|
70 | 70 | "execution_count": 3, |
71 | 71 | "metadata": { |
72 | 72 | "collapsed": false, |
73 | | - "scrolled": true |
| 73 | + "scrolled": false |
74 | 74 | }, |
75 | 75 | "outputs": [ |
76 | 76 | { |
|
213 | 213 | }, |
214 | 214 | { |
215 | 215 | "cell_type": "code", |
216 | | - "execution_count": 4, |
| 216 | + "execution_count": 5, |
217 | 217 | "metadata": { |
218 | 218 | "collapsed": false |
219 | 219 | }, |
|
326 | 326 | }, |
327 | 327 | { |
328 | 328 | "cell_type": "code", |
329 | | - "execution_count": 23, |
| 329 | + "execution_count": 7, |
330 | 330 | "metadata": { |
331 | 331 | "collapsed": false |
332 | 332 | }, |
|
355 | 355 | }, |
356 | 356 | { |
357 | 357 | "cell_type": "code", |
358 | | - "execution_count": 24, |
| 358 | + "execution_count": 8, |
359 | 359 | "metadata": { |
360 | 360 | "collapsed": false |
361 | 361 | }, |
|
364 | 364 | "name": "stdout", |
365 | 365 | "output_type": "stream", |
366 | 366 | "text": [ |
367 | | - "Accuracy:0.9894268224819144\n" |
| 367 | + "Accuracy:0.9860879243183084\n" |
368 | 368 | ] |
369 | 369 | } |
370 | 370 | ], |
|
485 | 485 | }, |
486 | 486 | { |
487 | 487 | "cell_type": "code", |
488 | | - "execution_count": 27, |
| 488 | + "execution_count": 9, |
489 | 489 | "metadata": { |
490 | 490 | "collapsed": false |
491 | 491 | }, |
|
494 | 494 | "name": "stdout", |
495 | 495 | "output_type": "stream", |
496 | 496 | "text": [ |
497 | | - "{'activation': 'tanh', 'epochs': 50, 'optimizer': 'adam', 'patience': 5, 'batch_size': 32, 'neurons': 32}\n", |
498 | | - "MSE:0.9961046188091264\n" |
| 497 | + "{'activation': 'relu', 'batch_size': 16, 'epochs': 50, 'neurons': 32, 'optimizer': 'adam', 'patience': 5}\n", |
| 498 | + "Accuracy:0.9994435169727324\n" |
499 | 499 | ] |
500 | 500 | } |
501 | 501 | ], |
|
514 | 514 | "grid = GridSearchCV(clf, rf_params, cv=3,scoring='accuracy')\n", |
515 | 515 | "grid.fit(X, y)\n", |
516 | 516 | "print(grid.best_params_)\n", |
517 | | - "print(\"MSE:\"+ str(grid.best_score_))" |
| 517 | + "print(\"Accuracy:\"+ str(grid.best_score_))" |
518 | 518 | ] |
519 | 519 | }, |
520 | 520 | { |
|
1494 | 1494 | }, |
1495 | 1495 | { |
1496 | 1496 | "cell_type": "code", |
1497 | | - "execution_count": 35, |
| 1497 | + "execution_count": 10, |
1498 | 1498 | "metadata": { |
1499 | 1499 | "collapsed": false |
1500 | 1500 | }, |
|
1503 | 1503 | "name": "stdout", |
1504 | 1504 | "output_type": "stream", |
1505 | 1505 | "text": [ |
1506 | | - "{'activation': 1.962890625, 'epochs': 21.611328125, 'optimizer': 2.6572265625, 'patience': 16.0322265625, 'batch_size': 1.041015625, 'neurons': 56.318359375}\n", |
1507 | | - "MSE:0.9905397885364496\n" |
| 1506 | + "{'optimizer': 1.614455714955763, 'activation': 0.41885608906506233, 'batch_size': 1.1755859375, 'neurons': 79.7763671875, 'epochs': 21.244529758913554, 'patience': 3.140998456489326}\n", |
| 1507 | + "Accuracy:0.9907252828788723\n" |
1508 | 1508 | ] |
1509 | 1509 | } |
1510 | 1510 | ], |
|
1560 | 1560 | " **search\n", |
1561 | 1561 | " )\n", |
1562 | 1562 | "print(optimal_configuration)\n", |
1563 | | - "print(\"MSE:\"+ str(info.optimum))" |
| 1563 | + "print(\"Accuracy:\"+ str(info.optimum))" |
1564 | 1564 | ] |
1565 | 1565 | }, |
1566 | 1566 | { |
|
1999 | 1999 | "ga2.fit(X, y)" |
2000 | 2000 | ] |
2001 | 2001 | }, |
2002 | | - { |
2003 | | - "cell_type": "code", |
2004 | | - "execution_count": null, |
2005 | | - "metadata": { |
2006 | | - "collapsed": true |
2007 | | - }, |
2008 | | - "outputs": [], |
2009 | | - "source": [] |
2010 | | - }, |
2011 | 2002 | { |
2012 | 2003 | "cell_type": "code", |
2013 | 2004 | "execution_count": null, |
|
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