def main():
data = MNISTSeven("../data/mnist_seven.csv",
3000,
1000,
1000,
oneHot=False)
myMLPClassifier = MultilayerPerceptron(data.trainingSet,
data.validationSet,
data.testSet,
loss='ce',
layers=[128, 150],
learningRate=0.005,
epochs=10)
# Report the result #
# Train the classifiers
print("=========================")
print("Training..")
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("\nResult of the Multi Layer Perceptron recognizer:")
evaluator.printComparison(data.testSet, mlpPred)
evaluator.printAccuracy(data.testSet, mlpPred)
# Draw
plot = PerformancePlot("Multi Layer Perceptron validation")
plot.draw_performance_epoch(myMLPClassifier.performances,
myMLPClassifier.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=1e-2,
epochs=1000)
# 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()
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,
oneHot=False)
# myLRClassifier = LogisticRegression(data.trainingSet,
# data.validationSet,
# data.testSet,
# learningRate=0.005,
# epochs=30)
hidden_layers = [
LogisticLayer(128, 32, isClassifierLayer=True) for layer in range(1)
]
mlp = MultilayerPerceptron(data.trainingSet,
data.validationSet,
data.testSet,
hidden_layers,
learningRate=0.005,
epochs=30)
# Train the classifiers
#print("=========================")
print("Training...")
# print("\nLogistic Regression has been training..")
# myLRClassifier.train()
# print("Done..")
print("Training MLP...")
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 = mlp.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 recognizer:")
evaluator.printComparison(data.testSet, mlpPred)
evaluator.printAccuracy(data.testSet, mlpPred)
# Draw
plot = PerformancePlot("Logistic Regression validation")
# plot.draw_performance_epoch(myLRClassifier.performances,
# myLRClassifier.epochs)
plot.draw_performance_epoch(mlp.performances, mlp.epochs)
def main():
# 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=0.005,
# 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()
# Multi layer perceptron
layers = [LogisticLayer(data.training_set.input.shape[1], 30, None, 'sigmoid', False),
LogisticLayer(30, 10, None, 'softmax', True)]
myMLPClassifier = MultilayerPerceptron(data.training_set,
data.validation_set,
data.test_set,
layers=layers,
learning_rate=0.005, epochs=30)
print("\nLogistic Regression 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.test_set, mlpPred)
evaluator.printAccuracy(data.test_set, mlpPred)