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How to use get_test_label method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
varyParameters.py
Source:varyParameters.py 
...56 while (epoch < training_epochs):57 epoch = epoch + 158 for minibatch_index in xrange(n_train_batches):59 minibatch_avg_cost1 = r_train_fn(minibatch_index)60 predict_y, origin_y = r_get_test_label() 61 print 'first results from right DBN , presion, recall, F1, accuracy: '62 print >> saveFile,'origin_y: ',origin_y63 print >> saveFile,'predict_y: ',predict_y64 predict_y = change2PrimaryC(predict_y)65 origin_y = change2PrimaryC(origin_y)66 print >> saveFile,'first results from right DBN , presion, recall, F1, accuracy: '67 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)68 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)69 70 r_sigmoid_layers,r_output,r_params = rDBN.getParams(train_set_x)71 r_sigmoid_layersT,r_outputT,r_paramsT = rDBN.getParams(test_set_x)72 print shape(r_sigmoid_layers())73 74 new_features = []75 l_features = []76 new_featuresT = []77 for x in topic_x:78 l_features = append(l_features,x)79# l_features = [l_sigmoid_layers()[i] for i in xrange(len_left)]80# new_features = map(list,zip(*(map(list,zip(*a))+map(list,zip(*b)))))81 print shape(l_features)82 for r_feature in r_sigmoid_layers():83 new_features.append(append(l_features,r_feature))84 for r_feature in r_sigmoid_layersT():85 new_featuresT.append(append(l_features,r_feature))86 print shape(new_features),shape(new_featuresT)87 88# sim_result = similarity(r_sigmoid_layers(),topic_x)89# new_features = []90# for i in xrange(len(sim_result)):91# feature = append(topic_x[sim_result[i]],(r_sigmoid_layers()[i]))92# new_features.append(feature)93# sim_result = similarity(r_sigmoid_layersT(),topic_x)94# new_featuresT = []95# for i in xrange(len(sim_result)):96# feature = append(topic_x[sim_result[i]],(r_sigmoid_layersT()[i]))97# new_featuresT.append(feature)98# print shape(new_features),shape(new_featuresT)99 100 101 n_dim = shape(new_features)[1]102 shared_x = theano.shared(numpy.asarray(new_features,dtype=theano.config.floatX),borrow=True)103 shared_y = theano.shared(numpy.asarray(new_featuresT,dtype=theano.config.floatX),borrow=True)104 105 layers=[100]106 print >> saveFile,'top layer hidden units: ',layers107 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)108 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)109 for i in xrange(dbn.n_layers):110 for epoch in xrange(pretraining_epochs):111 c = []112 for batch_index in xrange(n_train_batches):113 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))114# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),115# print numpy.mean(c)116 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]117 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(118 datasets=datasets, batch_size=batch_size,119 learning_rate=finetune_lr)120 epoch = 0121 while (epoch < training_epochs):122 epoch = epoch + 1123 for minibatch_index in xrange(n_train_batches):124 minibatch_avg_cost = train_fn(minibatch_index)125 predict_y, origin_y = get_test_label() 126 print 'second results from LDADBN, presion, recall, F1, accuracy: '127 print >> saveFile,'predict_y: ',predict_y128 predict_y = change2PrimaryC(predict_y)129 origin_y = change2PrimaryC(origin_y)130 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '131 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)132 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)133 134 layers=[200]135 print >> saveFile,'top layer hidden units: ',layers136 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)137 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)138 for i in xrange(dbn.n_layers):139 for epoch in xrange(pretraining_epochs):140 c = []141 for batch_index in xrange(n_train_batches):142 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))143# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),144# print numpy.mean(c)145 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]146 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(147 datasets=datasets, batch_size=batch_size,148 learning_rate=finetune_lr)149 epoch = 0150 while (epoch < training_epochs):151 epoch = epoch + 1152 for minibatch_index in xrange(n_train_batches):153 minibatch_avg_cost = train_fn(minibatch_index)154 predict_y, origin_y = get_test_label() 155 print 'second results from LDADBN, presion, recall, F1, accuracy: '156 print >> saveFile,'predict_y: ',predict_y157 predict_y = change2PrimaryC(predict_y)158 origin_y = change2PrimaryC(origin_y)159 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '160 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)161 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)162 163 layers=[300]164 print >> saveFile,'top layer hidden units: ',layers165 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)166 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)167 for i in xrange(dbn.n_layers):168 for epoch in xrange(pretraining_epochs):169 c = []170 for batch_index in xrange(n_train_batches):171 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))172# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),173# print numpy.mean(c)174 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]175 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(176 datasets=datasets, batch_size=batch_size,177 learning_rate=finetune_lr)178 epoch = 0179 while (epoch < training_epochs):180 