class MNISTConvNetTrainingTest(unittest.TestCase):
def setUp(self):
configPath = "MNIST2and3Config.ini"
self.net = ConvNet(configPath)
# import just images of 2s and 3s for now
self.imagesOf2s = extract_mnist.getMNISTTrainingSamplesNum(2)
self.imagesOf3s = extract_mnist.getMNISTTrainingSamplesNum(3)
def testTrain2sAnd3s(self):
numImages = 10 # number of images of each category
# combine two data sets
twos = self.imagesOf2s[0:numImages]
threes = self.imagesOf3s[0:numImages]
data = np.concatenate((twos, threes))
# set up labels
labels = []
for i in xrange(numImages):
labels.append("two")
for i in xrange(numImages):
labels.append("three")
# epochs
maxEpochs = 50
epochsPerSave = 50
# run test
print "Start"
self.net.trainSet(data, labels, maxEpochs, epochsPerSave)
self.net.testSet(data, labels)
self.net.saveFilters(maxEpochs)
class MNISTConvNetTestingTest(unittest.TestCase):
def setUp(self):
configPath = "MNIST2and3and7Config_twoConv-trained-100.ini"
self.net = ConvNet(configPath)
# import just images of 2s and 3s for now
self.imagesOf2s = extract_mnist.getMNISTTestSamplesNum(2)
self.imagesOf3s = extract_mnist.getMNISTTestSamplesNum(3)
self.imagesOf7s = extract_mnist.getMNISTTestSamplesNum(7)
def testTest2sAnd3sAnd7s(self):
numImages = 1 # number of images of each category
# combine two data sets
#twos = self.imagesOf2s[0:numImages]
threes = self.imagesOf3s[0:numImages]
#sevens = self.imagesOf7s[0:numImages]
#data = np.concatenate((twos, threes, sevens))
# set up labels
labels = []
#for i in xrange(numImages):
# labels.append("two")
for i in xrange(numImages):
labels.append("three")
#for i in xrange(numImages):
# labels.append("seven")
# run test
self.net.testSet(threes, labels)
self.net.saveFilters(100)
self.net.saveActivations(100)
class ToyConvNetTrainingTest(unittest.TestCase): def setUp(self): configPath = "MultipleConvPoolLayersConfig.ini" imagePathPrefix = "../test_imgs/lines/" imagePaths = ["horiz_line0.png", "horiz_line1.png", "horiz_line2.png", "vert_line0.png", "vert_line1.png", "vert_line2.png"] self.labels = np.array(["horiz", "horiz", "horiz", "vert", "vert", "vert"]) self.imageDim = 32 self.numImages = 6 assert len(self.labels) == self.numImages, "Not enough labels to run!" assert len(imagePaths) == self.numImages, "Not enough images to run!" self.net = ConvNet(configPath) self.images = np.zeros((self.numImages, self.imageDim, self.imageDim)) for i in xrange(self.numImages): self.images[i] = misc.loadImage(imagePathPrefix + imagePaths[i]) # def testTrain1Image(self): # data = np.zeros((1, self.imageDim, self.imageDim)) # data[0] = np.array([self.images[0]]) # maxEpochs = 100 # epochsPerSave = 101 # no saving in this test # self.net.trainSet(data, np.array([self.labels[0]]), maxEpochs, epochsPerSave) # self.net.testSet(data, np.array([self.labels[0]])) # def testTrain6Images(self): # data = self.images # maxEpochs = 1000 # epochsPerSave = 10001 # no saving in this test either # self.net.trainSet(data, self.labels, maxEpochs, epochsPerSave) def testTrain6ImagesAndTest(self): data = self.images maxEpochs = 500 epochsPerSave = 500 # save at the end of this test self.net.trainSet(data, self.labels, maxEpochs, epochsPerSave) self.net.testSet(data, self.labels) self.net.saveFilters(maxEpochs) # for visualization
class MNISTConvNetTestingTest(unittest.TestCase):
def setUp(self):
configPath = "MNIST_10_Config.ini"
self.net = ConvNet(configPath)
# import all images
self.imagesOf0s = extract_mnist.getMNISTTestSamplesNum(0)
self.imagesOf1s = extract_mnist.getMNISTTestSamplesNum(1)
self.imagesOf2s = extract_mnist.getMNISTTestSamplesNum(2)
self.imagesOf3s = extract_mnist.getMNISTTestSamplesNum(3)
self.imagesOf4s = extract_mnist.getMNISTTestSamplesNum(4)
self.imagesOf5s = extract_mnist.getMNISTTestSamplesNum(5)
self.imagesOf6s = extract_mnist.getMNISTTestSamplesNum(6)
self.imagesOf7s = extract_mnist.getMNISTTestSamplesNum(7)
self.imagesOf8s = extract_mnist.getMNISTTestSamplesNum(8)
self.imagesOf9s = extract_mnist.getMNISTTestSamplesNum(9)
def testTestAll(self):
numImages = 100 # number of images of each category
# combine all data sets
zeros = self.imagesOf0s[0:numImages]
ones = self.imagesOf1s[0:numImages]
twos = self.imagesOf2s[0:numImages]
threes = self.imagesOf3s[0:numImages]
fours = self.imagesOf4s[0:numImages]
fives = self.imagesOf5s[0:numImages]
sixs = self.imagesOf6s[0:numImages]
sevens = self.imagesOf7s[0:numImages]
eights = self.imagesOf8s[0:numImages]
nines = self.imagesOf9s[0:numImages]
data = np.concatenate((zeros, ones, twos, threes, fours, fives, sixs, \
sevens, eights, nines))
# set up labels
labels = []
for i in xrange(numImages):
labels.append("zero")
for i in xrange(numImages):
labels.append("one")
for i in xrange(numImages):
labels.append("two")
for i in xrange(numImages):
labels.append("three")
for i in xrange(numImages):
labels.append("four")
for i in xrange(numImages):
labels.append("five")
for i in xrange(numImages):
labels.append("six")
for i in xrange(numImages):
labels.append("seven")
for i in xrange(numImages):
labels.append("eight")
for i in xrange(numImages):
labels.append("nine")
# run test
maxEpochs = 1000
epochsPerSave = 5
self.net.trainSet(data, labels, maxEpochs, epochsPerSave)
self.net.testSet(data, labels)
self.net.saveFilters(1010)
self.net.saveActivations(1010)