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classification/.envrc
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,summary,config,name
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0,"{'test/epoch_acc': 0.7333333333333334, 'test/precision': 0.8285714285714286, 'test/epoch_loss': 0.5664619127909343, 'train/epoch_acc': 0.8230958230958231, '_step': 2059, 'epoch': 9, '_timestamp': 1680692970.2016854, 'test/f1-score': 0.7073170731707318, 'train/batch_loss': 0.33577921986579895, 'train/epoch_loss': 0.4241055610431793, '_wandb': {'runtime': 363}, '_runtime': 367.13677954673767, 'test/recall': 0.6170212765957447}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003}",fiery-sweep-26
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0,"{'_step': 2059, '_timestamp': 1680692970.2016854, 'test/recall': 0.6170212765957447, 'test/f1-score': 0.7073170731707318, 'test/epoch_acc': 0.7333333333333334, 'test/epoch_loss': 0.5664619127909343, 'train/epoch_loss': 0.4241055610431793, 'epoch': 9, '_wandb': {'runtime': 363}, '_runtime': 367.13677954673767, 'test/precision': 0.8285714285714286, 'train/epoch_acc': 0.8230958230958231, 'train/batch_loss': 0.33577921986579895}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003}",fiery-sweep-26
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1,"{'epoch': 9, '_wandb': {'runtime': 338}, '_runtime': 341.8420207500458, 'test/precision': 0.6851851851851852, 'train/epoch_acc': 0.7125307125307125, 'train/epoch_loss': 0.649790015355375, '_step': 1039, 'test/recall': 0.8222222222222222, 'test/f1-score': 0.7474747474747475, 'test/epoch_acc': 0.7222222222222222, 'test/epoch_loss': 0.6454579922888014, 'train/batch_loss': 0.7014500498771667, '_timestamp': 1680692589.503975}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0003}",radiant-sweep-25
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1,"{'test/f1-score': 0.7474747474747475, 'test/precision': 0.6851851851851852, 'test/epoch_loss': 0.6454579922888014, 'train/batch_loss': 0.7014500498771667, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 338}, '_runtime': 341.8420207500458, 'train/epoch_loss': 0.649790015355375, '_timestamp': 1680692589.503975, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.7222222222222222, 'train/epoch_acc': 0.7125307125307125}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0003}",radiant-sweep-25
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2,"{'test/recall': 0.7837837837837838, 'test/precision': 0.935483870967742, 'test/epoch_loss': 0.34812947780333664, 'train/epoch_loss': 0.01614290558709019, '_step': 1039, 'epoch': 9, '_timestamp': 1680692234.39516, 'test/epoch_acc': 0.888888888888889, 'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.01956617273390293, '_wandb': {'runtime': 333}, '_runtime': 336.8275649547577, 'test/f1-score': 0.8529411764705881}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003}",blooming-sweep-24
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2,"{'train/epoch_acc': 0.9987714987714988, 'train/epoch_loss': 0.01614290558709019, '_step': 1039, 'epoch': 9, '_runtime': 336.8275649547577, '_timestamp': 1680692234.39516, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.935483870967742, '_wandb': {'runtime': 333}, 'test/recall': 0.7837837837837838, 'test/f1-score': 0.8529411764705881, 'test/epoch_loss': 0.34812947780333664, 'train/batch_loss': 0.01956617273390293}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003}",blooming-sweep-24
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3,"{'_wandb': {'runtime': 327}, '_runtime': 331.57809829711914, '_timestamp': 1680691883.3877182, 'test/precision': 0.7608695652173914, 'test/epoch_loss': 0.5553177932898203, 'train/batch_loss': 0.5222326517105103, 'train/epoch_loss': 0.5324229019572753, 'epoch': 9, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7954545454545455, 'test/epoch_acc': 0.8, 'train/epoch_acc': 0.8353808353808354, '_step': 529}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0003}",visionary-sweep-23
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3,"{'train/epoch_acc': 0.8353808353808354, 'train/epoch_loss': 0.5324229019572753, 'epoch': 9, '_runtime': 331.57809829711914, '_timestamp': 1680691883.3877182, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7954545454545455, 'test/precision': 0.7608695652173914, '_step': 529, '_wandb': {'runtime': 327}, 'test/epoch_acc': 0.8, 'test/epoch_loss': 0.5553177932898203, 'train/batch_loss': 0.5222326517105103}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0003}",visionary-sweep-23
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4,"{'train/epoch_loss': 0.7508098256090057, 'epoch': 1, '_timestamp': 1680691538.7247725, 'test/recall': 0.8846153846153846, 'test/epoch_acc': 0.5777777777777778, 'train/epoch_acc': 0.5577395577395577, 'train/batch_loss': 0.5083656311035156, '_step': 410, '_wandb': {'runtime': 70}, '_runtime': 71.64615154266357, 'test/f1-score': 0.7076923076923076, 'test/precision': 0.5897435897435898, 'test/epoch_loss': 1.5602711306677923}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01}",ancient-sweep-22
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4,"{'test/precision': 0.5897435897435898, 'train/epoch_acc': 0.5577395577395577, '_step': 410, 'epoch': 1, '_runtime': 71.64615154266357, '_timestamp': 1680691538.7247725, 'test/f1-score': 0.7076923076923076, 'test/epoch_acc': 0.5777777777777778, 'train/batch_loss': 0.5083656311035156, '_wandb': {'runtime': 70}, 'test/recall': 0.8846153846153846, 'test/epoch_loss': 1.5602711306677923, 'train/epoch_loss': 0.7508098256090057}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01}",ancient-sweep-22
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5,"{'_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}, '_timestamp': 1680691453.5148375, 'test/precision': 0.6885245901639344, 'train/epoch_loss': 0.49390909720111537, '_runtime': 331.44886469841003, 'test/recall': 0.9545454545454546, 'test/f1-score': 0.8, 'test/epoch_acc': 0.7666666666666667, 'test/epoch_loss': 0.4844042791260613, 'train/epoch_acc': 0.769041769041769, 'train/batch_loss': 0.4559023082256317}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.003}",fresh-sweep-22
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5,"{'_wandb': {'runtime': 328}, 'test/recall': 0.9545454545454546, '_step': 529, 'epoch': 9, '_runtime': 331.44886469841003, '_timestamp': 1680691453.5148375, 'test/f1-score': 0.8, 'test/epoch_acc': 0.7666666666666667, 'test/precision': 0.6885245901639344, 'test/epoch_loss': 0.4844042791260613, 'train/epoch_acc': 0.769041769041769, 'train/batch_loss': 0.4559023082256317, 'train/epoch_loss': 0.49390909720111537}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.003}",fresh-sweep-22
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6,"{'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.26263883135527266, 'train/epoch_acc': 0.9975429975429976, 'epoch': 9, '_wandb': {'runtime': 355}, '_timestamp': 1680691110.042932, 'test/recall': 0.8867924528301887, 'test/f1-score': 0.9306930693069309, '_step': 2059, '_runtime': 358.66950702667236, 'test/precision': 0.9791666666666666, 'train/batch_loss': 0.0031523401848971844, 'train/epoch_loss': 0.018423480946079804}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.01}",pleasant-sweep-21
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6,"{'test/f1-score': 0.9306930693069309, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.26263883135527266, 'train/epoch_loss': 0.018423480946079804, 'epoch': 9, '_runtime': 358.66950702667236, '_timestamp': 1680691110.042932, 'test/precision': 0.9791666666666666, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.0031523401848971844, '_step': 2059, '_wandb': {'runtime': 355}, 'test/recall': 0.8867924528301887}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.01}",pleasant-sweep-21
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7,"{'train/epoch_loss': 0.0014873178028192654, 'epoch': 9, '_runtime': 332.6156196594238, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.8865979381443299, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.3669874522421095, 'train/batch_loss': 0.003317732596769929, '_step': 279, '_wandb': {'runtime': 329}, '_timestamp': 1680690741.3215847, 'test/precision': 0.86, 'train/epoch_acc': 1}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.01}",fragrant-sweep-20
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7,"{'epoch': 9, '_wandb': {'runtime': 329}, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.86, 'train/epoch_acc': 1, 'train/batch_loss': 0.003317732596769929, '_step': 279, '_runtime': 332.6156196594238, '_timestamp': 1680690741.3215847, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.8865979381443299, 'test/epoch_loss': 0.3669874522421095, 'train/epoch_loss': 0.0014873178028192654}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.01}",fragrant-sweep-20
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8,"{'epoch': 9, 'test/recall': 0.82, 'test/precision': 0.7592592592592593, 'test/epoch_loss': 0.5786970999505785, 'train/epoch_acc': 0.8206388206388207, 'train/batch_loss': 0.58731609582901, '_step': 149, '_runtime': 342.05230498313904, '_timestamp': 1680690397.165603, 'test/f1-score': 0.7884615384615384, 'test/epoch_acc': 0.7555555555555555, 'train/epoch_loss': 0.5623220165765842, '_wandb': {'runtime': 338}}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001}",treasured-sweep-19
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8,"{'test/epoch_loss': 0.5786970999505785, 'train/epoch_acc': 0.8206388206388207, 'train/epoch_loss': 0.5623220165765842, '_wandb': {'runtime': 338}, 'test/recall': 0.82, 'test/precision': 0.7592592592592593, '_timestamp': 1680690397.165603, 'test/f1-score': 0.7884615384615384, 'test/epoch_acc': 0.7555555555555555, 'train/batch_loss': 0.58731609582901, '_step': 149, 'epoch': 9, '_runtime': 342.05230498313904}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001}",treasured-sweep-19
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9,"{'_timestamp': 1680690042.488695, 'test/f1-score': 0.7865168539325843, 'test/precision': 0.8536585365853658, 'train/batch_loss': 0.5736206769943237, 'epoch': 9, '_wandb': {'runtime': 357}, '_runtime': 360.5366156101227, 'test/epoch_loss': 0.6037532766660054, 'train/epoch_acc': 0.7788697788697788, 'train/epoch_loss': 0.5984062318134074, '_step': 2059, 'test/recall': 0.7291666666666666, 'test/epoch_acc': 0.788888888888889}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.0001}",desert-sweep-18
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9,"{'test/recall': 0.7291666666666666, 'test/f1-score': 0.7865168539325843, 'test/precision': 0.8536585365853658, 'test/epoch_loss': 0.6037532766660054, 'train/batch_loss': 0.5736206769943237, 'train/epoch_loss': 0.5984062318134074, '_step': 2059, 'epoch': 9, '_timestamp': 1680690042.488695, 'test/epoch_acc': 0.788888888888889, 'train/epoch_acc': 0.7788697788697788, '_wandb': {'runtime': 357}, '_runtime': 360.5366156101227}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.0001}",desert-sweep-18
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10,"{'_timestamp': 1680689670.8310964, 'test/f1-score': 0.8333333333333334, 'test/epoch_loss': 0.3740654948684904, 'train/epoch_acc': 0.8697788697788698, '_step': 2059, 'epoch': 9, 'test/recall': 0.7446808510638298, 'test/epoch_acc': 0.8444444444444444, 'test/precision': 0.945945945945946, 'train/batch_loss': 0.5778521299362183, 'train/epoch_loss': 0.3086323318522451, '_wandb': {'runtime': 362}, '_runtime': 365.3367943763733}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.003}",celestial-sweep-17
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10,"{'epoch': 9, '_runtime': 365.3367943763733, 'test/recall': 0.7446808510638298, 'test/f1-score': 0.8333333333333334, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8697788697788698, 'train/epoch_loss': 0.3086323318522451, '_step': 2059, '_wandb': {'runtime': 362}, '_timestamp': 1680689670.8310964, 'test/epoch_acc': 0.8444444444444444, 'test/epoch_loss': 0.3740654948684904, 'train/batch_loss': 0.5778521299362183}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.003}",celestial-sweep-17
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11,"{'test/recall': 0.9285714285714286, 'test/f1-score': 0.9176470588235294, 'test/precision': 0.9069767441860463, 'train/epoch_acc': 1, 'epoch': 9, '_wandb': {'runtime': 337}, '_runtime': 340.39124369621277, '_timestamp': 1680689237.7951498, 'train/epoch_loss': 0.0053219743558098115, '_step': 149, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.18080708616309696, 'train/batch_loss': 0.004256190732121468}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.01}",cosmic-sweep-15
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11,"{'train/batch_loss': 0.004256190732121468, 'train/epoch_loss': 0.0053219743558098115, 'epoch': 9, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9069767441860463, 'test/epoch_loss': 0.18080708616309696, 'train/epoch_acc': 1, 'test/f1-score': 0.9176470588235294, '_step': 149, '_wandb': {'runtime': 337}, '_runtime': 340.39124369621277, '_timestamp': 1680689237.7951498, 'test/recall': 0.9285714285714286}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.01}",cosmic-sweep-15
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12,"{'_timestamp': 1680688886.363035, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.925, 'train/epoch_loss': 0.09628425111664636, 'test/epoch_loss': 0.23811448697621623, 'train/epoch_acc': 0.968058968058968, 'train/batch_loss': 0.21692615747451785, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 356}, '_runtime': 359.0396990776062, 'test/f1-score': 0.8705882352941177}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.001}",stilted-sweep-14
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12,"{'test/f1-score': 0.8705882352941177, 'test/epoch_loss': 0.23811448697621623, 'train/epoch_acc': 0.968058968058968, 'train/batch_loss': 0.21692615747451785, 'train/epoch_loss': 0.09628425111664636, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.925, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 356}, '_runtime': 359.0396990776062, '_timestamp': 1680688886.363035}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.001}",stilted-sweep-14
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13,"{'_step': 149, 'test/f1-score': 0.9278350515463918, 'test/epoch_loss': 0.16714997291564945, 'train/epoch_acc': 1, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9574468085106383, 'train/batch_loss': 0.007201554253697395, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 336.5640392303467, '_timestamp': 1680688517.0028613, 'test/recall': 0.9, 'train/epoch_loss': 0.007631345846546077}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01}",frosty-sweep-13
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13,"{'_wandb': {'runtime': 333}, '_runtime': 336.5640392303467, 'test/recall': 0.9, 'test/f1-score': 0.9278350515463918, 'test/epoch_acc': 0.9222222222222224, 'train/epoch_loss': 0.007631345846546077, '_step': 149, 'epoch': 9, 'test/epoch_loss': 0.16714997291564945, 'train/epoch_acc': 1, 'train/batch_loss': 0.007201554253697395, '_timestamp': 1680688517.0028613, 'test/precision': 0.9574468085106383}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01}",frosty-sweep-13
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14,"{'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.32556109494633145, 'train/epoch_loss': 0.17368088453934877, '_runtime': 331.98337984085083, '_timestamp': 1680688162.2054858, 'test/recall': 0.8181818181818182, 'test/f1-score': 0.8674698795180724, 'test/precision': 0.9230769230769232, 'train/epoch_acc': 0.9496314496314496, 'train/batch_loss': 0.27152174711227417, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001}",young-sweep-12
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14,"{'_timestamp': 1680688162.2054858, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8777777777777778, 'train/batch_loss': 0.27152174711227417, 'train/epoch_acc': 0.9496314496314496, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}, '_runtime': 331.98337984085083, 'test/f1-score': 0.8674698795180724, 'test/precision': 0.9230769230769232, 'test/epoch_loss': 0.32556109494633145, 'train/epoch_loss': 0.17368088453934877}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001}",young-sweep-12
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||||||
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||||||
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|
||||||
28,"{'_timestamp': 1680629513.1781075, 'test/epoch_loss': 3.395405118153546e+20, 'train/batch_loss': 82027960, 'train/epoch_loss': 60563307.6520902, 'epoch': 3, '_wandb': {'runtime': 135}, '_runtime': 132.22715950012207, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.6721311475409836, 'test/epoch_acc': 0.5555555555555556, 'test/precision': 0.5324675324675324, 'train/epoch_acc': 0.5282555282555282, '_step': 210}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.003}",smooth-sweep-12
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28,"{'train/epoch_acc': 0.5282555282555282, 'train/batch_loss': 82027960.0, 'train/epoch_loss': 60563307.6520902, 'epoch': 3, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.6721311475409836, 'test/precision': 0.5324675324675324, 'test/epoch_acc': 0.5555555555555556, 'test/epoch_loss': 3.395405118153546e+20, '_step': 210, '_wandb': {'runtime': 135}, '_runtime': 132.22715950012207, '_timestamp': 1680629513.1781075}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.003}",smooth-sweep-12
|
||||||
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29,"{'_timestamp': 1680629374.0562296, 'test/epoch_acc': 0.6333333333333333, 'train/batch_loss': 0.5836847424507141, 'train/epoch_loss': 0.6072891213970044, '_step': 279, '_wandb': {'runtime': 326}, '_runtime': 327.2181556224823, 'test/recall': 0.8888888888888888, 'test/f1-score': 0.6597938144329897, 'test/precision': 0.5245901639344263, 'test/epoch_loss': 0.6240786300765143, 'train/epoch_acc': 0.7469287469287469, 'epoch': 9}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.0003}",resilient-sweep-11
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||||||
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||||||
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||||||