epoch = epoch + 1181 for minibatch_index in xrange(n_train_batches):182 minibatch_avg_cost = train_fn(minibatch_index)183 predict_y, origin_y = get_test_label() 184 print 'second results from LDADBN, presion, recall, F1, accuracy: '185 print >> saveFile,'predict_y: ',predict_y186 predict_y = change2PrimaryC(predict_y)187 origin_y = change2PrimaryC(origin_y)188 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '189 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)190 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)191 192 layers=[400]193 print >> saveFile,'top layer hidden units: ',layers194 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)195 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)196 for i in xrange(dbn.n_layers):197 for epoch in xrange(pretraining_epochs):198 c = []199 for batch_index in xrange(n_train_batches):200 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))201# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),202# print numpy.mean(c)203 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]204 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(205 datasets=datasets, batch_size=batch_size,206 learning_rate=finetune_lr)207 epoch = 0208 while (epoch < training_epochs):209 epoch = epoch + 1210 for minibatch_index in xrange(n_train_batches):211 minibatch_avg_cost = train_fn(minibatch_index)212 predict_y, origin_y = get_test_label() 213 print 'second results from LDADBN, presion, recall, F1, accuracy: '214 print >> saveFile,'predict_y: ',predict_y215 predict_y = change2PrimaryC(predict_y)216 origin_y = change2PrimaryC(origin_y)217 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '218 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)219 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)220 221 layers=[500]222 print >> saveFile,'top layer hidden units: ',layers223 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)224 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)225 for i in xrange(dbn.n_layers):226 for epoch in xrange(pretraining_epochs):227 c = []228 for batch_index in xrange(n_train_batches):229 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))230# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),231# print numpy.mean(c)232 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]233 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(234 datasets=datasets, batch_size=batch_size,235 learning_rate=finetune_lr)236 epoch = 0237 while (epoch < training_epochs):238 epoch = epoch + 1239 for minibatch_index in xrange(n_train_batches):240 minibatch_avg_cost = train_fn(minibatch_index)241 predict_y, origin_y = get_test_label() 242 print 'second results from LDADBN, presion, recall, F1, accuracy: '243 print >> saveFile,'predict_y: ',predict_y244 predict_y = change2PrimaryC(predict_y)245 origin_y = change2PrimaryC(origin_y)246 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '247 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)248 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)249 250 layers=[600]251 print >> saveFile,'top layer hidden units: ',layers252 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)253 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)254 for i in xrange(dbn.n_layers):255 for epoch in xrange(pretraining_epochs):256 c = []257 for batch_index in xrange(n_train_batches):258 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))259# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),260# print numpy.mean(c)261 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]262 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(263 datasets=datasets, batch_size=batch_size,264 learning_rate=finetune_lr)265 epoch = 0266 while (epoch < training_epochs):267 epoch = epoch + 1268 for minibatch_index in xrange(n_train_batches):269 minibatch_avg_cost = train_fn(minibatch_index)270 predict_y, origin_y = get_test_label() 271 print 'second results from LDADBN, presion, recall, F1, accuracy: '272 print >> saveFile,'predict_y: ',predict_y273 predict_y = change2PrimaryC(predict_y)274 origin_y = change2PrimaryC(origin_y)275 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '276 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)277 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)278 279 layers=[700]280 print >> saveFile,'top layer hidden units: ',layers281 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)282 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)283 for i in xrange(dbn.n_layers):284 for epoch in xrange(pretraining_epochs):285 c = []286 for batch_index in xrange(n_train_batches):287 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))288# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),289# print numpy.mean(c)290 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]291 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(292 datasets=datasets, batch_size=batch_size,293 learning_rate=finetune_lr)294 epoch = 0295 while (epoch < training_epochs):296 epoch = epoch + 1297 for minibatch_index in xrange(n_train_batches):298 minibatch_avg_cost = train_fn(minibatch_index)299 predict_y, origin_y = get_test_label() 300 print 'second results from LDADBN, presion, recall, F1, accuracy: '301 print >> saveFile,'predict_y: ',predict_y302 predict_y = change2PrimaryC(predict_y)303 origin_y = change2PrimaryC(origin_y)304 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '305 