def run(self):
"""
Function: Run
-------------
This function will evaluate your solution! You do not need to
write any code in this file, however you SHOULD understand this
function!
"""
print "Running the full pipeline!"
K=25
trainImages = util.loadTrainImages()[:1000]
testImages = util.loadTestImages()
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'Testing...........'
testPredictions = classifier.test(testImages)
testAccuracy = self.evaluate(testPredictions, testImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
self.reportAccuracy(testAccuracy, 'Test Accuracy')
def run(grad,view,pixels,maxIter,numTrain):
k = 25
maxIter_kMeans = 20
trainUImages = util.loadTrainImages()[:numTrain]
trainSImages = trainUImages[:500]
testImages = util.loadTestImages()
if pixels is False:
# Compile all patches into one big 2-D array (patchSize x numPatches)
patches = np.hstack([np.array(image.getPatches()).transpose() for image in trainUImages])
print("Training K-means using %d images..." % numTrain)
centroids = submission.runKMeans(k,patches,maxIter_kMeans)
print("Finished running K-means")
if view:
util.viewPatches(centroids)
trainX,trainY = util.kMeansFeatures(trainSImages,centroids,submission.extractFeatures)
testX, testY = util.kMeansFeatures(testImages,centroids,submission.extractFeatures)
else:
maxIter = 100
trainX,trainY = util.pixelFeatures(trainSImages)
testX,testY = util.pixelFeatures(testImages)
clf = util.Classifier(maxIter=maxIter,alpha=5e-5,gradient=grad)
print("Training supervised classifier with %d images..."%len(trainSImages))
clf.train(trainX,trainY)
predictions = clf.test(trainX)
acc = np.sum(trainY==predictions)/float(trainY.size)
print("Train accuracy is %f"%acc)
predictions = clf.test(testX)
acc = np.sum(testY==predictions)/float(testY.size)
print("Test accuracy is %f"%acc)
def run(self):
"""
Function: Run
-------------
This function will evaluate your solution! You do not need to
write any code in this file, however you SHOULD understand this
function!
"""
print "Running the full pipeline!"
K = 25
trainImages = util.loadTrainImages()[:1000]
testImages = util.loadTestImages()
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'Testing...........'
testPredictions = classifier.test(testImages)
testAccuracy = self.evaluate(testPredictions, testImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
self.reportAccuracy(testAccuracy, 'Test Accuracy')
def run(grad,view,pixels,maxIter,numTrain):
k = 25
maxIter_kMeans = 20
trainUImages = util.loadTrainImages()[:numTrain]
trainSImages = trainUImages[:500]
testImages = util.loadTestImages()
if pixels is False:
# Compile all patches into one big 2-D array (patchSize x numPatches)
patches = np.hstack([np.array(image.getPatches()).transpose() for image in trainUImages])
print "Training K-means using %d images"%numTrain
centroids = submission.runKMeans(k,patches,maxIter_kMeans)
trainX,trainY = util.kMeansFeatures(trainSImages,centroids,submission.extractFeatures)
testX, testY = util.kMeansFeatures(testImages,centroids,submission.extractFeatures)
if view:
util.viewPatches(centroids)
else:
maxIter = 100
trainX,trainY = util.pixelFeatures(trainSImages)
testX,testY = util.pixelFeatures(testImages)
clf = util.Classifier(maxIter=maxIter,alpha=5e-5,gradient=grad)
clf.train(trainX,trainY)
predictions = clf.test(trainX)
acc = np.sum(trainY==predictions)/float(trainY.size)
print "Train accuracy is %f"%acc
predictions = clf.test(testX)
acc = np.sum(testY==predictions)/float(testY.size)
print "Test accuracy is %f"%acc
def runDev(self):
print "Running in development mode"
K=5
trainImages = util.loadTrainImages()[:100]
testImages = util.loadTestImages()[:100]
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
def run(self):
print "Running the full pipeline!"
K=25
trainImages = util.loadTrainImages()[:1000]
testImages = util.loadTestImages()
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'Testing...........'
testPredictions = classifier.test(testImages)
testAccuracy = self.evaluate(testPredictions, testImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
self.reportAccuracy(testAccuracy, 'Test Accuracy')
def runDev(self):
"""
Function: runDev
-------------
This function will run the full pipeline in development mode.
I.e. it will use only 10 centroids and 100 images.
"""
print "Running in development mode"
K=5
trainImages = util.loadTrainImages()[:100]
testImages = util.loadTestImages()[:100]
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
def runDev(self):
"""
Function: runDev
-------------
This function will run the full pipeline in development mode.
I.e. it will use only 10 centroids and 100 images.
"""
print "Running in development mode"
K = 5
trainImages = util.loadTrainImages()[:100]
testImages = util.loadTestImages()[:100]
classifier = Classifier()
print 'Training..........'
classifier.train(trainImages, K)
trainPredictions = classifier.test(trainImages)
trainAccuracy = self.evaluate(trainPredictions, trainImages)
print 'All done. Here is your summary:'
self.reportAccuracy(trainAccuracy, 'Train Accuracy')