40,"{'train/epoch_loss': 0.02368298517580857, 'epoch': 9, 'test/recall': 0.8653846153846154, 'test/f1-score': 0.9, 'test/precision': 0.9375, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.25786760796585845, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.05631007254123688, '_step': 2059, '_wandb': {'runtime': 346}, '_runtime': 347.9354045391083, '_timestamp': 1680624250.2654595}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.1}",valiant-sweep-23
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||||||
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||||||
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||||||
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43,"{'_runtime': 327.10622239112854, 'test/f1-score': 0.780952380952381, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5943129923608568, 'train/batch_loss': 0.6166229844093323, 'train/epoch_loss': 0.5714027147914034, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 325}, '_timestamp': 1680623221.0825984, 'test/recall': 0.8723404255319149, 'test/precision': 0.7068965517241379, 'train/epoch_acc': 0.7911547911547911}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001}",lemon-sweep-20
|
||||||
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50,"{'train/epoch_acc': 0.6523341523341524, 'train/batch_loss': 0.6023905277252197, '_wandb': {'runtime': 351}, '_timestamp': 1680620790.920825, 'test/recall': 0.675, 'test/f1-score': 0.6585365853658537, 'test/precision': 0.6428571428571429, 'test/epoch_loss': 0.661226307021247, 'train/epoch_loss': 0.6673213337211703, '_step': 2059, 'epoch': 9, '_runtime': 352.6435329914093, 'test/epoch_acc': 0.6888888888888889}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.0001}",glorious-sweep-13
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||||||
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||||||
53,"{'train/epoch_acc': 0.8611793611793611, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 322}, '_timestamp': 1680619755.0191748, 'test/f1-score': 0.7659574468085105, 'train/batch_loss': 0.5281365513801575, 'train/epoch_loss': 0.46212616409072127, '_runtime': 324.3058567047119, 'test/recall': 0.7659574468085106, 'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.7659574468085106, 'test/epoch_loss': 0.5337554746203952}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003}",different-sweep-10
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||||||
54,"{'test/epoch_loss': 0.5470490535100301, 'train/batch_loss': 0.6183260083198547, '_step': 279, 'epoch': 9, '_runtime': 327.0705659389496, '_timestamp': 1680619423.656795, 'test/recall': 0.9523809523809524, 'test/precision': 0.7843137254901961, '_wandb': {'runtime': 325}, 'test/f1-score': 0.8602150537634408, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.8058968058968059, 'train/epoch_loss': 0.5580001385557564}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.003}",lilac-sweep-9
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||||||
55,"{'test/f1-score': 0.7956989247311828, 'test/precision': 0.8409090909090909, 'train/batch_loss': 0.6300776600837708, '_step': 529, 'epoch': 9, '_runtime': 328.68579959869385, '_timestamp': 1680619089.5332966, 'test/recall': 0.7551020408163265, 'train/epoch_loss': 0.46969629490990605, '_wandb': {'runtime': 327}, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.46168507006433274, 'train/epoch_acc': 0.773955773955774}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1}",crimson-sweep-8
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|
||||||
56,"{'test/recall': 0.8181818181818182, 'test/epoch_loss': 0.44089303129391433, 'train/epoch_acc': 0.9938574938574938, 'train/epoch_loss': 0.02176519967463292, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.9375, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 349}, '_runtime': 350.2308712005615, '_timestamp': 1680618753.2361271, 'test/f1-score': 0.8737864077669902, 'train/batch_loss': 0.011611333116889}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",still-sweep-7
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|
||||||
57,"{'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.85, 'test/epoch_loss': 0.24035142682841976, 'train/epoch_acc': 0.9938574938574938, 'epoch': 9, '_wandb': {'runtime': 333}, 'test/recall': 0.8717948717948718, 'test/f1-score': 0.8607594936708861, 'train/epoch_loss': 0.02099113287724536, '_step': 1039, '_runtime': 334.69481587409973, '_timestamp': 1680618396.0194488, 'train/batch_loss': 0.030084805563092232}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.01}",charmed-sweep-6
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57,"{'train/epoch_loss': 0.02099113287724536, '_timestamp': 1680618396.0194488, 'test/f1-score': 0.8607594936708861, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.85, 'train/batch_loss': 0.030084805563092232, 'test/epoch_loss': 0.24035142682841976, 'train/epoch_acc': 0.9938574938574938, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 334.69481587409973, 'test/recall': 0.8717948717948718}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.01}",charmed-sweep-6
|
||||||
58,"{'epoch': 9, '_wandb': {'runtime': 335}, '_timestamp': 1680618051.044084, 'train/epoch_acc': 0.9963144963144964, 'train/epoch_loss': 0.010693324584853135, '_step': 1039, 'test/recall': 0.8780487804878049, 'test/f1-score': 0.8674698795180722, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.8571428571428571, 'test/epoch_loss': 0.5385394818252988, 'train/batch_loss': 0.001848929445259273, '_runtime': 336.1621870994568}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0003}",restful-sweep-5
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58,"{'train/epoch_acc': 0.9963144963144964, 'train/batch_loss': 0.001848929445259273, 'train/epoch_loss': 0.010693324584853135, '_wandb': {'runtime': 335}, '_runtime': 336.1621870994568, 'test/recall': 0.8780487804878049, 'test/f1-score': 0.8674698795180722, 'test/precision': 0.8571428571428571, '_step': 1039, 'epoch': 9, '_timestamp': 1680618051.044084, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.5385394818252988}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0003}",restful-sweep-5
|
||||||
59,"{'train/epoch_acc': 1, 'train/batch_loss': 0.004928763955831528, 'train/epoch_loss': 0.004462716538065481, '_step': 149, '_runtime': 334.4848310947418, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, 'test/precision': 0.8409090909090909, 'epoch': 9, '_wandb': {'runtime': 333}, '_timestamp': 1680617708.075962, 'test/recall': 0.8409090909090909, 'test/epoch_loss': 0.6238909363746643}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1}",proud-sweep-4
|
59,"{'test/recall': 0.8409090909090909, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, '_wandb': {'runtime': 333}, '_timestamp': 1680617708.075962, '_runtime': 334.4848310947418, 'test/precision': 0.8409090909090909, 'test/epoch_loss': 0.6238909363746643, 'train/epoch_acc': 1, 'train/batch_loss': 0.004928763955831528, 'train/epoch_loss': 0.004462716538065481, '_step': 149, 'epoch': 9}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1}",proud-sweep-4
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||||||
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||||||
70,"{'train/batch_loss': 0.711412787437439, 'train/epoch_loss': 0.09577267487700432, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 372}, '_timestamp': 1678796117.3062005, 'test/f1-score': 0.868421052631579, 'test/epoch_acc': 0.888888888888889, '_runtime': 378.4032835960388, 'test/recall': 0.8048780487804879, 'test/precision': 0.9428571428571428, 'test/epoch_loss': 0.2378266812198692, 'train/epoch_acc': 0.9705159705159704}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.001}",misty-sweep-31
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||||||
71,"{'_step': 529, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 336.8808288574219, '_timestamp': 1678795725.918603, 'test/recall': 0.8260869565217391, 'test/f1-score': 0.8636363636363636, 'test/epoch_acc': 0.8666666666666667, 'train/epoch_acc': 0.9926289926289926, 'test/precision': 0.9047619047619048, 'test/epoch_loss': 0.27924135790930854, 'train/batch_loss': 0.04936826974153519, 'train/epoch_loss': 0.05967479737370254}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.001}",flowing-sweep-30
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71,"{'train/epoch_acc': 0.9926289926289926, 'train/epoch_loss': 0.05967479737370254, '_step': 529, 'epoch': 9, '_runtime': 336.8808288574219, 'test/recall': 0.8260869565217391, 'test/f1-score': 0.8636363636363636, 'test/precision': 0.9047619047619048, '_wandb': {'runtime': 333}, '_timestamp': 1678795725.918603, 'test/epoch_acc': 0.8666666666666667, 'test/epoch_loss': 0.27924135790930854, 'train/batch_loss': 0.04936826974153519}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.001}",flowing-sweep-30
|
||||||
72,"{'_step': 279, 'epoch': 9, '_wandb': {'runtime': 336}, '_runtime': 339.73244285583496, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9, 'test/precision': 0.9523809523809524, 'test/epoch_loss': 0.37525106337335373, 'train/epoch_loss': 0.3784469199122024, '_timestamp': 1678795319.518895, 'test/recall': 0.851063829787234, 'train/epoch_acc': 0.8722358722358722, 'train/batch_loss': 0.4592914581298828}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.001}",deep-sweep-28
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72,"{'epoch': 9, 'test/recall': 0.851063829787234, 'test/epoch_loss': 0.37525106337335373, 'train/epoch_acc': 0.8722358722358722, 'train/batch_loss': 0.4592914581298828, 'train/epoch_loss': 0.3784469199122024, 'test/precision': 0.9523809523809524, '_step': 279, '_wandb': {'runtime': 336}, '_runtime': 339.73244285583496, '_timestamp': 1678795319.518895, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.001}",deep-sweep-28
|
||||||
73,"{'_timestamp': 1678794965.2675128, 'test/f1-score': 0.6849315068493151, 'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.7575757575757576, 'test/epoch_loss': 0.5484810524516636, 'epoch': 9, '_wandb': {'runtime': 377}, '_runtime': 381.0768678188324, 'train/epoch_acc': 0.7899262899262899, 'train/batch_loss': 0.6763702630996704, 'train/epoch_loss': 0.5319552311733255, '_step': 2059, 'test/recall': 0.625}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.0001}",glorious-sweep-27
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73,"{'test/precision': 0.7575757575757576, 'train/epoch_acc': 0.7899262899262899, 'train/epoch_loss': 0.5319552311733255, '_wandb': {'runtime': 377}, '_timestamp': 1678794965.2675128, 'test/recall': 0.625, 'test/f1-score': 0.6849315068493151, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5484810524516636, 'train/batch_loss': 0.6763702630996704, '_step': 2059, 'epoch': 9, '_runtime': 381.0768678188324}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.0001}",glorious-sweep-27
|
||||||
74,"{'_step': 529, 'epoch': 9, '_wandb': {'runtime': 334}, '_runtime': 338.11463618278503, '_timestamp': 1678794572.9156363, 'test/recall': 0.813953488372093, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.5729872869120703, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.4391788542270661, 'test/f1-score': 0.7608695652173914, 'test/precision': 0.7142857142857143, 'train/epoch_loss': 0.2699748155379471}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1}",stoic-sweep-26
|
74,"{'epoch': 9, '_runtime': 338.11463618278503, 'test/recall': 0.813953488372093, 'test/precision': 0.7142857142857143, 'train/epoch_loss': 0.2699748155379471, '_step': 529, '_timestamp': 1678794572.9156363, 'test/f1-score': 0.7608695652173914, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.5729872869120703, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.4391788542270661, '_wandb': {'runtime': 334}}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1}",stoic-sweep-26
|
||||||
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||||||
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||||||
81,"{'_wandb': {'runtime': 337}, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.931818181818182, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 0.9534883720930232, 'test/epoch_loss': 0.17397157057291932, 'epoch': 9, '_runtime': 340.5063774585724, '_timestamp': 1678792015.2579195, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0077079650945961475, 'train/epoch_loss': 0.018187719287696302, '_step': 1039}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003}",autumn-sweep-19
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||||||
82,"{'epoch': 9, '_wandb': {'runtime': 344}, 'test/recall': 0.8205128205128205, 'train/epoch_loss': 0.4784781006542412, 'test/epoch_loss': 0.4940012666914198, 'train/epoch_acc': 0.8218673218673218, '_step': 1039, '_runtime': 347.40152740478516, '_timestamp': 1678791661.9692383, 'test/f1-score': 0.7804878048780488, 'test/epoch_acc': 0.8, 'test/precision': 0.7441860465116279, 'train/batch_loss': 0.4317986071109772}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0001}",crisp-sweep-18
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||||||
83,"{'_runtime': 337.956387758255, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.9090909090909092, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.19624250796106127, '_step': 279, '_wandb': {'runtime': 335}, '_timestamp': 1678791236.6172178, 'test/epoch_acc': 0.9111111111111112, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.15555259585380554, 'train/epoch_loss': 0.08830470366618558, 'epoch': 9}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.003}",deep-sweep-16
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83,"{'_step': 279, 'epoch': 9, '_wandb': {'runtime': 335}, '_runtime': 337.956387758255, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.9090909090909092, 'train/epoch_loss': 0.08830470366618558, '_timestamp': 1678791236.6172178, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.19624250796106127, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.15555259585380554}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.003}",deep-sweep-16
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||||||
84,"{'_step': 279, '_timestamp': 1678790886.952144, 'test/f1-score': 0.7818181818181819, 'test/precision': 0.7049180327868853, 'test/epoch_loss': 0.6228035251299541, 'train/epoch_acc': 0.7493857493857494, 'train/batch_loss': 0.6377201080322266, 'epoch': 9, '_wandb': {'runtime': 331}, '_runtime': 334.2993712425232, 'test/recall': 0.8775510204081632, 'test/epoch_acc': 0.7333333333333334, 'train/epoch_loss': 0.6127705679478751}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.0003}",confused-sweep-15
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||||||
85,"{'train/epoch_loss': 0.3545121966840594, '_step': 529, 'epoch': 9, '_runtime': 345.0617377758026, '_timestamp': 1678790542.286384, 'test/f1-score': 0.7809523809523811, 'train/epoch_acc': 0.8415233415233415, 'train/batch_loss': 0.1340156048536301, '_wandb': {'runtime': 342}, 'test/recall': 0.8541666666666666, 'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.7192982456140351, 'test/epoch_loss': 0.6144241677390204}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.1}",ancient-sweep-14
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85,"{'test/precision': 0.7192982456140351, 'test/epoch_loss': 0.6144241677390204, 'train/epoch_acc': 0.8415233415233415, 'train/batch_loss': 0.1340156048536301, 'train/epoch_loss': 0.3545121966840594, '_wandb': {'runtime': 342}, '_timestamp': 1678790542.286384, '_runtime': 345.0617377758026, 'test/recall': 0.8541666666666666, 'test/f1-score': 0.7809523809523811, 'test/epoch_acc': 0.7444444444444445, '_step': 529, 'epoch': 9}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.1}",ancient-sweep-14
|
||||||
86,"{'_step': 529, '_timestamp': 1678790183.7024884, 'test/f1-score': 0.7422680412371134, 'train/batch_loss': 0.6280461549758911, 'test/precision': 0.7058823529411765, 'test/epoch_loss': 0.6392196734746297, 'train/epoch_acc': 0.7457002457002457, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 346.86587953567505, 'test/recall': 0.782608695652174, 'test/epoch_acc': 0.7222222222222222, 'train/epoch_loss': 0.6374555861334836}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.0003}",revived-sweep-13
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86,"{'test/epoch_loss': 0.6392196734746297, '_runtime': 346.86587953567505, '_timestamp': 1678790183.7024884, '_wandb': {'runtime': 344}, 'test/recall': 0.782608695652174, 'test/f1-score': 0.7422680412371134, 'test/epoch_acc': 0.7222222222222222, 'test/precision': 0.7058823529411765, 'train/epoch_acc': 0.7457002457002457, '_step': 529, 'epoch': 9, 'train/batch_loss': 0.6280461549758911, 'train/epoch_loss': 0.6374555861334836}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.0003}",revived-sweep-13
|
||||||
87,"{'_wandb': {'runtime': 348}, '_runtime': 350.9660577774048, 'test/recall': 0.9111111111111112, 'train/epoch_acc': 0.9987714987714988, 'epoch': 9, '_timestamp': 1678789826.0085878, 'test/f1-score': 0.9010989010989012, 'test/epoch_acc': 0.9, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.24115624560250176, 'train/batch_loss': 0.04231283441185951, 'train/epoch_loss': 0.02119528235872196, '_step': 149}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.0003}",swift-sweep-12
|
87,"{'_wandb': {'runtime': 348}, '_runtime': 350.9660577774048, '_timestamp': 1678789826.0085878, 'test/recall': 0.9111111111111112, 'train/epoch_acc': 0.9987714987714988, 'train/epoch_loss': 0.02119528235872196, '_step': 149, 'epoch': 9, 'test/f1-score': 0.9010989010989012, 'test/epoch_acc': 0.9, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.24115624560250176, 'train/batch_loss': 0.04231283441185951}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.0003}",swift-sweep-12
|
||||||
88,"{'test/recall': 0.8333333333333334, 'test/epoch_loss': 0.5769641452365452, 'train/batch_loss': 0.6127220392227173, 'train/epoch_loss': 0.5840219159676929, 'epoch': 9, '_wandb': {'runtime': 393}, '_timestamp': 1678789464.8040044, 'test/f1-score': 0.7894736842105262, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.75, 'train/epoch_acc': 0.757985257985258, '_step': 2059, '_runtime': 397.1281135082245}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0001}",rosy-sweep-11
