| <html> | |
| <head> | |
| <title>GENE_FAMILY: ARF</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=ARF' target='_blank'>ARF</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 4578 50.16 | |
| 0 4549 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9127</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9967 0.9934 0.9950 910 | |
| Class 1 0.9935 0.9967 0.9951 916 | |
| accuracy 0.9951 1826 | |
| macro avg 0.9951 0.9951 0.9951 1826 | |
| weighted avg 0.9951 0.9951 0.9951 1826 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 913</li> | |
| <li>True Negatives (TN): 904</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 6</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) │ 2,269,440 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 6,938,117 (26.47 MB) | |
| Trainable params: 2,312,705 (8.82 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 4,625,412 (17.64 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 4578 50.16 | |
| 0 4549 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9127</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9989 1.0000 0.9995 910 | |
| Class 1 1.0000 0.9989 0.9995 916 | |
| accuracy 0.9995 1826 | |
| macro avg 0.9995 0.9995 0.9995 1826 | |
| weighted avg 0.9995 0.9995 0.9995 1826 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 915</li> | |
| <li>True Negatives (TN): 910</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</li> | |
| <li>False Negatives (FN): 1</li> | |
| </ul> | |
| </div> | |
| </div> | |
| <div class="section confusion-matrix"> | |
| <h2>Confusion Matrix</h2> | |
| <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3GklEQVR4nO3de3iMd/7/8dfkNJKQkGBGCKXSViqtoJRatI4lVT2hbA9butqisg4pVUSRoFasc2lLV9dqS7W2tUrpUs1Pq06Nw9aWoJYIbZoQkWjcvz+s+e4I3cjcyR2T52Ov+7q4T/Oe7Ozm5f35zOe2GYZhCAAAAKbxsboAAAAAb0PAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZH5WF3AjecEWYnUJQKmbl/uD1SUApS8otNRu/ZyHvysWGDkmVQIrEbAAADARQ0OQCFgAAJjKx2azugSUAwRtAAAAk9HBAgDARHQuIBGwAAAwlQ8jhBABCwAAU9HBgkTAAgDAVExyh0TAAgDAVHSwIPE5AAAAMB0dLAAATMQkd0gELAAATMXQECQCFgAAprIxyR0iYAEAYCo6WJAIWAAAmIo5WJAI2gAAAKajgwUAgInoXEAiYAEAYCpWcodEwAIAwFR0sCARsAAAMBWT3CERsAAAMBUdLEh8DgAAAExHBwsAABP5iDFCELAAADAVc7AgEbAAADAVc28gEbAAADAVHSxIBCwAAEzFHCxIdDIBAABMRwcLAAATMUQIiYAFAICpGBqCRMACAMBUdLAgEbAAADAVk9whEbAAADAVHSxIDBUDAACYjoAFAICJbB5uxfXLL7/olVdeUf369RUYGKgGDRro1Vdf1cWLF13nGIahxMRERUREKDAwUO3bt9fevXvd7pOfn68hQ4aoevXqCg4OVo8ePXTs2LGSvn38BwELAAAT+dg824pr6tSpWrBggebMmaP9+/dr2rRpeu211zR79mzXOdOmTdOMGTM0Z84cbdu2TU6nU506ddKZM2dc58THx2vVqlVavny5tmzZorNnzyouLk6FhYVm/lgqHJthGIbVRdwoXrCFWF0CUOrm5f5gdQlA6QsKLbVb/zm0pkfXP5mdWazz4uLi5HA49Oabb7r2PfLIIwoKCtLSpUtlGIYiIiIUHx+vl156SdKlbpXD4dDUqVM1cOBAZWdnq0aNGlq6dKl69+4tSTp+/LgiIyO1Zs0adenSxaP3UpHRwQIAwERl1cFq06aNNmzYoAMHDkiSdu/erS1btqhbt26SpPT0dGVkZKhz586ua+x2u9q1a6fU1FRJ0vbt23XhwgW3cyIiItS4cWPXOSgZvkUIAEA5kp+fr/z8fLd9drtddrvdbd9LL72k7Oxs3XbbbfL19VVhYaEmT56sxx9/XJKUkZEhSXI4HG7XORwOHTlyxHVOQECAqlWrVuScy9ejZOhgAQBgIh8Pt+TkZIWGhrptycnJRV7n3Xff1TvvvKNly5Zpx44devvttzV9+nS9/fbbbufZbO5tMcMwiuy7UnHOwa+jgwUAgIk8jSWjR4/WsGHD3PZd2b2SpJEjR2rUqFHq06ePJCkmJkZHjhxRcnKynnrqKTmdTkmXulS1atVyXZeZmenqajmdThUUFCgrK8uti5WZmanWrVt7+E4qNjpYAACYyMdm82iz2+0KCQlx264WsM6dOycfH/df476+vq5lGurXry+n06n169e7jhcUFGjTpk2u8NSsWTP5+/u7nXPixAnt2bOHgOUhOlgAAJiorAbWHnjgAU2ePFl169bV7bffrp07d2rGjBl65plnLtVhsyk+Pl5JSUmKiopSVFSUkpKSFBQUpL59+0qSQkND1b9/fw0fPlzh4eEKCwvTiBEjFBMTo44dO5bRO/FOBCwAAExUVgFr9uzZGjt2rF544QVlZmYqIiJCAwcO1Lhx41znJCQkKC8vTy+88IKysrLUsmVLrVu3TlWqVHGdk5KSIj8/P/Xq1Ut5eXnq0KGDlixZIl9f3zJ6J96JdbCuA+tgoSJgHSxUCKW4Dtb71Rz/+6Rf8VjWSZMqgZXoYAEAYCK+eweJgAUAgKlY3gASAQsAAFMRryARsAAAMBXrH0EiYAEAYCpGCCERtAEAAExHBwsAABPZmIUFEbAAADAV8QoSAQsAAFMRsCB5YcDKzc3VsmXLlJqaqoyMDNlsNjkcDt1zzz16/PHHFRwcbHWJAAAv5kPCgrxskvu+fft0yy23KCEhQVlZWapbt67q1KmjrKwsjRw5Urfeeqv27dtndZkAAC9m8/A/8A5e1cEaNGiQ2rZtq7ffflsBAQFuxwoKCvT0009r0KBB+vzzzy2qEAAAVAReFbC++uorffPNN0XClSQFBATo5ZdfVosWLSyoDABQUdCDguRlQ4TVqlXTv/71r2se//7771WtWrUyrAgAUNHYbJ5t8A5e1cF69tln9dRTT+mVV15Rp06d5HA4ZLPZlJGRofXr1yspKUnx8fFWlwkA8GJkJEheFrASExMVGBioGTNmKCEhwfVEc8Mw5HQ6NWrUKCUkJFhcJQDAm/kQsSDJZhiGYXURpSE9PV0ZGRmSJKfTqfr163t8zxdsIR7fAyjv5uX+YHUJQOkLCi21W2+oUduj6zuc+rdJlcBKXtXB+m/169c3JVQBAABcL68NWAAAWIGJ6pAIWAAAmIp8BYmABQCAqViNHRIBCwAAU/EsQkhettDoZWvXrtWWLVtcf587d66aNGmivn37Kisry8LKAADezubhBu/glQFr5MiRysnJkSSlpaVp+PDh6tatmw4dOqRhw4ZZXB0AAPB2XjlEmJ6erujoaEnSypUrFRcXp6SkJO3YsUPdunWzuDoAgDejCwXJSztYAQEBOnfunCTps88+U+fOnSVJYWFhrs4WAAClwebhf+AdvDJgtWnTRsOGDdPEiRP19ddfq3v37pKkAwcOqE6dOhZXB3vlyno0ZYomHt6jmedOasSX61WveVPX8SYPPaDBa1dp2ql0zTNyVOfOmCL38AsIUK9Zr2naqXSlnD2h5z5arqq1I8rybQAe+8t7K3Rf9wcV07KNHu77pL7ZsdPqkmACHvYMyUsD1pw5c+Tn56cVK1Zo/vz5ql370mML/v73v6tr164WV4ffvjFbt3W6V28/8XtNjmml/es26sXPPlJoRC1JUkBwsA5+uVUfjhp/zXs8OnOK7nwoTm/2+Z3+2KaL7JWD9fzH78nm45UfaXihNZ+uV/JrM/R8/9/pw78uVbPYJnp2cLyOn8iwujR4yMfDDd7Ba59FWBp4FqHn/CtV0owzx/X6g49rz5pPXftH79yiPR9/qr+NnejaF1avriYd3qOkJvfo2O401/5KISGaduqQ3n7i99r+3geSpNBaTk3+Yb/mdntU+9dtKLs35IV4FmHZeOyJ3yn6tls1Ycwo1777H+6lju3bafiLgyysrIIoxWcRfuWM9Oj6lhn8b9AbeGVY3rFjh9LS/u8X8kcffaSePXvq5ZdfVkFBgYWVwcfPT75+frpw/rzb/gt553Vzm7uLdY+6zZrILyBA+9ZtdO3LPpGh43v2qUHrlqbWC5SGggsXtHf/P9Wmlfvn9Z67W2rn7m8tqgqAmbwyYA0cOFAHDhyQJB06dEh9+vRRUFCQ3n//fSUkJFhcXcWWf/asDqV+pfvHJii0llM2Hx+16NdbN7VsrtBazmLdI8Tp0IX8fOX9/LPb/jMnTynEWbMUqgbMlZX1swoLCxUeFu62v3p4mE79+KNFVcEsNpvNow3ewSsD1oEDB9SkSRNJ0vvvv6+2bdtq2bJlWrJkiVauXFmse+Tn5ysnJ8dtKxSjqWZY8sTvZbPZlHz8gGbln1b7F5/TN8ve18XCQs9ubJPEiDduIFf+LjUMg1+wXoCFRiF5acAyDEMXL16UdGmZhstrX0VGRur06dPFukdycrJCQ0Pdth1ieNEMpw+lK6V9N8UHOzUmspGmtbxXvv5++jH9SLGuz8k4KX+7XYFVq7rtr1KzhnJOniqFigFzVatWVb6+vjp9Rbfqx5+yVD0szKKqYBYCFiQvDVjNmzfXpEmTtHTpUm3atMm1TEN6erocDkex7jF69GhlZ2e7bU0VUJplVzgF584pJ+OkAqtWVaMuHbT7o0+Kdd3R7bv0S0GBGnW617UvxOlQRONoHUr9qrTKBUwT4O+v2xvdpi+3fu22P3Xr14q98w6LqoJZGCKE5KUruc+cOVP9+vXThx9+qDFjxqhhw4aSpBUrVqh169bFuofdbpfdbnfb58u/LUzRqHMH2Ww2nfzuX6rRsIEeem2iTn73vf7f4nckSUHVqimsbh3Xsg2OW6MkXepc5ZzM1PmcHKW++Wc98sfJyv3xJ+X+lKWHp0/Sv9P26p+ffW7Z+wKux+9+21cJr4xX4+hGir0jRu9+sEonMjLU59GHrS4NHuJhz5C8NGDdcccdbt8ivOy1116Tr6+vBRXhvwWGhujB5ERVrROhcz9laefK1Vo95lVd/OUXSdIdPe7Xk0sWuM7v/+4SSdInicn6ZEKyJGnFH0br4i+F6v/e2woIrKTvNmzSgqd7y/jP0DBQ3nXr0klZ2dmat/BNZZ4+rVsa3qyFs1NU+z//sABwY2MdrOvAOlioCFgHCxVCKa6DtSvyJo+ub/LDYVPqgLW8soNVWFiolJQUvffeezp69GiRta9++ukniyoDAHg7plFB8tJJ7hMmTNCMGTPUq1cvZWdna9iwYXr44Yfl4+OjxMREq8sDAHgxnkUIyUsD1l/+8hctWrRII0aMkJ+fnx5//HG98cYbGjdunLZu3Wp1eQAAL8a3CCF5acDKyMhQTEyMJKly5crKzs6WJMXFxemTT4q3FAAAACVBBwuSlwasOnXq6MSJE5Kkhg0bat26dZKkbdu2FVl6AQAAwGxeGbAeeughbdiwQZI0dOhQjR07VlFRUXryySf1zDPPWFwdAMCbMUQIqYIs07B161alpqaqYcOG6tGjR4nvwzINqAhYpgEVQiku07Dv5gYeXR998JBJlcBKXrlMw5Xuvvtu3X333VaXAQCoAHzoQkFeFLBWr15d7HM96WIBAPBryFeQvChg9ezZs1jn2Ww2FRYWlm4xAIAKi3lUkLwoYF3kGXQAAKCc8JqABQBAeWDzyu/n43p51cdg48aNio6OVk5OTpFj2dnZuv3227V582YLKgMAVBQs0wDJywLWzJkz9eyzzyokpOhyCqGhoRo4cKBSUlIsqAwAUFGwkjskLwtYu3fvVteuXa95vHPnztq+fXsZVgQAqGjoYEHysjlYJ0+elL+//zWP+/n56dSpU2VYEQCgoiEjQfKyDlbt2rWVlpZ2zePffvutatWqVYYVAQCAisirAla3bt00btw4nT9/vsixvLw8jR8/XnFxcRZUBgCoKHxsNo82eAevehbhyZMn1bRpU/n6+mrw4MG69dZbZbPZtH//fs2dO1eFhYXasWOHHA5Hie7PswhREfAsQlQIpfgswqN33OrR9XW//c6kSmAlr5qD5XA4lJqaqueff16jR4/W5exos9nUpUsXzZs3r8ThCgCA4mCiOiQvGyKUpHr16mnNmjU6ffq0vvrqK23dulWnT5/WmjVrdNNNN1ldHgDAy5XlMg3//ve/9dvf/lbh4eEKCgpSkyZN3L4tbxiGEhMTFRERocDAQLVv31579+51u0d+fr6GDBmi6tWrKzg4WD169NCxY8fM+FFUaF4XsC6rVq2a7rrrLrVo0ULVqlWzuhwAQAVRVgErKytL99xzj/z9/fX3v/9d+/bt0x//+EdVrVrVdc60adM0Y8YMzZkzR9u2bZPT6VSnTp105swZ1znx8fFatWqVli9fri1btujs2bOKi4vjub0e8qo5WKWNOVioCJiDhQqhFOdg/Tv2No+ur73zn8U6b9SoUfryyy/1xRdfXPW4YRiKiIhQfHy8XnrpJUmXulUOh0NTp07VwIEDlZ2drRo1amjp0qXq3bu3JOn48eOKjIzUmjVr1KVLF4/eS0XmtR0sAACsYPOxebTl5+crJyfHbcvPzy/yOqtXr1bz5s312GOPqWbNmoqNjdWiRYtcx9PT05WRkaHOnTu79tntdrVr106pqamSpO3bt+vChQtu50RERKhx48auc1AyBCwAAEzk6RBhcnKyQkND3bbk5OQir3Po0CHNnz9fUVFR+vTTT/Xcc8/pxRdf1J///GdJUkZGhiQV+XKXw+FwHcvIyFBAQECRqTT/fQ5Kxqu+RQgAgNU8Xctq9OjRGjZsmNs+u91e5LyLFy+qefPmSkpKkiTFxsZq7969mj9/vp588knXeVd+q9EwjP/5TcfinINfRwcLAAATedrBstvtCgkJcduuFrBq1aql6Ohot32NGjXS0aNHJUlOp1OSinSiMjMzXV0tp9OpgoICZWVlXfMclAwBCwAAE5XVw57vueceffed+6KkBw4cUL169SRJ9evXl9Pp1Pr1613HCwoKtGnTJrVu3VqS1KxZM/n7+7udc+LECe3Zs8d1DkqGIUIAAG5Af/jDH9S6dWslJSWpV69e+vrrr7Vw4UItXLhQ0qWgFx8fr6SkJEVFRSkqKkpJSUkKCgpS3759JUmhoaHq37+/hg8frvDwcIWFhWnEiBGKiYlRx44drXx7NzwCFgAAJiqrqUt33XWXVq1apdGjR+vVV19V/fr1NXPmTPXr1891TkJCgvLy8vTCCy8oKytLLVu21Lp161SlShXXOSkpKfLz81OvXr2Ul5enDh06aMmSJfL19S2bN+KlWAfrOrAOFioC1sFChVCK62D92KqxR9eH/789JlUCK9HBAgDARHz5DhIBCwAAU7G8ASS+RQgAAGA6OlgAAJjIRusCImABAGAqhgghEbAAADCXDwELBCwAAMxFBwsiYAEAYCqGCCHxLUIAAADT0cECAMBMzMGCCFgAAJiLIUKIgAUAgKlsdLAgAhYAAOaigwURsAAAMBUdLEh8ixAAAMB0dLAAADATQ4QQAQsAAHMxRAgRsAAAMBUruUMiYAEAYC46WBABCwAAc9HBgvgWIQAAgOnoYAEAYCIbrQvI4oC1evXqYp/bo0ePUqwEAACTMEQIWRywevbsWazzbDabCgsLS7cYAABMwErukCwOWBcvXrTy5QEAMB8dLIg5WAAAmIsOFlTOAlZubq42bdqko0ePqqCgwO3Yiy++aFFVAAAA16fcBKydO3eqW7duOnfunHJzcxUWFqbTp08rKChINWvWJGABAG4IrOQOqRytg/WHP/xBDzzwgH766ScFBgZq69atOnLkiJo1a6bp06dbXR4AAMXjY/Nsg1coNwFr165dGj58uHx9feXr66v8/HxFRkZq2rRpevnll60uDwCA4rHZPNvgFcpNwPL393e1VR0Oh44ePSpJCg0Ndf0ZAIDyzmazebTBO5SbOVixsbH65ptvdMstt+jee+/VuHHjdPr0aS1dulQxMTFWlwcAQPEwzAeVow5WUlKSatWqJUmaOHGiwsPD9fzzzyszM1MLFy60uDoAAIDiKzcdrObNm7v+XKNGDa1Zs8bCagAAKBmG+SCVo4AFAIBXYIgQKkcBq379+r+a+g8dOlSG1QAAUEJ0sKByFLDi4+Pd/n7hwgXt3LlTa9eu1ciRI60pCgCA68TDniGVo4A1dOjQq+6fO3euvvnmmzKuBgCAEqKDBZWjbxFey/3336+VK1daXQYAAECxlZsO1rWsWLFCYWFhVpcBAEDxMEQIlaOAFRsb6zbJ3TAMZWRk6NSpU5o3b56FlQEAUHws0wCpHAWsBx980O1D6ePjoxo1aqh9+/a67bbbLKzs/8zL/cHqEoBS91xwpNUlAKVugZFTejengwWVo4CVmJhodQkAAHiODhZUjia5+/r6KjMzs8j+H3/8Ub6+vhZUBABACdhsnm3wCuUmYBmGcdX9+fn5CggIKONqAAAASs7yIcJZs2ZJujQp8I033lDlypVdxwoLC7V58+ZyMwcLAID/iS4UVA4CVkpKiqRLHawFCxa4DQcGBATopptu0oIFC6wqDwCA6+NTbgaHYCHLA1Z6erok6d5779UHH3ygatWqWVwRAAAeoIMFlYOAddnnn39udQkAAHiOgAWVo0nujz76qKZMmVJk/2uvvabHHnvMgooAACgBvkUIlaOAtWnTJnXv3r3I/q5du2rz5s0WVAQAAFAy5WaI8OzZs1ddjsHf3185OaW44i4AAGZikjtUjjpYjRs31rvvvltk//LlyxUdHW1BRQAAlABDhFA56mCNHTtWjzzyiA4ePKj77rtPkrRhwwYtW7ZMK1assLg6AACKiZAElaOA1aNHD3344YdKSkrSihUrFBgYqDvvvFMbN25USEiI1eUBAFA8BCyoHAUsSerevbtrovvPP/+sv/zlL4qPj9fu3btVWFhocXUAABQDc7CgcjQH67KNGzfqt7/9rSIiIjRnzhx169ZN33zzjdVlAQBQriUnJ8tmsyk+Pt61zzAMJSYmKiIiQoGBgWrfvr327t3rdl1+fr6GDBmi6tWrKzg4WD169NCxY8fKuHrvUy4C1rFjxzRp0iQ1aNBAjz/+uKpVq6YLFy5o5cqVmjRpkmJjY60uEQCA4rFgkvu2bdu0cOFC3XHHHW77p02bphkzZmjOnDnatm2bnE6nOnXqpDNnzrjOiY+P16pVq7R8+XJt2bJFZ8+eVVxcHCNHHrI8YHXr1k3R0dHat2+fZs+erePHj2v27NlWlwUAQMmUccA6e/as+vXrp0WLFrk9bs4wDM2cOVNjxozRww8/rMaNG+vtt9/WuXPntGzZMklSdna23nzzTf3xj39Ux44dFRsbq3feeUdpaWn67LPPTPuRVESWB6x169ZpwIABmjBhgrp37+72sGcAAG44Hgas/Px85eTkuG35+fnXfLlBgwape/fu6tixo9v+9PR0ZWRkqHPnzq59drtd7dq1U2pqqiRp+/btunDhgts5ERERaty4sesclIzlAeuLL77QmTNn1Lx5c7Vs2VJz5szRqVOnrC4LAIASsfn4eLQlJycrNDTUbUtOTr7qay1fvlw7duy46vGMjAxJksPhcNvvcDhcxzIyMhQQEODW+bryHJSM5QGrVatWWrRokU6cOKGBAwdq+fLlql27ti5evKj169e7jRMDAFDuedjBGj16tLKzs9220aNHF3mZH374QUOHDtU777yjSpUq/Uo57sOOhmEU2Xel4pyDX2d5wLosKChIzzzzjLZs2aK0tDQNHz5cU6ZMUc2aNdWjRw+rywMAoEzY7XaFhIS4bXa7vch527dvV2Zmppo1ayY/Pz/5+flp06ZNmjVrlvz8/Fydqys7UZmZma5jTqdTBQUFysrKuuY5KJlyE7D+26233qpp06bp2LFj+utf/2p1OQAAFF8ZTXLv0KGD0tLStGvXLtfWvHlz9evXT7t27VKDBg3kdDq1fv161zUFBQXatGmTWrduLUlq1qyZ/P393c45ceKE9uzZ4zoHJVOuFhq9kq+vr3r27KmePXtaXQoAAMVTRkNrVapUUePGjd32BQcHKzw83LU/Pj5eSUlJioqKUlRUlJKSkhQUFKS+fftKkkJDQ9W/f38NHz5c4eHhCgsL04gRIxQTE1Nk0jyuT7kOWAAA3HDK0UruCQkJysvL0wsvvKCsrCy1bNlS69atU5UqVVznpKSkyM/PT7169VJeXp46dOigJUuW8K1+D9kMwzCsLuKGcS7b6gqAUvdccKTVJQClboGRU2r3Lkz6vUfX+7680KRKYCU6WAAAmIlv30HldJI7AADAjYwOFgAAZqKDBRGwAAAwVzma5A7rELAAADATHSyIgAUAgLkIWBABCwAAczFECPEtQgAAANPRwQIAwEwMEUIELAAAzEXAgghYAACYi4AFEbAAADAXk9whJrkDAACYjg4WAABmYogQImABAGAuAhZEwAIAwFw2Zt+AgAUAgLl86GCBgAUAgLnoYEF8ixAAAMB0dLAAADATk9whAhYAAOZioVGIgAUAgLnoYEEELAAAzMUkd4iABQCAuehgQXyLEAAAwHR0sAAAMBOT3CECFgAA5mKIECJgAQBgLia5QxVsDtbJkyf16quvWl0GAMCb+dg82+AVKlTAysjI0IQJE6wuAwDgzWw+nm3wCl41RPjtt9/+6vHvvvuujCoBAAAVmVcFrCZNmshms8kwjCLHLu+3MfkQAFCa+D0DeVnACg8P19SpU9WhQ4erHt+7d68eeOCBMq4KAFChMMwHeVnAatasmY4fP6569epd9fjPP/981e4WAACmYaI65GUBa+DAgcrNzb3m8bp162rx4sVlWBEAoMJhiBDysoD10EMP/erxatWq6amnniqjagAAFRJDhFAFW6YBAACgLHhVBwsAAMsxBwsiYAEAYC6GCCECFgAA5mKSO0TAAgDAXHSwIC+d5L527Vpt2bLF9fe5c+eqSZMm6tu3r7KysiysDADg9XjYM+SlAWvkyJHKycmRJKWlpWn48OHq1q2bDh06pGHDhllcHQAA8HZeOUSYnp6u6OhoSdLKlSsVFxenpKQk7dixQ926dbO4OgCAV2OIEPLSDlZAQIDOnTsnSfrss8/UuXNnSVJYWJirswUAQKmw2Tzb4BW8MmC1adNGw4YN08SJE/X111+re/fukqQDBw6oTp06FleH/2Xb9h16bugwtenUTbfGttBnn//D6pKA62avXFmPpUzR5MN7NOvcSY38cr3qNW/qOt7koQc0ZO0qTT+VrgVGjurcGVPkHsM+/0QLjBy3rf9fedxXuefj49kGr+CV/03OmTNHfn5+WrFihebPn6/atWtLkv7+97+ra9euFleH/+Vc3nndekuUxo0aaXUpQIk98cZsNep0rxY/8XtNjGml/es2Kv6zj1Q1opYkyR4crINfbtWqUeN/9T5fLFysBGdD1/aXgUPLonx4gg4W5KVzsOrWrauPP/64yP6UlBQLqsH1atemtdq1aW11GUCJ+VeqpNhHHtT8Bx/X91+kSpI+npCsO3t2V9vnB2j12In66p3lkqTwenV/9V4F5/KUczKz1GuGiZiDBXlpB2vHjh1KS0tz/f2jjz5Sz5499fLLL6ugoMDCygBUBD5+fvL189OF8+fd9l/IO6+Gbe6+rnu16NdL00+la9yer/TIa5Nkr1zZzFIBlBKvDFgDBw7UgQMHJEmHDh1Snz59FBQUpPfff18JCQkWVwfA2+WfPauDqV+p+9gEhdZyyubjoxb9euumls0VUstZ7Pt8/Zf39Objz2hG+25aM3GaYh/poec+eKcUK4cpGCKEvHSI8MCBA2rSpIkk6f3331fbtm21bNkyffnll+rTp49mzpz5P++Rn5+v/Px8t332wnzZ7fZSqBiAt1n8xO/15FtzNfX4ARX+8ot+2LFb25a9r7pN7yz2Pba88bbrz8f37lfmvw7q5e2bFRl7p37Yubs0yoYZmKgOeWkHyzAMXbx4UdKlZRour30VGRmp06dPF+seycnJCg0NdduSp88otZoBeJfTh9I1o303vRjs1OjIRprS8l75+vvpdPqREt/z6I5d+qWgQDWjbjaxUpiODhbkpR2s5s2ba9KkSerYsaM2bdqk+fPnS7q0AKnD4SjWPUaPHl1k1Xd74flrnA0AV1dw7pwKzp1TUNWqiu7SQR8kjCvxvSJubyS/gABln8gwsUKYjknukJcGrJkzZ6pfv3768MMPNWbMGDVs2FCStGLFCrVuXbxvp9nt9qLDgecMs0vFVeSeO6ejPxxz/f3Yv49r/3cHFBoSoojrmL8CWCm6cwfJZtPJ7/6lmg0b6OHXJurkd98rdfGlOVRB1aoprG4d17INjlujJEk5GSeVczJT1RvUV4t+vbRnzTrlnv5RtaJv0yN/nKyjO3bp4JdbLXtfKAa6UJBkMwyjwqSG8+fPy9fXV/7+/iW7wblscwvCVX31zXY9+ezzRfY/9EB3TXn119cMgueeC460ugSv0Oyxh9QzOVFV60To3E9Z2rlytT4c86rO/+dpEq2e6qunliwoct3Hicn6eEKyqtWprd+9s0gRjaNlrxysrB/+rT2ffKqPJ0zROR5a77EFRuk91aPw82UeXe97b1+TKoGVKlTA8hgBCxUAAQsVQakGrH8s9+h63/Z9TKoEVvLKgeLCwkJNnz5dLVq0kNPpVFhYmNsGAECp8bF5thVTcnKy7rrrLlWpUkU1a9ZUz5499d1337mdYxiGEhMTFRERocDAQLVv31579+51Oyc/P19DhgxR9erVFRwcrB49eujYsWOCZ7wyYE2YMEEzZsxQr169lJ2drWHDhunhhx+Wj4+PEhMTrS4PAODNbD6ebcW0adMmDRo0SFu3btX69ev1yy+/qHPnzsrNzXWdM23aNM2YMUNz5szRtm3b5HQ61alTJ505c8Z1Tnx8vFatWqXly5dry5YtOnv2rOLi4lRYWGjqj6Wi8cohwptvvlmzZs1S9+7dVaVKFe3atcu1b+vWrVq2rITj4wwRogJgiBAVQakOEW5Z4dH1vm0eLdF1p06dUs2aNbVp0ya1bdtWhmEoIiJC8fHxeumllyRd6lY5HA5NnTpVAwcOVHZ2tmrUqKGlS5eqd+/ekqTjx48rMjJSa9asUZcuXTx6LxWZV3awMjIyFBNz6cn0lStXVnb2pWAUFxenTz75xMrSAADerow6WFe6/Lvu8lSY9PR0ZWRkqHPnzq5z7Ha72rVrp9TUS8/I3L59uy5cuOB2TkREhBo3buw6ByXjlQGrTp06OnHihCSpYcOGWrdunSRp27ZtrMQOACjX8vPzlZOT47Zd+WSRKxmGoWHDhqlNmzZq3LixpEvNBklF1n90OByuYxkZGQoICFC1atWueQ5KxisD1kMPPaQNGzZIkoYOHaqxY8cqKipKTz75pJ555hmLqwMAeDObzebRdtUniSQn/+prDh48WN9++63++te/XrWe/2YYRpF9VyrOOfh1XrnQ6JQpU1x/fvTRR1WnTh2lpqaqYcOG6tGjh4WVAQC8nocruV/1SSK/MvoyZMgQrV69Wps3b1adOnVc+53OSwszZ2RkqFatWq79mZmZrq6W0+lUQUGBsrKy3LpYmZmZxV6YG1fnlR2sK919990aNmwY4QoAUPo8nINlt9sVEhLitl0tYBmGocGDB+uDDz7Qxo0bVb9+fbfj9evXl9Pp1Pr16137CgoKtGnTJld4atasmfz9/d3OOXHihPbs2UPA8pDXdLBWr15d7HMJWgCAUnMda1l5YtCgQVq2bJk++ugjValSxTVnKjQ0VIGBgbLZbIqPj1dSUpKioqIUFRWlpKQkBQUFqW/fvq5z+/fvr+HDhys8PFxhYWEaMWKEYmJi1LFjxzJ5H97KawJWz549i3WezWZjbQ8AQOkpo4c9z58/X5LUvn17t/2LFy/W008/LUlKSEhQXl6eXnjhBWVlZally5Zat26dqlSp4jo/JSVFfn5+6tWrl/Ly8tShQwctWbJEvr6+ZfI+vJVXroNValgHCxUA62ChIijNdbAublvj0fU+d3UzqRJYyWs6WAAAlAt8+w7ysknuGzduVHR0tHJyiv7LJDs7W7fffrs2b95sQWUAgArDooVGUb541X+TM2fO1LPPPquQkJAix0JDQzVw4EClpKRYUBkAoMKw2Tzb4BW8KmDt3r1bXbt2vebxzp07a/v27WVYEQCgwqGDBXnZHKyTJ0/K39//msf9/Px06tSpMqwIAFDhlNEyDSjfvCoq165dW2lpadc8/u2337qtZgsAAFAavCpgdevWTePGjdP58+eLHMvLy9P48eMVFxdnQWUAgAqDIULIy9bBOnnypJo2bSpfX18NHjxYt956q2w2m/bv36+5c+eqsLBQO3bsKPJk8WJjHSxUAKyDhYqgVNfBSvuHR9f7xLQ3owxYzKvmYDkcDqWmpur555/X6NGjdTk72mw2denSRfPmzSt5uAIAoDjoQkFeFrAkqV69elqzZo2ysrL0/fffyzAMRUVFuT0lHACAUsNSC5AXBqzLqlWrprvuusvqMgAAFQ0dLMjLJrkDAACUB17bwQIAwBI+9C5AwAIAwFQ25mBBBCwAAMzFHCyIgAUAgLnoYEEELAAAzEUHC+JbhAAAAKajgwUAgJkYIoQIWAAAmItlGiACFgAA5qKDBRGwAAAwF5PcISa5AwAAmI4OFgAAZmKIECJgAQBgMgIWCFgAAJiLDhZEwAIAwFwELIiABQCAyQhY4FuEAAAApqODBQCAmRgihAhYAACYi3wFEbAAADAZCQsELAAAzMUQIUTAAgDAXAQsiG8RAgAAmI4OFgAApqKDBQIWAADmYogQImABAGAyAhYIWAAAmIsOFkTAAgDAXAQsiG8RAgAAmI4OFgAApqKDBQIWAACmsjFECBGwAAAwFwELImABAGAyAhYIWAAAmIsOFsS3CAEAAExHBwsAADPRwYIIWAAAmIyABQIWAADmooMFEbAAADAX+QoiYAEAYDISFvgWIQAAgOnoYAEAYCbmYEEELAAAzEXAgghYAACYjIAFAhYAAOaigwUxyR0AAHPZbJ5t12nevHmqX7++KlWqpGbNmumLL74ohTeF60XAAgDgBvXuu+8qPj5eY8aM0c6dO/Wb3/xG999/v44ePWp1aRWezTAMw+oibhjnsq2uACh1zwVHWl0CUOoWGDmld/Pcnz27PrhqsU9t2bKlmjZtqvnz57v2NWrUSD179lRycrJndcAjzMECAMBMHs7Bys/PV35+vts+u90uu93utq+goEDbt2/XqFGj3PZ37txZqampHtUAzxGwrkdQqNUVVCj5+flKTk7W6NGji/wfC0pPqf7LHkXwOfdCHv6uSE5M1IQJE9z2jR8/XomJiW77Tp8+rcLCQjkcDrf9DodDGRkZHtUAzzFEiHIrJydHoaGhys7OVkhIiNXlAKWCzzmuVNwO1vHjx1W7dm2lpqaqVatWrv2TJ0/W0qVL9c9//rNM6sXV0cECAKAcuVqYuprq1avL19e3SLcqMzOzSFcLZY9vEQIAcAMKCAhQs2bNtH79erf969evV+vWrS2qCpfRwQIA4AY1bNgwPfHEE2revLlatWqlhQsX6ujRo3ruueesLq3CI2Ch3LLb7Ro/fjwTf+HV+JzDE71799aPP/6oV199VSdOnFDjxo21Zs0a1atXz+rSKjwmuQMAAJiMOVgAAAAmI2ABAACYjIAFAABgMgIWyoTNZtOHH35odRlAqeJzDuAyAhY8lpGRoSFDhqhBgway2+2KjIzUAw88oA0bNlhdmiTJMAwlJiYqIiJCgYGBat++vfbu3Wt1WbjBlPfP+QcffKAuXbqoevXqstls2rVrl9UlARUaAQseOXz4sJo1a6aNGzdq2rRpSktL09q1a3Xvvfdq0KBBVpcnSZo2bZpmzJihOXPmaNu2bXI6nerUqZPOnDljdWm4QdwIn/Pc3Fzdc889mjJlitWlAJAkA/DA/fffb9SuXds4e/ZskWNZWVmuP0syVq1a5fp7QkKCERUVZQQGBhr169c3XnnlFaOgoMB1fNeuXUb79u2NypUrG1WqVDGaNm1qbNu2zTAMwzh8+LARFxdnVK1a1QgKCjKio6ONTz755Kr1Xbx40XA6ncaUKVNc+86fP2+EhoYaCxYs8PDdo6Io75/z/5aenm5IMnbu3Fni9wvAcyw0ihL76aeftHbtWk2ePFnBwcFFjletWvWa11apUkVLlixRRESE0tLS9Oyzz6pKlSpKSEiQJPXr10+xsbGaP3++fH19tWvXLvn7+0uSBg0apIKCAm3evFnBwcHat2+fKleufNXXSU9PV0ZGhjp37uzaZ7fb1a5dO6WmpmrgwIEe/ARQEdwIn3MA5Q8BCyX2/fffyzAM3Xbbbdd97SuvvOL680033aThw4fr3Xffdf3iOXr0qEaOHOm6d1RUlOv8o0eP6pFHHlFMTIwkqUGDBtd8ncsPQb3ywacOh0NHjhy57rpR8dwIn3MA5Q9zsFBixn8eAmCz2a772hUrVqhNmzZyOp2qXLmyxo4dq6NHj7qODxs2TAMGDFDHjh01ZcoUHTx40HXsxRdf1KRJk3TPPfdo/Pjx+vbbb//n611Zo2EYJaobFc+N9DkHUH4QsFBiUVFRstls2r9//3Vdt3XrVvXp00f333+/Pv74Y+3cuVNjxoxRQUGB65zExETt3btX3bt318aNGxUdHa1Vq1ZJkgYMGKBDhw7piSeeUFpampo3b67Zs2df9bWcTqek/+tkXZaZmVmkqwVczY3wOQdQDlk6Aww3vK5du1735N/p06cbDRo0cDu3f//+Rmho6DVfp0+fPsYDDzxw1WOjRo0yYmJirnrs8iT3qVOnuvbl5+czyR3Xpbx/zv8bk9yB8oEOFjwyb948FRYWqkWLFlq5cqX+9a9/af/+/Zo1a5ZatWp11WsaNmyoo0ePavny5Tp48KBmzZrl+le7JOXl5Wnw4MH6xz/+oSNHjujLL7/Utm3b1KhRI0lSfHy8Pv30U6Wnp2vHjh3auHGj69iVbDab4uPjlZSUpFWrVmnPnj16+umnFRQUpL59+5r/A4FXKu+fc+nSZPxdu3Zp3759kqTvvvtOu3btKtK9BVBGrE54uPEdP37cGDRokFGvXj0jICDAqF27ttGjRw/j888/d52jK76+PnLkSCM8PNyoXLmy0bt3byMlJcX1L/v8/HyjT58+RmRkpBEQEGBEREQYgwcPNvLy8gzDMIzBgwcbN998s2G3240aNWoYTzzxhHH69Olr1nfx4kVj/PjxhtPpNOx2u9G2bVsjLS2tNH4U8GLl/XO+ePFiQ1KRbfz48aXw0wDwv9gM4z8zOAEAAGAKhggBAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELKACS0xMVJMmTVx/f/rpp9WzZ88yr+Pw4cOy2WzatWtXmb82AJQGAhZQDj399NOy2Wyy2Wzy9/dXgwYNNGLECOXm5pbq6/7pT3/SkiVLinUuoQgArs3P6gIAXF3Xrl21ePFiXbhwQV988YUGDBig3NxczZ8/3+28CxcuyN/f35TXDA0NNeU+AFDR0cECyim73S6n06nIyEj17dtX/fr104cffuga1nvrrbfUoEED2e12GYah7Oxs/f73v1fNmjUVEhKi++67T7t373a755QpU+RwOFSlShX1799f58+fdzt+5RDhxYsXNXXqVDVs2FB2u11169bV5MmTJUn169eXJMXGxspms6l9+/au6xYvXqxGjRqpUqVKuu222zRv3jy31/n6668VGxurSpUqqXnz5tq5c6eJPzkAsB4dLOAGERgYqAsXLkiSvv/+e7333ntauXKlfH19JUndu3dXWFiY1qxZo9DQUL3++uvq0KGDDhw4oLCwML333nsaP3685s6dq9/85jdaunSpZs2apQYNGlzzNUePHq1FixYpJSVFbdq00YkTJ/TPf/5T0qWQ1KJFC3322We6/fbbFRAQIElatGiRxo8frzlz5ig2NlY7d+7Us88+q+DgYD311FPKzc1VXFyc7rvvPr3zzjtKT0/X0KFDS/mnBwBlzOKHTQO4iqeeesp48MEHXX//6quvjPDwcKNXr17G+PHjDX9/fyMzM9N1fMOGDUZISIhx/vx5t/vcfPPNxuuvv24YhmG0atXKeO6559yOt2zZ0rjzzjuv+ro5OTmG3W43Fi1adNUa09PTDUnGzp073fZHRkYay5Ytc9s3ceJEo1WrVoZhGMbrr79uhIWFGbm5ua7j8+fPv+q9AOBGxRAhUE59/PHHqly5sipVqqRWrVqpbdu2mj17tiSpXr16qlGjhuvc7du36+zZswoPD1flypVdW3p6ug4ePChJ2r9/v1q1auX2Glf+/b/t379f+fn56tChQ7FrPnXqlH744Qf179/frY5Jkya51XHnnXcqKCioWHUAwI2IIUKgnLr33ns1f/58+fv7KyIiwm0ie3BwsNu5Fy9eVK1atfSPf/yjyH2qVq1aotcPDAy87msuXrwo6dIwYcuWLd2OXR7KNAyjRPUAwI2EgAWUU8HBwWrYsGGxzm3atKkyMjLk5+enm2666arnNGrUSFu3btWTTz7p2rd169Zr3jMqKkqBgYHasGGDBgwYUOT45TlXhYWFrn0Oh0O1a9fWoUOH1K9fv6veNzo6WkuXLlVeXp4rxP1aHQBwI2KIEPACHTt2VKtWrdSzZ099+umnOnz4sFJTU/XKK6/om2++kSQNHTpUb731lt566y0dOHBA48eP1969e695z0qVKumll15SQkKC/vznP+vgwYPaunWr3nzzTUlSzZo1FRgYqLVr1+rkyZPKzs6WdGnx0uTkZP3pT3/SgQMHlJaWpsWLF2vGjBmSpL59+8rHx0f9+/fXvn37tGbNGk2fPr2Uf0IAULYIWIAXsNlsWrNmjdq2batnnnlGt9xyi/r06aPDhw/L4XBIknr37q1x48bppZdeUrNmzXTkyBE9//zzv3rfsWPHavjw4Ro3bpwaNWqk3r17KzMzU5Lk5+enWbNm6fXXX1dERIQefPBBSdKAAQP0xhtvaMmSJYqJiVG7du20ZMkS17IOlStX1t/+9jft27dPsbGxGjNmjKZOnVqKPx0AKHs2gwkRAAAApqKDBQAAYDICFgAAgMkIWAAAACb7/8lLBt7n6ZCdAAAAAElFTkSuQmCC" 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) │ 39,033,088 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 117,222,917 (447.17 MB) | |
| Trainable params: 39,074,305 (149.06 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 78,148,612 (298.11 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 4578 50.16 | |
| 0 4549 49.84</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 9127</li> | |
| <li>Imbalance Ratio: 1.01</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9967 1.0000 0.9984 910 | |
| Class 1 1.0000 0.9967 0.9984 916 | |
| accuracy 0.9984 1826 | |
| macro avg 0.9984 0.9984 0.9984 1826 | |
| weighted avg 0.9984 0.9984 0.9984 1826 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 913</li> | |
| <li>True Negatives (TN): 910</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 0</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> | |
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