def convolutionalNNMnist():
training = args.trainSize
testing = args.testSize
# building the layers
layer1 = ConvolutionalLayer(50, (5, 5) , Sigmoid())
layer2 = PoolingLayer((2, 2))
layer3 = ConvolutionalLayer(20, (5, 5), Sigmoid())
layer4 = PoolingLayer((2, 2))
# TODO: add fully connected layer
layer5 = SoftmaxLayer(10)
layers = [layer1, layer2, layer3, layer4, layer5]
net = convNet.ConvolutionalNN(layers, TrainingOptions(10, 1.0, momentumMax=0.9))
trainData, trainLabels =\
readmnist.read(0, training, digits=None, bTrain=True, path="MNIST", returnImages=True)
# transform the labels into vector (one hot encoding)
trainLabels = labelsToVectors(trainLabels, 10)
net.train(trainData, trainLabels, epochs=args.maxEpochs)
testData, testLabels =\
readmnist.read(0, testing, digits=None, bTrain=False, path="MNIST", returnImages=True)
outputData, labels = net.test(testData)
print " "
print "accuracy"
print sum(labels == testLabels) * 1.0 / testing
def convolutionalNNMnist():
training = args.trainSize
testing = args.testSize
# building the layers
layer1 = ConvolutionalLayer(50, (5, 5), Sigmoid())
layer2 = PoolingLayer((2, 2))
layer3 = ConvolutionalLayer(20, (5, 5), Sigmoid())
layer4 = PoolingLayer((2, 2))
# TODO: add fully connected layer
layer5 = SoftmaxLayer(10)
layers = [layer1, layer2, layer3, layer4, layer5]
net = convNet.ConvolutionalNN(layers,
TrainingOptions(10, 1.0, momentumMax=0.9))
trainData, trainLabels =\
readmnist.read(0, training, digits=None, bTrain=True, path="MNIST", returnImages=True)
# transform the labels into vector (one hot encoding)
trainLabels = labelsToVectors(trainLabels, 10)
net.train(trainData, trainLabels, epochs=args.maxEpochs)
testData, testLabels =\
readmnist.read(0, testing, digits=None, bTrain=False, path="MNIST", returnImages=True)
outputData, labels = net.test(testData)
print " "
print "accuracy"
print sum(labels == testLabels) * 1.0 / testing
def annMNIST():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
if args.train:
# Try 1200, 1200, 1200
# [784, 500, 500, 2000, 10
net = ann.ANN(5, [784, 1000, 1000, 1000, 10],
supervisedLearningRate=0.001,
nesterovMomentum=args.nesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
miniBatchSize=args.miniBatchSize,
normConstraint=15)
net.train(trainingScaledVectors, vectorLabels,
maxEpochs=args.maxEpochs, validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def annMNIST():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
if args.train:
# Try 1200, 1200, 1200
# [784, 500, 500, 2000, 10
net = ann.ANN(5, [784, 1000, 1000, 1000, 10],
supervisedLearningRate=0.001,
nesterovMomentum=args.nesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
miniBatchSize=args.miniBatchSize,
normConstraint=15)
net.train(trainingScaledVectors, vectorLabels,
maxEpochs=args.maxEpochs, validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def trainDBN(unsupervisedLearningRate,
supervisedLearningRate,
visibleDropout,
hiddenDropout,
miniBatchSize,
momentumMax,
maxEpochs):
print 'in trainDBN'
trainVectors, trainLabels =\
readmnist.read(0, TRAIN, digits=None, bTrain=True, path=PATH)
testVectors, testLabels =\
readmnist.read(TRAIN, TRAIN + TEST,
digits=None, bTrain=True, path=PATH)
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
print 'done reading'
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
trainVectorLabels = labelsToVectors(trainLabels, 10)
print 'done scaling data'
print 'creating DBN'
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
rmsprop=True,
visibleDropout=visibleDropout,
hiddenDropout=hiddenDropout,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=miniBatchSize,
adversarial_training=True,
# TODO: make this a learned param
preTrainEpochs=100,
sparsityConstraintRbm=False,
sparsityTragetRbm=0.01,
sparsityRegularizationRbm=None)
net.train(trainingScaledVectors, trainVectorLabels,
maxEpochs=200, validation=False)
proabilities, predicted = net.classify(testingScaledVectors)
error = getClassificationError(predicted, testLabels)
print "error", error
return error
def trainDBN(unsupervisedLearningRate, supervisedLearningRate, visibleDropout,
hiddenDropout, miniBatchSize, momentumMax, maxEpochs):
print 'in trainDBN'
trainVectors, trainLabels =\
readmnist.read(0, TRAIN, digits=None, bTrain=True, path=PATH)
testVectors, testLabels =\
readmnist.read(TRAIN, TRAIN + TEST,
digits=None, bTrain=True, path=PATH)
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
print 'done reading'
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
