| <html> | |
| <head> | |
| <title>GENE_FAMILY: HSF</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=HSF' target='_blank'>HSF</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 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 4574 50.16 | |
| 0 4545 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9119</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 0.9472 0.9712 909 | |
| Class 1 0.9500 0.9967 0.9728 915 | |
| accuracy 0.9720 1824 | |
| macro avg 0.9733 0.9720 0.9720 1824 | |
| weighted avg 0.9732 0.9720 0.9720 1824 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 912</li> | |
| <li>True Negatives (TN): 861</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 48</li> | |
| <li>False Negatives (FN): 3</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) │ 2,279,936 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 6,969,605 (26.59 MB) | |
| Trainable params: 2,323,201 (8.86 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 4,646,404 (17.72 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 4574 50.16 | |
| 0 4545 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9119</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9934 0.9989 0.9962 909 | |
| Class 1 0.9989 0.9934 0.9962 915 | |
| accuracy 0.9962 1824 | |
| macro avg 0.9962 0.9962 0.9962 1824 | |
| weighted avg 0.9962 0.9962 0.9962 1824 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 909</li> | |
| <li>True Negatives (TN): 908</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 1</li> | |
| <li>False Negatives (FN): 6</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3LElEQVR4nO3de1hVZd7/8c/mtAU5eGaLWmGSSTppmqY5HvKQKRnW5HGamsyhUpNQMTxXBmqNmIe0rLSxx5+VpjmTlaalGZdlHgoPI6momSI6MqCI4MD6/eG0px1aCAsWLt6vudZ1yVr32vu7aT+PH7/3ve/tMAzDEAAAAEzjZXUBAAAAdkPAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZD5WF3AtedwRbHUJQLlbmPuD1SUA5S8gpNweuqx/Vyw0ckyqBFYiYAEAYCKmhiARsAAAMJWXw2F1CagECNoAAAAmo4MFAICJ6FxAImABAGAqL2YIIQIWAACmooMFiYAFAICpWOQOiYAFAICp6GBB4n0AAABgOjpYAACYiEXukAhYAACYiqkhSAQsAABM5WCRO0TAAgDAVHSwIBGwAAAwFWuwIBG0AQAATEcHCwAAE9G5gETAAgDAVOzkDomABQCAqehgQSJgAQBgKha5QyJgAQBgKjpYkHgfAAAAmI4OFgAAJvISc4QgYAEAYCrWYEEiYAEAYCrW3kAiYAEAYCo6WJAIWAAAmIo1WJDoZAIAAJiODhYAACZiihASAQsAAFMxNQSJgAUAgKnoYEEiYAEAYCoWuUOikwkAgKm8HGU7Suo///mPJk6cqPDwcPn7+6tx48Z67rnnVFRU5B5jGIamTp2qsLAw+fv7q0uXLtqzZ4/H4+Tn52vkyJGqU6eOqlevrr59++rYsWNm/TqqLAIWAADXoBkzZmjhwoWaN2+e9u3bp5kzZ+rFF1/U3Llz3WNmzpypWbNmad68edq2bZtcLpd69Oihs2fPusfExsZq1apVWr58ubZs2aJz584pKipKhYWFVrws23AYhmFYXcS14nFHsNUlAOVuYe4PVpcAlL+AkHJ76DeD65bp/kdzTpVoXFRUlEJDQ/XGG2+4zz3wwAMKCAjQ0qVLZRiGwsLCFBsbq3Hjxkm61K0KDQ3VjBkzFBMTo+zsbNWtW1dLly7VgAEDJEnHjx9Xo0aNtHbtWt19991lei1VGR0sAABMVFFThB07dtSGDRuUlpYmSfr222+1ZcsW9e7dW5KUnp6ujIwM9ezZ032P0+lU586dlZKSIknavn27Ll686DEmLCxMzZs3d49B6bDIHQAAE5V1kXt+fr7y8/M9zjmdTjmdTo9z48aNU3Z2tm6++WZ5e3ursLBQL7zwggYNGiRJysjIkCSFhoZ63BcaGqojR464x/j5+almzZrFxvx0P0qHDhYAACYqawcrKSlJISEhHkdSUlKx53nnnXf09ttva9myZdqxY4feeustvfTSS3rrrbc8xjkcnoHPMIxi536pJGPw6+hgAQBQiSQkJCguLs7j3C+7V5I0duxYPfPMMxo4cKAkqUWLFjpy5IiSkpL08MMPy+VySbrUpapfv777vszMTHdXy+VyqaCgQFlZWR5drMzMTHXo0MH011aV0MECAMBEXmU8nE6ngoODPY7LBazz58/Ly8vzr3Fvb2/3Ng3h4eFyuVxav369+3pBQYE2bdrkDk+tW7eWr6+vx5gTJ05o9+7dBKwyooMFAICJKmpi7d5779ULL7yg6667Trfccot27typWbNm6dFHH71Uh8Oh2NhYJSYmKiIiQhEREUpMTFRAQIAGDx4sSQoJCdHQoUM1evRo1a5dW7Vq1dKYMWPUokULde/evYJeiT0RsAAAMJFXBa1dmjt3riZNmqQnn3xSmZmZCgsLU0xMjCZPnuweEx8fr7y8PD355JPKyspSu3bttG7dOgUFBbnHJCcny8fHR/3791deXp66deumJUuWyNvbu0Jeh12xD9ZVYB8sVAXsg4UqoRz3wXq3ZuhvD/oV/bNOmlQJrEQHCwAAE/HZO0gscgcAADAdHSwAAExEBwsSAQsAAFOxQSckAhYAAKYiXkEiYAEAYCoWN0MiYAEAYCpmCCERtAEAAExHBwsAABM5WIUFEbAAADAV8QoSAQsAAFMRsCDZMGDl5uZq2bJlSklJUUZGhhwOh0JDQ3XnnXdq0KBBql69utUlAgBszIuEBdlskfvevXt10003KT4+XllZWbruuuvUsGFDZWVlaezYsWratKn27t1rdZkAABtzlPF/sAdbdbCGDx+uTp066a233pKfn5/HtYKCAj3yyCMaPny4PvvsM4sqBAAAVYGtAtZXX32lb775pli4kiQ/Pz+NHz9ebdu2taAyAEBVQQ8Kks2mCGvWrKnvv//+itcPHDigmjVrVmBFAICqxuEo2wF7sFUHa9iwYXr44Yc1ceJE9ejRQ6GhoXI4HMrIyND69euVmJio2NhYq8sEANgYGQmSzQLW1KlT5e/vr1mzZik+Pt79jeaGYcjlcumZZ55RfHy8xVUCAOzMi4gFSQ7DMAyriygP6enpysjIkCS5XC6Fh4eX+TEfdwSX+TGAym5h7g9WlwCUv4CQcnvoDXUblOn+bqd+NKkSWMlWHayfCw8PNyVUAQAAXC3bBiwAAKzAQnVIBCwAAExFvoJEwAIAwFTsxg6JgAUAgKn4LkJINtto9Ccff/yxtmzZ4v55/vz5atmypQYPHqysrCwLKwMA2J2jjAfswZYBa+zYscrJyZEkpaamavTo0erdu7cOHTqkuLg4i6sDAAB2Z8spwvT0dEVGRkqSVq5cqaioKCUmJmrHjh3q3bu3xdUBAOyMLhQkm3aw/Pz8dP78eUnSp59+qp49e0qSatWq5e5sAQBQHhxl/B/swZYBq2PHjoqLi9Pzzz+vr7/+Wn369JEkpaWlqWHDhhZXB2dgoB5Mnq4XDu/WnPMnNfbL9bq+zW0eY6KmJGj6j/s15/xJxX32oepH3uxxPTi0nh7522uaceJ7vXzuhMZv36zbHrivIl8GUCbbtu/Q46Pi1LFHbzVt1Vaffva51SXBJHzZMySbBqx58+bJx8dHK1as0IIFC9SgwaWvLfjoo4/Uq1cvi6vDQ6/PVbMeXbX4ob/o+RbttW/dRsV++oFqhNWXJPWMj1W3uOFaPmKMpt/eRdkZmRq1/gM5AwPdj/Hnpa/J1TRCC/oO1PMt2mvn+3/XY+8sUaOWv7PqZQFX5XzeBTW9KUKTnxlrdSkwmVcZD9iDbb+LsDzwXYRl51utmmafPa4F9w3S7rWfuM9P2LlFqf/4RGsmPa8Zx9O0YfYrWjdztiTJx89PM08e0KpxU/TFa4slSbPPHtf/eyJOX7293P0YL50+rPfjJynlzaUV+prshu8irHhNW7XV/Fkz1b1rF6tLqTrK8bsIv3I1KtP97TL4v0E7sGVY3rFjh1JTU90/f/DBB4qOjtb48eNVUFBgYWXw8vGRt4+PLl644HH+Yt4FNel4h+qE36CQ+i7tW7fRfe0/BQX6ftOXatyhnfvcwS1b1XrA/QqoWVMOh0NtBjwgH6ef0j7fIgAArGbLgBUTE6O0tDRJ0qFDhzRw4EAFBATovffeU3x8vMXVVW35587pYMpX6jMpXiH1XXJ4eantkAG6oV0bBdd3KdhVT5KUczLT476ck5kKdoW6f1404BF5+/ho1pkjmpd/WkNena1X+w3R6UPpFfp6AOCXHA5HmQ7Ygy0DVlpamlq2bClJeu+999SpUyctW7ZMS5Ys0cqVK0v0GPn5+crJyfE4CsVsqhkWP/QXyeHQjONpmpd/Wnc99bi2LXtPRmGhe8wvZ64dDof0s3P3TZukgJo1lNztXiW26axPZ83XsPfeUljzyAp7HQBwOWw0CsmmAcswDBUVFUm6tE3DT3tfNWrUSKdPny7RYyQlJSkkJMTj2CmmF81w+lC6ZnXpraequ5TQqJmmt+sqb18fnU4/opyMS52rkJ91qyQpqF5dd1erTuNwdR0Zo789+qT2b9ykH7/brQ+fm64j3+xUl+HDKvz1AMDPEbAg2TRgtWnTRtOmTdPSpUu1adMm9zYN6enpCg0N/Y27L0lISFB2drbH0Up+5Vl2lVNw/rxyMk4qoEYNRd7dTd9+8KFOpx9W9okMNevR1T3O29dXEZ3v1KGUryRJfgH+kiTjvyH6J0WFRXJ42fItDeAawhQhJJvu5D579mwNGTJEq1ev1oQJE9SkSRNJ0ooVK9ShQ4cSPYbT6ZTT6fQ4582/LUwR2bOb5HDo5P7vVa9JY93/4vM6uf+AUha/LUnaMPsV9Ro/WpnfH1Tm9wfVa/wYFZzP09fL3pMkZfwzTZnfH9SQV1/WyjETde5fZ9Qyuo+a9eiqV6L6W/nSgBLLPX9eR3845v752I/HtW9/mkKCgxVW32VhZSgrvuwZUhXbpuHChQvy9vaWr69vqe5nmwZztH6wn6KTpqpGwzCdP5OlnSvXaPWE53ThZ7vsR01J0O9j/qyAmjWU/tU3Wj58tI7v2ee+Xq/JjYqePlVNOraXM7C6Th04pPUvzfXYtgGlwzYNFeOrb7brT8OeKHa+3719NP25KRZUVMWU4zYNOxtcX6b7W/14xKRKYKUqFbDKioCFqoCAhSqhHAPWrkY3lOn+lj8cNqUOWMuWU4SFhYVKTk7Wu+++q6NHjxbb++rMmTMWVQYAsDuWUUGy6SL3Z599VrNmzVL//v2VnZ2tuLg43X///fLy8tLUqVOtLg8AYGN8FyEkmwas//u//9OiRYs0ZswY+fj4aNCgQXr99dc1efJkbd261eryAAA2xqcIIdk0YGVkZKhFixaSpMDAQGVnZ0uSoqKi9OGHH1pZGgDA5uhgQbJpwGrYsKFOnDghSWrSpInWrVsnSdq2bVuxrRcAAADMZsuA1a9fP23YsEGSNGrUKE2aNEkRERH605/+pEcffdTi6gAAdsYUIaQqsk3D1q1blZKSoiZNmqhv376lfhy2aUBVwDYNqBLKcZuGvTc2LtP9kQcPmVQJrGTLbRp+6Y477tAdd9xhdRkAgCrAiy4UZKOAtWbNmhKPLUsXCwCAX0O+gmSjgBUdHV2icQ6HQ4WFheVbDACgymIdFSQbBayioiKrSwAAAJBko4AFAEBl4LDl5/NxtWz1Nti4caMiIyOVk5NT7Fp2drZuueUWbd682YLKAABVBds0QLJZwJo9e7aGDRum4ODi2ymEhIQoJiZGycnJFlQGAKgq2Mkdks0C1rfffqtevXpd8XrPnj21ffv2CqwIAFDV0MGCZLM1WCdPnpSvr+8Vr/v4+OjUqVMVWBEAoKohI0GyWQerQYMGSk1NveL17777TvXr16/AigAAQFVkq4DVu3dvTZ48WRcuXCh2LS8vT1OmTFFUVJQFlQEAqgovh6NMB+zBVgFr4sSJOnPmjG666SbNnDlTH3zwgdasWaMZM2aoadOmOnPmjCZMmGB1mQAAG6vIRe4//vij/vjHP6p27doKCAhQy5YtPdYaG4ahqVOnKiwsTP7+/urSpYv27Nnj8Rj5+fkaOXKk6tSpo+rVq6tv3746duyYGb+KKs1WASs0NFQpKSlq3ry5EhIS1K9fP0VHR2v8+PFq3ry5vvzyS4WGhlpdJgDAxipqkXtWVpbuvPNO+fr66qOPPtLevXv117/+VTVq1HCPmTlzpmbNmqV58+Zp27Ztcrlc6tGjh86ePeseExsbq1WrVmn58uXasmWLzp07p6ioKL71pIwchmEYVhdRHrKysnTgwAEZhqGIiAjVrFmzzI/5uKP49g+A3SzM/cHqEoDyFxBSbg99rOXNZbq/4a5/lmjcM888oy+//FJffPHFZa8bhqGwsDDFxsZq3Lhxki51q0JDQzVjxgzFxMQoOztbdevW1dKlSzVgwABJ0vHjx9WoUSOtXbtWd999d5leS1Vmqw7Wz9WsWVO333672rZta0q4AgCgJCpqinDNmjVq06aNHnzwQdWrV0+tWrXSokWL3NfT09OVkZGhnj17us85nU517txZKSkpkqTt27fr4sWLHmPCwsLUvHlz9xiUjm0DFgAA16L8/Hzl5OR4HPn5+cXGHTp0SAsWLFBERIQ++eQTPf7443rqqaf0t7/9TZKUkZEhScWWxoSGhrqvZWRkyM/Pr1gj4udjUDoELAAATOTwcpTpSEpKUkhIiMeRlJRU7HmKiop02223KTExUa1atVJMTIyGDRumBQsWeNbzi7aYYRi/udarJGPw6whYAACYqKxThAkJCcrOzvY4EhISij1P/fr1FRkZ6XGuWbNmOnr0qCTJ5XJJUrFOVGZmprur5XK5VFBQoKysrCuOQekQsAAAMFFZ98FyOp0KDg72OJxOZ7HnufPOO7V//36Pc2lpabr++uslSeHh4XK5XFq/fr37ekFBgTZt2qQOHTpIklq3bi1fX1+PMSdOnNDu3bvdY1A6tvqqHAAArFZRM2tPP/20OnTooMTERPXv319ff/21XnvtNb322mv/rcOh2NhYJSYmKiIiQhEREUpMTFRAQIAGDx4sSQoJCdHQoUM1evRo1a5dW7Vq1dKYMWPUokULde/evWJeiE0RsAAAMFFFrV26/fbbtWrVKiUkJOi5555TeHi4Zs+erSFDhrjHxMfHKy8vT08++aSysrLUrl07rVu3TkFBQe4xycnJ8vHxUf/+/ZWXl6du3bppyZIl8vb2rpDXYVe23QerPLAPFqoC9sFClVCO+2CdandLme6v+9We3x6ESo8OFgAAJuLDd5AIWAAAmIrtDSARsAAAMBX5ChIBCwAAU9HBgkTAAgDAVA52mITYaBQAAMB0dLAAADARU4SQCFgAAJjLi4AFAhYAAOaigwURsAAAMBVThJBY5A4AAGA6OlgAAJiJNVgQAQsAAHMxRQgRsAAAMJWDDhZEwAIAwFx0sCACFgAApqKDBYlPEQIAAJiODhYAAGZiihAiYAEAYC6mCCECFgAApmInd0gELAAAzEUHCyJgAQBgLjpYEJ8iBAAAMB0dLAAATOSgdQFZHLDWrFlT4rF9+/Ytx0oAADAJU4SQxQErOjq6ROMcDocKCwvLtxgAAEzATu6QLA5YRUVFVj49AADmo4MFsQYLAABz0cGCKlnAys3N1aZNm3T06FEVFBR4XHvqqacsqgoAAODqVJqAtXPnTvXu3Vvnz59Xbm6uatWqpdOnTysgIED16tUjYAEArgns5A6pEu2D9fTTT+vee+/VmTNn5O/vr61bt+rIkSNq3bq1XnrpJavLAwCgZLwcZTtgC5UmYO3atUujR4+Wt7e3vL29lZ+fr0aNGmnmzJkaP3681eUBAFAyDkfZDthCpQlYvr6+7rZqaGiojh49KkkKCQlx/xkAgMrO4XCU6YA9VJo1WK1atdI333yjm266SV27dtXkyZN1+vRpLV26VC1atLC6PAAASoZpPqgSdbASExNVv359SdLzzz+v2rVr64knnlBmZqZee+01i6sDAAAouUrTwWrTpo37z3Xr1tXatWstrAYAgNJhmg9SJQpYAADYAlOEUCUKWOHh4b+a+g8dOlSB1QAAUEp0sKBKFLBiY2M9fr548aJ27typjz/+WGPHjrWmKAAArhJf9gypEgWsUaNGXfb8/Pnz9c0331RwNQAAlBIdLKgSfYrwSu655x6tXLnS6jIAAABKrNJ0sK5kxYoVqlWrltVlAABQMkwRQpUoYLVq1cpjkbthGMrIyNCpU6f0yiuvWFgZAAAlxzYNkCpRwLrvvvs83pReXl6qW7euunTpoptvvtnCyv5n4Tm+sgf293j1RlaXAJS7hUZO+T04HSyoEgWsqVOnWl0CAABlRwcLqkSL3L29vZWZmVns/L/+9S95e3tbUBEAAKXgcJTtgC1UmoBlGMZlz+fn58vPz6+CqwEAACg9y6cI58yZI+nSosDXX39dgYGB7muFhYXavHlzpVmDBQDAb6ILBVWCgJWcnCzpUgdr4cKFHtOBfn5+uuGGG7Rw4UKrygMA4Op4VZrJIVjI8oCVnp4uSeratavef/991axZ0+KKAAAoAzpYUCUIWD/57LPPrC4BAICyI2BBlWiR+x/+8AdNnz692PkXX3xRDz74oAUVAQBQCnyKEKpEAWvTpk3q06dPsfO9evXS5s2bLagIAACgdCrNFOG5c+cuux2Dr6+vcnLKccddAADMxCJ3qBJ1sJo3b6533nmn2Pnly5crMjLSgooAACgFpgihStTBmjRpkh544AEdPHhQd911lyRpw4YNWrZsmVasWGFxdQAAlBAhCapEAatv375avXq1EhMTtWLFCvn7++vWW2/Vxo0bFRwcbHV5AACUDAELqkRThJLUp08fffnll8rNzdWBAwd0//33KzY2Vq1bt7a6NAAASsbLq2xHKSUlJcnhcCg2NtZ9zjAMTZ06VWFhYfL391eXLl20Z88ej/vy8/M1cuRI1alTR9WrV1ffvn117NixUteBSypVwJKkjRs36o9//KPCwsI0b9489e7dW998843VZQEAUGlt27ZNr732mn73u995nJ85c6ZmzZqlefPmadu2bXK5XOrRo4fOnj3rHhMbG6tVq1Zp+fLl2rJli86dO6eoqCgVFhZW9MuwlUoRsI4dO6Zp06apcePGGjRokGrWrKmLFy9q5cqVmjZtmlq1amV1iQAAlEwFL3I/d+6chgwZokWLFnl8G4phGJo9e7YmTJig+++/X82bN9dbb72l8+fPa9myZZKk7OxsvfHGG/rrX/+q7t27q1WrVnr77beVmpqqTz/91LRfSVVkecDq3bu3IiMjtXfvXs2dO1fHjx/X3LlzrS4LAIDSqeCANXz4cPXp00fdu3f3OJ+enq6MjAz17NnTfc7pdKpz585KSUmRJG3fvl0XL170GBMWFqbmzZu7x6B0LF/kvm7dOj311FN64oknFBERYXU5AACUTRkXuefn5ys/P9/jnNPplNPpLDZ2+fLl2rFjh7Zt21bsWkZGhiQpNDTU43xoaKiOHDniHuPn51fse4BDQ0Pd96N0LO9gffHFFzp79qzatGmjdu3aad68eTp