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)306 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)307 308 layers=[800]309 print >> saveFile,'top layer hidden units: ',layers310 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)311 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)312 for i in xrange(dbn.n_layers):313 for epoch in xrange(pretraining_epochs):314 c = []315 for batch_index in xrange(n_train_batches):316 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))317# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),318# print numpy.mean(c)319 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]320 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(321 datasets=datasets, batch_size=batch_size,322 learning_rate=finetune_lr)323 epoch = 0324 while (epoch < training_epochs):325 epoch = epoch + 1326 for minibatch_index in xrange(n_train_batches):327 minibatch_avg_cost = train_fn(minibatch_index)328 predict_y, origin_y = get_test_label() 329 print 'second results from LDADBN, presion, recall, F1, accuracy: '330 print >> saveFile,'predict_y: ',predict_y331 predict_y = change2PrimaryC(predict_y)332 origin_y = change2PrimaryC(origin_y)333 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '334 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)335 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)336 337 layers=[900]338 print >> saveFile,'top layer hidden units: ',layers339 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)340 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)341 for i in xrange(dbn.n_layers):342 for epoch in xrange(pretraining_epochs):343 c = []344 for batch_index in xrange(n_train_batches):345 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))346# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),347# print numpy.mean(c)348 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]349 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(350 datasets=datasets, batch_size=batch_size,351 learning_rate=finetune_lr)352 epoch = 0353 while (epoch < training_epochs):354 epoch = epoch + 1355 for minibatch_index in xrange(n_train_batches):356 minibatch_avg_cost = train_fn(minibatch_index)357 predict_y, origin_y = get_test_label() 358 print 'second results from LDADBN, presion, recall, F1, accuracy: '359 print >> saveFile,'predict_y: ',predict_y360 predict_y = change2PrimaryC(predict_y)361 origin_y = change2PrimaryC(origin_y)362 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '363 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)364 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)365 366 layers=[1000]367 print >> saveFile,'top layer hidden units: ',layers368 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)369 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)370 for i in xrange(dbn.n_layers):371 for epoch in xrange(pretraining_epochs):372 c = []373 for batch_index in xrange(n_train_batches):374 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))375# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),376# print numpy.mean(c)377 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]378 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(379 datasets=datasets, batch_size=batch_size,380 learning_rate=finetune_lr)381 epoch = 0382 while (epoch < training_epochs):383 epoch = epoch + 1384 for minibatch_index in xrange(n_train_batches):385 minibatch_avg_cost = train_fn(minibatch_index)386 predict_y, origin_y = get_test_label() 387 print 'second results from LDADBN, presion, recall, F1, accuracy: '388 print >> saveFile,'predict_y: ',predict_y389 predict_y = change2PrimaryC(predict_y)390 origin_y = change2PrimaryC(origin_y)391 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '392 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)393 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)394 395 layers=[1100]396 print >> saveFile,'top layer hidden units: ',layers397 dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)398 pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)399 for i in xrange(dbn.n_layers):400 for epoch in xrange(pretraining_epochs):401 c = []402 for batch_index in xrange(n_train_batches):403 c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))404# print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),405# print numpy.mean(c)406 datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]407 train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(408 datasets=datasets, batch_size=batch_size,409 learning_rate=finetune_lr)410 epoch = 0411 while (epoch < training_epochs):412 epoch = epoch + 1413 for minibatch_index in xrange(n_train_batches):414 minibatch_avg_cost = train_fn(minibatch_index)415 predict_y, origin_y = get_test_label() 416 print 'second results from LDADBN, presion, recall, F1, accuracy: '417 print >> saveFile,'predict_y: ',predict_y418 predict_y = change2PrimaryC(predict_y)419 origin_y = change2PrimaryC(origin_y)420 print >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '421 print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)422 print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)423 424 425 end_time = time.clock()426 print >> saveFile,'Finish all using %.2f mins' % ((end_time - start_time) / 60.)427 print 'Finish all using %.2f mins' % ((end_time - start_time) / 60.)428 print >>saveFile,'------------------------------------------------------------------------------'429# layers = []...
