CS231n Assignment1--Q5

2019-11-06 07:35:37

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Q5: Higher Level RePResentations: Image Features

features.ipynb

Load data

Extract Features

Done extracting features for 1000 / 49000 images Done extracting features for 2000 / 49000 images Done extracting features for 3000 / 49000 images Done extracting features for 4000 / 49000 images Done extracting features for 5000 / 49000 images Done extracting features for 6000 / 49000 images Done extracting features for 7000 / 49000 images Done extracting features for 8000 / 49000 images Done extracting features for 9000 / 49000 images Done extracting features for 10000 / 49000 images Done extracting features for 11000 / 49000 images Done extracting features for 12000 / 49000 images Done extracting features for 13000 / 49000 images Done extracting features for 14000 / 49000 images Done extracting features for 15000 / 49000 images Done extracting features for 16000 / 49000 images Done extracting features for 17000 / 49000 images Done extracting features for 18000 / 49000 images Done extracting features for 19000 / 49000 images Done extracting features for 20000 / 49000 images Done extracting features for 21000 / 49000 images Done extracting features for 22000 / 49000 images Done extracting features for 23000 / 49000 images Done extracting features for 24000 / 49000 images Done extracting features for 25000 / 49000 images Done extracting features for 26000 / 49000 images Done extracting features for 27000 / 49000 images Done extracting features for 28000 / 49000 images Done extracting features for 29000 / 49000 images Done extracting features for 30000 / 49000 images Done extracting features for 31000 / 49000 images Done extracting features for 32000 / 49000 images Done extracting features for 33000 / 49000 images Done extracting features for 34000 / 49000 images Done extracting features for 35000 / 49000 images Done extracting features for 36000 / 49000 images Done extracting features for 37000 / 49000 images Done extracting features for 38000 / 49000 images Done extracting features for 39000 / 49000 images Done extracting features for 40000 / 49000 images Done extracting features for 41000 / 49000 images Done extracting features for 42000 / 49000 images Done extracting features for 43000 / 49000 images Done extracting features for 44000 / 49000 images Done extracting features for 45000 / 49000 images Done extracting features for 46000 / 49000 images Done extracting features for 47000 / 49000 images Done extracting features for 48000 / 49000 images

Train SVM on features

Use the validation set to tune the learning rate and regularization strength

from cs231n.classifiers.linear_classifier import LinearSVMlearning_rates = [1e-9, 1e-8, 1e-7]regularization_strengths = [1e5, 1e6, 1e7]results = {}best_val = -1best_svm = Nonepass################################################################################# TODO: ## Use the validation set to set the learning rate and regularization strength. ## This should be identical to the validation that you did for the SVM; save ## the best trained classifer in best_svm. You might also want to play ## with different numbers of bins in the color histogram. If you are careful ## you should be able to get accuracy of near 0.44 on the validation set. #################################################################################passfor lr in learning_rates: for rs in regularization_strengths: svm = LinearSVM() svm.train(X_train_feats, y_train, learning_rate=lr, reg=rs,num_iters=1500, verbose=True) y_train_pred = svm.predict(X_train_feats) train_acc = (np.mean(y_train == y_train_pred)) y_val_pred = svm.predict(X_val_feats) valid_acc = (np.mean(y_val == y_val_pred)) results[(lr,rs)] = train_acc,valid_acc if valid_acc > best_val: best_val = valid_acc best_svm = svm################################################################################# END OF YOUR CODE ################################################################################## Print out results.for lr, reg in sorted(results): train_accuracy, val_accuracy = results[(lr, reg)] print 'lr %e reg %e train accuracy: %f val accuracy: %f' % ( lr, reg, train_accuracy, val_accuracy)print 'best validation accuracy achieved during cross-validation: %f' % best_val