16pTVZQEAUCoOL68yHUlJSQoJCfE4kpKSij3PDz/8oFGjRuntt99WtWrVrlzPLwKfYRjFzv1SScbg11kesNq3b69FixbpxIkTiomJ0fLly9WgQQMVFRVp/fr1HgvxAACo9Mo4RZiQkKDs7GyPIyEhodjTbN++XZmZmWrdurV8fHzk4+OjTZs2ac6cOfLx8XF3rn7ZicrMzHRfc7lcKigoUFZW1hXHoHQsD1g/CQgI0KOPPqotW7YoNTVVo0eP1vTp01WvXj317dvX6vIAAKgQTqdTwcHBHsflpge7deum1NRU7dq1y320adNGQ4YM0a5du9S4cWO5XC6tX7/efU9BQYE2bdqkDh06SJJat24tX19fjzEnTpzQ7t273WNQOpavwbqcpk2baubMmUpKStLf//53vfnmm1aXBABAyVTQ1FpQUJCaN2/uca569eqqXbu2+3xsbKwSExMVERGhiIgIJSYmKiAgQIMHD5YkhYSEaOjQoRo9erRq166tWrVqacyYMWrRokWxRfO4OpUyYP3E29tb0dHRio6OtroUAABKphKtXYqPj1deXp6efPJJZWVlqV27dlq3bp2CgoLcY5KTk+Xj46P+/fsrLy9P3bp105IlS+Tt7W1h5dc+h2EYhtVFXDNy/211BUC5ezzwOqtLAMrdQiOn3B678IVhZbrfe8IikyqBlSp1BwsAgGtOJepgwToELAAAzETAgirRpwgBAADsgg4WAABmooMFEbAAADCXF5NDIGABAGAuOlgQAQsAAHMRsCACFgAA5mKKEOJThAAAAKajgwUAgJmYIoQIWAAAmIuABRGwAAAwFwELImABAGAuFrlDLHIHAAAwHR0sAADMxBQhRMACAMBcBCyIgAUAgLkcrL4BAQsAAHN50cECAQsAAHPRwYL4FCEAAIDp6GABAGAmFrlDBCwAAMzFRqMQAQsAAHPRwYIIWAAAmItF7hABCwAAc9HBgvgUIQAAgOnoYAEAYCYWuUMELAAAzMUUIUTAAgDAXCxyh6rYGqyTJ0/queees7oMAICdeTnKdsAWqlTAysjI0LPPPmt1GQAAO3N4le2ALdhqivC777771ev79++voEoAAEBVZquA1bJlSzkcDhmGUezaT+cdLD4EAJQn/p6BbBawateurRkzZqhbt26Xvb5nzx7de++9FVwVAKBKYZoPslnAat26tY4fP67rr7/+stf//e9/X7a7BQCAaVioDtksYMXExCg3N/eK16+77jotXry4AisCAFQ5TBFCNgtY/fr1+9XrNWvW1MMPP1xB1QAAqiSmCKEqtk0DAABARbBVBwsAAMuxBgsiYAEAYC6mCCECFgAA5mKRO0TAAgDAXHSwIJsucv/444+1ZcsW98/z589Xy5YtNXjwYGVlZVlYGQDA9viyZ8imAWvs2LHKycmRJKWmpmr06NHq3bu3Dh06pLi4OIurAwAAdmfLKcL09HRFRkZKklauXKmoqCglJiZqx44d6t27t8XVAQBsjSlCyKYdLD8/P50/f16S9Omnn6pnz56SpFq1ark7WwAAlAuHo2wHbMGWAatjx46Ki4vT888/r6+//lp9+vSRJKWlpalhw4YWV4eSOJmZqTETpqhd1x66tUMn3Tfwj9q9d5/VZQEl5gwM1IPJ0/XC4d2ac/6kxn65Xte3uc1jTNSUBE3/cb/mnD+puM8+VP3Imz2u12kcrsff/z+9mHlIydnHNOydJQqqV7ciXwZKw8urbAdswZb/JefNmycfHx+tWLFCCxYsUIMGDSRJH330kXr16mVxdfgt2Tk5GvTnv8jXx1uL5s7WhyuW65mnRyk4KMjq0oASe+j1uWrWo6sWP/QXPd+ivfat26jYTz9QjbD6kqSe8bHqFjdcy0eM0fTbuyg7I1Oj1n8gZ2CgJMkvIECj1q2WYRhKvitKL97ZU95+fhr+93floMtRudHBgiSHYRiG1UVcM3L/bXUFVcJLc+Zrx65vtezN16wupUp6PPA6q0u45vlWq6bZZ49rwX2DtHvtJ+7zE3ZuUeo/PtGaSc9rxvE0bZj9itbNnC1J8vHz08yTB7Rq3BR98dpiNetxl0Z+tFJxNa/ThbNnJUkBNWpoVtZRze7eV//c8LkFr8w+Fhrlt1yk8JPFZbrf++4/m1QJrGTLDtaOHTuUmprq/vmDDz5QdHS0xo8fr4KCAgsrQ0ls3LRZzSOb6an4BLXv1kvRgx7Su++vtrosoMS8fHzk7eOjixcueJy/mHdBTTreoTrhNyikvkv71m10X/tPQYG+3/SlGndoJ0nycfrJMAz9Jz//f/dfuKCiwkI16di+Yl4IgFKzZcCKiYlRWlqaJOnQoUMaOHCgAgIC9N577yk+Pt7i6vBbfvjxuP7fivd1Q6NGemP+yxr4QD9Ne3GWVv9jrdWlASWSf+6cDqZ8pT6T4hVS3yWHl5faDhmgG9q1UXB9l4Jd9SRJOSczPe7LOZmpYFeoJCl96zYV5Oaq34zn5OvvL7+AAD3w4jR5eXsruH5ohb8mXAWmCCGbBqy0tDS1bNlSkvTee++pU6dOWrZsmZYsWaKVK1eW6DHy8/OVk5PjceT/7F+SKD9GUZFuubmp4kY+qcibm2rgH+5X/3736f+9V7L/dkBlsPihv