def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, epochs=10) # Train the classifiers print("=========================") print("Training..") myStupidClassifier.train() myPerceptronClassifier.train() # Do the recognizer stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) myLRClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() lrPred = myLRClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, lrPred)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, oneHot=True) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) myLRClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() lrPred = myLRClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, lrPred)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) # Uncomment this to make your Perceptron evaluated myPerceptronClassifier = Perceptron( data.trainingSet, data.validationSet, data.testSet, learningRate=1.0, #0.005, epochs=1 #30 ) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # Uncomment this to make your Perceptron evaluated perceptronPred = myPerceptronClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) # Uncomment this to make your Perceptron evaluated evaluator.printAccuracy(data.testSet, perceptronPred) evaluator.printConfusionMatrix(data.testSet, perceptronPred) evaluator.printClassificationResult(data.testSet, perceptronPred, ['class 0', 'class 1']) #target_names)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, oneHot=False) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) #myPerceptronClassifier = Perceptron(data.trainingSet, #data.validationSet, #data.testSet, #learningRate=0.005, #epochs=30) #myLRClassifier = LogisticRegression(data.trainingSet, #data.validationSet, #data.testSet, #learningRate=0.005, #epochs=30) mlp = MultilayerPerceptron(data.trainingSet, data.validationSet, data.testSet, layers=None, inputWeights=None, outputTask='classification', outputActivation='softmax', loss='cee', learningRate=0.01, epochs=50) # Report the result # print("=========================") evaluator = Evaluator() # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") #myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") #myLRClassifier.train() print("Done..") print("\nmlp has been training..") mlp.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() #perceptronPred = myPerceptronClassifier.evaluate() #lrPred = myLRClassifier.evaluate() mlppred = MultilayerPerceptron.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) #evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") #evaluator.printComparison(data.testSet, lrPred) #evaluator.printAccuracy(data.testSet, lrPred) print("Result of the mlp:") evaluator.printAccuracy(data.testSet, mlppred) # Draw #plot = PerformancePlot("Logistic Regression validation") #plot.draw_performance_epoch(myLRClassifier.performances, #myLRClassifier.epochs) ####可能有问题 plot = PerformancePlot("mlp validation") plot.draw_performance_epoch(mlp.performances, mlp.epochs)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) myLRClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) MlpClassifier = MultilayerPerceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.1, epochs = 30) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") # print("\nPerceptron has been training..") # myPerceptronClassifier.train() # print("Done..") # print("\nLogistic Regression has been training..") # myLRClassifier.train() # print("Done..") print("\nStarting Backpropagation MLP training...") MlpClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # perceptronPred = myPerceptronClassifier.evaluate() # lrPred = myLRClassifier.evaluate() mlpPred = MlpClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) # print("\nResult of the Perceptron recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) # evaluator.printAccuracy(data.testSet, perceptronPred) # print("\nResult of the Logistic Regression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) # evaluator.printAccuracy(data.testSet, lrPred) print("\nResult of the MLP recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, mlpPred) # eval.printConfusionMatrix(data.testSet, pred) # eval.printClassificationResult(data.testSet, pred, target_names) print("=========================")
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) # parameters learnRate = 0.005 maxEpochs = 20 #epochNumber = 30 xEpochs = [] yAccuracyPerceptron = [] yAccuracyLogistic = [] # loop for gathering data for graph plotting for epochNumber in xrange(1, maxEpochs + 1): myPerceptronClassifier = Perceptron( data.trainingSet, data.validationSet, data.testSet, learningRate=learnRate, #0.005, epochs=epochNumber) # Uncomment this to run Logistic Neuron Layer myLRClassifier = LogisticRegression( data.trainingSet, data.validationSet, data.testSet, learningRate=learnRate, #0.005, epochs=epochNumber #30 ) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() lrPred = myLRClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") #evaluator.printComparison(data.testSet, lrPred) evaluator.printAccuracy(data.testSet, lrPred) # accumulate plotting data xEpochs.append(epochNumber) yAccuracyPerceptron.append( accuracy_score(data.testSet.label, perceptronPred) * 100) yAccuracyLogistic.append( accuracy_score(data.testSet.label, lrPred) * 100) # === end of for loop === # plot the graph plt.plot(xEpochs, yAccuracyPerceptron, marker='o', label='Perceptron') plt.plot(xEpochs, yAccuracyLogistic, marker='o', color='r', label='Logistic Neuron') plt.xlabel('Number of epochs') plt.ylabel('Accuracy [%]') plt.title( 'Performance on different epochs\n(using: testSet | learningRate: ' + str(learnRate) + ')') #plt.legend() plt.legend(loc=4) #plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.) #plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.show()