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88,"{'_step': 2059, '_wandb': {'runtime': 393}, '_runtime': 397.1281135082245, '_timestamp': 1678789464.8040044, 'epoch': 9, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7894736842105262, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.75, 'test/epoch_loss': 0.5769641452365452, 'train/epoch_acc': 0.757985257985258, 'train/batch_loss': 0.6127220392227173, 'train/epoch_loss': 0.5840219159676929}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0001}",rosy-sweep-11
|
||||||
89,"{'epoch': 9, 'test/recall': 0.8076923076923077, 'test/f1-score': 0.8842105263157894, 'test/epoch_loss': 0.2696530275874668, 'train/epoch_acc': 0.9938574938574938, 'train/batch_loss': 0.11590295284986496, '_step': 149, '_wandb': {'runtime': 352}, '_runtime': 355.46944642066956, '_timestamp': 1678789057.5684297, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.9767441860465116, 'train/epoch_loss': 0.06967324825777176}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003}",deft-sweep-10
|
89,"{'_wandb': {'runtime': 352}, '_timestamp': 1678789057.5684297, 'test/f1-score': 0.8842105263157894, 'test/epoch_acc': 0.8777777777777778, 'train/epoch_acc': 0.9938574938574938, 'train/batch_loss': 0.11590295284986496, 'train/epoch_loss': 0.06967324825777176, '_step': 149, '_runtime': 355.46944642066956, 'test/recall': 0.8076923076923077, 'test/precision': 0.9767441860465116, 'test/epoch_loss': 0.2696530275874668, 'epoch': 9}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003}",deft-sweep-10
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||||||
90,"{'_step': 279, '_wandb': {'runtime': 340}, '_runtime': 342.3234579563141, '_timestamp': 1678788683.006292, 'test/recall': 0.9069767441860463, 'test/f1-score': 0.7959183673469388, 'test/epoch_acc': 0.7777777777777778, 'test/precision': 0.7090909090909091, 'test/epoch_loss': 0.6248881856600443, 'train/epoch_acc': 0.7014742014742015, 'train/batch_loss': 0.5820533037185669, 'train/epoch_loss': 0.6400203514450599, 'epoch': 9}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001}",atomic-sweep-9
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90,"{'epoch': 9, '_wandb': {'runtime': 340}, '_runtime': 342.3234579563141, 'test/recall': 0.9069767441860463, 'test/f1-score': 0.7959183673469388, 'test/precision': 0.7090909090909091, 'test/epoch_loss': 0.6248881856600443, '_step': 279, 'train/batch_loss': 0.5820533037185669, 'train/epoch_acc': 0.7014742014742015, 'test/epoch_acc': 0.7777777777777778, 'train/epoch_loss': 0.6400203514450599, '_timestamp': 1678788683.006292}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001}",atomic-sweep-9
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||||||
91,"{'_step': 1039, '_wandb': {'runtime': 351}, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6140350877192983, 'test/epoch_loss': 0.6175267219543457, 'train/epoch_acc': 0.7432432432432432, 'epoch': 9, '_runtime': 353.4816448688507, '_timestamp': 1678788328.1196988, 'test/recall': 0.7954545454545454, 'test/f1-score': 0.693069306930693, 'train/batch_loss': 0.3377891480922699, 'train/epoch_loss': 0.5329857344855841}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.1}",cosmic-sweep-8
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91,"{'_step': 1039, '_wandb': {'runtime': 351}, '_timestamp': 1678788328.1196988, 'test/f1-score': 0.693069306930693, 'test/epoch_loss': 0.6175267219543457, 'train/batch_loss': 0.3377891480922699, 'epoch': 9, '_runtime': 353.4816448688507, 'test/recall': 0.7954545454545454, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6140350877192983, 'train/epoch_acc': 0.7432432432432432, 'train/epoch_loss': 0.5329857344855841}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.1}",cosmic-sweep-8
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||||||
92,"{'epoch': 9, '_wandb': {'runtime': 390}, '_runtime': 392.4064960479736, '_timestamp': 1678787961.3400052, 'test/f1-score': 0.6999999999999998, 'test/precision': 0.5932203389830508, 'train/epoch_loss': 0.5631518808058498, '_step': 2059, 'test/recall': 0.8536585365853658, 'test/epoch_acc': 0.6666666666666667, 'test/epoch_loss': 0.6419186863634322, 'train/epoch_acc': 0.7186732186732187, 'train/batch_loss': 0.17200787365436554}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01}",lunar-sweep-7
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92,"{'test/epoch_acc': 0.6666666666666667, 'test/epoch_loss': 0.6419186863634322, 'train/epoch_acc': 0.7186732186732187, 'train/batch_loss': 0.17200787365436554, 'epoch': 9, '_runtime': 392.4064960479736, '_timestamp': 1678787961.3400052, 'test/recall': 0.8536585365853658, 'train/epoch_loss': 0.5631518808058498, '_step': 2059, '_wandb': {'runtime': 390}, 'test/f1-score': 0.6999999999999998, 'test/precision': 0.5932203389830508}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01}",lunar-sweep-7
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||||||
93,"{'train/epoch_acc': 0.9975429975429976, 'train/epoch_loss': 0.03237721893286529, 'epoch': 9, '_wandb': {'runtime': 343}, '_runtime': 345.9260220527649, 'test/f1-score': 0.8988764044943819, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.04353119805455208, '_step': 529, '_timestamp': 1678787557.992564, 'test/recall': 0.8888888888888888, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.24278527200222016}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01}",zany-sweep-6
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93,"{'_runtime': 345.9260220527649, 'test/recall': 0.8888888888888888, 'test/epoch_acc': 0.9, 'test/epoch_loss': 0.24278527200222016, 'train/epoch_acc': 0.9975429975429976, 'train/epoch_loss': 0.03237721893286529, 'epoch': 9, '_wandb': {'runtime': 343}, 'test/f1-score': 0.8988764044943819, 'test/precision': 0.9090909090909092, 'train/batch_loss': 0.04353119805455208, '_step': 529, '_timestamp': 1678787557.992564}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01}",zany-sweep-6
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||||||
94,"{'_step': 529, '_runtime': 346.5414688587189, 'test/f1-score': 0.9130434782608696, 'train/epoch_acc': 0.9336609336609336, 'test/epoch_loss': 0.32114719019995797, 'train/batch_loss': 0.21811823546886444, 'epoch': 9, '_wandb': {'runtime': 344}, '_timestamp': 1678787192.9954038, 'test/recall': 0.8571428571428571, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9767441860465116, 'train/epoch_loss': 0.2347587838000103}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001}",absurd-sweep-5
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94,"{'test/epoch_acc': 0.9111111111111112, 'test/epoch_loss': 0.32114719019995797, 'train/batch_loss': 0.21811823546886444, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 346.5414688587189, '_timestamp': 1678787192.9954038, 'test/recall': 0.8571428571428571, 'test/f1-score': 0.9130434782608696, 'test/precision': 0.9767441860465116, 'train/epoch_acc': 0.9336609336609336, 'train/epoch_loss': 0.2347587838000103}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001}",absurd-sweep-5
|
||||||
95,"{'_timestamp': 1678786835.7254088, 'test/f1-score': 0.8799999999999999, 'test/epoch_loss': 0.22436124781767527, 'train/epoch_loss': 0.02646600444977348, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 345.9469966888428, 'test/precision': 0.9166666666666666, 'train/epoch_acc': 1, 'train/batch_loss': 0.06225413456559181, '_step': 279, 'test/recall': 0.8461538461538461, 'test/epoch_acc': 0.9}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003}",radiant-sweep-4
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95,"{'train/epoch_loss': 0.02646600444977348, '_timestamp': 1678786835.7254088, 'test/precision': 0.9166666666666666, 'test/epoch_loss': 0.22436124781767527, 'train/epoch_acc': 1, 'test/recall': 0.8461538461538461, 'test/f1-score': 0.8799999999999999, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.06225413456559181, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 345.9469966888428}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003}",radiant-sweep-4
|
||||||
96,"{'_wandb': {'runtime': 353}, '_runtime': 355.012455701828, 'test/recall': 0.875, 'test/f1-score': 0.8045977011494252, 'test/epoch_acc': 0.8111111111111111, 'test/precision': 0.7446808510638298, '_step': 1039, 'epoch': 9, 'train/epoch_loss': 0.45506354690476775, 'train/epoch_acc': 0.8341523341523341, 'train/batch_loss': 0.5456343293190002, '_timestamp': 1678786479.0865147, 'test/epoch_loss': 0.4459853092829386}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",sandy-sweep-3
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96,"{'test/f1-score': 0.8045977011494252, 'test/precision': 0.7446808510638298, 'train/epoch_acc': 0.8341523341523341, 'epoch': 9, '_wandb': {'runtime': 353}, 'test/recall': 0.875, 'test/epoch_acc': 0.8111111111111111, 'test/epoch_loss': 0.4459853092829386, 'train/batch_loss': 0.5456343293190002, 'train/epoch_loss': 0.45506354690476775, '_step': 1039, '_runtime': 355.012455701828, '_timestamp': 1678786479.0865147}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",sandy-sweep-3
|
||||||
97,"{'_wandb': {'runtime': 342}, '_timestamp': 1678786112.108075, 'test/recall': 0.7894736842105263, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.31915653232071134, 'train/batch_loss': 0.026765840128064156, 'train/epoch_loss': 0.045762457081668206, '_step': 529, 'epoch': 9, '_runtime': 344.01046657562256, 'test/f1-score': 0.8450704225352113, 'test/epoch_acc': 0.8777777777777778, 'train/epoch_acc': 0.9926289926289926}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01}",pretty-sweep-2
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97,"{'test/epoch_loss': 0.31915653232071134, 'train/epoch_acc': 0.9926289926289926, 'train/epoch_loss': 0.045762457081668206, '_wandb': {'runtime': 342}, '_runtime': 344.01046657562256, 'test/recall': 0.7894736842105263, 'test/f1-score': 0.8450704225352113, 'test/precision': 0.9090909090909092, '_step': 529, 'epoch': 9, '_timestamp': 1678786112.108075, 'test/epoch_acc': 0.8777777777777778, 'train/batch_loss': 0.026765840128064156}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01}",pretty-sweep-2
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||||||
98,"{'train/batch_loss': 0.7150550484657288, 'train/epoch_loss': 0.7011552195291262, '_step': 149, '_wandb': {'runtime': 357}, '_runtime': 359.66486382484436, '_timestamp': 1678785758.376562, 'test/f1-score': 0.379746835443038, 'test/precision': 0.42857142857142855, 'epoch': 9, 'test/recall': 0.3409090909090909, 'test/epoch_acc': 0.45555555555555555, 'test/epoch_loss': 0.7006691349877252, 'train/epoch_acc': 0.4815724815724816}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.0003}",rose-sweep-1
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98,"{'_runtime': 359.66486382484436, 'test/f1-score': 0.379746835443038, 'test/precision': 0.42857142857142855, 'test/epoch_loss': 0.7006691349877252, 'train/batch_loss': 0.7150550484657288, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 357}, '_timestamp': 1678785758.376562, 'test/recall': 0.3409090909090909, 'test/epoch_acc': 0.45555555555555555, 'train/epoch_acc': 0.4815724815724816, 'train/epoch_loss': 0.7011552195291262}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.0003}",rose-sweep-1
|
||||||
99,"{'train/epoch_loss': 0.023103852647056927, '_step': 74, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8791208791208791, 'train/batch_loss': 0.0016211483161896467, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.851063829787234, 'test/epoch_loss': 0.5091631063156657, 'train/epoch_acc': 0.995085995085995, 'epoch': 4, '_wandb': {'runtime': 181}, '_runtime': 180.05384421348572, '_timestamp': 1678785370.5563953}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.1}",cosmic-sweep-2
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99,"{'_timestamp': 1678785370.5563953, 'test/recall': 0.9090909090909092, 'train/epoch_acc': 0.995085995085995, 'train/epoch_loss': 0.023103852647056927, '_step': 74, '_runtime': 180.05384421348572, 'test/f1-score': 0.8791208791208791, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.851063829787234, 'test/epoch_loss': 0.5091631063156657, 'train/batch_loss': 0.0016211483161896467, 'epoch': 4, '_wandb': {'runtime': 181}}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.1}",cosmic-sweep-2
|
||||||
100,"{'test/f1-score': 0.9166666666666666, 'test/precision': 0.9166666666666666, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.0724378228187561, 'train/epoch_loss': 0.11044558714297244, '_step': 279, '_runtime': 347.11417746543884, 'test/recall': 0.9166666666666666, 'test/epoch_acc': 0.9111111111111112, 'test/epoch_loss': 0.2461573594146305, 'epoch': 9, '_wandb': {'runtime': 344}, '_timestamp': 1678743707.9633043}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.003}",ethereal-sweep-14
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100,"{'test/f1-score': 0.9166666666666666, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9166666666666666, 'test/epoch_loss': 0.2461573594146305, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.0724378228187561, '_step': 279, 'test/recall': 0.9166666666666666, '_runtime': 347.11417746543884, '_timestamp': 1678743707.9633043, 'train/epoch_loss': 0.11044558714297244, 'epoch': 9, '_wandb': {'runtime': 344}}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.003}",ethereal-sweep-14
|
||||||
101,"{'_step': 149, 'epoch': 9, '_wandb': {'runtime': 346}, 'test/recall': 0.9130434782608696, 'test/precision': 0.9545454545454546, 'train/batch_loss': 0.05796322599053383, 'train/epoch_loss': 0.043383844352398226, '_runtime': 349.69085454940796, '_timestamp': 1678743349.8008895, 'test/f1-score': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'test/epoch_loss': 0.16449517243438297, 'train/epoch_acc': 1}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003}",northern-sweep-13
|
101,"{'_timestamp': 1678743349.8008895, 'test/recall': 0.9130434782608696, 'test/f1-score': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 0.9545454545454546, 'test/epoch_loss': 0.16449517243438297, '_step': 149, '_runtime': 349.69085454940796, 'train/epoch_loss': 0.043383844352398226, 'train/epoch_acc': 1, 'train/batch_loss': 0.05796322599053383, 'epoch': 9, '_wandb': {'runtime': 346}}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003}",northern-sweep-13
|
||||||
102,"{'_runtime': 560.5539684295654, '_timestamp': 1678743376.8770983, 'test/recall': 0.85, 'test/f1-score': 0.7816091954022989, 'train/epoch_acc': 0.8255528255528255, 'train/epoch_loss': 0.40511614706651, '_wandb': {'runtime': 559}, 'epoch': 9, 'test/epoch_acc': 0.788888888888889, 'test/precision': 0.723404255319149, 'test/epoch_loss': 0.5102662573258082, 'train/batch_loss': 0.42048144340515137, '_step': 2059}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.001}",faithful-sweep-12
|
102,"{'epoch': 9, 'test/precision': 0.723404255319149, 'train/epoch_loss': 0.40511614706651, '_step': 2059, '_wandb': {'runtime': 559}, '_runtime': 560.5539684295654, '_timestamp': 1678743376.8770983, 'test/recall': 0.85, 'test/f1-score': 0.7816091954022989, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.5102662573258082, 'train/epoch_acc': 0.8255528255528255, 'train/batch_loss': 0.42048144340515137}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.001}",faithful-sweep-12
|
||||||
103,"{'_timestamp': 1678742986.9751594, 'test/recall': 0.7777777777777778, 'test/epoch_loss': 0.3378064884079827, 'epoch': 9, '_runtime': 358.3485324382782, 'test/f1-score': 0.8536585365853658, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8955773955773956, 'train/batch_loss': 0.5923706889152527, 'train/epoch_loss': 0.27216847456936755, '_step': 1039, '_wandb': {'runtime': 355}}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003}",zany-sweep-12
|
103,"{'epoch': 9, '_wandb': {'runtime': 355}, '_timestamp': 1678742986.9751594, 'test/epoch_loss': 0.3378064884079827, 'train/epoch_acc': 0.8955773955773956, '_step': 1039, '_runtime': 358.3485324382782, 'test/recall': 0.7777777777777778, 'test/f1-score': 0.8536585365853658, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.945945945945946, 'train/batch_loss': 0.5923706889152527, 'train/epoch_loss': 0.27216847456936755}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003}",zany-sweep-12
|
||||||
104,"{'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.6226415094339622, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 358}, '_timestamp': 1678742619.1453717, 'test/recall': 0.9166666666666666, 'test/f1-score': 0.7415730337078651, 'train/epoch_loss': 0.613342459283824, '_runtime': 362.78373169898987, 'test/epoch_loss': 0.615033131175571, 'train/epoch_acc': 0.7481572481572482, 'train/batch_loss': 0.6421169638633728}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",ruby-sweep-11
|
104,"{'train/epoch_acc': 0.7481572481572482, 'train/epoch_loss': 0.613342459283824, 'epoch': 9, '_wandb': {'runtime': 358}, '_runtime': 362.78373169898987, 'test/precision': 0.6226415094339622, 'test/epoch_loss': 0.615033131175571, 'train/batch_loss': 0.6421169638633728, '_step': 1039, '_timestamp': 1678742619.1453717, 'test/recall': 0.9166666666666666, 'test/f1-score': 0.7415730337078651, 'test/epoch_acc': 0.7444444444444445}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",ruby-sweep-11
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||||||
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108,"{'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.6, 'train/epoch_acc': 0.5921375921375921, 'train/batch_loss': 0.6228023767471313, '_step': 279, '_runtime': 344.49258494377136, 'test/f1-score': 0.6451612903225806, 'test/recall': 0.6976744186046512, 'test/epoch_loss': 0.6676742302046882, 'train/epoch_loss': 0.6766868150204932, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678741869.828495}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001}",spring-sweep-9
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108,"{'train/epoch_loss': 0.6766868150204932, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678741869.828495, 'test/epoch_loss': 0.6676742302046882, 'train/epoch_acc': 0.5921375921375921, 'train/batch_loss': 0.6228023767471313, '_runtime': 344.49258494377136, 'test/recall': 0.6976744186046512, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.6}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001}",spring-sweep-9
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109,"{'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.0022799931466579437, '_step': 1039, 'test/recall': 0.9318181818181818, 'test/f1-score': 0.9213483146067416, '_timestamp': 1678741623.0662856, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9111111111111112, 'test/epoch_loss': 0.16872049139605627, 'train/epoch_loss': 0.02303326028314504, 'epoch': 9, '_wandb': {'runtime': 451}, '_runtime': 452.4322986602783}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003}",elated-sweep-8
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110,"{'test/recall': 0.9318181818181818, 'test/epoch_acc': 0.9555555555555556, 'test/epoch_loss': 0.2148759490913815, 'train/epoch_loss': 0.1359616077759049, 'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 345.3405177593231, 'test/precision': 0.9761904761904762, 'train/epoch_acc': 0.9606879606879608, 'train/batch_loss': 0.11643347889184952, '_step': 149, '_timestamp': 1678741511.9070578, 'test/f1-score': 0.9534883720930232}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.003}",hardy-sweep-8
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111,"{'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 345.1732180118561, '_timestamp': 1678741156.130327, 'test/recall': 0.8048780487804879, 'test/epoch_acc': 0.888888888888889, 'train/epoch_acc': 1, '_step': 279, 'train/epoch_loss': 0.008645273717600824, 'test/precision': 0.9428571428571428, 'test/epoch_loss': 0.2181672462158733, 'train/batch_loss': 0.042314428836107254, 'test/f1-score': 0.868421052631579}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.1}",sweepy-sweep-7
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111,"{'test/recall': 0.8048780487804879, 'test/epoch_loss': 0.2181672462158733, 'train/epoch_acc': 1, 'train/epoch_loss': 0.008645273717600824, '_wandb': {'runtime': 342}, '_runtime': 345.1732180118561, '_timestamp': 1678741156.130327, 'test/f1-score': 0.868421052631579, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9428571428571428, 'train/batch_loss': 0.042314428836107254, '_step': 279, 'epoch': 9}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.1}",sweepy-sweep-7
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112,"{'_step': 1039, 'test/f1-score': 0.7222222222222222, 'test/epoch_acc': 0.7777777777777778, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4768455002042982, 'train/epoch_acc': 0.8292383292383292, 'train/epoch_loss': 0.45283343838825274, 'epoch': 9, '_wandb': {'runtime': 453}, '_runtime': 454.0593776702881, '_timestamp': 1678741159.4683807, 'test/recall': 0.6341463414634146, 'train/batch_loss': 0.3791900873184204}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0001}",glorious-sweep-7
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112,"{'test/recall': 0.6341463414634146, 'test/epoch_acc': 0.7777777777777778, 'train/epoch_acc': 0.8292383292383292, 'train/batch_loss': 0.3791900873184204, 'epoch': 9, '_wandb': {'runtime': 453}, '_runtime': 454.0593776702881, '_timestamp': 1678741159.4683807, 'test/f1-score': 0.7222222222222222, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4768455002042982, 'train/epoch_loss': 0.45283343838825274, '_step': 1039}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0001}",glorious-sweep-7
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||||||
113,"{'test/epoch_loss': 0.1931780371401045, 'epoch': 9, '_wandb': {'runtime': 346}, 'test/f1-score': 0.9333333333333332, 'test/precision': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'train/epoch_acc': 1, 'train/batch_loss': 0.001889266073703766, 'train/epoch_loss': 0.0030514685945077376, '_step': 149, '_runtime': 348.53755164146423, '_timestamp': 1678740798.1400597, 'test/recall': 0.9333333333333332}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01}",rural-sweep-6
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114,"{'epoch': 9, 'test/recall': 0.8666666666666667, 'test/f1-score': 0.896551724137931, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.1385842263698578, '_step': 2059, '_runtime': 560.7404127120972, '_timestamp': 1678740696.0305526, 'test/precision': 0.9285714285714286, 'test/epoch_loss': 0.22745563416845269, 'train/epoch_acc': 0.984029484029484, 'train/epoch_loss': 0.07075482415817952, '_wandb': {'runtime': 560}}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",smart-sweep-6
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114,"{'train/epoch_acc': 0.984029484029484, 'train/batch_loss': 0.1385842263698578, 'train/epoch_loss': 0.07075482415817952, '_step': 2059, '_timestamp': 1678740696.0305526, 'test/recall': 0.8666666666666667, 'test/f1-score': 0.896551724137931, 'test/epoch_loss': 0.22745563416845269, 'epoch': 9, '_wandb': {'runtime': 560}, '_runtime': 560.7404127120972, 'test/epoch_acc': 0.9, 'test/precision': 0.9285714285714286}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",smart-sweep-6
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||||||
115,"{'_runtime': 345.5716743469238, '_timestamp': 1678740438.4959724, 'test/recall': 0.7755102040816326, 'test/f1-score': 0.8172043010752688, 'train/epoch_acc': 0.7616707616707616, 'train/epoch_loss': 0.5191410552225183, 'epoch': 9, '_wandb': {'runtime': 342}, 'test/precision': 0.8636363636363636, 'test/epoch_loss': 0.507676590151257, 'train/batch_loss': 0.44296249747276306, '_step': 529, 'test/epoch_acc': 0.8111111111111111}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1}",giddy-sweep-5
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115,"{'test/epoch_acc': 0.8111111111111111, 'test/epoch_loss': 0.507676590151257, '_runtime': 345.5716743469238, 'test/f1-score': 0.8172043010752688, '_wandb': {'runtime': 342}, '_timestamp': 1678740438.4959724, 'test/recall': 0.7755102040816326, 'test/precision': 0.8636363636363636, 'train/epoch_acc': 0.7616707616707616, 'train/batch_loss': 0.44296249747276306, '_step': 529, 'epoch': 9, 'train/epoch_loss': 0.5191410552225183}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1}",giddy-sweep-5
|
||||||
116,"{'_step': 529, 'epoch': 9, '_runtime': 345.28623247146606, 'test/f1-score': 0.6842105263157895, 'train/epoch_acc': 0.8538083538083537, 'train/batch_loss': 0.4066888689994812, 'train/epoch_loss': 0.32492415251837314, '_wandb': {'runtime': 342}, '_timestamp': 1678740073.5443084, 'test/recall': 0.6666666666666666, 'test/epoch_acc': 0.7333333333333334, 'test/precision': 0.7027027027027027, 'test/epoch_loss': 0.6657861550649007}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1}",lilac-sweep-4
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116,"{'_step': 529, '_wandb': {'runtime': 342}, '_runtime': 345.28623247146606, '_timestamp': 1678740073.5443084, 'test/recall': 0.6666666666666666, 'test/precision': 0.7027027027027027, 'test/epoch_loss': 0.6657861550649007, 'epoch': 9, 'test/f1-score': 0.6842105263157895, 'test/epoch_acc': 0.7333333333333334, 'train/epoch_acc': 0.8538083538083537, 'train/batch_loss': 0.4066888689994812, 'train/epoch_loss': 0.32492415251837314}","{'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1}",lilac-sweep-4
|
||||||
117,"{'_step': 1039, 'epoch': 9, '_wandb': {'runtime': 454}, '_runtime': 454.98564982414246, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.2600655794143677, 'train/batch_loss': 0.01167443674057722, '_timestamp': 1678740126.212114, 'test/recall': 0.8367346938775511, 'test/f1-score': 0.8913043478260869, 'test/precision': 0.9534883720930232, 'train/epoch_acc': 0.9803439803439804, 'train/epoch_loss': 0.08152788232426166}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",hearty-sweep-5
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117,"{'_timestamp': 1678740126.212114, 'test/f1-score': 0.8913043478260869, 'test/epoch_loss': 0.2600655794143677, 'train/epoch_acc': 0.9803439803439804, '_step': 1039, 'epoch': 9, '_runtime': 454.98564982414246, 'test/precision': 0.9534883720930232, 'train/batch_loss': 0.01167443674057722, 'train/epoch_loss': 0.08152788232426166, '_wandb': {'runtime': 454}, 'test/recall': 0.8367346938775511, 'test/epoch_acc': 0.888888888888889}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",hearty-sweep-5
|
||||||
118,"{'train/epoch_acc': 0.8144963144963144, 'epoch': 9, '_wandb': {'runtime': 354}, '_timestamp': 1678739717.8250418, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.4899995631641812, 'train/batch_loss': 0.6180618405342102, 'train/epoch_loss': 0.5079173609724209, '_step': 1039, '_runtime': 356.9382667541504, 'test/recall': 0.875, 'test/f1-score': 0.7865168539325842, 'test/precision': 0.7142857142857143}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0001}",silvery-sweep-3
|
118,"{'epoch': 9, '_wandb': {'runtime': 354}, '_timestamp': 1678739717.8250418, 'test/f1-score': 0.7865168539325842, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.4899995631641812, 'train/epoch_acc': 0.8144963144963144, '_step': 1039, 'train/epoch_loss': 0.5079173609724209, 'test/recall': 0.875, 'test/precision': 0.7142857142857143, 'train/batch_loss': 0.6180618405342102, '_runtime': 356.9382667541504}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0001}",silvery-sweep-3
|
||||||
119,"{'_wandb': {'runtime': 453}, 'test/precision': 0.9142857142857144, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.2711101472377777, 'test/epoch_loss': 0.3028925802972582, '_step': 1039, 'epoch': 9, '_runtime': 454.2519624233246, '_timestamp': 1678739662.5458224, 'test/recall': 0.8205128205128205, 'test/f1-score': 0.8648648648648648, 'test/epoch_acc': 0.888888888888889, 'train/epoch_loss': 0.28549219298128414}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003}",dulcet-sweep-4
|
119,"{'_runtime': 454.2519624233246, 'test/recall': 0.8205128205128205, 'epoch': 9, '_wandb': {'runtime': 453}, 'test/f1-score': 0.8648648648648648, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9142857142857144, 'test/epoch_loss': 0.3028925802972582, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.2711101472377777, '_step': 1039, '_timestamp': 1678739662.5458224, 'train/epoch_loss': 0.28549219298128414}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003}",dulcet-sweep-4
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||||||
120,"{'train/epoch_loss': 0.6479796424544707, '_step': 529, '_runtime': 343.88807487487793, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.5454545454545454, 'test/epoch_loss': 0.6651701913939582, 'train/epoch_acc': 0.6928746928746928, 'train/batch_loss': 0.6685948967933655, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678739351.1315958, 'test/recall': 0.7894736842105263}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.001}",glamorous-sweep-2
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120,"{'train/epoch_loss': 0.6479796424544707, '_step': 529, '_wandb': {'runtime': 341}, '_runtime': 343.88807487487793, 'test/recall': 0.7894736842105263, 'test/precision': 0.5454545454545454, 'test/epoch_loss': 0.6651701913939582, 'epoch': 9, '_timestamp': 1678739351.1315958, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'train/epoch_acc': 0.6928746928746928, 'train/batch_loss': 0.6685948967933655}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.001}",glamorous-sweep-2
|
||||||
121,"{'_runtime': 469.65283608436584, 'epoch': 9, '_wandb': {'runtime': 469}, 'test/recall': 0.875, 'test/f1-score': 0.7608695652173914, 'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.6730769230769231, 'test/epoch_loss': 0.6144020875295003, 'train/epoch_acc': 0.7542997542997543, '_step': 1039, '_timestamp': 1678739200.083605, 'train/batch_loss': 0.6510805487632751, 'train/epoch_loss': 0.6267796501480684}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",hopeful-sweep-3
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121,"{'test/precision': 0.6730769230769231, 'train/epoch_acc': 0.7542997542997543, 'train/batch_loss': 0.6510805487632751, 'train/epoch_loss': 0.6267796501480684, '_step': 1039, '_runtime': 469.65283608436584, 'test/f1-score': 0.7608695652173914, 'test/recall': 0.875, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.6144020875295003, 'epoch': 9, '_wandb': {'runtime': 469}, '_timestamp': 1678739200.083605}","{'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",hopeful-sweep-3
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||||||
122,"{'test/precision': 0.8409090909090909, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.0980801358819008, '_step': 279, '_wandb': {'runtime': 353}, '_runtime': 357.5890119075775, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, 'train/epoch_loss': 0.03763626415181805, 'epoch': 9, '_timestamp': 1678738994.027642, 'test/recall': 0.8409090909090909, 'test/epoch_loss': 0.3028163850307465}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003}",lunar-sweep-1
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122,"{'_timestamp': 1678738994.027642, 'test/recall': 0.8409090909090909, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 353}, '_runtime': 357.5890119075775, 'test/epoch_loss': 0.3028163850307465, 'train/epoch_acc': 0.9975429975429976, 'test/precision': 0.8409090909090909, 'train/batch_loss': 0.0980801358819008, 'train/epoch_loss': 0.03763626415181805}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003}",lunar-sweep-1
|
||||||
123,"{'test/f1-score': 0.7157894736842105, 'test/epoch_loss': 0.5541173484590318, '_timestamp': 1678738720.9443874, 'test/recall': 0.8947368421052632, 'test/epoch_acc': 0.7000000000000001, 'test/precision': 0.5964912280701754, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 529}, '_runtime': 529.6096863746643, 'train/epoch_acc': 0.6658476658476659, 'train/batch_loss': 0.7896618843078613, 'train/epoch_loss': 0.618659178367118}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.1}",stoic-sweep-2
|
123,"{'test/recall': 0.8947368421052632, 'test/f1-score': 0.7157894736842105, '_wandb': {'runtime': 529}, '_timestamp': 1678738720.9443874, '_runtime': 529.6096863746643, 'test/epoch_acc': 0.7000000000000001, 'test/precision': 0.5964912280701754, 'test/epoch_loss': 0.5541173484590318, 'train/epoch_acc': 0.6658476658476659, 'train/batch_loss': 0.7896618843078613, '_step': 2059, 'epoch': 9, 'train/epoch_loss': 0.618659178367118}","{'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.1}",stoic-sweep-2
|
||||||
124,"{'train/epoch_loss': 0.016353931551580648, 'epoch': 9, '_wandb': {'runtime': 353}, '_runtime': 355.4184715747833, '_timestamp': 1678738469.1834886, 'test/recall': 0.6578947368421053, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0014543599681928754, '_step': 529, 'test/f1-score': 0.7575757575757577, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.8928571428571429, 'test/epoch_loss': 0.4269479903909895}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0001}",dark-sweep-2
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124,"{'_runtime': 355.4184715747833, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.8928571428571429, 'test/epoch_loss': 0.4269479903909895, 'epoch': 9, '_wandb': {'runtime': 353}, '_timestamp': 1678738469.1834886, 'test/recall': 0.6578947368421053, 'test/f1-score': 0.7575757575757577, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0014543599681928754, 'train/epoch_loss': 0.016353931551580648, '_step': 529}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0001}",dark-sweep-2
|
||||||
125,"{'_wandb': {'runtime': 381}, '_timestamp': 1678738101.018471, 'test/f1-score': 0.8470588235294119, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.40116495291392007, 'epoch': 9, '_runtime': 384.5172441005707, 'test/recall': 0.8181818181818182, 'test/precision': 0.8780487804878049, 'train/epoch_acc': 0.8673218673218673, 'train/batch_loss': 0.31195682287216187, 'train/epoch_loss': 0.3623260387038716, '_step': 1039}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",trim-sweep-1
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125,"{'_step': 1039, '_runtime': 384.5172441005707, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8780487804878049, 'test/epoch_loss': 0.40116495291392007, 'train/epoch_acc': 0.8673218673218673, 'epoch': 9, '_wandb': {'runtime': 381}, '_timestamp': 1678738101.018471, 'test/f1-score': 0.8470588235294119, 'train/batch_loss': 0.31195682287216187, 'train/epoch_loss': 0.3623260387038716}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",trim-sweep-1
|
||||||
126,"{'epoch': 9, '_runtime': 560.7235152721405, 'test/f1-score': 0.8602150537634408, 'test/precision': 0.8163265306122449, 'train/epoch_acc': 0.7567567567567567, 'train/batch_loss': 0.6653294563293457, '_step': 2059, '_wandb': {'runtime': 560}, '_timestamp': 1678738182.1088202, 'test/recall': 0.9090909090909092, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_loss': 0.6107166709712448}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.001}",sparkling-sweep-1
|
126,"{'epoch': 9, '_wandb': {'runtime': 560}, '_runtime': 560.7235152721405, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8602150537634408, 'train/epoch_loss': 0.6107166709712448, '_step': 2059, '_timestamp': 1678738182.1088202, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8163265306122449, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_acc': 0.7567567567567567, 'train/batch_loss': 0.6653294563293457}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.001}",sparkling-sweep-1
|
||||||
127,"{'_step': 555, 'epoch': 1, '_timestamp': 1678737059.0375042, 'test/recall': 0.6818181818181818, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6382978723404256, '_wandb': {'runtime': 118}, '_runtime': 122.13349413871764, 'test/f1-score': 0.6593406593406593, 'test/epoch_loss': 0.6796493821673923, 'train/epoch_acc': 0.5515970515970516, 'train/batch_loss': 0.6759337782859802, 'train/epoch_loss': 0.6851893525744539}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003}",serene-sweep-1
|
127,"{'test/epoch_loss': 0.6796493821673923, 'train/epoch_acc': 0.5515970515970516, 'train/batch_loss': 0.6759337782859802, 'epoch': 1, '_wandb': {'runtime': 118}, '_runtime': 122.13349413871764, 'test/recall': 0.6818181818181818, 'test/precision': 0.6382978723404256, '_step': 555, '_timestamp': 1678737059.0375042, 'test/f1-score': 0.6593406593406593, 'test/epoch_acc': 0.6555555555555556, 'train/epoch_loss': 0.6851893525744539}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003}",serene-sweep-1
|
||||||