0gOh2YcT9O8/NO666nHtW3ZezIKC91jfrlCw+FwSP89d+70v/Tagw/rd/feo5fPnVBy9jFVCwnWke07ZRQWVehrwVVikTtk04BlGIaKii79P6BPP/3UvfdVo0aNdPr06RI9RlJSkkJCQjyOpJeSy61m/E/dOnV0Y+Nwj3ONw2/Q8YyTFlUEXL3Th9I1q0tvPVXdpYRGzTS9XVd5+/rodPoR5WRc6lyFuDw7UUH16np0tfat36hJTW7V2HqNNaZOuJb86S+q0SBMp9MPV+RLwdWigwXZNGC1adNG06ZN09KlS7Vp0yb3Ng3p6ekKDS1Zaz0hIUHZ2dkeR8KYp8uzbPzXbS1/p/TDRzzOHT5yVA3quyyqCCi9gvPnlZNxUgE1aijy7m769oMPdTr9sLJPZKhZj67ucd6+vorofKcOpXxV7DFy/3VGednZatq1k4Lq1dV3a5gur9QcXmU7YAu23Ml99uzZGjJkiFavXq0JEyaoSZMmkqQVK1aoQ4cOJXoMp9Mpp9PpeTKXtnxFeHjIIA3682Na+MYS3dOjm77bs1fvvr9az01MsLo0oMQie3aTHA6d3P+96jVprPtffF4n9x9QyuK3JUkbZr+iXuNHK/P7g8r8/qB6jR+jgvN5+nrZe+7HaP/IEGXsS9PZU6fVuH1b9X95hjYkz9fJtANWvSyUBF0oqIpt03DhwgV5e3vL19e3dA/ANg0V5rPNWzRr3is6fPQHNQwL05//OEj974+2uqwqgW0azNH6wX6KTpqqGg3DdP5MlnauXKPVE57ThZ99m0TUlAT9PubPCqhZQ+lffaPlw0fr+J7/bagbnTRV7R8Zouq1aupfh49q88I3tCF5vhUvx3bKdZuGz5aV6X7vroNNqgRWqlIBq8wIWKgCCFioCso1YH2+vEz3e3cZaFIlsJItJ3sLCwv10ksvqW3btnK5XKpVq5bHAQBAufFylO0ooaSkJN1+++0KCgpSvXr1FB0drf3793uMMQxDU6dOVVhYmPz9/dWlSxft2bPHY0x+fr5GjhypOnXqqHr16urbt6+OHTtmyq+iKrNlwHr22Wc1a9Ys9e/fX9nZ2YqLi9P9998vLy8vTZ061eryAAB2VkGL3Ddt2qThw4dr69atWr9+vf7zn/+oZ8+eys3NdY+ZOXOmZs2apXnz5mnbtm1yuVzq0aOHzv732wEkKTY2VqtWrdLy5cu1ZcsWnTt3TlFRUSr82ZYiuHq2nCK88cYbNWfOHPXp00dBQUHatWuX+9zWrVu1bFkp58eZIkQVwBQhqoJynSLcsqJM93t3/EOp7jt16pTq1aunTZs2qVOnTjIMQ2FhYYqNjdW4ceMkXepWhYaGasaMGYqJiVF2drbq1q2rpUuXasCAAZKk48ePq1GjRlq7dq3uvvvuMr2WqsyWHayMjAy1aNFCkhQYGKjs7GxJUlRUlD788EMrSwMA2F0ZO1il3ej6p7/rfloKk56eroyMDPXs2dM9xul0qnPnzkpJSZEkbd++XRcvXvQYExYWpubNm7vHoHRsGbAaNmyoEydOSJKaNGmidevWSZK2bdtWfOsFAAAqkctudJ2U9Kv3GIahuLg4dezYUc2bN5d0qdkgqdj+j6Ghoe5rGRkZ8vPzU82aNa84BqVjy32w+vXrpw0bNqhdu3YaNWqUBg0apDfeeENHjx7V00+zWSgAoPw4yrgPVkJCguLi4jzO/VZzYMSIEfruu++0ZcuW36zHMIzfrLEkY/DrbBmwpk+f7v7zH/7wBzVs2FApKSlq0qSJ+vbta2FlAADbK+Nu7Jfd6PpXjBw5UmvWrNHmzZvVsGFD93mX69K3X2RkZKh+/fru85mZme6ulsvlUkFBgbKysjy6WJmZmSXemBuXZ8spwl+64447FBcXR7gCAJS/CvoUoWEYGjFihN5//31t3LhR4eGe3+EaHh4ul8ul9evXu88VFBRo06ZN7vDUunVr+fr6eow5ceKEdu/eTcAqI9t0sNasWVPisQQtAEC5uYq9rMpi+PDhWrZsmT744AMFBQW510yFhITI399fDodDsbGxSkxMVEREhCIiIpSYmKiAgAANHjzYPXbo0KEaPXq0ateurVq1amnMmDFq0aKFunfvXiGvw65sE7Cio6NLNM7hcLC3BwCg/FTQFzYvWLBAktSlSxeP84sXL9YjjzwiSYqPj1deXp6efPJJZWVlqV27dlq3bp2CgoLc45OTk+Xj46P+/fsrLy9P3bp105IlS+Tt7V0hr8OubLkPVrlhHyxUAeyDhaqgPPfBKtq2tkz3e93e26RKYCXbdLAAAKgU+PQdZLNF7hs3blRkZKRycor/yyQ7O1u33HKLNm/ebEFlAIAqo4IWuaNys9V/ydmzZ2vYsGEKDg4udi0kJEQxMTFKTk62oDIAQJXhcJTtgC3YKmB9++236tWr1xWv9+zZU9u3b6/AigAAVQ4dLMhma7BOnjwpX1/fK1738fHRqVOnKrAiAECVU0HbNKBys1VUbtCggVJTU694/bvvvvPYzRYAAKA82Cpg9e7dW5MnT9aFCxeKXcvLy9OUKVMUFRVlQWUAgCqDKULIZvtgnTx5Urfddpu8vb01YsQINW3aVA6HQ/v27dP8+fNVWFioHTt2FPtm8RJjHyxUAeyDhaqgXPfBSv28TPd7tehiRhmwmK3WYIWGhiolJUVPPPGEEhIS9FN2dDgcuvvuu/XKK6+UPlwBAFASdKEgmwUsSbr++uu1du1aZWVl6cCBAzIMQxERER7fEg4AQLlhqwXIhgHrJzVr1tTtt99udRkAgKqGDhZks0XuAAAAlYFtO1gAAFjCi94FCFgAAJjKwRosiIAFAIC5WIMFEbAAADAXHSyIgAUAgLnoYEF8ihAAAMB0dLAAADATU4QQAQsAAHOxTQNEwAIAwFx0sCACFgAA5mKRO8QidwAAANPRwQIAwExMEUIELAAATEbAAgELAABz0cGCCFgAAJiLgAURsAAAMBkBC3yKEAAAwHR0sAAAMBNThBABCwAAc5GvIAIWAAAmI2GBgAUAgLmYIoQIWAAAmIuABfEpQgAAANPRwQIAwFR0sEDAAgDAXEwRQgQsAABMRsACAQsAAHPRwYIIWAAAmIuABfEpQgAAANPRwQIAwFR0sEDAAgDAVA6mCCECFgAA5iJgQQQsAABMRsACAQsAAHPRwYL4FCEAAIDp6GABAGAmOlgQAQsAAJMRsEDAAgDAXHSwIAIWAADmIl9BBCwAAExGwgKfIgQAADAdHSwAAMzEGiyIgAUAgLkIWBABCwAAkxGwQMACAMBcdLAgAhYAAOYiYEF8ihAAAMB0dLAAADAVHSwQsAAAMBdThJDkMAzDsLoI4HLy8/OVlJSkhIQEOZ1Oq8sBygXvc8CeCFiotHJychQSEqLs7GwFBwdbXQ5QLnifA/bEIncAAACTEbAAAABMRsACAAAwGQELlZbT6dSUKVNY+Atb430O2BOL3AEAAExGBwsAAMBkBCwAAACTEbAAAABMRsBChXA4HFq9erXVZQDlivc5gJ8QsFBmGRkZGjlypBo3biyn06lGjRrp3nvv1YYNG6wuTZJkGIamTp2qsLAw+fv7q0uXLtqzZ4/VZeEaU9nf5++//77uvvtu1alTRw6HQ7t27bK6JKBKI2ChTA4fPqzWrVtr48aNmjlzplJTU/Xxxx+ra9euGj58uNXlSZJmzpypWbNmad68edq2bZtcLpd69Oihs2fPWl0arhHXwvs8NzdXd955p6ZPn251KQAkyQDK4J577jEaNGhgnDt3rti1rKws958lGatWrXL/HB8fb0RERBj+/v5GeHi4MXHiRKOgoMB9fdeuXUaXLl2MwMBAIygoyLjtttuMbdu2GYZhGIcPHzaioqKMGjVqGAEBAUZkZKTx4YcfXra+oqIiw+VyGdOnT3efu3DhghESEmIsXLiwjK8eVUVlf5//XHp6uiHJ2LlzZ6lfL4Cy87E43+EadubMGX388cd64YUXVL169WLXa9SoccV7g4KCtGTJEoWFhSk1NVXDhg1TUFCQ4uPjJUlDhgxRq1attGDBAnl7e2vXrl3y9fWVJA0fPlwFBQXavHmzqlevrr179yowMPCyz5Oenq6MjAz17NnTfc7pdKpz585KSUlRTExMGX4DqAquhfc5gMqHgIVSO3DggAzD0M0333zV906cONH95xtuuEGjR4/WO++84/6L5+jRoxo7dqz7sSMiItzjjx49qgceeEAtWrSQJDVu3PiKz5ORkSFJCg0N9TgfGhqqI0eOXHXdqHquhfc5gMqHNVgoNeO/XwLgcDiu+t4VK1aoY8eOcrlcCgwM1KRJk3T06FH39bi4OD322GPq3r27pk+froMHD7qvPfXUU5o2bZruvPNOTZkyRd99991vPt8vazQMo1R1o+q5lt7nACoPAhZKLSIiQg6HQ/v27buq+7Zu3aqBAwfqnnvu0T/+8Q/t3LlTEyZMUEFBgXvM1KlTtWfPHvXp00cbN25UZGSkVq1aJUl67LHHdOjQIT300ENKTU1VmzZtNHfu3Ms+l8vlkvS/TtZPMjMzi3W1gMu5Ft7nACohS1eA4ZrXq1evq178+9JLLxmNGzf2GDt06FAjJCTkis8zcOBA4957773stWeeecZo0aLFZa/9tMh9xowZ7nP5+fkscsdVqezv859jkTtQOdDBQpm88sorKiwsVNu2bbVy5Up9//332rdvn+bMmaP27dtf9p4mTZro6NGjWr58uQ4ePKg5c+a4/9UuSXl5eRoxYoQ+//xzHTlyRF9++aW2bdumZs2aSZJiY2P1ySefKD09XTt27NDGjRvd137J4XAoNjZWiYmJWrVqlXbv3q1HHnlEAQEBGjx4sPm/ENhSZX+fS5cW4+/atUt79+6VJO3fv1+7du0q1r0FUEGsTni49h0/ftwYPny4cf311xt+fn5GgwYNjL59+xqfffaZe4x+8fH1sWPHGrVr1zYCAwONAQMGGMnJye5/2efn5xsDBw40GjVqZPj5+RlhYWHGiBEjjLy8PMMwDGPEiBHGjTfeaDidTqNu3brGQw89ZJw+ffqK9RUVFRlTpkwxXC6X4XQ6jU6dOhmpqanl8auAjVX29/nixYsNScWOKVOmlMNvA8BvcRjGf1dwAgAAwBRMEQIAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYQBU2depUtWzZ0v3zI488oujo6Aqv4/Dhw3I4HNq1a1eFPzcAlAcCFlAJPfLII3I4HHI4HPL19VXjxo01ZswY5ebmluvzvvzyy1qyZEmJxhKKAODKfKwuAMDl9erVS4sXL9bFixf1xRdf6LHHHlNubq4WLFjgMe7ixYvy9fU15TlDQkJMeRwAqOroYAGVlNPplMvlUqNGjTR48GANGTJEq1evdk/rvfnmm2rcuLGcTqcMw1B2drb+8pe/qF69egoODtZdd92lb7/91uMxp0+frtDQUAUFBWno0KG6cOGCx/VfThEWFRVpxowZatKkiZxOp6677jq98MILkqTw8HBJUqtWreRwONSlSxf3fYsXL1azZs1UrVo13XzzzXrllVc8nufrr79Wq1atVK1aNbVp00Y7d+408TcHANajgwVcI/z9/XXx4kVJ0oEDB/Tuu+9q5cqV8vb2liT16dNHtWrV0tq1axUSEqJXX31V3bp1U1pammrVqqV3331XU6ZM0fz58/X73/9eS5cu1Zw5c9S4ceMrPmdCQoIWLVqk5ORkdezYUSdOnNA///lPSZdCUtu2bfXpp5/qlltukZ+fnyRp0aJFmjJliubNm6dWrVpp586dGjZsmKpXr66HH35Yubm5ioqK0l133aW3335b6enpGjVqVDn/9gCggln8ZdMALuPhhx827rvvPvfPX331lVG7dm2jf//+xpQpUwxfX18jMzPTfX3Dhg1GcHCwceHCBY/HufHGG41XX33VMAzDaN++vfH44497XG/Xrp1x6623XvZ5c3JyDKfTaSxatOiyNaanpxuSjJ07d3qcb9SokbFs2TKPc88//7zRvn17wzAM49VXXzVq1apl5Obmuq8vWLDgso8FANcqpgiBSuof//iHAgMDVa1aNbVv316dOnXS3LlzJUnXX3+96tat6x67fft2nTt3TrVr11ZgYKD7SE9P18GDByVJ+/btU/v27T2e45c//9y+ffuUn5+vbt26lbjmU6dO6YcfftDQoUM96pg2bZpHHbfeeqsCAgJKVAcAXIuYIgQqqa5du2rBggXy9fVVWFiYx0L26tWre4wtKipS/fr19fnnnxd7nBo1apTq+f39/a/6nqKiIkmXpgnbtWvnce2nqUzDMEpVDwBcSwhYQCVVvXp1NWnSpERjb7vtNmVkZMjHx0c33HDDZcc0a9ZMW7du1Z/+9Cf3ua1bt17xMSMiIuTv768NGzboscceK3b9pzVXhYWF7nOhoaFq0KCBDh06pCFDhlz2cSMjI7V06VLl5eW5Q9yv1QEA1yKmCAEb6N69u9q3b6/o6Gh98sknOnz4sFJSUjRx4kR98803kqRRo0bpzTff1Jtvvqm0tDRNmTJFe/bsueJjVqtWTePGjVN8fLz+9re/6eDBg9q6daveeOMNSVK9evXk7++vjz/+WCdPnlR2drakS5uXJiUl6eWXX1ZaWppSU1O1ePFizZo1S5I0ePBgeXl5aejQodq7d6/Wrl2rl156qZx/QwBQsQhYgA04HA6tXbtWnTp10qOPPqqbbrpJAwcO1OHDhxUaGipJGjBggCZPnqxx48apdevWOnLkiJ544olffdxJkyZp9OjRmjx5spo1a6YBAwYoMzNTkuTj46M5c+bo1VdfVVhYmO677z5J0mOPPabXX39dS5YsUYsWLdS5c2ctWbLEva1DYGCg/v73v2vv3r1q1aqVJkyYoBkzZpTjbwcAKp7DYEEEAACAqehgAQAAmIyABQAAYDICFgAAgMn+P6P1P7waDAvGAAAAAElFTkSuQmCC" 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) │ 38,727,424 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 116,305,925 (443.67 MB) | |
| Trainable params: 38,768,641 (147.89 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 77,537,284 (295.78 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 4574 50.16 | |
| 0 4545 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9119</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9913 1.0000 0.9956 909 | |
| Class 1 1.0000 0.9913 0.9956 915 | |
| accuracy 0.9956 1824 | |
| macro avg 0.9956 0.9956 0.9956 1824 | |
| weighted avg 0.9957 0.9956 0.9956 1824 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 907</li> | |
| <li>True Negatives (TN): 909</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 8</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> | |
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