def classify_one(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, one_hot=True, target_digit='7') # NOTE: # Comment out the MNISTSeven instantiation above and # uncomment the following to work with full MNIST task # data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, # one_hot=False) # NOTE: # Other 1-digit classifiers do not make sense now for comparison purpose # So you should comment them out, let alone the MLP training and evaluation # Train the classifiers # print("=========================") print("Training..") # Stupid Classifier myStupidClassifier = StupidRecognizer(data.training_set, data.validation_set, data.test_set) print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # Perceptron myPerceptronClassifier = Perceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.005, epochs=10) print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated perceptronPred = myPerceptronClassifier.evaluate() # Logistic Regression myLRClassifier = LogisticRegression(data.training_set, data.validation_set, data.test_set, learning_rate=0.20, epochs=30) print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated lrPred = myLRClassifier.evaluate() # Logistic Regression myMLPClassifier = MultilayerPerceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.30, epochs=50) print("\nMultilayer Perceptron has been training..") myMLPClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated mlpPred = myMLPClassifier.evaluate() # Report the result # print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.test_set, stupidPred) print("\nResult of the Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, perceptronPred) print("\nResult of the Logistic Regression recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, lrPred) print("\nResult of the Multi-layer Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, mlpPred) # Draw plot = PerformancePlot("Logistic Regression") plot.draw_performance_epoch(myLRClassifier.performances, myLRClassifier.epochs)
def classify_one(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, one_hot=True, target_digit='7') # NOTE: # Comment out the MNISTSeven instantiation above and # uncomment the following to work with full MNIST task # data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, # one_hot=False) # NOTE: # Other 1-digit classifiers do not make sense now for comparison purpose # So you should comment them out, let alone the MLP training and evaluation # Train the classifiers # print("=========================") print("Training..") # Stupid Classifier myStupidClassifier = StupidRecognizer(data.training_set, data.validation_set, data.test_set) print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # Perceptron myPerceptronClassifier = Perceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.005, epochs=10) print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated perceptronPred = myPerceptronClassifier.evaluate() # Logistic Regression myLRClassifier = LogisticRegression(data.training_set, data.validation_set, data.test_set, learning_rate=0.20, epochs=30) print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated lrPred = myLRClassifier.evaluate() # Logistic Regression myMLPClassifier = MultilayerPerceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.30, epochs=50) print("\nMultilayer Perceptron has been training..") myMLPClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated mlpPred = myMLPClassifier.evaluate() # Report the result # print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.test_set, stupidPred) print("\nResult of the Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, perceptronPred) print("\nResult of the Logistic Regression recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, lrPred) print("\nResult of the Multi-layer Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, mlpPred) # Draw plot = PerformancePlot("Logistic Regression") plot.draw_performance_epoch(myLRClassifier.performances, myLRClassifier.epochs)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) mylogisticClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nLogsticregression has been training..") mylogisticClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = mylogisticClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\n Result of the Logsticregression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) for i in range(2): for j in range(2): learningRate = (i + 1) * 0.002 epochs = (j + 1) * 20 mylogisticClassifier = LogisticRegression( data.trainingSet, data.validationSet, data.testSet, learningRate=learningRate, epochs=epochs) # Train the classifiers print("=========================") print("learning rate :" + str(learningRate)) print("epoch :" + str(epochs)) print("Training..") print("\nLogsticregression has been training..") mylogisticClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated perceptronPred = mylogisticClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("\n Result of the Logsticregression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred)