyEhQbm5uQ5be/lUZtm4hFoBAXpk9QoVFxXpxZgh+qWw0OH4kW07df7cOYW0jLDv8/DyUr3mTfXjYRaLovrx8fbWta2u0RebvnLYn77pK7Vve52LqoJZmCKE5KZ3cp8zZ46GDRumFStWaPLkyQoPD5ckLV26VJ07dy7XOaxWq6xWq8M+T/7b4rJZ/f3VILyF/ef6Yc3VuG2k8n/KUc73R+UXFKTgpo1VN/QqSbKHpLysE8o7kS1r7dp6ePUK+fj56rW/PCDfOgHyrRMgSTp98pSMkhKdO31aG+a/poFTH1fO9z/op8NH1HvSI5Kk7e+sqNoPDJTTvX8ZqvgnEtWmdSu1vy5Sb727XMezsjTkzjtcXRqcxMOeIdWw2zScO3dOnp6e8vb2rtDruU3D5bu6e1eN/2xlqf1fpr2p1+8dragRQzUibX6p4x8kpeiDqSmXfL0kTW7eRj8ePiLpQsfq9pQkdRo+RN6+tXRo81a9HfeYw7QhyofbNFSdN99eqlfTFin71CldHf4HJUyI0w0drnd1WTVDJd6mYUejZk69vv0Ph02qBK5UowKWswhYqAkIWKgRKjFg7WzS3KnXt/v+kCl1wLXccoqwuLhYs2fP1ttvv60jR46UuvfVTz/95KLKAADujmVUkNx0kfvUqVM1a9YsDRo0SLm5uRo/frzuuOMOeXh4KCkpydXlAQDcGM8ihOSmAevNN9/UggULNHHiRHl5eemuu+7SK6+8oilTpmjTpk2uLg8A4Ma4ihCSmwasrKwsRUZGSpJq166t3NxcSVJ0dLQ+/LD0c/AAADBLVXWwfvnlFz3xxBMKCwuTr6+vWrRooaeeesp+H0jpwn0hk5KSFBoaKl9fX/Xo0UN79uxxOE9hYaHGjRun+vXry9/fXzExMTp69KhZv44ayy0DVuPGjXX8+HFJUnh4uFavXi1J2rJlS6lbLwAAcCWaMWOG5s+fr9TUVO3bt08zZ87Us88+q+eff94+ZubMmZo1a5ZSU1O1ZcsW2Ww29e7dW6dPn7aPiYuL0/Lly7VkyRJt3LhRZ86cUXR0tIr/+7xXVIxbBqzbb79da9eulSQ98sgjevLJJxUREaG7775b9913n4urAwC4s6qaIvzyyy912223acCAAWrevLnuvPNO9enTR1u3bpV0oXs1Z84cTZ48WXfccYfatGmj119/XWfPntXixYslSbm5uXr11Vf197//Xb169VL79u31xhtvKCMjQ5988kml/H5qCre8inD69On2P995551q3Lix0tPTFR4erpiYGBdWBgBwd84uoyosLFThb55YUdbNr7t27ar58+dr//79uvrqq7Vr1y5t3LhRc+bMkXTh6SVZWVnq06ePw3m6d++u9PR0jRo1Stu2bdP58+cdxoSGhqpNmzZKT09X3759nfswNZhbBqzfuummm3TTTTe5ugwAQA3g4WTCSklJ0dSpUx32JSYmlroK/tFHH1Vubq6uueYaeXp6qri4WM8884zuuusuSRfWI0sq9Yi4kJAQHT582D7Gx8dHQUFBpcZcfD0qxm0C1vvvv1/usXSxAACVxdkOVkJCgsaPH++wr6z1w2+99ZbeeOMNLV68WNdee6127typuLg4hYaGasSIEb+qx7EgwzD+51Rkecbg97lNwIqNjS3XOIvFwsI9AEClcTaYlDUdWJZJkybpscce05AhQyRJkZGROnz4sFJSUjRixAjZbDZJF7pUV111lf112dnZ9q6WzWZTUVGRcnJyHLpY2dnZ5X52L8rmNovcS0pKyrURrgAA7uDs2bPy8HD8a9zT09N+m4awsDDZbDatWbPGfryoqEjr16+3h6cOHTrI29vbYczx48e1e/duApaT3KaDBQBAdWCpotbFwIED9cwzz6hp06a69tprtWPHDs2aNct+tbzFYlFcXJySk5MVERGhiIgIJScny8/PT0OHDpUkBQYGauTIkZowYYLq1aun4OBgTZw4UZGRkerVq1fVfBA35TYdLElat26dWrdurby8vFLHcnNzde2112rDhg0uqAwAUFNU1W0ann/+ed1555166KGH1KpVK02cOFGjRo3S008/bR8THx+vuLg4PfTQQ+rYsaN++OEHrV69WgEBAfYxs2fPVmxsrAYNGqQuXbrIz89P//73v+Xp6Wnq76WmsRiGYbi6CLPExMTo5ptv1t/+9rcyj8+dO1effvqpli9fXqHzP2ip40x5wBVhfv73ri4BqHx+gZV26gOtI5x6/R/2/sekSuBKbtXB2rVrl/r163fJ43369NG2bduqsCIAQE3DswghudkarBMnTsjb2/uSx728vHTy5MkqrAgAUNOQkSC5WQerUaNGysjIuOTxr7/+2uFSVQAAgMrgVgGrf//+mjJlis6dO1fqWEFBgRITExUdHe2CygAANYWHxeLUBvfgVovcT5w4oeuvv16enp4aO3asWrZsKYvFon379umFF15QcXGxtm/fXuqxAeXFInfUBCxyR41QiYvcj1zX0qnXN/36W5MqgSu51RqskJAQpaena/To0UpISNDF7GixWNS3b1+9+OKLFQ5XAACUBwvVIblZwJKkZs2aaeXKlcrJydF3330nwzAUERFR6kGWAABUBvIVJDcMWBcFBQXphhtucHUZAIAahoAFyc0WuQMAAFQHbtvBAgDAFSwetLBAwAIAwFRMEUIiYAEAYCruZQWJgAUAgKnIV5AIWAAAmIr7YEHiKkIAAADT0cECAMBENLAgEbAAADAVU4SQCFgAAJiKfAWJgAUAgKnoYEFikTsAAIDp6GABAGAiC60LiIAFAICpmCKERMACAMBcPOwZImABAGAuOlgQAQsAAFMxRQiJqwgBAABMRwcLAAAzsQYLImABAGAupgghAhYAAKay0MGCCFgAAJiLDhZEwAIAwFR0sCBxFSEAAIDp6GABAGAmpgghAhYAAOZiihAiYAEAYCru5A6JgAUAgLnoYEEELAAAzEUHC+IqQgAAANPRwQIAwEQWWheQiwPW+++/X+6xMTExlVgJAAAmYYoQcnHAio2NLdc4i8Wi4uLiyi0GAAATcCd3SC4OWCUlJa58ewAAzEcHC2INFgAA5qKDBVWzgJWfn6/169fryJEjKioqcjj28MMPu6gqAACAy1NtrnXYsWOHwsPDddddd2ns2LGaNm2a4uLi9Pjjj2vOnDmuLg8AgHKxWCxObZfjhx9+0F/+8hfVq1dPfn5+ateunbZt22Y/bhiGkpKSFBoaKl9fX/Xo0UN79uxxOEdhYaHGjRun+vXry9/fXzExMTp69Kgpv4uarNoErL/97W8aOHCgfvrpJ/n6+mrTpk06fPiwOnTooOeee87V5QEAUD4eFue2csrJyVGXLl3k7e2tjz76SHv37tXf//531a1b1z5m5syZmjVrllJTU7VlyxbZbDb17t1bp0+fto+Ji4vT8uXLtWTJEm3cuFFnzpxRdHQ0F5c5yWIYhuHqIiSpbt262rx5s1q2bKm6devqyy+/VKtWrbR582aNGDFC33zzjatL1IOWOq4uAah08/O/d3UJQOXzC6y0U58bfotTr6+1aF25xj322GP64osv9Pnnn5d53DAMhYaGKi4uTo8++qikC92qkJAQzZgxQ6NGjVJubq4aNGigRYsWafDgwZKkY8eOqUmTJlq5cqX69u3r1GepyapNB8vb29veGg0JCdGRI0ckSYGBgfY/AwBQ3Tk7RVhYWKi8vDyHrbCwsNT7vP/+++rYsaP+/Oc/q2HDhmrfvr0WLFhgP56ZmamsrCz16dPHvs9qtap79+5KT0+XJG3btk3nz593GBMaGqo2bdrYx6Biqk3Aat++vbZu3SpJuvnmmzVlyhS9+eabiouLU2RkpIurAwCgnJycIkxJSVFgYKDDlpKSUuptDh48qHnz5ikiIkIff/yxHnzwQT388MP65z//KUnKysqSdKFp8WshISH2Y1lZWfLx8VFQUNAlx6Biqs1VhMnJyfY54aefflojRozQ6NGjFR4eroULF7q4OgAAqkZCQoLGjx/vsM9qtZYaV1JSoo4dOyo5OVnShUbFnj17NG/ePN199932cb9dOG8Yxv9cTF+eMfh91SZgdezY0f7nBg0aaOXKlS6sBgCAinE2mPhYrWUGqt+66qqr1Lp1a4d9rVq10rJlyyRJNptN0oUu1VVXXWUfk52dbe9q2Ww2FRUVKScnx6GLlZ2drc6dOzv1OWq6ajNFCACAW6iiqwi7dOmib7/91mHf/v371axZM0lSWFiYbDab1qxZYz9eVFSk9evX28NThw4d5O3t7TDm+PHj2r17NwHLSdWmgxUWFva7qf/gwYNVWA0AABVURVNrf/vb39S5c2clJydr0KBB+uqrr/Tyyy/r5Zdf/m8ZFsXFxSk5OVkRERGKiIhQcnKy/Pz8NHToUEkXLiQbOXKkJkyYoHr16ik4OFgTJ05UZGSkevXqVSWfw11Vm4AVFxfn8PP58+e1Y8cOrVq1SpMmTXJNUQAAXKaqetjzDTfcoOXLlyshIUFPPfWUwsLCNGfOHA0bNsw+Jj4+XgUFBXrooYeUk5OjTp06afXq1QoICLCPmT17try8vDRo0CAVFBSoZ8+eSktLk6enZ5V8DndVbe6DdSkvvPCCtm7dWi0WunMfLNQE3AcLNUIl3gfr/KhbnXq990sfmVQJXKnar8G69dZb7Qv2AAAArgTVZorwUpYuXarg4GBXlwEAQPlU0RQhqrdqE7Dat2/vsMjdMAxlZWXp5MmTevHFF11YGQAA5cf9oyBVo4B12223OXwpPTw81KBBA/Xo0UPXXHONCyv7f6xNQU3woH8TV5cAVLr5Rl7lnZwOFlSNAlZSUpKrSwAAwHl0sKBqtMjd09NT2dnZpfb/+OOPXCoKALhyWCzObXAL1SZgXepuEYWFhfLx8aniagAAACrO5VOEc+fOlXRhUeArr7yi2rVr248VFxdrw4YN1WYNFgAA/xNdKKgaBKzZs2dLutDBmj9/vsN0oI+Pj5o3b6758+e7qjwAAC6PR7WZHIILuTxgZWZmSpJuvvlmvfvuuw5P8wYA4IpDBwuqBgHrok8//dTVJQAA4DwCFlSNFrnfeeedmj59eqn9zz77rP785z+7oCIAACqAqwihahSw1q9frwEDBpTa369fP23YsMEFFQEAAFRMtZkiPHPmTJm3Y/D29lZeXiXecRcAADOxyB2qRh2sNm3a6K233iq1f8mSJWrdurULKgIAoAKYIoSqUQfrySef1J/+9CcdOHBAt9xyiyRp7dq1Wrx4sZYuXeri6gAAKCdCElSNAlZMTIxWrFih5ORkLV26VL6+vmrbtq3WrVunOnXquLo8AADKh4AFVaOAJUkDBgywL3T/+eef9eabbyouLk67du1ScXGxi6sDAKAcWIMFVaM1WBetW7dOf/nLXxQaGqrU1FT1799fW7dudXVZAAAA5VYtOlhHjx5VWlqaXnvtNeXn52vQoEE6f/68li1bxgJ3AMCVhSlCqBp0sPr376/WrVtr7969ev7553Xs2DE9//zzri4LAICK4SpCqBp0sFavXq2HH35Yo0ePVkREhKvLAQDAOYQkqBp0sD7//HOdPn1aHTt2VKdOnZSamqqTJ0+6uiwAACrE4uHh1Ab34PJ/k1FRUVqwYIGOHz+uUaNGacmSJWrUqJFKSkq0Zs0anT592tUlAgBQfkwRQtUgYF3k5+en++67Txs3blRGRoYmTJig6dOnq2HDhoqJiXF1eQAAAOVWbQLWr7Vs2VIzZ87U0aNH9a9//cvV5QAAUH50sKBqsMj993h6eio2NlaxsbGuLgUAgPIhJEHVPGABAHDFYaE6RMACAMBcdLAgAhYAAOYiYEHVdJE7AADAlYwOFgAAZqKDBRGwAAAwF4vcIQIWAADmooMFEbAAADAXAQsiYAEAYC6mCCGuIgQAADAdHSwAAMzEFCFEwAIAwFwELIiABQCAuQhYEAELAABzscgdYpE7AACA6ehgAQBgJqYIITpYAACYy2JxbquglJQUWSwWxcXF2fcZhqGkpCSFhobK19dXPXr00J49exxeV1hYqHHjxql+/fry9/dXTEyMjh49WuE6cAEBCwAAM1k8nNsqYMuWLXr55Zd13XXXOeyfOXOmZs2apdTUVG3ZskU2m029e/fW6dOn7WPi4uK0fPlyLVmyRBs3btSZM2cUHR2t4uJip34NNR0BCwAAM3lYnNsu05kzZzRs2DAtWLBAQUFB9v2GYWjOnDmaPHmy7rjjDrVp00avv/66zp49q8WLF0uScnNz9eqrr+rvf/+7evXqpfbt2+uNN95QRkaGPvnkE9N+JTURAQsAADNVcQdrzJgxGjBggHr16uWwPzMzU1lZWerTp499n9VqVffu3ZWeni5J2rZtm86fP+8wJjQ0VG3atLGPQcWwyB0AgGqksLBQhYWFDvusVqusVmupsUuWLNH27du1ZcuWUseysrIkSSEhIQ77Q0JCdPjwYfsYHx8fh87XxTEXX4+KoYMFAICZnFzknpKSosDAQIctJSWl1Nt8//33euSRR/TGG2+oVq1av1OO47SjYRil9v1Wecbg9xGwAAAwk4eHU1tCQoJyc3MdtoSEhFJvs23bNmVnZ6tDhw7y8vKSl5eX1q9fr7lz58rLy8veufptJyo7O9t+zGazqaioSDk5OZccg4ohYAEAYCYnO1hWq1V16tRx2MqaHuzZs6cyMjK0c+dO+9axY0cNGzZMO3fuVIsWLWSz2bRmzRr7a4qKirR+/Xp17txZktShQwd5e3s7jDl+/Lh2795tH4OKYQ0WAABmquCtFi5XQECA2rRp47DP399f9erVs++Pi4tTcnKyIiIiFBERoeTkZPn5+Wno0KGSpMDAQI0cOVITJkxQvXr1FBwcrIkTJyoyMrLUonlcHgIWAABmqkZrl+Lj41VQUKCHHnpIOTk56tSpk1avXq2AgAD7mNmzZ8vLy0uDBg1SQUGBevbsqbS0NHl6erqw8iufxTAMw9VFXDHO5rq6AqDSPejfxNUlAJVuvpFXaecuXjjVqdd73ptoUiVwJTpYAACYyYPlzSBgAQBgrmo0RQjXIWABAGCmKlrkjuqtRn0LTpw4oaeeesrVZQAA3FkVP4sQ1VONClhZWVmaOtW5xYcAAPyuKn4WIaont5oi/Prrr3/3+LfffltFlQAAgJrMrQJWu3btZLFYVNadJy7u59lKAIBKxd8zkJsFrHr16mnGjBnq2bNnmcf37NmjgQMHVnFVAIAahWk+yM0CVocOHXTs2DE1a9aszOM///xzmd0tAABMw0J1yM0C1qhRo5Sfn3/J402bNtXChQursCIAQI3DFCHkZgHr9ttv/93jQUFBGjFiRBVVAwCokZgihGrYbRoAAACqglt1sAAAcDnWYEEELAAAzMUUIUTAAgDAXCxyhwhYAACYiw4W5KaL3FetWqWNGzfaf37hhRfUrl07DR06VDk5OS6sDADg9njYM+SmAWvSpEnKy8uTJGVkZGjChAnq37+/Dh48qPHjx7u4OgAA4O7ccoowMzNTrVu3liQtW7ZM0dHRSk5O1vbt29W/f38XVwcAcGtMEUJu2sHy8fHR2bNnJUmffPKJ+vTpI0kKDg62d7YAAKgUFotzG9yCWwasrl27avz48Xr66af11VdfacCAAZKk/fv3q3Hjxi6uDpfjpVfT1LL9jXrm2VmuLgW4pPA/dtZD77+l6T98q/lGntreNsDheLvbB2rcquV67mSm5ht5atw20uG4X1CQBs99VknfbNPc/CwlH96jQf+YqVp16tjHXN29q+YbeWVuzTpeXyWfE+Xk4eHcBrfglv8mU1NT5eXlpaVLl2revHlq1KiRJOmjjz5Sv379XFwdyuvrPXv11rvL1TIi3NWlAL/L6u+vo7t2a8nYiZc8fuCLTVr+WGKZx+uG2hQYatOyiZP1VGSUXr9ntK7t10t3v5pqH3MgfbPibeEO28YFaTqVeUiHt26vlM+FCqKDBbnpGqymTZvqgw8+KLV/9uzZLqgGFZF/9qwmPf6kpj05WfNeec3V5QC/a8+qNdqzas0lj29+Y4kkqV6zpmUeP7Znn16+c7j951MHM/Xe5Kd07xsL5OHpqZLiYhWfP6+8E9n2MR5eXroupr8+S33ZpE8B07AGC3LTDtb27duVkZFh//m9995TbGysHn/8cRUVFbmwMpTXUykz1f2PXdT5phtdXQrgEr6BdXQu77RKiovLPN42pr9q16+nL9PerOLKAJSHWwasUaNGaf/+/ZKkgwcPasiQIfLz89M777yj+Ph4F1eH/+XDVau195tvNWHcGFeXAriEf3Cw+j8Zr89fWnjJMV1G3q29H69VztEfqrAylAtThJCbBqz9+/erXbt2kqR33nlH3bp10+LFi5WWlqZly5aV6xyFhYXKy8tz2AoLCyuxakjS8awTeubZWXp22lRZrVZXlwNUuVoBARrz4Ts6vvdbfTA1pcwxdRuFqnXfnvri1X9WcXUoFxa5Q24asAzDUElJiaQLt2m4eO+rJk2a6NSpU+U6R0pKigIDAx22lOe4kq2y7dm3Tz/+9JPuGDZCrTtGqXXHKH21bbsW/estte4YpeJLTJcA7sBau7bGrXpXhWfOaP7tQ1Xyyy9ljut871905seftOv9lVVcIcqFDhbkpovcO3bsqGnTpqlXr15av3695s2bJ+nCDUhDQkLKdY6EhIRSd323Fp8zvVY4uunGG/Tvd/7lsC8h8Sm1CGuuB+65W56eni6qDKhctQIC9PDHy/VLYaFejBmiX36nYx517zBt/ue/LhnA4GIscofcNGDNmTNHw4YN04oVKzR58mSFh1+4zH/p0qXq3Llzuc5htVpLT1GdNcwuFb9R299fV4f/wWGfn6+v6gYGltoPVBdWf381CG9h/7l+WHM1bhup/J9ylPP9UfkFBSm4aWPVDb1KkhTSMkKSlJd1QnknsmWtXVsPr14hHz9fvfaXB+RbJ0C+dQIkSadPnpLx3468JLW8pbsatAhjerA6owsFuWnAuu666xyuIrzo2WefpQMCwHTNOrbX+M/+f7ruz7MvrJ36Mu1NvX7vaLWNuVUj0ubbjz/wVpok6YOkFH0wNUXNOrRTi5tukCRNO7DL4dyTm7fRj4eP2H/uMvJuHfhik7K+2V9ZHweACSyGYdCWKa+zua6uAKh0D/o3cXUJQKWbb1TeY9OKP1vi1Os9ewwxqRK4klt2sIqLizV79my9/fbbOnLkSKl7X/30008uqgwA4PY8mCKEm15FOHXqVM2aNUuDBg1Sbm6uxo8frzvuuEMeHh5KSkpydXkAAHdm8XBug1twy3+Tb775phYsWKCJEyfKy8tLd911l1555RVNmTJFmzZtcnV5AAB3xm0aIDcNWFlZWYqMvPC0+tq1ays398LaqejoaH344YeuLA0A4O7oYEFuGrAaN26s48ePS5LCw8O1evVqSdKWLVu4OzgAAKh0bhmwbr/9dq1du1aS9Mgjj+jJJ59URESE7r77bt13330urg4A4M4sFotTG9yDW15FOH36dPuf77zzTjVu3Fjp6ekKDw9XTEyMCysDALg9pvkgNw1Yv3XTTTfppptucnUZAICagIAFuVHAev/998s9li4WAKDScB8syI0CVmxsbLnGWSwWFRcXV24xAICaiw4W5EYBq+RXD0MFAABwJbcJWAAAVAtcCQi52W0a1q1bp9atWysvr/RDPHNzc3Xttddqw4YNLqgMAFBjcKNRyM0C1pw5c/TAAw+oTp06pY4FBgZq1KhRmj17tgsqAwDUGFX0qJyUlBTdcMMNCggIUMOGDRUbG6tvv/3WYYxhGEpKSlJoaKh8fX3Vo0cP7dmzx2FMYWGhxo0bp/r168vf318xMTE6evSoKb+KmsytAtauXbvUr1+/Sx7v06ePtm3bVoUVAQBqnCrqYK1fv15jxozRpk2btGbNGv3yyy/q06eP8vPz7WNmzpypWbNmKTU1VVu2bJHNZlPv3r11+vRp+5i4uDgtX75cS5Ys0caNG3XmzBlFR0dzQZiTLIZhGK4uwiy1atXS7t27FR4eXubx7777TpGRkSooKKjYG5zNdaI64MrwoH8TV5cAVLr5RumlJGYp2bXWqdd7tO1ZodedPHlSDRs21Pr169WtWzcZhqHQ0FDFxcXp0UcflXShWxUSEqIZM2Zo1KhRys3NVYMGDbRo0SINHjxYknTs2DE1adJEK1euVN++fZ36LDWZW3WwGjVqpIyMjEse//rrr3XVVVdVYUUAAFyewsJC5eXlOWyFhYX/83W5uReaAMHBwZKkzMxMZWVlqU+fPvYxVqtV3bt3V3p6uiRp27ZtOn/+vMOY0NBQtWnTxj4GFeNWAat///6aMmWKzp07V+pYQUGBEhMTFR0d7YLKAAA1hpNThCkpKQoMDHTYUlJSfvctDcPQ+PHj1bVrV7Vp00aSlJWVJUkKCQlxGBsSEmI/lpWVJR8fHwUFBV1yDCrGrW7T8MQTT+jdd9/V1VdfrbFjx6ply5ayWCzat2+fXnjhBRUXF2vy5MmuLhMA4M6cvE1DQkKCxo8f77DParX+7mvGjh2rr7/+Whs3biyjHMd6DMP4nw+VLs8Y/D63ClghISFKT0/X6NGjlZCQoIvLyywWi/r27asXX3yxVJIHAMBUTt5qwWq1/s9A9Wvjxo3T+++/rw0bNqhx48b2/TabTdKFLtWvl8dkZ2fb/y602WwqKipSTk6OQxcrOztbnTt3dupz1HRuNUUoSc2aNdPKlSt16tQpbd68WZs2bdKpU6e0cuVKNW/e3NXlAQDcXRXdpsEwDI0dO1bvvvuu1q1bp7CwMIfjYWFhstlsWrNmjX1fUVGR1q9fbw9PHTp0kLe3t8OY48ePa/fu3QQsJ7lVB+vXgoKCdMMNN7i6DABATVNFNwsdM2aMFi9erPfee08BAQH2NVOBgYHy9fWVxWJRXFyckpOTFRERoYiICCUnJ8vPz09Dhw61jx05cqQmTJigevXqKTg4WBMnTlRkZKR69epVJZ/DXbltwAIAwJ3NmzdPktSjRw+H/QsXLtQ999wjSYqPj1dBQYEeeugh5eTkqFOnTlq9erUCAgLs42fPni0vLy8NGjRIBQUF6tmzp9LS0uTp6VlVH8UtudV9sCod98FCDcB9sFATVOp9sL750qnXe1wTZVIlcCU6WAAAmIir7yARsAAAMBcPbIYIWAAAmIsOFkTAAgDAXHSwIDe8DxYAAICr0cECAMBMTBFCBCwAAMzlweQQCFgAAJiLDhZEwAIAwFwscodY5A4AAGA6OlgAAJiJKUKIgAUAgMkIWCBgAQBgLjpYEAELAABzEbAgAhYAACYjYIGrCAEAAExHBwsAADMxRQgRsAAAMBf5CiJgAQBgMhIWCFgAAJiLKUKIgAUAgLkIWBBXEQIAAJiODhYAAKaigwUCFgAA5mKKECJgAQBgMgIWCFgAAJiLDhZEwAIAwFwELIirCAEAAExHBwsAAFPRwQIBCwAAU1mYIoQIWAAAmIuABRGwAAAwGQELBCwAAMxFBwviKkIAAADT0cECAMBMdLAgAhYAACYjYIGABQCAuehgQQQsAADMRb6CCFgAAJiMhAWuIgQAADAdHSwAAMzEGiyIgAUAgLkIWBABCwAAkxGwQMACAMBcdLAgFrkDAGAui8W57TK9+OKLCgsLU61atdShQwd9/vnnlfChcLkIWAAAXKHeeustxcXFafLkydqxY4f++Mc/6tZbb9WRI0dcXVqNZzEMw3B1EVeMs7murgCodA/6N3F1CUClm2/kVd7J83927vX+dcs9tFOnTrr++us1b948+75WrVopNjZWKSkpztUBp7AGCwAAMzm5BquwsFCFhYUO+6xWq6xWq8O+oqIibdu2TY899pjD/j59+ig9Pd2pGuA8Atbl8At0dQU1SmFhoVJSUpSQkFDq/1hQeSr1v+xRCt9zN+Tk3xUpSUmaOnWqw77ExEQlJSU57Dt16pSKi4sVEhLisD8kJERZWVlO1QDnMUWIaisvL0+BgYHKzc1VnTp1XF0OUCn4nuO3ytvBOnbsmBo1aqT09HRFRUXZ9z/zzDNatGiRvvnmmyqpF2WjgwUAQDVSVpgqS/369eXp6VmqW5WdnV2qq4Wqx1WEAABcgXx8fNShQwetWbPGYf+aNWvUuXNnF1WFi+hgAQBwhRo/fryGDx+ujh07KioqSi+//LKOHDmiBx980NWl1XgELFRbVqtViYmJLPyFW+N7DmcMHjxYP/74o5566ikdP35cbdq00cqVK9WsWTNXl1bjscgdAADAZKzBAgAAMBkBCwAAwGQELAAAAJMRsFAlLBaLVqxY4eoygErF9xzARQQsOC0rK0vjxo1TixYtZLVa1aRJEw0cOFBr1651dWmSJMMwlJSUpNDQUPn6+qpHjx7as2ePq8vCFaa6f8/fffdd9e3bV/Xr15fFYtHOnTtdXRJQoxGw4JRDhw6pQ4cOWrdunWbOnKmMjAytWrVKN998s8aMGePq8iRJM2fO1KxZs5SamqotW7bIZrOpd+/eOn36tKtLwxXiSvie5+fnq0uXLpo+fbqrSwEgSQbghFtvvdVo1KiRcebMmVLHcnJy7H+WZCxfvtz+c3x8vBEREWH4+voaYWFhxhNPPGEUFRXZj+/cudPo0aOHUbt2bSMgIMC4/vrrjS1bthiGYRiHDh0yoqOjjbp16xp+fn5G69atjQ8//LDM+kpKSgybzWZMnz7dvu/cuXNGYGCgMX/+fCc/PWqK6v49/7XMzExDkrFjx44Kf14AzuNGo6iwn376SatWrdIzzzwjf3//Usfr1q17ydcGBAQoLS1NoaGhysjI0AMPPKCAgADFx8dLkoYNG6b27dtr3rx58vT01M6dO+Xt7S1JGjNmjIqKirRhwwb5+/tr7969ql27dpnvk5mZqaysLPXp08e+z2q1qnv37kpPT9eoUaOc+A2gJrgSvucAqh8CFirsu+++k2EYuuaaay77tU888YT9z82bN9eECRP01ltv2f/iOXLkiCZNmmQ/d0REhH38kSNH9Kc//UmRkZGSpBYtWlzyfS4+BPW3Dz4NCQnR4cOHL7tu1DxXwvccQPXDGixUmPHfhwBYLJbLfu3SpUvVtWtX2Ww21a5dW08++aSOHDliPz5+/Hjdf//96tWrl6ZPn64DBw7Yjz388MOaNm2aunTposTERH399df/8/1+W6NhGBWqGzXPlfQ9B1B9ELBQYREREbJYLNq3b99lvW7Tpk0aMmSIbr31Vn3wwQfasWOHJk+erKKiIvuYpKQk7dmzRwMGDNC6devUunVrLV++XJJ0//336+DBgxo+fLgyMjLUsWNHPf/882W+l81mk/T/nayLsrOzS3W1gLJcCd9zANWQS1eA4YrXr1+/y178+9xzzxktWrRwGDty5EgjMDDwku8zZMgQY+DAgWUee+yxx4zIyMgyj11c5D5jxgz7vsLCQha547JU9+/5r7HIHage6GDBKS+++KKKi4t14403atmyZfrPf/6jffv2ae7cuYqKiirzNeHh4Tpy5IiWLFmiAwcOaO7cufb/apekgoICjR07Vp999pkOHz6sL774Qlu2bFGrVq0kSXFxcfr444+VmZmp7du3a926dfZjv2WxWBQXF6fk5GQtX75cu3fv1j333CM/Pz8NHTrU/F8I3FJ1/55LFxbj79y5U3v37pUkffvtt9q5c2ep7i2AKuLqhIcr37Fjx4wxY8YYzZo1M3x8fIxGjRoZMTExxqeffmofo99cvj5p0iSjXr16Ru3atY3Bgwcbs2fPtv+XfWFhoTFkyBCjSZMmho+PjxEaGmqMHTvWKCgoMAzDMMaOHWv84Q9/MKxWq9GgQQNj+PDhxqlTpy5ZX0lJiZGYmGjYbDbDarUa3bp1MzIyMirjVwE3Vt2/5wsXLjQkldoSExMr4bcB4H+xGMZ/V3ACAADAFEwRAgAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFhADZaUlKR27drZf77nnnsUGxtb5XUcOnRIFotFO3furPL3BoDKQMACqqF77rlHFotFFotF3t7eatGihSZOnKj8/PxKfd9//OMfSktLK9dYQhEAXJqXqwsAULZ+/fpp4cKFOn/+vD7//HPdf//9ys/P17x58xzGnT9/Xt7e3qa8Z2BgoCnnAYCajg4WUE1ZrVbZbDY1adJEQ4cO1bBhw7RixQr7tN5rr72mFi1ayGq1yjAM5ebm6q9//asaNmyoOnXq6JZbbtGuXbsczjl9+nSFhIQoICBAI0eO1Llz5xyO/3aKsKSkRDNmzFB4eLisVquaNm2qZ555RpIUFhYmSWrfvr0sFot69Ohhf93ChQvVqlUr1apVS9dcc41efPFFh/f56quv1L59e9WqVUsdO3bUjh07TPzNAYDr0cECrhC+vr46f/68JOm7777T22+/rWXLlsnT01OSNGDAAAUHB2vlypUKDAzUSy+9pJ49e2r//v0KDg7W22+/rcTERL3wwgv64x//qEWLFmnu3Llq0aLFJd8zISFBCxYs0OzZs9W1a1cdP35c33zzjaQLIenGG2/UJ598omuvvVY+Pj6SpAULFigxMVGpqalq3769duzYoQceeED+/v4aMWKE8vPzFR0drVtuuUVvvPGGMjMz9cgjj1Tybw8AqpiLHzYNoAwjRowwbrvtNvvPmzdvNurVq2cMGjTISExMNLy9vY3s7Gz78bVr1xp16tQxzp0753CeP/zhD8ZLL71kGIZhREVFGQ8++KDD8U6dOhlt27Yt833z8vIMq9VqLFiwoMwaMzMzDUnGjh07HPY3adLEWLx4scO+p59+2oiKijIMwzBeeuklIzg42MjPz7cfnzdvXpnnAoArFVOEQDX1wQcfqHbt2qpVq5aioqLUrVs3Pf/885KkZs2aqUGDBvax27Zt05kzZ1SvXj3Vrl3bvmVmZurAgQOSpH379ikqKsrhPX7786/t27dPhYWF6tmzZ7lrPnnypL7//nuNHDnSoY5p06Y51NG2bVv5+fmVqw4AuBIxRQhUUzfffLPmzZsnb29vhYaGOixk9/f3dxhbUlKiq666Sp999lmp89StW7dC7+/r63vZrykpKZF0YZqwU6dODscuTmUahlGhegDgSkLAAqopf39/hYeHl2vs9ddfr6ysLHl5eal58+ZljmnVqpU2bdqku+++275v06ZNlzxnRESEfH19tXbtWt1///2ljl9cc1VcXGzfFxISokaNGungwYMaNmxYmedt3bq1Fi1apIKCAnuI+706AOBKxBQh4AZ69eqlqKgoxcbG6uOPP9ahQ4eUnp6uJ554Qlu3bpUkPfLII3rttdf02muvaf/+/UpMTNSePXsuec5atWrp0UcfVXx8vP75z3/qwIED2rRpk1599VVJUsOGDeXr66tVq1bpxIkTys3NlXTh5qUpKSn6xz/+of379ysjI0MLFy7UrFmzJElDhw6Vh4eHRo4cqb1792rlypV67rnnKvk3BABVi4AFuAGLxaKVK1eqW7duuu+++3T11VdryJAhOnTokEJCQiRJgwcP1pQpU/Too4+qQ4cOOnz4sEaPHv27533yySc1YcIETZkyRa1atdLgwYOVnZ0tSfLy8tLcuXP10ksvKTQ0VLfddpsk6f7779crr7yitLQ0RUZGqnv37kpLS7Pf1qF27dr697//rb1796p9+/aaPHmyZsyYUYm/HQCoehaDBREAAACmooMFAABgMgIWAACAyQhYAAAAJvs/sb55adD8qe0AAAAASUVORK5CYII=" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="container"> | |
| <div class="header">MODEL: FEEDFORWARD_k4</div> | |
| <div style="display: flex;"> | |
| <div class="section"> | |
| <h2 class='mod_sum'>Model Architecture</h2> | |
| <pre>Model: "FEEDFORWARD_k4" | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ | |
| ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ | |
| │ dense_10 (Dense) │ (None, 256) │ 39,403,264 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_8 (Dropout) │ (None, 256) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_11 (Dense) │ (None, 128) │ 32,896 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_9 (Dropout) │ (None, 128) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_12 (Dense) │ (None, 64) │ 8,256 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dropout_10 (Dropout) │ (None, 64) │ 0 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ dense_13 (Dense) │ (None, 1) │ 65 │ | |
| └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ | |
| Total params: 118,333,445 (451.41 MB) | |
| Trainable params: 39,444,481 (150.47 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 78,888,964 (300.94 MB) | |
| </pre> | |
| </div> | |
| <div class="section learning-curve"> | |
| <h2>Learning Curve</h2> | |
| <img src="data:image/png;base64,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" alt="Learning Curve"> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div class="section class_dist"> | |
| <h2>Class Distribution</h2> | |
| <pre> Count Percentage | |
| class | |
| 1 5655 50.13 | |
| 0 5626 49.87</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 11281</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9921 1.0000 0.9960 1126 | |
| Class 1 1.0000 0.9920 0.9960 1131 | |
| accuracy 0.9960 2257 | |
| macro avg 0.9960 0.9960 0.9960 2257 | |
| weighted avg 0.9960 0.9960 0.9960 2257 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 1122</li> | |
| <li>True Negatives (TN): 1126</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 9</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,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" alt="Confusion Matrix"> | |
| </div> | |
| </div> | |
| </div> | |
| </body> | |
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