128,"{'_wandb': {'runtime': 455}, 'train/epoch_acc': 0.9914004914004914, 'test/precision': 0.9361702127659576, 'test/batch_loss': 0.1311825066804886, 'train/epoch_loss': 0.032788554922144414, '_runtime': 456.3002746105194, '_timestamp': 1678734250.8076646, 'test/f1-score': 0.8888888888888888, 'train/batch_loss': 0.003167948452755809, '_step': 1159, 'test/recall': 0.8461538461538461, 'test/epoch_loss': 0.45068282733360926, 'epoch': 9, 'test/epoch_acc': 0.8777777777777778}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003}",super-sweep-10
|
128,"{'_runtime': 456.3002746105194, 'train/epoch_acc': 0.9914004914004914, 'train/epoch_loss': 0.032788554922144414, 'test/epoch_loss': 0.45068282733360926, 'train/batch_loss': 0.003167948452755809, 'test/f1-score': 0.8888888888888888, 'test/epoch_acc': 0.8777777777777778, 'test/batch_loss': 0.1311825066804886, 'test/precision': 0.9361702127659576, 'epoch': 9, '_wandb': {'runtime': 455}, 'test/recall': 0.8461538461538461, '_step': 1159, '_timestamp': 1678734250.8076646}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003}",super-sweep-10
|
||||||
129,"{'_wandb': {'runtime': 563}, '_runtime': 564.230875492096, 'test/f1-score': 0.7173913043478259, 'test/batch_loss': 0.9658783674240112, 'train/epoch_loss': 0.5984233345387902, '_step': 2289, 'test/precision': 0.673469387755102, 'test/recall': 0.7674418604651163, 'train/epoch_acc': 0.687960687960688, 'train/batch_loss': 0.3260266184806824, 'epoch': 9, 'test/epoch_acc': 0.7111111111111111, 'test/epoch_loss': 0.5302444166607327, '_timestamp': 1678733784.6976814}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",distinctive-sweep-9
|
129,"{'test/epoch_loss': 0.5302444166607327, '_wandb': {'runtime': 563}, '_runtime': 564.230875492096, '_timestamp': 1678733784.6976814, 'test/precision': 0.673469387755102, 'train/epoch_acc': 0.687960687960688, '_step': 2289, 'epoch': 9, 'test/epoch_acc': 0.7111111111111111, 'train/epoch_loss': 0.5984233345387902, 'test/batch_loss': 0.9658783674240112, 'train/batch_loss': 0.3260266184806824, 'test/recall': 0.7674418604651163, 'test/f1-score': 0.7173913043478259}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",distinctive-sweep-9
|
||||||
130,"{'_step': 2289, 'test/f1-score': 0.9268292682926828, '_timestamp': 1678733210.1129615, 'test/epoch_acc': 0.9333333333333332, 'test/epoch_loss': 0.17092165086004468, 'epoch': 9, 'train/batch_loss': 0.007875862531363964, 'train/epoch_loss': 0.1743801347293527, 'test/precision': 1, 'test/batch_loss': 0.1419784128665924, 'train/epoch_acc': 0.9496314496314496, '_wandb': {'runtime': 527}, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003}",winter-sweep-8
|
130,"{'test/epoch_loss': 0.17092165086004468, '_step': 2289, '_wandb': {'runtime': 527}, '_timestamp': 1678733210.1129615, 'test/batch_loss': 0.1419784128665924, 'train/epoch_acc': 0.9496314496314496, 'epoch': 9, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636, 'test/precision': 1, 'train/batch_loss': 0.007875862531363964, 'test/f1-score': 0.9268292682926828, 'test/epoch_acc': 0.9333333333333332, 'train/epoch_loss': 0.1743801347293527}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003}",winter-sweep-8
|
||||||
131,"{'test/f1-score': 0.9066666666666668, '_runtime': 453.52900218963623, 'test/recall': 0.8292682926829268, 'test/precision': 1, 'test/batch_loss': 0.27116066217422485, '_step': 1159, '_wandb': {'runtime': 452}, 'test/epoch_loss': 0.21558621691332924, 'train/epoch_loss': 0.07730489082323246, 'epoch': 9, '_timestamp': 1678732673.1225052, 'test/epoch_acc': 0.9222222222222224, 'train/epoch_acc': 0.9791154791154792, 'train/batch_loss': 0.04383014515042305}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",stilted-sweep-7
|
131,"{'epoch': 9, '_runtime': 453.52900218963623, '_timestamp': 1678732673.1225052, 'train/epoch_loss': 0.07730489082323246, 'test/epoch_loss': 0.21558621691332924, 'train/batch_loss': 0.04383014515042305, '_step': 1159, 'test/recall': 0.8292682926829268, 'test/f1-score': 0.9066666666666668, 'train/epoch_acc': 0.9791154791154792, '_wandb': {'runtime': 452}, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 1, 'test/batch_loss': 0.27116066217422485}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",stilted-sweep-7
|
||||||
132,"{'_timestamp': 1678732212.5530572, 'test/f1-score': 0.7010309278350516, 'test/epoch_acc': 0.6777777777777778, 'epoch': 9, 'test/batch_loss': 0.4716488718986511, 'train/batch_loss': 0.48304444551467896, '_step': 2289, '_wandb': {'runtime': 561}, '_runtime': 561.7993631362915, 'test/precision': 0.6538461538461539, 'test/recall': 0.7555555555555555, 'test/epoch_loss': 0.6190193812052409, 'train/epoch_acc': 0.7272727272727273, 'train/epoch_loss': 0.5549268187263967}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",summer-sweep-6
|
132,"{'_timestamp': 1678732212.5530572, 'test/recall': 0.7555555555555555, 'test/precision': 0.6538461538461539, '_wandb': {'runtime': 561}, '_runtime': 561.7993631362915, 'test/epoch_loss': 0.6190193812052409, '_step': 2289, 'test/epoch_acc': 0.6777777777777778, 'test/f1-score': 0.7010309278350516, 'test/batch_loss': 0.4716488718986511, 'train/epoch_acc': 0.7272727272727273, 'train/batch_loss': 0.48304444551467896, 'train/epoch_loss': 0.5549268187263967, 'epoch': 9}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",summer-sweep-6
|
||||||
133,"{'test/epoch_acc': 0.8222222222222223, 'test/batch_loss': 0.5068956017494202, 'train/epoch_loss': 0.5186349417126442, '_step': 1159, '_wandb': {'runtime': 453}, 'test/f1-score': 0.813953488372093, 'test/epoch_loss': 0.4936415394147237, 'train/batch_loss': 0.4434223175048828, 'test/recall': 0.7142857142857143, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8218673218673218, 'epoch': 9, '_runtime': 454.3645238876343, '_timestamp': 1678731639.156168}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001}",different-sweep-5
|
133,"{'epoch': 9, '_wandb': {'runtime': 453}, 'test/batch_loss': 0.5068956017494202, '_step': 1159, '_runtime': 454.3645238876343, 'test/f1-score': 0.813953488372093, 'test/epoch_acc': 0.8222222222222223, 'train/batch_loss': 0.4434223175048828, 'train/epoch_loss': 0.5186349417126442, 'test/recall': 0.7142857142857143, 'train/epoch_acc': 0.8218673218673218, '_timestamp': 1678731639.156168, 'test/precision': 0.945945945945946, 'test/epoch_loss': 0.4936415394147237}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001}",different-sweep-5
|
||||||
134,"{'_wandb': {'runtime': 453}, '_runtime': 454.26038885116577, 'test/batch_loss': 0.5159374475479126, 'test/epoch_loss': 0.5482642173767089, '_step': 1159, 'epoch': 9, 'train/batch_loss': 0.5655931830406189, '_timestamp': 1678731176.111379, 'test/f1-score': 0.8354430379746836, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.825, 'train/epoch_acc': 0.812039312039312, 'train/epoch_loss': 0.5429200196149016, 'test/recall': 0.8461538461538461}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",wise-sweep-4
|
134,"{'_runtime': 454.26038885116577, 'test/f1-score': 0.8354430379746836, 'test/batch_loss': 0.5159374475479126, 'test/epoch_loss': 0.5482642173767089, 'epoch': 9, '_wandb': {'runtime': 453}, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.812039312039312, 'train/epoch_loss': 0.5429200196149016, '_step': 1159, '_timestamp': 1678731176.111379, 'test/recall': 0.8461538461538461, 'test/precision': 0.825, 'train/batch_loss': 0.5655931830406189}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",wise-sweep-4
|
||||||
135,"{'test/batch_loss': 1.7588363885879517, 'train/batch_loss': 0.00470334617421031, 'train/epoch_loss': 0.02060394324720534, '_step': 2289, 'epoch': 9, 'test/f1-score': 0.8493150684931509, 'train/epoch_acc': 0.9963144963144964, '_runtime': 528.9760706424713, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.9393939393939394, 'test/epoch_loss': 0.24194780117250048, '_wandb': {'runtime': 528}, '_timestamp': 1678730714.7711067, 'test/recall': 0.775}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003}",misty-sweep-3
|
135,"{'_runtime': 528.9760706424713, 'test/f1-score': 0.8493150684931509, 'train/epoch_loss': 0.02060394324720534, 'epoch': 9, '_timestamp': 1678730714.7711067, 'test/batch_loss': 1.7588363885879517, 'train/batch_loss': 0.00470334617421031, '_wandb': {'runtime': 528}, 'test/recall': 0.775, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.24194780117250048, '_step': 2289, 'train/epoch_acc': 0.9963144963144964, 'test/precision': 0.9393939393939394}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003}",misty-sweep-3
|
||||||
136,"{'test/batch_loss': 0.455120325088501, 'train/batch_loss': 0.5347514748573303, 'test/precision': 0.8387096774193549, 'train/epoch_acc': 0.8329238329238329, '_runtime': 455.41485929489136, 'test/recall': 0.6842105263157895, 'test/epoch_acc': 0.8111111111111111, 'test/f1-score': 0.7536231884057972, 'train/epoch_loss': 0.42904984072326735, 'epoch': 9, '_wandb': {'runtime': 454}, '_timestamp': 1678730177.1362092, '_step': 1159, 'test/epoch_loss': 0.4792341656155056}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",unique-sweep-2
|
136,"{'epoch': 9, '_wandb': {'runtime': 454}, 'test/epoch_acc': 0.8111111111111111, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4792341656155056, '_step': 1159, 'test/recall': 0.6842105263157895, 'train/batch_loss': 0.5347514748573303, 'train/epoch_loss': 0.42904984072326735, 'train/epoch_acc': 0.8329238329238329, '_runtime': 455.41485929489136, '_timestamp': 1678730177.1362092, 'test/f1-score': 0.7536231884057972, 'test/batch_loss': 0.455120325088501}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",unique-sweep-2
|
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137,"{'epoch': 9, '_wandb': {'runtime': 527}, 'test/recall': 0.8636363636363636, 'test/batch_loss': 2.5320074558258057, 'train/epoch_acc': 0.9901719901719902, 'train/batch_loss': 0.005740344058722258, 'train/epoch_loss': 0.024021292951151657, '_step': 2289, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9047619047619048, 'test/epoch_loss': 0.5442472649919283, '_runtime': 528.4356484413147, '_timestamp': 1678729705.2001765, 'test/f1-score': 0.8837209302325582}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",polar-sweep-1
|
137,"{'_step': 2289, '_runtime': 528.4356484413147, '_timestamp': 1678729705.2001765, 'test/batch_loss': 2.5320074558258057, 'test/epoch_loss': 0.5442472649919283, 'epoch': 9, 'test/recall': 0.8636363636363636, 'train/epoch_acc': 0.9901719901719902, 'test/epoch_acc': 0.888888888888889, '_wandb': {'runtime': 527}, 'test/f1-score': 0.8837209302325582, 'test/precision': 0.9047619047619048, 'train/batch_loss': 0.005740344058722258, 'train/epoch_loss': 0.024021292951151657}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",polar-sweep-1
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" fpr, tpr, thresh = metrics.roc_curve(best_y_true, best_y_score)\n",
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" fpr, tpr, thresh = metrics.roc_curve(best_y_true, best_y_score)\n",
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" ax.plot(fpr,\n",
|
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" label=r\"Fold %d (AUC = %0.2f)\" % (fold, best_test_auc),\n",
|
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|
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|
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|
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" alpha=0.5)\n",
|
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" interp_tpr = np.interp(mean_fpr, fpr, tpr)\n",
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"Create sweep with ID: bq0rvyfn\n",
|
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"Sweep URL: https://wandb.ai/flower-classification/classifier-optimized/sweeps/bq0rvyfn\n"
|
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: qxhbaz0l with config:\n",
|
"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: puf6qvta with config:\n",
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|
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|
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" $ pip install wandb --upgrade"
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"Syncing run <strong><a href='https://wandb.ai/flower-classification/classifier-optimized/runs/qxhbaz0l' target=\"_blank\">good-sweep-1</a></strong> to <a href='https://wandb.ai/flower-classification/classifier-optimized' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/flower-classification/classifier-optimized/sweeps/bq0rvyfn' target=\"_blank\">https://wandb.ai/flower-classification/classifier-optimized/sweeps/bq0rvyfn</a>"
|
"Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20240309_202329-puf6qvta</code>"
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"Syncing run <strong><a href='https://wandb.ai/flower-classification/classifier-optimized/runs/puf6qvta' target=\"_blank\">faithful-sweep-1</a></strong> to <a href='https://wandb.ai/flower-classification/classifier-optimized' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/flower-classification/classifier-optimized/sweeps/fp9p6hei' target=\"_blank\">https://wandb.ai/flower-classification/classifier-optimized/sweeps/fp9p6hei</a>"
|
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" View sweep at <a href='https://wandb.ai/flower-classification/classifier-optimized/sweeps/bq0rvyfn' target=\"_blank\">https://wandb.ai/flower-classification/classifier-optimized/sweeps/bq0rvyfn</a>"
|
" View sweep at <a href='https://wandb.ai/flower-classification/classifier-optimized/sweeps/fp9p6hei' target=\"_blank\">https://wandb.ai/flower-classification/classifier-optimized/sweeps/fp9p6hei</a>"
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"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:561: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
|
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|
||||||
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|
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|
||||||
|
" dataset = build_dataset(config.batch_size)\n",
|
||||||
|
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|
||||||
|
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|
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|
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|
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||||||
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||||||
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|
|
||||||
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|
|
||||||
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|
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||||||
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|
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||||||
"\n",
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
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|
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|
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||||||
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|
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||||||
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|
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||||||
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|
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||||||
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||||||
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|||||||
|
[tool.poetry]
|
||||||
|
name = "thesis"
|
||||||
|
version = "0.1.0"
|
||||||
|
description = ""
|
||||||
|
authors = ["Tobias Eidelpes <e1527193@student.tuwien.ac.at>"]
|
||||||
|
readme = "README.md"
|
||||||
|
|
||||||
|
[tool.poetry.dependencies]
|
||||||
|
python = "^3.10"
|
||||||
|
flask = "^2.0.3"
|
||||||
|
apscheduler = "^3.10.0"
|
||||||
|
albumentations = "^1.3.0"
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||||||
|
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|
||||||
|
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|
||||||
|
opencv-python = "^4.7.0"
|
||||||
|
torch = "^2.1.2"
|
||||||
|
torchvision = "^0.16.2"
|
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|
numpy = "^1.18.0"
|
||||||
|
scipy = "^1.11.4"
|
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|
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|
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|
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|
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|
requires = ["poetry-core"]
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
|||||||
|
\documentclass{beamer}
|
||||||
|
|
||||||
|
\beamertemplatenavigationsymbolsempty
|
||||||
|
|
||||||
|
\usetheme{default}
|
||||||
|
\usecolortheme{dolphin}
|
||||||
|
|
||||||
|
\usepackage{graphicx}
|
||||||
|
\usepackage{caption}
|
||||||
|
\usepackage{tikz}
|
||||||
|
\usepackage{dsfont}
|
||||||
|
\usepackage{siunitx}
|
||||||
|
\usepackage{booktabs}
|
||||||
|
\usepackage[labelformat=empty]{caption}
|
||||||
|
\usetikzlibrary{shapes,arrows}
|
||||||
|
|
||||||
|
% Define block styles
|
||||||
|
\tikzstyle{decision} = [diamond, draw, fill=blue!20, text width=4.5em, text badly centered, node distance=3cm, inner sep=0pt]
|
||||||
|
\tikzstyle{block} = [rectangle, draw, fill=blue!20, text width=5em, text centered, rounded corners, minimum height=4em]
|
||||||
|
\tikzstyle{line} = [draw, -latex']
|
||||||
|
\tikzstyle{cloud} = [draw, ellipse,fill=red!20, node distance=3cm, minimum height=2em]
|
||||||
|
|
||||||
|
\setbeamerfont{caption}{size=\tiny}
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
|
||||||
|
\title[Plant Detection and State Classification]{Plant Detection and
|
||||||
|
State Classification with Machine Learning}
|
||||||
|
\author{Tobias Eidelpes}
|
||||||
|
\date{March 12, 2024}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\maketitle
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Introduction}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Problem Statement}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Automated detection of water stress \pause
|
||||||
|
\item Automated watering of household plants \pause
|
||||||
|
\item Decision-making \emph{in the field} \pause
|
||||||
|
\item No research so far in this context
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Research Questions}
|
||||||
|
\begin{enumerate}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item How well does the model work in theory and how well in
|
||||||
|
practice? \pause
|
||||||
|
\item What are possible reasons for it to work/not work? \pause
|
||||||
|
\item What are possible improvements to the system in the future?
|
||||||
|
\end{enumerate}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Methodological Approach}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Methods}
|
||||||
|
\begin{columns}[c]
|
||||||
|
\column{.5\textwidth}
|
||||||
|
\begin{enumerate}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Literature Review
|
||||||
|
\item Dataset Curation
|
||||||
|
\item Model Training
|
||||||
|
\item Optimization
|
||||||
|
\item Deployment
|
||||||
|
\item Evaluation
|
||||||
|
\end{enumerate}
|
||||||
|
\column{.5\textwidth}
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/wilted\_007.jpg}
|
||||||
|
\end{center}
|
||||||
|
\end{columns}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Prototype Design}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Design: Requirements} \pause
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Detect and Classify \pause
|
||||||
|
\item Publish Results via REST-API \pause
|
||||||
|
\item Reasonable Inference Time \pause
|
||||||
|
\item Reasonable Model Performance
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Design}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\centerline{\includegraphics[width=0.9\textwidth]{graphics/setup.pdf}}
|
||||||
|
\label{fig:design}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Prototype Implementation}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: YOLOv7n}
|
||||||
|
\begin{minipage}[bt]{.49\textwidth}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Pretrained on COCO
|
||||||
|
\item OID classes \emph{Houseplant} and \emph{Plant}
|
||||||
|
\item Training Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{79204} images
|
||||||
|
\item \num{284130} bounding boxes
|
||||||
|
\end{itemize}
|
||||||
|
\item Validation Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{3091} images
|
||||||
|
\item \num{4092} bounding boxes
|
||||||
|
\end{itemize}
|
||||||
|
\end{itemize}
|
||||||
|
\end{minipage}
|
||||||
|
\begin{minipage}[bt]{.49\textwidth}
|
||||||
|
\vspace{.5cm}
|
||||||
|
\begin{figure}
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=.85\textwidth]{graphics/houseplant.jpg}
|
||||||
|
\caption{Earthy Tones For Fallsurlevif by Flickr User decor8
|
||||||
|
under CC BY 2.0}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{minipage}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: YOLOv7n}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/model_fitness.pdf}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: YOLOv7n}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/val\_box\_obj\_loss.pdf}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{YOLOv7n Hyperparameter Optimization} \pause
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Mutate 26 out of 30 hyperparameters \pause
|
||||||
|
\item Parent chosen randomly from top five previous generations \pause
|
||||||
|
\item 3 epochs per iteration \pause
|
||||||
|
\item 87 iterations \pause
|
||||||
|
\item Best with 0.6076 fitness
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{YOLOv7n Hyperparameter Optimization}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/model_fitness\_final.pdf}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: ResNet-50}
|
||||||
|
\begin{minipage}[bt]{.49\textwidth}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Pretrained on ImageNet
|
||||||
|
\item Training Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{384} healthy
|
||||||
|
\item \num{384} stressed
|
||||||
|
\end{itemize}
|
||||||
|
\item Validation Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{68} healthy
|
||||||
|
\item \num{68} stressed
|
||||||
|
\end{itemize}
|
||||||
|
\end{itemize}
|
||||||
|
\end{minipage}
|
||||||
|
\begin{minipage}[bt]{.49\textwidth}
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/classifier-cam-cropped.pdf}
|
||||||
|
\end{center}
|
||||||
|
\end{minipage}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: ResNet-50 Accuracy}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/classifier-metrics-acc.pdf}
|
||||||
|
\caption{\normalsize Maximum validation accuracy of 0.9118 at epoch 27}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Prototype Implementation: ResNet-50 Loss}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\begin{center}
|
||||||
|
\includegraphics[width=\textwidth]{graphics/classifier-metrics-loss.pdf}
|
||||||
|
\end{center}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{ResNet-50 Hyperparameter Optimization}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Random search \pause
|
||||||
|
\item 10 epochs per iteration \pause
|
||||||
|
\item 138 iterations \pause
|
||||||
|
\item Best with 0.9783 $\mathrm{F}_{1}$-score
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{ResNet-50 Hyperparameter Optimization}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\centerline{\includegraphics[width=\textwidth]{graphics/classifier-hyp-metrics.pdf}}
|
||||||
|
\caption{\normalsize Learning rate and batch size effect on
|
||||||
|
$\mathrm{F}_{1}$-score}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Evaluation}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{YOLOv7n Evaluation}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Test Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{9000} images
|
||||||
|
\item \num{12238} bounding boxes \pause
|
||||||
|
\end{itemize}
|
||||||
|
\end{itemize}
|
||||||
|
\begin{table}[h]
|
||||||
|
\centering
|
||||||
|
\begin{tabular}{lrrrr}
|
||||||
|
\toprule
|
||||||
|
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
|
||||||
|
\midrule
|
||||||
|
Plant & \num{0.5476} & \num{0.7379} & \num{0.6286} & \num{12238} \\
|
||||||
|
\bottomrule
|
||||||
|
\end{tabular}
|
||||||
|
\caption{\scriptsize Results for the non-optimized object detection model}
|
||||||
|
\label{tab:yolo-metrics}
|
||||||
|
\end{table}
|
||||||
|
\begin{table}[h]
|
||||||
|
\centering
|
||||||
|
\begin{tabular}{lrrrr}
|
||||||
|
\toprule
|
||||||
|
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
|
||||||
|
\midrule
|
||||||
|
Plant & \num{0.6334} & \num{0.7028} & \num{0.6663} & \num{12238} \\
|
||||||
|
\bottomrule
|
||||||
|
\end{tabular}
|
||||||
|
\caption{\scriptsize Results for the optimized object detection model}
|
||||||
|
\label{tab:yolo-metrics-hyp}
|
||||||
|
\end{table}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{ResNet-50 Evaluation}
|
||||||
|
\begin{center}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\includegraphics[width=0.65\textwidth]{graphics/classifier-hyp-folds.pdf}
|
||||||
|
\caption{\scriptsize ROC curves and AUC for classifier 10-fold
|
||||||
|
cross-validation}
|
||||||
|
\end{figure}
|
||||||
|
\end{center}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Aggregate Model Evaluation}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item Pre-annotated Test Set
|
||||||
|
\begin{itemize}
|
||||||
|
\item \num{640} images
|
||||||
|
\item \num{766} bounding boxes healthy
|
||||||
|
\item \num{494} bounding boxes stressed \pause
|
||||||
|
\end{itemize}
|
||||||
|
\item Non-optimized model $\mathrm{mAP} = 0.3581$ \pause
|
||||||
|
\item Optimized model $\mathrm{mAP} = 0.3838$
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\section{Conclusion}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Limitations and Conclusions}
|
||||||
|
\begin{itemize}
|
||||||
|
\setlength{\itemsep}{0.75\baselineskip}
|
||||||
|
\item I am \emph{not} an expert labeler! \pause
|
||||||
|
\item Object detection performs well (mAP 0.5727) \pause
|
||||||
|
\item Optimized detector worse than non-optimized \pause
|
||||||
|
\item Inconsistent ground truth \pause
|
||||||
|
\item Robust classification
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Research Questions Revisited}
|
||||||
|
\begin{enumerate}
|
||||||
|
\setlength{\itemsep}{1.1\baselineskip}
|
||||||
|
\item How well does the model work in theory and how well in practice? \pause
|
||||||
|
\begin{itemize}
|
||||||
|
\item Plant detection comparable to benchmarks \pause
|
||||||
|
\item Impressive stress classification \pause
|
||||||
|
\end{itemize}
|
||||||
|
\item What are possible reasons for it to work/not work? \pause
|
||||||
|
\begin{itemize}
|
||||||
|
\item Dataset curation \pause
|
||||||
|
\end{itemize}
|
||||||
|
\item What are possible improvements to the system in the future? \pause
|
||||||
|
\begin{itemize}
|
||||||
|
\item Use more computational resources \pause
|
||||||
|
\item Expert labeling
|
||||||
|
\end{itemize}
|
||||||
|
\end{enumerate}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\centering
|
||||||
|
\Large
|
||||||
|
Thank you for your attention!
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{ResNet-50 CAM}
|
||||||
|
\begin{figure}[htbp]
|
||||||
|
\centerline{\includegraphics[width=0.9\textwidth]{graphics/classifier-cam.pdf}}
|
||||||
|
\caption[]{\label{fig:classifier-cam} Top-right: CAM for
|
||||||
|
\emph{healthy}. Bot-right: CAM for \emph{stressed}}
|
||||||
|
\end{figure}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}
|
||||||
|
\frametitle{Aggregate Model Evaluation}
|
||||||
|
\begin{table}
|
||||||
|
\centering
|
||||||
|
\begin{tabular}{lrrrr}
|
||||||
|
\toprule
|
||||||
|
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
|
||||||
|
\midrule
|
||||||
|
Healthy & \num{0.665} & \num{0.554} & \num{0.604} & \num{766} \\
|
||||||
|
Stressed & \num{0.639} & \num{0.502} & \num{0.562} & \num{494} \\
|
||||||
|
Weighted Avg & \num{0.655} & \num{0.533} & \num{0.588} & \num{1260} \\
|
||||||
|
\bottomrule
|
||||||
|
\end{tabular}
|
||||||
|
\caption{Metrics for the non-optimized aggregate model}
|
||||||
|
\label{tab:model-metrics}
|
||||||
|
\end{table}
|
||||||
|
\begin{table}
|
||||||
|
\centering
|
||||||
|
\begin{tabular}{lrrrr}
|
||||||
|
\toprule
|
||||||
|
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
|
||||||
|
\midrule
|
||||||
|
Healthy & 0.711 & 0.555 & 0.623 & 766 \\
|
||||||
|
Stressed & 0.570 & 0.623 & 0.596 & 494 \\
|
||||||
|
Weighted Avg & 0.656 & 0.582 & 0.612 & 1260 \\
|
||||||
|
\bottomrule
|
||||||
|
\end{tabular}
|
||||||
|
\caption{Metrics for the optimized aggregate model}
|
||||||
|
\label{tab:model-metrics-hyp}
|
||||||
|
\end{table}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
%%% Local Variables:
|
||||||
|
%%% mode: LaTeX
|
||||||
|
%%% TeX-master: t
|
||||||
|
%%% End:
|
||||||
Binary file not shown.
BIN
thesis/graphics/classifier-metrics-acc.pdf
Normal file
BIN
thesis/graphics/classifier-metrics-acc.pdf
Normal file
Binary file not shown.
BIN
thesis/graphics/classifier-metrics-loss.pdf
Normal file
BIN
thesis/graphics/classifier-metrics-loss.pdf
Normal file
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Binary file not shown.
Binary file not shown.
@ -64,7 +64,7 @@
|
|||||||
\setadvisor{Ao.Univ.-Prof. Dr.}{Horst Eidenberger}{}{male}
|
\setadvisor{Ao.Univ.-Prof. Dr.}{Horst Eidenberger}{}{male}
|
||||||
|
|
||||||
\setregnumber{01527193}
|
\setregnumber{01527193}
|
||||||
\setdate{27}{12}{2023} % Set date with 3 arguments: {day}{month}{year}.
|
\setdate{30}{12}{2023} % Set date with 3 arguments: {day}{month}{year}.
|
||||||
\settitle{\thesistitle}{Plant Detection and State Classification with Machine Learning} % Sets English and German version of the title (both can be English or German).
|
\settitle{\thesistitle}{Plant Detection and State Classification with Machine Learning} % Sets English and German version of the title (both can be English or German).
|
||||||
|
|
||||||
% Select the thesis type: bachelor / master / doctor / phd-school.
|
% Select the thesis type: bachelor / master / doctor / phd-school.
|
||||||
@ -190,36 +190,90 @@ Challenge}
|
|||||||
|
|
||||||
\begin{kurzfassung}
|
\begin{kurzfassung}
|
||||||
Wassermangel in Zimmerpflanzen kann ihr Wachstum negativ
|
Wassermangel in Zimmerpflanzen kann ihr Wachstum negativ
|
||||||
beeinflussen. Derzeitige Lösungen zur Überwachung von Wasserstress
|
beeinflussen. Bestehende Lösungen zur Überwachung von Wasserstress
|
||||||
sind hauptsächlich für landwirtschaftliche Anwendungen
|
sind in erster Linie für landwirtschaftliche Kontexte gedacht, bei
|
||||||
vorgesehen. Wir präsentieren den ersten Deep-Learning-basierten
|
denen nur eine kleine Auswahl an Pflanzen von Interesse ist. Bislang
|
||||||
Prototyp zur Klassifizierung des Wasserstresslevels gängiger
|
gab es keine Forschung im Haushaltskontext, wo die Vielfalt der
|
||||||
Zimmerpflanzen. Unser zweistufiger Ansatz besteht aus einem
|
Pflanzen wesentlich größer ist und es daher schwieriger ist,
|
||||||
Erkennungs- und einem Klassifizierungsschritt und wird anhand eines
|
Wasserstress zu erfassen. Außerdem beinhalten derzeitige Ansätze
|
||||||
eigens erstellten Datensatzes evaluiert. Die Parameter des Modells
|
entweder keinen eigenen Pflanzenerkennungsschritt oder es kommt
|
||||||
werden mit gängigen Methoden der Hyperparameteroptimierung
|
traditionelle Feature Extraction zur Anwendung. Wir entwickeln einen
|
||||||
ausgewählt. Der Prototyp wird auf einem embedded Computer
|
Prototyp zur Erkennung und nachfolgender Klassifizierung des
|
||||||
bereitgestellt, der eine autonome Pflanzenüberwachung
|
Wasserstresses von Pflanzen, der ausschließlich auf Deep Learning
|
||||||
ermöglicht. Die Vorhersagen unseres Modells werden kontinuierlich
|
basiert.
|
||||||
über eine API veröffentlicht, wodurch nachgelagerte
|
|
||||||
Bewässerungssysteme automatisch Zimmerpflanzen ohne menschliche
|
Unser zweistufiger Ansatz besteht aus einem Erkennungs- und einem
|
||||||
Intervention bewässern können. Unser optimiertes Modell erreicht
|
Klassifizierungsschritt. In der Erkennungsphase werden die Pflanzen
|
||||||
einen mAP-Wert von \num{0.3838}.
|
identifiziert und aus dem Originalbild ausgeschnitten. Die
|
||||||
|
Ausschnitte werden an das Klassifizierungsmodell weitergeleitet, das
|
||||||
|
die Wahrscheinlichkeit für Wasserstress ausgibt. Wir verwenden
|
||||||
|
Transfer Learning und führen die Feinabstimmung der beiden Modelle
|
||||||
|
anhand zweier Datensätze durch. Jedes Modell wird mithilfe einer
|
||||||
|
Hyperparameter-Suche optimiert und zunächst einzeln und dann im
|
||||||
|
Aggregat auf einem eigens erstellten Datensatz evaluiert. Wir
|
||||||
|
stellen beide Modelle auf einem Nvidia Jetson Nano bereit, der in
|
||||||
|
der Lage ist, Pflanzen autonom über eine angeschlossene Kamera zu
|
||||||
|
klassifizieren. Die Ergebnisse der Pipeline werden kontinuierlich
|
||||||
|
über eine API veröffentlicht. Nachgeschaltete Bewässerungssysteme
|
||||||
|
können die Vorhersagen zum Wasserstress nutzen, um die Hauspflanzen
|
||||||
|
ohne menschliches Zutun zu bewässern.
|
||||||
|
|
||||||
|
Die beiden Modelle zusammengenommen erreichen einen mAP-Wert von
|
||||||
|
\num{0.3581} in der nicht optimierten Version. Beide Modelle sind in
|
||||||
|
der Lage, mit verschiedenen Lichtverhältnissen, verschiedenen
|
||||||
|
Blickwinkeln und einer Vielfalt an Pflanzen umzugehen. Die
|
||||||
|
optimierte Pipeline erreicht einen mAP-Wert von \num{0.3838}. Im
|
||||||
|
Vergleich zur nicht optimierten Version ist die Genauigkeit für
|
||||||
|
nicht gestresste Pflanzen höher, aber geringer für die gestresste
|
||||||
|
Klasse. Die Spezifität für die nicht gestresste Klasse bleibt im
|
||||||
|
Vergleich zur nicht optimierten Basislinie auf demselben Niveau, ist
|
||||||
|
aber um \num{12.1} Prozentpunkte höher für die gestresste
|
||||||
|
Klasse. Das gewichtete harmonische Mittel ($F_{1}$-score) für beide
|
||||||
|
Klassen konnte um \num{2.4} Prozentpunkte verbessert werden. Diese
|
||||||
|
Ergebnisse zeigen, dass unser zweistufiger Ansatz funktioniert und
|
||||||
|
ein vielversprechender erster Schritt zur Klassifizierung des
|
||||||
|
Zustands von Zimmerpflanzen ist.
|
||||||
\end{kurzfassung}
|
\end{kurzfassung}
|
||||||
|
|
||||||
\begin{abstract}
|
\begin{abstract}
|
||||||
Water deficiency in household plants can adversely affect
|
Water deficiency in household plants can adversely affect
|
||||||
growth. Existing solutions to monitor water stress are primarily
|
growth. Existing solutions to monitor water stress are primarily
|
||||||
intended for agricultural contexts. We present the first deep
|
intended for agricultural contexts where only a small selection of
|
||||||
learning based prototype to classify water stress of common
|
plants are of interest. To date, there has been no research in
|
||||||
household plants. Our two-stage approach consists of a detection and
|
household settings where the variety of plants is considerably
|
||||||
a classification step and is evaluated on a new dataset. The model
|
higher and it is thus more difficult to obtain accurate measures of
|
||||||
parameters are optimized with a hyperparameter search. The prototype
|
water stress. Furthermore, current approaches either do not detect
|
||||||
is deployed to an embedded device enabling autonomous plant
|
plants in images first or use traditional feature extraction for
|
||||||
monitoring. The predictions of our model are published continuously
|
plant detection. We develop a prototype to detect plants and
|
||||||
via an API, allowing downstream watering systems to automatically
|
classify them into water-stressed or not using deep learning based
|
||||||
water household plants without human intervention. Our optimized
|
methods exclusively.
|
||||||
model achieves a mAP of \num{0.3838} on unseen images.
|
|
||||||
|
Our two-stage approach consists of a detection and a classification
|
||||||
|
step. In the detection step, plants are identified and cut out from
|
||||||
|
the original image. The cutouts are passed to the classifier which
|
||||||
|
outputs a probability for water stress. We use transfer learning to
|
||||||
|
start from a robust base and fine-tune both models for their
|
||||||
|
respective tasks. Each model is optimized using hyperparameter
|
||||||
|
optimization and first evaluated individually and then in aggregate
|
||||||
|
on a custom dataset. We deploy both models to an Nvidia Jetson Nano
|
||||||
|
which is able to survey plants autonomously via an attached
|
||||||
|
camera. The results of the pipeline are published continuously via
|
||||||
|
an API. Downstream watering systems can use the water stress
|
||||||
|
predictions to water the plants without human intervention.
|
||||||
|
|
||||||
|
The two models in aggregate achieve a mAP of \num{0.3581} for the
|
||||||
|
non-optimized version. Both constituent models have robust feature
|
||||||
|
extraction capabilities and are able to cope with various lighting
|
||||||
|
conditions, different angles and a wide variety of household
|
||||||
|
plants. The optimized pipeline achieves a mAP of \num{0.3838} on
|
||||||
|
unseen images with higher precision for the non-stressed but lower
|
||||||
|
precision for the stressed class. Recall for the non-stressed class
|
||||||
|
remains at the same level compared to the non-optimized baseline but
|
||||||
|
is \num{12.1} percentage points higher for the stressed class. The
|
||||||
|
weighted $F_{1}$-score across both classes was improved by \num{2.4}
|
||||||
|
percentage points. These results show that our two-stage approach is
|
||||||
|
viable and a promising first step for plant state classification for
|
||||||
|
household plants.
|
||||||
\end{abstract}
|
\end{abstract}
|
||||||
|
|
||||||
% Select the language of the thesis, e.g., english or naustrian.
|
% Select the language of the thesis, e.g., english or naustrian.
|
||||||
@ -466,7 +520,7 @@ models. Chapter~\ref{chap:implementation} expands on how the datasets
|
|||||||
are used during training as well as how the prototype publishes its
|
are used during training as well as how the prototype publishes its
|
||||||
classification results. Chapter~\ref{chap:evaluation} shows the
|
classification results. Chapter~\ref{chap:evaluation} shows the
|
||||||
results of the testing phases as well as the performance of the
|
results of the testing phases as well as the performance of the
|
||||||
aggregate model. Futhermore, the results are compared with the
|
aggregate model. Furthermore, the results are compared with the
|
||||||
expectations and it is discussed whether they are explainable in the
|
expectations and it is discussed whether they are explainable in the
|
||||||
context of the task at hand as well as benchmark results from other
|
context of the task at hand as well as benchmark results from other
|
||||||
datasets (\gls{coco} \cite{lin2015}). Chapter~\ref{chap:conclusion}
|
datasets (\gls{coco} \cite{lin2015}). Chapter~\ref{chap:conclusion}
|
||||||
@ -496,12 +550,14 @@ The term machine learning was first used by \textcite{samuel1959} in
|
|||||||
1959 in the context of teaching a machine how to play the game
|
1959 in the context of teaching a machine how to play the game
|
||||||
Checkers. \textcite{mitchell1997a} defines learning in the context of
|
Checkers. \textcite{mitchell1997a} defines learning in the context of
|
||||||
programs as:
|
programs as:
|
||||||
\begin{quote}
|
|
||||||
|
\begin{quote}{\cite[p.2]{mitchell1997a}}
|
||||||
A computer program is said to \textbf{learn} from experience $E$
|
A computer program is said to \textbf{learn} from experience $E$
|
||||||
with respect to some class of tasks $T$ and performance measure $P$,
|
with respect to some class of tasks $T$ and performance measure $P$,
|
||||||
if its performance at tasks in $T$, as measured by $P$, improves
|
if its performance at tasks in $T$, as measured by $P$, improves
|
||||||
with experience $E$. \cite[p.2]{mitchell1997a}
|
with experience $E$.
