| <html> | |
| <head> | |
| <title>GENE_FAMILY: FAR1</title> | |
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| body { font-family: Arial, sans-serif; } | |
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| .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=FAR1' target='_blank'>FAR1</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 2505 50.1 | |
| 0 2495 49.9</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9775 0.9559 0.9666 499 | |
| Class 1 0.9570 0.9780 0.9674 501 | |
| accuracy 0.9670 1000 | |
| macro avg 0.9672 0.9670 0.9670 1000 | |
| weighted avg 0.9672 0.9670 0.9670 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 490</li> | |
| <li>True Negatives (TN): 477</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 22</li> | |
| <li>False Negatives (FN): 11</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) │ 2,241,536 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 6,854,405 (26.15 MB) | |
| Trainable params: 2,284,801 (8.72 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 4,569,604 (17.43 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 2505 50.1 | |
| 0 2495 49.9</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9960 0.9860 0.9909 499 | |
| Class 1 0.9862 0.9960 0.9911 501 | |
| accuracy 0.9910 1000 | |
| macro avg 0.9911 0.9910 0.9910 1000 | |
| weighted avg 0.9910 0.9910 0.9910 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 499</li> | |
| <li>True Negatives (TN): 492</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 7</li> | |
| <li>False Negatives (FN): 2</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_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) │ 37,925,888 │ | |
| ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ | |
| │ 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: 113,901,317 (434.50 MB) | |
| Trainable params: 37,967,105 (144.83 MB) | |
| Non-trainable params: 0 (0.00 B) | |
| Optimizer params: 75,934,212 (289.67 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 2505 50.1 | |
| 0 2495 49.9</pre> | |
| <h3>Additional Metrics</h3> | |
| <ul> | |
| <li>Total Samples: 5000</li> | |
| <li>Imbalance Ratio: 1.00</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>Classification Report</h2> | |
| <pre> precision recall f1-score support | |
| Class 0 0.9822 0.9960 0.9891 499 | |
| Class 1 0.9960 0.9820 0.9889 501 | |
| accuracy 0.9890 1000 | |
| macro avg 0.9891 0.9890 0.9890 1000 | |
| weighted avg 0.9891 0.9890 0.9890 1000 | |
| </pre> | |
| <h3>Metrics</h3> | |
| <div class="metrics"> | |
| <ul> | |
| <li>True Positives (TP): 492</li> | |
| <li>True Negatives (TN): 497</li> | |
| </ul> | |
| <ul> | |
| <li>False Positives (FP): 2</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> | |
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