SVMModel.py
Source:SVMModel.py
...30 clf, predicted = svm_imbalanced.train_and_predict()31 svm_imbalanced.display_confusion_matrix(predicted)32 svm_imbalanced.print_accuracy(predicted)33 print(f"Classification report for classifier {clf}:\n"34 f"{metrics.classification_report(svm_imbalanced.get_test_label(), predicted)}\n")35 # test_image, actual_label, predicted_label = svm_imbalanced.get_misclassified(predicted)36 # svm_imbalanced.display_samples(test_image, actual_label, predicted_label, False)37 print('Imbalanced with Symmetrical Noise')38 sym_non_balanced = SVMModel(balanced=False, noise_type='sym', noise_ratio=40)39 sym_non_balanced.scale_pixels()40 sym_non_balanced.show_class_distribution(False)41 clf2, predicted2 = sym_non_balanced.train_and_predict()42 sym_non_balanced.display_confusion_matrix(predicted2)43 sym_non_balanced.print_accuracy(predicted2)44 print(f"Classification report for classifier {clf2}:\n"45 f"{metrics.classification_report(sym_non_balanced.get_test_label(), predicted2)}\n")46 print('Imbalanced with Asymmetrical Noise')47 asym_non_balanced = SVMModel(balanced=False, noise_type='asym', noise_ratio=40)48 asym_non_balanced.scale_pixels()49 asym_non_balanced.show_class_distribution(False)50 clf3, predicted3 = asym_non_balanced.train_and_predict()51 asym_non_balanced.display_confusion_matrix(predicted3)52 asym_non_balanced.print_accuracy(predicted3)53 print(f"Classification report for classifier {clf3}:\n"54 f"{metrics.classification_report(asym_non_balanced.get_test_label(), predicted3)}\n")55 print('Balanced with No Noise')56 svm_balanced = SVMModel(balanced=True, noise_type='No Noise', noise_ratio=0)57 svm_balanced.scale_pixels()58 svm_balanced.show_class_distribution(False)59 clf6, predicted6 = svm_balanced.train_and_predict()60 svm_balanced.display_confusion_matrix(predicted6)61 svm_balanced.print_accuracy(predicted6)62 print(f"Classification report for classifier {clf6}:\n"63 f"{metrics.classification_report(svm_balanced.get_test_label(), predicted6)}\n")64 print('Balanced with Symmetrical Noise')65 sym_balanced = SVMModel(balanced=True, noise_type='sym', noise_ratio=40)66 sym_balanced.scale_pixels()67 sym_balanced.show_class_distribution(False)68 clf4, predicted4 = sym_balanced.train_and_predict()69 sym_balanced.display_confusion_matrix(predicted4)70 sym_balanced.print_accuracy(predicted4)71 print(f"Classification report for classifier {clf4}:\n"72 f"{metrics.classification_report(sym_balanced.get_test_label(), predicted4)}\n")73 print('Balanced with Asymmetrical Noise')74 asym_balanced = SVMModel(balanced=True, noise_type='asym', noise_ratio=40)75 asym_balanced.scale_pixels()76 asym_balanced.show_class_distribution(False)77 clf5, predicted5 = asym_balanced.train_and_predict()78 asym_balanced.display_confusion_matrix(predicted5)79 asym_balanced.print_accuracy(predicted5)80 print(f"Classification report for classifier {clf5}:\n"...
train_classifier.py
Source:train_classifier.py
...33 print("The length of image are train: {} test: {}".format(len(train_image_paths),len(test_image_paths)))34 print("Ambiguity will be filtered")35 #load label file36 meta = pd.read_csv(meta)37 train_X, train_y = get_test_label(meta,train_image_paths)38 val_X, val_y = train_test_split(train_X,train_y,validation_proportion)39 test_X, test_y = get_test_label(meta,test_image_paths)40 print("*"*50)41 print("Train: {} Validation: {} Test: {}".format(len(train_X),len(val_X),len(test_X)))42 # Create Dataset43 train_dataset = ClassifierDataset(train_X,train_y,config['augmentation'])44 val_dataset = ClassifierDataset(val_X,val_y,config['augmentation'])...
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