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@cerlymarco
cerlymarco committed May 5, 2021
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168 changes: 104 additions & 64 deletions notebooks/Advance Usage.ipynb
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"Epoch 00041: early stopping\n",
"SCORE: 0.94431 at epoch 31\n"
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"Epoch 00046: early stopping\n",
"SCORE: 0.9406 at epoch 36\n"
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"8 trials detected for ('unit', 'kernel', 'lr', 'layer_types', 'epochs', 'batch_size')\n",
"\n",
"***** (1/8) *****\n",
"Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00020: early stopping\n",
"SCORE: 0.92913 at epoch 18\n",
"\n",
"***** (2/8) *****\n",
"Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00008: early stopping\n",
"SCORE: 0.108 at epoch 3\n",
"\n",
"***** (3/8) *****\n",
"Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"***** (2/8) *****\n",
"Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00013: early stopping\n",
"SCORE: 0.96423 at epoch 13\n",
"Epoch 00020: early stopping\n",
"SCORE: 0.92913 at epoch 18\n",
"\n",
"***** (4/8) *****\n",
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"Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00040: early stopping\n",
"SCORE: 0.71785 at epoch 35\n",
"\n",
"***** (5/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"***** (4/8) *****\n",
"Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00013: early stopping\n",
"SCORE: 0.8866 at epoch 9\n",
"SCORE: 0.96423 at epoch 13\n",
"\n",
"***** (6/8) *****\n",
"***** (5/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00006: early stopping\n",
"SCORE: 0.1134 at epoch 1\n",
"\n",
"***** (7/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"***** (6/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00012: early stopping\n",
"SCORE: 0.96625 at epoch 8\n",
"Epoch 00013: early stopping\n",
"SCORE: 0.8866 at epoch 9\n",
"\n",
"***** (8/8) *****\n",
"***** (7/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00054: early stopping\n",
"SCORE: 0.77354 at epoch 49\n"
"SCORE: 0.77354 at epoch 49\n",
"\n",
"***** (8/8) *****\n",
"Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00012: early stopping\n",
"SCORE: 0.96625 at epoch 8\n"
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"16 trials detected for ('unit_1', 'unit_2', 'opt', 'lr', 'epochs', 'batch_size')\n",
"\n",
"***** (1/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00016: early stopping\n",
"SCORE: 0.95174 at epoch 13\n",
"\n",
"***** (2/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00033: early stopping\n",
"SCORE: 0.95005 at epoch 28\n",
"\n",
"***** (3/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00012: early stopping\n",
"SCORE: 0.95073 at epoch 7\n",
"\n",
"***** (4/16) *****\n",
"***** (2/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00030: early stopping\n",
"SCORE: 0.94938 at epoch 30\n",
"\n",
"***** (5/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"***** (3/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00019: early stopping\n",
"SCORE: 0.9514 at epoch 14\n",
"Epoch 00016: early stopping\n",
"SCORE: 0.95174 at epoch 13\n",
"\n",
"***** (6/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"***** (4/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00029: early stopping\n",
"SCORE: 0.94533 at epoch 24\n",
"Epoch 00033: early stopping\n",
"SCORE: 0.95005 at epoch 28\n",
"\n",
"***** (7/16) *****\n",
"***** (5/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00015: early stopping\n",
"SCORE: 0.94668 at epoch 10\n",
"\n",
"***** (8/16) *****\n",
"***** (6/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00027: early stopping\n",
"SCORE: 0.94398 at epoch 22\n",
"\n",
"***** (9/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"***** (7/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00019: early stopping\n",
"SCORE: 0.95073 at epoch 14\n",
"SCORE: 0.9514 at epoch 14\n",
"\n",
"***** (10/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"***** (8/16) *****\n",
"Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00029: early stopping\n",
"SCORE: 0.93824 at epoch 28\n",
"SCORE: 0.94533 at epoch 24\n",
"\n",
"***** (11/16) *****\n",
"***** (9/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00019: early stopping\n",
"SCORE: 0.94701 at epoch 17\n",
"\n",
"***** (12/16) *****\n",
"***** (10/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00035: early stopping\n",
"SCORE: 0.9406 at epoch 34\n",
"\n",
"***** (13/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"***** (11/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00023: early stopping\n",
"SCORE: 0.9487 at epoch 18\n",
"Epoch 00019: early stopping\n",
"SCORE: 0.95073 at epoch 14\n",
"\n",
"***** (14/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"***** (12/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00042: early stopping\n",
"SCORE: 0.93925 at epoch 37\n",
"Epoch 00029: early stopping\n",
"SCORE: 0.93824 at epoch 28\n",
"\n",
"***** (15/16) *****\n",
"***** (13/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00017: early stopping\n",
"SCORE: 0.94735 at epoch 12\n",
"\n",
"***** (16/16) *****\n",
"***** (14/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00027: early stopping\n",
"SCORE: 0.93486 at epoch 22\n"
"SCORE: 0.93486 at epoch 22\n",
"\n",
"***** (15/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00023: early stopping\n",
"SCORE: 0.9487 at epoch 18\n",
"\n",
"***** (16/16) *****\n",
"Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n",
"Restoring model weights from the end of the best epoch.\n",
"Epoch 00042: early stopping\n",
"SCORE: 0.93925 at epoch 37\n"
]
},
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"data": {
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"execution_count": 15,
"metadata": {},
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"source": [
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"name": "python",
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"nbformat": 4,
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