def main():
data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000)
myPerceptronClassifier = Perceptron(data.trainingSet,
data.validationSet,
data.testSet,
learningRate=0.01,
epochs=10)
# Train the classifiers
print("=========================")
print("Training..")
print("\nTraining the Perceptron..")
myPerceptronClassifier.train()
print("Done..")
# Do the recognizer
perceptronPred = myPerceptronClassifier.evaluate()
# Report the result
print("=========================")
evaluator = Evaluator()
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,
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)
myPerceptronClassifier = Perceptron(data.trainingSet,
data.validationSet,
data.testSet,
learningRate=0.01,
epochs=10)
# Train the classifiers
print("=========================")
print("Training..")
print("\nTraining the Perceptron..")
myPerceptronClassifier.train()
print("Done..")
# Do the recognizer
perceptronPred = myPerceptronClassifier.evaluate()
# Report the result
print("=========================")
evaluator = Evaluator()
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)
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)