iteration 0 / 1500: loss 86.964746 iteration 100 / 1500: loss 85.433916 iteration 200 / 1500: loss 83.901478 iteration 300 / 1500: loss 82.417817 iteration 400 / 1500: loss 80.979968 iteration 500 / 1500: loss 79.543434 iteration 600 / 1500: loss 78.147781 iteration 700 / 1500: loss 76.778501 iteration 800 / 1500: loss 75.435538 iteration 900 / 1500: loss 74.130870 iteration 1000 / 1500: loss 72.833356 iteration 1100 / 1500: loss 71.567940 iteration 1200 / 1500: loss 70.330666 iteration 1300 / 1500: loss 69.101906 iteration 1400 / 1500: loss 67.924721 iteration 0 / 1500: loss 799.393635 iteration 100 / 1500: loss 656.061690 iteration 200 / 1500: loss 538.715564 iteration 300 / 1500: loss 442.643978 iteration 400 / 1500: loss 364.011190 iteration 500 / 1500: loss 299.622833 iteration 600 / 1500: loss 246.917134 iteration 700 / 1500: loss 203.777157 iteration 800 / 1500: loss 168.454207 iteration 900 / 1500: loss 139.528692 iteration 1000 / 1500: loss 115.863211 iteration 1100 / 1500: loss 96.479661 iteration 1200 / 1500: loss 80.622802 iteration 1300 / 1500: loss 67.628478 iteration 1400 / 1500: loss 56.998456 iteration 0 / 1500: loss 7322.316997 iteration 100 / 1500: loss 988.834726 iteration 200 / 1500: loss 140.277063 iteration 300 / 1500: loss 26.587858 iteration 400 / 1500: loss 11.356381 iteration 500 / 1500: loss 9.315762 iteration 600 / 1500: loss 9.042241 iteration 700 / 1500: loss 9.005658 iteration 800 / 1500: loss 9.000754 iteration 900 / 1500: loss 9.000099 iteration 1000 / 1500: loss 9.000009 iteration 1100 / 1500: loss 8.999998 iteration 1200 / 1500: loss 8.999996 iteration 1300 / 1500: loss 8.999996 iteration 1400 / 1500: loss 8.999996 iteration 0 / 1500: loss 82.759342 iteration 100 / 1500: loss 69.381527 iteration 200 / 1500: loss 58.430912 iteration 300 / 1500: loss 49.461105 iteration 400 / 1500: loss 42.121674 iteration 500 / 1500: loss 36.118543 iteration 600 / 1500: loss 31.201188 iteration 700 / 1500: loss 27.179788 iteration 800 / 1500: 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iteration 300 / 1500: loss 8.999961 iteration 400 / 1500: loss 8.999968 iteration 500 / 1500: loss 8.999968 iteration 600 / 1500: loss 8.999966 iteration 700 / 1500: loss 8.999969 iteration 800 / 1500: loss 8.999963 iteration 900 / 1500: loss 8.999969 iteration 1000 / 1500: loss 8.999966 iteration 1100 / 1500: loss 8.999966 iteration 1200 / 1500: loss 8.999958 iteration 1300 / 1500: loss 8.999981 iteration 1400 / 1500: loss 8.999965 iteration 0 / 1500: loss 7426.789686 iteration 100 / 1500: loss 8.999999 iteration 200 / 1500: loss 9.000000 iteration 300 / 1500: loss 9.000000 iteration 400 / 1500: loss 9.000000 iteration 500 / 1500: loss 9.000001 iteration 600 / 1500: loss 9.000000 iteration 700 / 1500: loss 8.999999 iteration 800 / 1500: loss 9.000000 iteration 900 / 1500: loss 8.999999 iteration 1000 / 1500: loss 9.000000 iteration 1100 / 1500: loss 9.000001 iteration 1200 / 1500: loss 9.000001 iteration 1300 / 1500: loss 9.000001 iteration 1400 / 1500: loss 9.000001 lr 1.000000e-09 reg 1.000000e+05 train accuracy: 0.097939 val accuracy: 0.085000 lr 1.000000e-09 reg 1.000000e+06 train accuracy: 0.100755 val accuracy: 0.107000 lr 1.000000e-09 reg 1.000000e+07 train accuracy: 0.414163 val accuracy: 0.426000 lr 1.000000e-08 reg 1.000000e+05 train accuracy: 0.101796 val accuracy: 0.101000 lr 1.000000e-08 reg 1.000000e+06 train accuracy: 0.417449 val accuracy: 0.416000 lr 1.000000e-08 reg 1.000000e+07 train accuracy: 0.391143 val accuracy: 0.374000 lr 1.000000e-07 reg 1.000000e+05 train accuracy: 0.414245 val accuracy: 0.419000 lr 1.000000e-07 reg 1.000000e+06 train accuracy: 0.403694 val accuracy: 0.407000 lr 1.000000e-07 reg 1.000000e+07 train accuracy: 0.345694 val accuracy: 0.352000 best validation accuracy achieved during cross-validation: 0.426000

0.431

这里写图片描述

(49000, 155)

from cs231n.classifiers.neural_net import TwoLayerNetinput_dim = X_train_feats.shape[1]hidden_dim = 500num_classes = 10net = TwoLayerNet(input_dim, hidden_dim, num_classes)best_net = Nonelearning_rate_choice = [1.8,1.7, 1.6, 1.5]reg_choice = [0.01, 0.011]batch_size_choice = [1024,2048]num_iters_curr = 1500best_acc = -1best_stats = Nonefor batch_size_curr in batch_size_choice: for reg_cur in reg_choice: for learning_rate_curr in learning_rate_choice: print "current training learning_rate:",learning_rate_curr print "current training reg:",reg_cur print "current training batch_size:",batch_size_curr net = TwoLayerNet(input_dim, hidden_dim, num_classes) stats = net.train(X_train_feats, y_train, X_val_feats, y_val, num_iters=num_iters_curr, batch_size=batch_size_curr, learning_rate=learning_rate_curr, learning_rate_decay=0.95, reg=reg_cur, verbose=True) val_acc = (net.predict(X_val_feats) == y_val).mean() print "current val_acc:",val_acc if val_acc>best_acc: best_acc = val_acc best_net = net best_stats = stats print "best_acc:",best_acc print "best learning_rate:",best_net.hyper_params['learning_rate'] print "best reg:",best_net.hyper_params['reg'] print "best batch_size:",best_net.hyper_params['batch_size']

current training learning_rate: 1.8 current training reg: 0.01 current training batch_size: 1024 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.500305 iteration 200 / 1500: loss 1.529890 iteration 300 / 1500: loss 1.380687 iteration 400 / 1500: loss 1.460466 iteration 500 / 1500: loss 1.428169 iteration 600 / 1500: loss 1.472154 iteration 700 / 1500: loss 1.430816 iteration 800 / 1500: loss 1.398814 iteration 900 / 1500: loss 1.428572 iteration 1000 / 1500: loss 1.455034 iteration 1100 / 1500: loss 1.390804 iteration 1200 / 1500: loss 1.447808 iteration 1300 / 1500: loss 1.364305 iteration 1400 / 1500: loss 1.406287 current val_acc: 0.559 best_acc: 0.559 best learning_rate: 1.8 best reg: 0.01 best batch_size: 1024 current training learning_rate: 1.7 current training reg: 0.01 current training batch_size: 1024 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.540023 iteration 200 / 1500: loss 1.480771 iteration 300 / 1500: loss 1.434544 iteration 400 / 1500: loss 1.438121 iteration 500 / 1500: loss 1.436991 iteration 600 / 1500: loss 1.454054 iteration 700 / 1500: loss 1.416321 iteration 800 / 1500: loss 1.399309 iteration 900 / 1500: loss 1.411744 iteration 1000 / 1500: loss 1.392650 iteration 1100 / 1500: loss 1.398244 iteration 1200 / 1500: loss 1.389063 iteration 1300 / 1500: loss 1.395841 iteration 1400 / 1500: loss 1.405926 current val_acc: 0.568 best_acc: 0.568 best learning_rate: 1.7 best reg: 0.01 best batch_size: 1024 current training learning_rate: 1.6 current training reg: 0.01 current training batch_size: 1024 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.470494 iteration 200 / 1500: loss 1.506132 iteration 300 / 1500: loss 1.462313 iteration 400 / 1500: loss 1.452474 iteration 500 / 1500: loss 1.489785 iteration 600 / 1500: loss 1.397515 iteration 700 / 1500: loss 1.367555 iteration 800 / 1500: loss 1.402279 iteration 900 / 1500: loss 1.440599 iteration 1000 / 1500: loss 1.379100 iteration 1100 / 1500: loss 1.421882 iteration 1200 / 1500: loss 1.464033 iteration 1300 / 1500: loss 1.441130 iteration 1400 / 1500: loss 1.410320 current val_acc: 0.573 best_acc: 0.573 best learning_rate: 1.6 best reg: 0.01 best batch_size: 1024 current training learning_rate: 1.5 current training reg: 0.01 current training batch_size: 1024 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.546656 iteration 200 / 1500: loss 1.486320 iteration 300 / 1500: loss 1.401493 iteration 400 / 1500: loss 1.442899 iteration 500 / 1500: loss 1.470813 iteration 600 / 1500: loss 1.444808 iteration 700 / 1500: loss 1.429762 iteration 800 / 1500: loss 1.414738 iteration 900 / 1500: loss 1.369698 iteration 1000 / 1500: loss 1.409698 iteration 1100 / 1500: loss 1.384700 iteration 1200 / 1500: loss 1.406450 iteration 1300 / 1500: loss 1.402708 iteration 1400 / 1500: loss 1.382739 current val_acc: 0.571 current training learning_rate: 1.8 current training reg: 0.011 current training batch_size: 1024 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.563979 iteration 200 / 1500: loss 1.515331 iteration 300 / 1500: loss 1.492799 iteration 400 / 1500: loss 1.435600 iteration 500 / 1500: loss 1.442067 iteration 600 / 1500: loss 1.437993 iteration 700 / 1500: loss 1.380245 iteration 800 / 1500: loss 1.431763 iteration 900 / 1500: loss 1.456615 iteration 1000 / 1500: loss 1.384013 iteration 1100 / 1500: loss 1.374626 iteration 1200 / 1500: loss 1.435926 iteration 1300 / 1500: loss 1.413065 iteration 1400 / 1500: loss 1.417783 current val_acc: 0.56 current training learning_rate: 1.7 current training reg: 0.011 current training batch_size: 1024 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.523889 iteration 200 / 1500: loss 1.459294 iteration 300 / 1500: loss 1.547960 iteration 400 / 1500: loss 1.446798 iteration 500 / 1500: loss 1.488612 iteration 600 / 1500: loss 1.468087 iteration 700 / 1500: loss 1.446866 iteration 800 / 1500: loss 1.441728 iteration 900 / 1500: loss 1.473999 iteration 1000 / 1500: loss 1.379939 iteration 1100 / 1500: loss 1.436082 iteration 1200 / 1500: loss 1.445550 iteration 1300 / 1500: loss 1.396983 iteration 1400 / 1500: loss 1.477185 current val_acc: 0.56 current training learning_rate: 1.6 current training reg: 0.011 current training batch_size: 1024 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.620983 iteration 200 / 1500: loss 1.465891 iteration 300 / 1500: loss 1.508626 iteration 400 / 1500: loss 1.507472 iteration 500 / 1500: loss 1.501891 iteration 600 / 1500: loss 1.469078 iteration 700 / 1500: loss 1.415572 iteration 800 / 1500: loss 1.430231 iteration 900 / 1500: loss 1.428609 iteration 1000 / 1500: loss 1.438212 iteration 1100 / 1500: loss 1.461607 iteration 1200 / 1500: loss 1.407656 iteration 1300 / 1500: loss 1.403149 iteration 1400 / 1500: loss 1.509024 current val_acc: 0.551 current training learning_rate: 1.5 current training reg: 0.011 current training batch_size: 1024 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.560552 iteration 200 / 1500: loss 1.471533 iteration 300 / 1500: loss 1.495598 iteration 400 / 1500: loss 1.480390 iteration 500 / 1500: loss 1.458200 iteration 600 / 1500: loss 1.410563 iteration 700 / 1500: loss 1.403763 iteration 800 / 1500: loss 1.459880 iteration 900 / 1500: loss 1.390115 iteration 1000 / 1500: loss 1.444417 iteration 1100 / 1500: loss 1.459145 iteration 1200 / 1500: loss 1.421502 iteration 1300 / 1500: loss 1.411841 iteration 1400 / 1500: loss 1.435132 current val_acc: 0.569 current training learning_rate: 1.8 current training reg: 0.01 current training batch_size: 2048 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.503050 iteration 200 / 1500: loss 1.512787 iteration 300 / 1500: loss 1.419264 iteration 400 / 1500: loss 1.386965 iteration 500 / 1500: loss 1.456314 iteration 600 / 1500: loss 1.371561 iteration 700 / 1500: loss 1.367006 iteration 800 / 1500: loss 1.397520 iteration 900 / 1500: loss 1.393192 iteration 1000 / 1500: loss 1.401441 iteration 1100 / 1500: loss 1.405192 iteration 1200 / 1500: loss 1.375592 iteration 1300 / 1500: loss 1.354200 iteration 1400 / 1500: loss 1.348964 current val_acc: 0.571 current training learning_rate: 1.7 current training reg: 0.01 current training batch_size: 2048 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.500972 iteration 200 / 1500: loss 1.451483 iteration 300 / 1500: loss 1.450264 iteration 400 / 1500: loss 1.443042 iteration 500 / 1500: loss 1.394450 iteration 600 / 1500: loss 1.381958 iteration 700 / 1500: loss 1.391028 iteration 800 / 1500: loss 1.387771 iteration 900 / 1500: loss 1.400622 iteration 1000 / 1500: loss 1.372487 iteration 1100 / 1500: loss 1.375760 iteration 1200 / 1500: loss 1.394658 iteration 1300 / 1500: loss 1.396033 iteration 1400 / 1500: loss 1.403560 current val_acc: 0.563 current training learning_rate: 1.6 current training reg: 0.01 current training batch_size: 2048 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.570068 iteration 200 / 1500: loss 1.416525 iteration 300 / 1500: loss 1.451739 iteration 400 / 1500: loss 1.436536 iteration 500 / 1500: loss 1.410322 iteration 600 / 1500: loss 1.351524 iteration 700 / 1500: loss 1.439125 iteration 800 / 1500: loss 1.364394 iteration 900 / 1500: loss 1.408162 iteration 1000 / 1500: loss 1.387432 iteration 1100 / 1500: loss 1.368569 iteration 1200 / 1500: loss 1.403514 iteration 1300 / 1500: loss 1.396555 iteration 1400 / 1500: loss 1.374414 current val_acc: 0.564 current training learning_rate: 1.5 current training reg: 0.01 current training batch_size: 2048 iteration 0 / 1500: loss 2.302589 iteration 100 / 1500: loss 1.509225 iteration 200 / 1500: loss 1.469627 iteration 300 / 1500: loss 1.440074 iteration 400 / 1500: loss 1.427931 iteration 500 / 1500: loss 1.387515 iteration 600 / 1500: loss 1.399713 iteration 700 / 1500: loss 1.416284 iteration 800 / 1500: loss 1.451338 iteration 900 / 1500: loss 1.395987 iteration 1000 / 1500: loss 1.391280 iteration 1100 / 1500: loss 1.372650 iteration 1200 / 1500: loss 1.409513 iteration 1300 / 1500: loss 1.350978 iteration 1400 / 1500: loss 1.371274 current val_acc: 0.564 current training learning_rate: 1.8 current training reg: 0.011 current training batch_size: 2048 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.503954 iteration 200 / 1500: loss 1.528570 iteration 300 / 1500: loss 1.437264 iteration 400 / 1500: loss 1.457583 iteration 500 / 1500: loss 1.430647 iteration 600 / 1500: loss 1.439845 iteration 700 / 1500: loss 1.405331 iteration 800 / 1500: loss 1.409905 iteration 900 / 1500: loss 1.401408 iteration 1000 / 1500: loss 1.430758 iteration 1100 / 1500: loss 1.393267 iteration 1200 / 1500: loss 1.417903 iteration 1300 / 1500: loss 1.415154 iteration 1400 / 1500: loss 1.389908 current val_acc: 0.558 current training learning_rate: 1.7 current training reg: 0.011 current training batch_size: 2048 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.559359 iteration 200 / 1500: loss 1.499529 iteration 300 / 1500: loss 1.466741 iteration 400 / 1500: loss 1.411526 iteration 500 / 1500: loss 1.425862 iteration 600 / 1500: loss 1.426498 iteration 700 / 1500: loss 1.440676 iteration 800 / 1500: loss 1.400584 iteration 900 / 1500: loss 1.431244 iteration 1000 / 1500: loss 1.437300 iteration 1100 / 1500: loss 1.404868 iteration 1200 / 1500: loss 1.428941 iteration 1300 / 1500: loss 1.373555 iteration 1400 / 1500: loss 1.387955 current val_acc: 0.557 current training learning_rate: 1.6 current training reg: 0.011 current training batch_size: 2048 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.468813 iteration 200 / 1500: loss 1.505506 iteration 300 / 1500: loss 1.416367 iteration 400 / 1500: loss 1.464052 iteration 500 / 1500: loss 1.410479 iteration 600 / 1500: loss 1.409553 iteration 700 / 1500: loss 1.426082 iteration 800 / 1500: loss 1.429986 iteration 900 / 1500: loss 1.444137 iteration 1000 / 1500: loss 1.401925 iteration 1100 / 1500: loss 1.416147 iteration 1200 / 1500: loss 1.452663 iteration 1300 / 1500: loss 1.411526 iteration 1400 / 1500: loss 1.391227 current val_acc: 0.555 current training learning_rate: 1.5 current training reg: 0.011 current training batch_size: 2048 iteration 0 / 1500: loss 2.302590 iteration 100 / 1500: loss 1.476053 iteration 200 / 1500: loss 1.484842 iteration 300 / 1500: loss 1.463245 iteration 400 / 1500: loss 1.415583 iteration 500 / 1500: loss 1.417196 iteration 600 / 1500: loss 1.440323 iteration 700 / 1500: loss 1.443371 iteration 800 / 1500: loss 1.414975 iteration 900 / 1500: loss 1.405715 iteration 1000 / 1500: loss 1.404263 iteration 1100 / 1500: loss 1.407797 iteration 1200 / 1500: loss 1.404061 iteration 1300 / 1500: loss 1.417821 iteration 1400 / 1500: loss 1.373487 current val_acc: 0.554

0.546

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