|
||||||
\end{quote}
|
\end{quote}
|
||||||
|
|
||||||
In other words, if the aim is to learn to win at a game, the
|
In other words, if the aim is to learn to win at a game, the
|
||||||
performance measure $P$ is defined as the ability to win at that
|
performance measure $P$ is defined as the ability to win at that
|
||||||
game. The tasks in $T$ are playing the game multiple times, and the
|
game. The tasks in $T$ are playing the game multiple times, and the
|
||||||
@ -509,7 +565,7 @@ experience $E$ is gained by letting the program play the game against
|
|||||||
itself.
|
itself.
|
||||||
|
|
||||||
Machine learning is thought to be a sub-field of \gls{ai}. \gls{ai} is
|
Machine learning is thought to be a sub-field of \gls{ai}. \gls{ai} is
|
||||||
a more general term for the scientific endeavour of creating things
|
a more general term for the scientific endeavor of creating things
|
||||||
which possess the kind of intelligence we humans have. Since those
|
which possess the kind of intelligence we humans have. Since those
|
||||||
things will not have been created \emph{naturally}, their intelligence
|
things will not have been created \emph{naturally}, their intelligence
|
||||||
is termed \emph{artificial}. Within the field of \gls{ai} there have
|
is termed \emph{artificial}. Within the field of \gls{ai} there have
|
||||||
@ -628,7 +684,7 @@ The earliest attempts at describing learning machines were by
|
|||||||
\textcite{mcculloch1943} with the idea of the \emph{perceptron}. This
|
\textcite{mcculloch1943} with the idea of the \emph{perceptron}. This
|
||||||
idea was implemented in a more general sense by
|
idea was implemented in a more general sense by
|
||||||
\textcite{rosenblatt1957,rosenblatt1962} as a physical machine. At its
|
\textcite{rosenblatt1957,rosenblatt1962} as a physical machine. At its
|
||||||
core, the perceptron is the simplest artifical neural network with
|
core, the perceptron is the simplest artificial neural network with
|
||||||
only one neuron in the center. The neuron takes all its inputs,
|
only one neuron in the center. The neuron takes all its inputs,
|
||||||
aggregates them with a weighted sum and outputs 1 if the result is
|
aggregates them with a weighted sum and outputs 1 if the result is
|
||||||
above some threshold $\theta$ and 0 if it is not (see
|
above some threshold $\theta$ and 0 if it is not (see
|
||||||
@ -648,7 +704,7 @@ variables.
|
|||||||
|
|
||||||
Due to the inherent limitations of perceptrons to only be able to
|
Due to the inherent limitations of perceptrons to only be able to
|
||||||
classify linearly separable data, \glspl{mlp} are the bedrock of
|
classify linearly separable data, \glspl{mlp} are the bedrock of
|
||||||
modern artifical neural networks. By adding an input layer, a hidden
|
modern artificial neural networks. By adding an input layer, a hidden
|
||||||
layer, and an output layer as well as requiring the activation
|
layer, and an output layer as well as requiring the activation
|
||||||
function of each neuron to be non-linear, a \gls{mlp} can classify
|
function of each neuron to be non-linear, a \gls{mlp} can classify
|
||||||
also non-linear data. Every neuron in each layer is fully connected to
|
also non-linear data. Every neuron in each layer is fully connected to
|
||||||
@ -657,7 +713,7 @@ straightforward case of a feedforward
|
|||||||
network. Figure~\ref{fig:neural-network} shows the skeleton of a
|
network. Figure~\ref{fig:neural-network} shows the skeleton of a
|
||||||
\gls{mlp}.
|
\gls{mlp}.
|
||||||
|
|
||||||
There are two types of artifical neural networks: feedforward and
|
There are two types of artificial neural networks: feedforward and
|
||||||
recurrent networks. Their names refer to the way information flows
|
recurrent networks. Their names refer to the way information flows
|
||||||
through the network. In a feedforward network, the information enters
|
through the network. In a feedforward network, the information enters
|
||||||
the network and flows only uni-directionally to the output nodes. In a
|
the network and flows only uni-directionally to the output nodes. In a
|
||||||
@ -700,7 +756,7 @@ The simplest activation function is the identity function. It is defined as
|
|||||||
|
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:identity}
|
\label{eq:identity}
|
||||||
g(x) = x
|
g(x) = x.
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
If all layers in an artificial neural network use the identity
|
If all layers in an artificial neural network use the identity
|
||||||
@ -750,14 +806,14 @@ logistic function in machine learning. It is defined as
|
|||||||
|
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eq:sigmoid}
|
\label{eq:sigmoid}
|
||||||
\sigma(x) = \frac{1}{1 + e^{-x}}
|
\sigma(x) = \frac{1}{1 + e^{-x}}.
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
It has a characteristic S-shaped curve, mapping each input value to a
|
It has a characteristic S-shaped curve, mapping each input value to a
|
||||||
number between $0$ and $1$, regardless of input size. This
|
number between $0$ and $1$, regardless of input size. This
|
||||||
\emph{squashing} property is particularly desirable for binary
|
\emph{squashing} property is particularly desirable for binary
|
||||||
classification problems because the outputs can be interpreted as
|
classification problems because the outputs can be interpreted as
|
||||||
probabilities. Additionally to the squashing propery, it is also a
|
probabilities. Additionally to the squashing property, it is also a
|
||||||
saturating function because large values map to $1$ and very small
|
saturating function because large values map to $1$ and very small
|
||||||
values to $0$. If a learning algorithm has to update the weights in
|
values to $0$. If a learning algorithm has to update the weights in
|
||||||
the network, saturated neurons are very inefficient and difficult to
|
the network, saturated neurons are very inefficient and difficult to
|
||||||
@ -805,8 +861,8 @@ state, the model's capability of learning new patterns is
|
|||||||
diminished. To address this problem, there are two possibilities. One
|
diminished. To address this problem, there are two possibilities. One
|
||||||
solution is to make sure that the learning rate is not set too high,
|
solution is to make sure that the learning rate is not set too high,
|
||||||
which reduces the problem but does not fully remove it. Another
|
which reduces the problem but does not fully remove it. Another
|
||||||
solution is to use one of the several variants of the ReLU function
|
solution is to use one of the several variants of the \gls{relu}
|
||||||
such as leaky \gls{relu}, \gls{elu}, and \gls{silu}.
|
function such as leaky \gls{relu}, \gls{elu}, and \gls{silu}.
|
||||||
|
|
||||||
In recent years, the \gls{relu} function has become the most popular
|
In recent years, the \gls{relu} function has become the most popular
|
||||||
activation function for deep neural networks and is recommended as the
|
activation function for deep neural networks and is recommended as the
|
||||||
@ -959,15 +1015,15 @@ summation of the pixels above and to the left of it. This
|
|||||||
representation allows them to quickly and efficiently calculate
|
representation allows them to quickly and efficiently calculate
|
||||||
Haar-like features.
|
Haar-like features.
|
||||||
|
|
||||||
The Haar-like features are passed to a modified AdaBoost
|
The Haar-like features are passed to a modified AdaBoost algorithm
|
||||||
algorithm \cite{freund1995} which only selects the (presumably) most
|
\cite{freund1995} which only selects the (presumably) most important
|
||||||
important features. At the end there is a cascading stage of
|
features. At the end there is a cascading stage of classifiers where
|
||||||
classifiers where regions are only considered further if they are
|
regions are only considered further if they are promising. Every
|
||||||
promising. Every additional classifier adds complexity, but once a
|
additional classifier adds complexity, but once a classifier rejects a
|
||||||
classifier rejects a sub-window, the processing stops and the
|
sub-window, the processing stops and the algorithm moves on to the
|
||||||
algorithm moves on to the next window. Despite their final structure
|
next window. Despite their final structure containing \num{32}
|
||||||
containing 32 classifiers, the sliding-window approach is fast and
|
classifiers, the sliding-window approach is fast and achieves
|
||||||
achieves comparable results to the state of the art in 2001.
|
comparable results to the state of the art in 2001.
|
||||||
|
|
||||||
\subsubsection{HOG Detector}
|
\subsubsection{HOG Detector}
|
||||||
\label{sssec:obj-hog}
|
\label{sssec:obj-hog}
|
||||||
@ -987,12 +1043,13 @@ are then passed as a feature vector to a classifier.
|
|||||||
|
|
||||||
\textcite{dalal2005} successfully use the \gls{hog} with a linear
|
\textcite{dalal2005} successfully use the \gls{hog} with a linear
|
||||||
\gls{svm} for classification to detect humans in images. They work
|
\gls{svm} for classification to detect humans in images. They work
|
||||||
with images of 64 by 128 pixels and make sure that the image contains
|
with images of \num{64} by \num{128} pixels and make sure that the
|
||||||
a margin of 16 pixels around the person. Decreasing the border by
|
image contains a margin of \num{16} pixels around the
|
||||||
either enlarging the person or reducing the overall image size results
|
person. Decreasing the border by either enlarging the person or
|
||||||
in worse performance. Unfortunately, their method is far from being
|
reducing the overall image size results in worse
|
||||||
able to process images in real time—a $320$ by $240$ image takes
|
performance. Unfortunately, their method is far from being able to
|
||||||
roughly a second to process.
|
process images in real time—a $320$ by $240$ image takes roughly a
|
||||||
|
second to process.
|
||||||
|
|
||||||
\subsubsection{Deformable Part-Based Model}
|
\subsubsection{Deformable Part-Based Model}
|
||||||
\label{sssec:obj-dpm}
|
\label{sssec:obj-dpm}
|
||||||
@ -1028,20 +1085,21 @@ corresponding \gls{cnn} layer.
|
|||||||
\label{ssec:theory-dl-based}
|
\label{ssec:theory-dl-based}
|
||||||
|
|
||||||
After the publication of the \gls{dpm}, the field of object detection
|
After the publication of the \gls{dpm}, the field of object detection
|
||||||
did not make significant advances regarding speed or accuracy. Only
|
did not make significant advances regarding speed or accuracy until
|
||||||
the (re-)introduction of \glspl{cnn} by \textcite{krizhevsky2012} with
|
2012. Only the (re-)introduction of \glspl{cnn} by
|
||||||
their AlexNet architecture and their subsequent win of the
|
\textcite{krizhevsky2012} with their AlexNet architecture and their
|
||||||
\gls{ilsvrc} 2012 gave the field a new influx of ideas. The
|
subsequent win of the \gls{ilsvrc} 2012 gave the field a new influx of
|
||||||
availability of the 12 million labeled images in the ImageNet dataset
|
ideas. The availability of the \num{12e6} labeled images in the
|
||||||
\cite{deng2009} allowed a shift from focusing on better methods to
|
ImageNet dataset \cite{deng2009} allowed a shift from focusing on
|
||||||
being able to use more data to train models. Earlier models had
|
better methods to being able to use more data to train models. Earlier
|
||||||
difficulties with making use of the large dataset since training was
|
models had difficulties with making use of the large dataset since
|
||||||
unfeasible. AlexNet, however, provided an architecture which was able
|
training was unfeasible. AlexNet, however, provided an architecture
|
||||||
to be trained on two \glspl{gpu} within 6 days. For an in depth
|
which was able to be trained on two \glspl{gpu} within six days. For
|
||||||
overview of AlexNet see section~\ref{sssec:theory-alexnet}. Object
|
an in depth overview of AlexNet see
|
||||||
detection networks from 2014 onward either follow a \emph{one-stage}
|
section~\ref{sssec:theory-alexnet}. Object detection networks from
|
||||||
or \emph{two-stage} detection approach. The following sections go into
|
2014 onward either follow a \emph{one-stage} or \emph{two-stage}
|
||||||
detail about each model category.
|
detection approach. The following sections go into detail about each
|
||||||
|
model category.
|
||||||
|
|
||||||
\subsection{Two-Stage Detectors}
|
\subsection{Two-Stage Detectors}
|
||||||
\label{ssec:theory-two-stage}
|
\label{ssec:theory-two-stage}
|
||||||
@ -1059,7 +1117,7 @@ often not as efficient as one-stage detectors.
|
|||||||
|
|
||||||
\textcite{girshick2014} were the first to propose using feature
|
\textcite{girshick2014} were the first to propose using feature
|
||||||
representations of \glspl{cnn} for object detection. Their approach
|
representations of \glspl{cnn} for object detection. Their approach
|
||||||
consists of generating around $2000$ region proposals and passing
|
consists of generating around \num{2000} region proposals and passing
|
||||||
these on to a \gls{cnn} for feature extraction. The fixed-length
|
these on to a \gls{cnn} for feature extraction. The fixed-length
|
||||||
feature vector is used as input for a linear \gls{svm} which
|
feature vector is used as input for a linear \gls{svm} which
|
||||||
classifies the region. They name their method R-\gls{cnn}, where the R
|
classifies the region. They name their method R-\gls{cnn}, where the R
|
||||||
@ -1067,17 +1125,17 @@ stands for region.
|
|||||||
|
|
||||||
R-\gls{cnn} uses selective search to generate region proposals
|
R-\gls{cnn} uses selective search to generate region proposals
|
||||||
\cite{uijlings2013}.The authors use selective search's \emph{fast
|
\cite{uijlings2013}.The authors use selective search's \emph{fast
|
||||||
mode} to generate the $2000$ proposals and warp (i.e. aspect ratios
|
mode} to generate the \num{2000} proposals and warp (i.e. aspect
|
||||||
are not retained) each proposal into the image dimensions required by
|
ratios are not retained) each proposal into the image dimensions
|
||||||
the \gls{cnn}. The \gls{cnn}, which matches the architecture of
|
required by the \gls{cnn}. The \gls{cnn}, which matches the
|
||||||
AlexNet \cite{krizhevsky2012}, generates a $4096$-dimensional feature
|
architecture of AlexNet \cite{krizhevsky2012}, generates a
|
||||||
vector and each feature vector is scored by a linear \gls{svm} for
|
\num{4096}-dimensional feature vector and each feature vector is
|
||||||
each class. Scored regions are selected/discarded by comparing each
|
scored by a linear \gls{svm} for each class. Scored regions are
|
||||||
region to other regions within the same class and rejecting them if
|
selected/discarded by comparing each region to other regions within
|
||||||
there exists another region with a higher score and greater \gls{iou}
|
the same class and rejecting them if there exists another region with
|
||||||
than a threshold. The linear \gls{svm} classifiers are trained to only
|
a higher score and greater \gls{iou} than a threshold. The linear
|
||||||
label a region as positive if the overlap, as measured by \gls{iou},
|
\gls{svm} classifiers are trained to only label a region as positive
|
||||||
is above $0.3$.
|
if the overlap, as measured by \gls{iou}, is above $0.3$.
|
||||||
|
|
||||||
While the approach of generating region proposals is not new, using a
|
While the approach of generating region proposals is not new, using a
|
||||||
\gls{cnn} purely for feature extraction is. Unfortunately, R-\gls{cnn}
|
\gls{cnn} purely for feature extraction is. Unfortunately, R-\gls{cnn}
|
||||||
@ -1123,7 +1181,7 @@ set at a \gls{map} of 59.2\%.
|
|||||||
Fast R-\gls{cnn} was proposed by \textcite{girshick2015a} to fix the
|
Fast R-\gls{cnn} was proposed by \textcite{girshick2015a} to fix the
|
||||||
three main problems R-\gls{cnn} and \gls{spp}-net have. The first
|
three main problems R-\gls{cnn} and \gls{spp}-net have. The first
|
||||||
problem is that the training for both models is
|
problem is that the training for both models is
|
||||||
multi-stage. R-\gls{cnn} finetunes the convolutional network which is
|
multi-stage. R-\gls{cnn} fine-tunes the convolutional network which is
|
||||||
responsible for feature extraction and then trains \glspl{svm} to
|
responsible for feature extraction and then trains \glspl{svm} to
|
||||||
classify the feature vectors. The third stage consists of training the
|
classify the feature vectors. The third stage consists of training the
|
||||||
bounding box regressors. The second problem is the training time which
|
bounding box regressors. The second problem is the training time which
|
||||||
@ -1134,10 +1192,10 @@ the convolutional network) upwards of \qty{13}{\s\per image}.
|
|||||||
Fast R-\gls{cnn} deals with these problems by having an architecture
|
Fast R-\gls{cnn} deals with these problems by having an architecture
|
||||||
which allows it to take in images and object proposals at once and
|
which allows it to take in images and object proposals at once and
|
||||||
process them simultaneously to arrive at the results. The outputs of
|
process them simultaneously to arrive at the results. The outputs of
|
||||||
the network are the class an object proposal belongs to and 4 scalar
|
the network are the class an object proposal belongs to and four
|
||||||
values representing the bounding box of the object. Unfortunately,
|
scalar values representing the bounding box of the
|
||||||
this approach still requires a separate object proposal generator such
|
object. Unfortunately, this approach still requires a separate object
|
||||||
as selective search \cite{uijlings2013}.
|
proposal generator such as selective search \cite{uijlings2013}.
|
||||||
|
|
||||||
\subsubsection{Faster R-\gls{cnn}}
|
\subsubsection{Faster R-\gls{cnn}}
|
||||||
\label{sssec:theory-faster-rcnn}
|
\label{sssec:theory-faster-rcnn}
|
||||||
@ -1199,7 +1257,7 @@ feature map with high semantic information but also high spatial
|
|||||||
information.
|
information.
|
||||||
|
|
||||||
\textcite{lin2017} report results on \gls{coco} with a \gls{map}@0.5
|
\textcite{lin2017} report results on \gls{coco} with a \gls{map}@0.5
|
||||||
of 59.1\% with a Faster R-\gls{cnn} structure and a ResNet-101
|
of 59.1\% with a Faster R-\gls{cnn} structure and a \gls{resnet}-101
|
||||||
backbone. Their submission does not include any specific improvements
|
backbone. Their submission does not include any specific improvements
|
||||||
such as hard negative mining \cite{shrivastava2016} or data
|
such as hard negative mining \cite{shrivastava2016} or data
|
||||||
augmentation.
|
augmentation.
|
||||||
@ -1302,7 +1360,7 @@ on examples which are harder to achieve a good confidence score on.
|
|||||||
\textcite{lin2017b} implement their focal loss with a simple one-stage
|
\textcite{lin2017b} implement their focal loss with a simple one-stage
|
||||||
detector called \emph{RetinaNet}. It makes use of previous advances in
|
detector called \emph{RetinaNet}. It makes use of previous advances in
|
||||||
object detection and classification by including a \gls{fpn} on top of
|
object detection and classification by including a \gls{fpn} on top of
|
||||||
a ResNet \cite{he2016} as the backbone and using anchors for the
|
a \gls{resnet} \cite{he2016} as the backbone and using anchors for the
|
||||||
different levels in the feature pyramid. Attached to the backbone are
|
different levels in the feature pyramid. Attached to the backbone are
|
||||||
two subnetworks which classify anchor boxes and regress them to the
|
two subnetworks which classify anchor boxes and regress them to the
|
||||||
ground truth boxes. The results are that the RetinaNet-101-500 version
|
ground truth boxes. The results are that the RetinaNet-101-500 version
|
||||||
@ -1416,23 +1474,23 @@ increases the amount of feature maps to $16$ which aims to increase
|
|||||||
the richness of the learned representations. Another pooling layer
|
the richness of the learned representations. Another pooling layer
|
||||||
follows which reduces the size of each of the $16$ feature maps to
|
follows which reduces the size of each of the $16$ feature maps to
|
||||||
five by five pixels. A dense block of three fully-connected layers of
|
five by five pixels. A dense block of three fully-connected layers of
|
||||||
120, 84 and 10 neurons respectively serves as the actual classifier in
|
120, 84 and 10 neurons serves as the actual classifier in the
|
||||||
the network. The last layer uses the euclidean \gls{rbf} to compute
|
network. The last layer uses the euclidean \gls{rbf} to compute the
|
||||||
the class an image belongs to (0-9 digits).
|
class an image belongs to (0-9 digits).
|
||||||
|
|
||||||
The performance of LeNet-5 was measured on the \gls{mnist} database
|
The performance of LeNet-5 was measured on the \gls{mnist} database
|
||||||
which consists of $70000$ labeled images of handwritten digits. The
|
which consists of \num{70000} labeled images of handwritten
|
||||||
\gls{mse} on the test set is 0.95\%. This result is impressive
|
digits. The \gls{mse} on the test set is 0.95\%. This result is
|
||||||
considering that character recognition with a \gls{cnn} had not been
|
impressive considering that character recognition with a \gls{cnn} had
|
||||||
done before. However, standard machine learning methods of the time,
|
not been done before. However, standard machine learning methods of
|
||||||
such as manual feature engineering and \glspl{svm}, achieved a similar
|
the time, such as manual feature engineering and \glspl{svm}, achieved
|
||||||
error rate, even though they are much more memory-intensive. LeNet-5
|
a similar error rate, even though they are much more
|
||||||
was conceived to take advantage of the (then) large \gls{mnist}
|
memory-intensive. LeNet-5 was conceived to take advantage of the
|
||||||
database. Since there were not many datasets available at the time,
|
(then) large \gls{mnist} database. Since there were not many datasets
|
||||||
especially with more samples than in the \gls{mnist} database,
|
available at the time, especially with more samples than in the
|
||||||
\glspl{cnn} were not widely used even after their viability had been
|
\gls{mnist} database, \glspl{cnn} were not widely used even after
|
||||||
demonstrated by \textcite{lecun1998}. Only in 2012
|
their viability had been demonstrated by \textcite{lecun1998}. Only in
|
||||||
\textcite{krizhevsky2012} reintroduced \glspl{cnn} (see
|
2012 \textcite{krizhevsky2012} reintroduced \glspl{cnn} (see
|
||||||
section~\ref{ssec:theory-dl-based}) and since then most
|
section~\ref{ssec:theory-dl-based}) and since then most
|
||||||
state-of-the-art image classification methods have used them.
|
state-of-the-art image classification methods have used them.
|
||||||
|
|
||||||
@ -1479,7 +1537,7 @@ maximum values are then put back into each two by two area (depending
|
|||||||
on the kernel size). This process loses information because a
|
on the kernel size). This process loses information because a
|
||||||
max-pooling layer is not invertible. The subsequent \gls{relu}
|
max-pooling layer is not invertible. The subsequent \gls{relu}
|
||||||
function can be easily inverted because negative values are squashed
|
function can be easily inverted because negative values are squashed
|
||||||
to zero and and positive values are retained. The final deconvolution
|
to zero and positive values are retained. The final deconvolution
|
||||||
operation concerns the convolutional layer itself. In order to
|
operation concerns the convolutional layer itself. In order to
|
||||||
\emph{reconstruct} the original spatial dimensions (before
|
\emph{reconstruct} the original spatial dimensions (before
|
||||||
convolution), a transposed convolution is performed. This process
|
convolution), a transposed convolution is performed. This process
|
||||||
@ -1520,7 +1578,7 @@ other and a \emph{stem} with convolutions at the beginning as well as
|
|||||||
two auxiliary classifiers which help retain the gradient during
|
two auxiliary classifiers which help retain the gradient during
|
||||||
backpropagation. The auxiliary classifiers are only used during
|
backpropagation. The auxiliary classifiers are only used during
|
||||||
training. The authors submitted multiple model versions to the 2004
|
training. The authors submitted multiple model versions to the 2004
|
||||||
\gls{ilsvrc} and their ensemble prediction model consisting of 7
|
\gls{ilsvrc} and their ensemble prediction model consisting of seven
|
||||||
GoogleNets achieved a top-5 error rate of 6.67\%, which resulted in
|
GoogleNets achieved a top-5 error rate of 6.67\%, which resulted in
|
||||||
first place.
|
first place.
|
||||||
|
|
||||||
@ -1573,21 +1631,21 @@ section~\ref{sec:methods-classification}.
|
|||||||
\label{sssec:theory-densenet}
|
\label{sssec:theory-densenet}
|
||||||
|
|
||||||
The authors of DenseNet \cite{huang2017} go one step further than
|
The authors of DenseNet \cite{huang2017} go one step further than
|
||||||
ResNets by connecting every convolutional layer to every other layer
|
\glspl{resnet} by connecting every convolutional layer to every other
|
||||||
in the chain. Previously, each layer was connected in sequence with
|
layer in the chain. Previously, each layer was connected in sequence
|
||||||
the one before and the one after it. Residual connections establish a
|
with the one before and the one after it. Residual connections
|
||||||
link between the previous layer and the next one but still do not
|
establish a link between the previous layer and the next one but still
|
||||||
always propagate enough information forward. These \emph{shortcut
|
do not always propagate enough information forward. These
|
||||||
connections} from earlier layers to later layers are thus only taking
|
\emph{shortcut connections} from earlier layers to later layers are
|
||||||
place in an episodic way for short sections in the chain. DenseNets
|
thus only taking place in an episodic way for short sections in the
|
||||||
are structured in a way such that every layer receives the feature map
|
chain. DenseNets are structured in a way such that every layer
|
||||||
of every previous layer as input. In ResNets, information from
|
receives the feature map of every previous layer as input. In
|
||||||
previous layers is added on to the next layer via element-wise
|
\glspl{resnet}, information from previous layers is added on to the
|
||||||
addition. DenseNets concatenate the features of the previous
|
next layer via element-wise addition. DenseNets concatenate the
|
||||||
layers. The number of feature maps per layer has to be kept low so
|
features of the previous layers. The number of feature maps per layer
|
||||||
that the subsequent layers can still process their inputs. Otherwise,
|
has to be kept low so that the subsequent layers can still process
|
||||||
the last layer in each dense block would receive too many channels
|
their inputs. Otherwise, the last layer in each dense block would
|
||||||
which increases computational complexity.
|
receive too many channels which increases computational complexity.
|
||||||
|
|
||||||
The authors construct their network from multiple dense blocks which
|
The authors construct their network from multiple dense blocks which
|
||||||
are connected via a batch normalization layer, a one by one
|
are connected via a batch normalization layer, a one by one
|
||||||
@ -1919,14 +1977,14 @@ was trained with a dataset containing images of maize, okra, and
|
|||||||
soybean at different stages of growth and under stress and no
|
soybean at different stages of growth and under stress and no
|
||||||
stress. The researchers did not include an object detection step
|
stress. The researchers did not include an object detection step
|
||||||
before image classification and compiled a fairly small dataset of
|
before image classification and compiled a fairly small dataset of
|
||||||
$1200$ images. Of the three models, GoogLeNet beat the other two with
|
\num{1200} images. Of the three models, GoogLeNet beat the other two
|
||||||
a sizable lead at a classification accuracy of >94\% for all three
|
with a sizable lead at a classification accuracy of >94\% for all
|
||||||
types of crop. The authors attribute its success to its inherently
|
three types of crop. The authors attribute its success to its
|
||||||
deeper structure and application of multiple convolutional layers at
|
inherently deeper structure and application of multiple convolutional
|
||||||
different stages. Unfortunately, all of the images were taken at the
|
layers at different stages. Unfortunately, all of the images were
|
||||||
same $\ang{45}\pm\ang{5}$ angle and it stands to reason that the models
|
taken at the same $\ang{45}\pm\ang{5}$ angle and it stands to reason
|
||||||
would perform significantly worse on images taken under different
|
that the models would perform significantly worse on images taken
|
||||||
conditions.
|
under different conditions.
|
||||||
|
|
||||||
\textcite{ramos-giraldo2020} detected water stress in soybean and corn
|
\textcite{ramos-giraldo2020} detected water stress in soybean and corn
|
||||||
crops with a pretrained model based on DenseNet-121 (see
|
crops with a pretrained model based on DenseNet-121 (see
|
||||||
@ -1949,7 +2007,7 @@ classification scores on corn and soybean with a low-cost setup.
|
|||||||
\textcite{azimi2020} demonstrate the efficacy of deep learning models
|
\textcite{azimi2020} demonstrate the efficacy of deep learning models
|
||||||
versus classical machine learning models on chickpea plants. The
|
versus classical machine learning models on chickpea plants. The
|
||||||
authors created their own dataset in a laboratory setting for stressed
|
authors created their own dataset in a laboratory setting for stressed
|
||||||
and non-stressed plants. They acquired $8000$ images at eight
|
and non-stressed plants. They acquired \num{8000} images at eight
|
||||||
different angles in total. For the classical machine learning models,
|
different angles in total. For the classical machine learning models,
|
||||||
they extracted feature vectors using \gls{sift} and \gls{hog}. The
|
they extracted feature vectors using \gls{sift} and \gls{hog}. The
|
||||||
features are fed into three classical machine learning models:
|
features are fed into three classical machine learning models:
|
||||||
@ -1957,30 +2015,28 @@ features are fed into three classical machine learning models:
|
|||||||
algorithm. On the deep learning side, they used their own \gls{cnn}
|
algorithm. On the deep learning side, they used their own \gls{cnn}
|
||||||
architecture and the pretrained ResNet-18 (see
|
architecture and the pretrained ResNet-18 (see
|
||||||
section~\ref{sssec:theory-resnet}) model. The accuracy scores for the
|
section~\ref{sssec:theory-resnet}) model. The accuracy scores for the
|
||||||
classical models was in the range of $\qty{60}{\percent}$ to
|
classical models was in the range of 60\% to 73\% with the \gls{svm}
|
||||||
$\qty{73}{\percent}$ with the \gls{svm} outperforming the two
|
outperforming the two others. The \gls{cnn} achieved higher scores at
|
||||||
others. The \gls{cnn} achieved higher scores at $\qty{72}{\percent}$
|
72\% to 78\% and ResNet-18 achieved the highest scores at 82\% to
|
||||||
to $\qty{78}{\percent}$ and ResNet-18 achieved the highest scores at
|
86\%. The results clearly show the superiority of deep learning over
|
||||||
$\qty{82}{\percent}$ to $\qty{86}{\percent}$. The results clearly show
|
classical machine learning. A downside of their approach lies in the
|
||||||
the superiority of deep learning over classical machine learning. A
|
collection of the images. The background in all images was uniformly
|
||||||
downside of their approach lies in the collection of the images. The
|
white and the plants were prominently placed in the center. It should,
|
||||||
background in all images was uniformly white and the plants were
|
therefore, not be assumed that the same classification scores can be
|
||||||
prominently placed in the center. It should, therefore, not be assumed
|
achieved on plants in the field with messy and noisy backgrounds as
|
||||||
that the same classification scores can be achieved on plants in the
|
well as illumination changes and so forth.
|
||||||
field with messy and noisy backgrounds as well as illumination changes
|
|
||||||
and so forth.
|
|
||||||
|
|
||||||
\textcite{venal2019} combine a standard \gls{cnn} architecture with a
|
\textcite{venal2019} combine a standard \gls{cnn} architecture with a
|
||||||
\gls{svm} for classification. The \gls{cnn} acts as a feature
|
\gls{svm} for classification. The \gls{cnn} acts as a feature
|
||||||
extractor and instead of using the last fully-connected layers of an
|
extractor and instead of using the last fully-connected layers of an
|
||||||
off-the-shelf \gls{cnn}, they replace them with a \gls{svm}. They use
|
off-the-shelf \gls{cnn}, they replace them with a \gls{svm}. They use
|
||||||
this classifier to determine which biotic or abiotic stresses soybeans
|
this classifier to determine which biotic or abiotic stresses soybeans
|
||||||
suffer from. Their dataset consists of $65184$ $64$ by $64$ RGB
|
suffer from. Their dataset consists of \num{65184} $64$ by $64$ RGB
|
||||||
images of which around $40000$ were used for training and $6000$ for
|
images of which around \num{40000} were used for training and
|
||||||
testing. All images show a close-up of a soybean leaf. Their \gls{cnn}
|
\num{6000} for testing. All images show a close-up of a soybean
|
||||||
architecture makes use of three Inception modules (see
|
leaf. Their \gls{cnn} architecture makes use of three Inception
|
||||||
section~\ref{sssec:theory-googlenet}) with \gls{se} blocks and
|
modules (see section~\ref{sssec:theory-googlenet}) with \gls{se}
|
||||||
\gls{bn} layers in-between. Their model achieves an average
|
blocks and \gls{bn} layers in-between. Their model achieves an average
|
||||||
$\mathrm{F}_1$-score of 97\% and an average accuracy of 97.11\% on the
|
$\mathrm{F}_1$-score of 97\% and an average accuracy of 97.11\% on the
|
||||||
test set. Overall, the hybrid structure of their model is promising,
|
test set. Overall, the hybrid structure of their model is promising,
|
||||||
but it is not clear why only using the \gls{cnn} as a feature
|
but it is not clear why only using the \gls{cnn} as a feature
|
||||||
@ -2509,8 +2565,8 @@ phases, we will list a small selection of them.
|
|||||||
\item[HSV-saturation] Randomly change the saturation of the color
|
\item[HSV-saturation] Randomly change the saturation of the color
|
||||||
channels.
|
channels.
|
||||||
\item[HSV-value] Randomly change the value of the color channels.
|
\item[HSV-value] Randomly change the value of the color channels.
|
||||||
\item[Translation] Randomly \emph{translate}, that is, move the image
|
\item[Translation] Randomly \emph{translate}, i.e., move the image by
|
||||||
by a specified amount of pixels.
|
a specified amount of pixels.
|
||||||
\item[Scaling] Randomly scale the image up and down by a factor.
|
\item[Scaling] Randomly scale the image up and down by a factor.
|
||||||
\item[Rotation] Randomly rotate the image.
|
\item[Rotation] Randomly rotate the image.
|
||||||
\item[Inversion] Randomly flip the image along the $x$ or the
|
\item[Inversion] Randomly flip the image along the $x$ or the
|
||||||
@ -2622,7 +2678,7 @@ nor recall change materially during training. In fact, precision
|
|||||||
starts to decrease from the beginning, while recall experiences a
|
starts to decrease from the beginning, while recall experiences a
|
||||||
barely noticeable increase. Taken together with the box and object
|
barely noticeable increase. Taken together with the box and object
|
||||||
loss from figure~\ref{fig:box-obj-loss}, we speculate that the
|
loss from figure~\ref{fig:box-obj-loss}, we speculate that the
|
||||||
pre-trained model already generalizes well to plant detection because
|
pretrained model already generalizes well to plant detection because
|
||||||
one of the categories in the \gls{coco} \cite{lin2015} dataset is
|
one of the categories in the \gls{coco} \cite{lin2015} dataset is
|
||||||
\emph{potted plant}. Any further training solely impacts the
|
\emph{potted plant}. Any further training solely impacts the
|
||||||
confidence of detection but does not lead to higher detection
|
confidence of detection but does not lead to higher detection
|
||||||
@ -2840,14 +2896,14 @@ which is hyperparameter optimization \cite{bergstra2012}.
|
|||||||
\toprule
|
\toprule
|
||||||
Parameter & Values \\
|
Parameter & Values \\
|
||||||
\midrule
|
\midrule
|
||||||
optimizer & adam, sgd \\
|
Optimizer & Adam, \gls{sgd} \\
|
||||||
batch size & 4, 8, 16, 32, 64 \\
|
Batch Size & 4, 8, 16, 32, 64 \\
|
||||||
learning rate & 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1 \\
|
Learning Rate & 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1 \\
|
||||||
step size & 2, 3, 5, 7 \\
|
Step Size & 2, 3, 5, 7 \\
|
||||||
gamma & 0.1, 0.5 \\
|
Gamma & 0.1, 0.5 \\
|
||||||
beta one & 0.9, 0.99 \\
|
Beta One & 0.9, 0.99 \\
|
||||||
beta two & 0.5, 0.9, 0.99, 0.999 \\
|
Beta Two & 0.5, 0.9, 0.99, 0.999 \\
|
||||||
eps & 0.00000001, 0.1, 1 \\
|
Eps & 0.00000001, 0.1, 1 \\
|
||||||
\bottomrule
|
\bottomrule
|
||||||
\end{tabular}
|
\end{tabular}
|
||||||
\caption{Hyperparameters and their possible values during
|
\caption{Hyperparameters and their possible values during
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user