trainVectorLabels = labelsToVectors(trainLabels, 10)
print 'done scaling data'
print 'creating DBN'
net = db.DBN(
5,
[784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
rmsprop=True,
visibleDropout=visibleDropout,
hiddenDropout=hiddenDropout,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=miniBatchSize,
adversarial_training=True,
# TODO: make this a learned param
preTrainEpochs=100,
sparsityConstraintRbm=False,
sparsityTragetRbm=0.01,
sparsityRegularizationRbm=None)
net.train(trainingScaledVectors,
trainVectorLabels,
maxEpochs=200,
validation=False)
proabilities, predicted = net.classify(testingScaledVectors)
error = getClassificationError(predicted, testLabels)
print "error", error
return error
def pcaMain():
training = args.trainSize
testing = args.testSize
train, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print train[0].shape
pcaSklearn(train, dimension=400)
def pcaMain():
training = args.trainSize
testing = args.testSize
train, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print train[0].shape
pcaSklearn(train, dimension=400)
def svmMNIST():
with open(args.netFile, "rb") as f:
dbnNet = pickle.load(f)
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
svm.SVMCV(dbnNet, trainingScaledVectors, trainLabels,
testingScaledVectors, testLabels)
def makeMNISTpic():
f = lambda x: readmnist.read(0, 1000, digits=[x], path=args.path)[0][0].reshape((28,28))
img = np.hstack(map(f, xrange(10)))
print img.shape
plt.imshow(img, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('MNISTdigits.png', transparent=True)
def makeMNISTpic():
f = lambda x: readmnist.read(0, 1000, digits=[x], path=args.path)[0][0].reshape((28,28))
img = np.hstack(map(f, xrange(10)))
print img.shape
plt.imshow(img, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('MNISTdigits.png', transparent=True)
def displayWeightsAndDbSample():
# First let's display the nets layer weights:
with open(args.netFile, "rb") as f:
dbnNet = pickle.load(f)
for i in xrange(dbnNet.nrLayers - 1):
w = dbnNet.weights[i]
if i == 0:
t = visualizeWeights(w.T, (28,28), (10,10))
else:
t = visualizeWeights(w.T, (40, 25), (10, 10))
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weightslayer' + str(i) +'.png', transparent=True)
# then sample from the net
samples = dbnNet.sample(10)
reshaped = map(lambda x: x.reshape(28, 28), samples)
reshaped = np.hstack(reshaped)
plt.imshow(reshaped, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('samples.png', transparent=True)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
testVectors = testVectors / 255.0
activationList = dbnNet.getHiddenActivations(testVectors)
activations = activationList[-1]
nice = []
for activation in activationList:
reshaped = map(lambda x: x.reshape(25, 40), activation)
reshaped = np.hstack(reshaped)
nice += [reshaped]
nice = np.vstack(nice)
plt.imshow(activations[0].reshape(25,40), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('activationsingle.png', transparent=True)
plt.imshow(nice, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('allactivations.png', transparent=True)
def displayWeightsAndDbSample():
# First let's display the nets layer weights:
with open(args.netFile, "rb") as f:
dbnNet = pickle.load(f)
for i in xrange(dbnNet.nrLayers - 1):
w = dbnNet.weights[i]
if i == 0:
t = visualizeWeights(w.T, (28,28), (10,10))
else:
t = visualizeWeights(w.T, (40, 25), (10, 10))
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weightslayer' + str(i) +'.png', transparent=True)
# then sample from the net
samples = dbnNet.sample(10)
reshaped = map(lambda x: x.reshape(28, 28), samples)
reshaped = np.hstack(reshaped)
plt.imshow(reshaped, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('samples.png', transparent=True)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
testVectors = testVectors / 255.0
activationList = dbnNet.getHiddenActivations(testVectors)
activations = activationList[-1]
nice = []
for activation in activationList:
reshaped = map(lambda x: x.reshape(25, 40), activation)
reshaped = np.hstack(reshaped)
nice += [reshaped]
nice = np.vstack(nice)
plt.imshow(activations[0].reshape(25,40), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('activationsingle.png', transparent=True)
plt.imshow(nice, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('allactivations.png', transparent=True)
def getMissclassifiedDigits():
# First let's display the nets layer weights:
with open(args.netFile, "rb") as f:
dbnNet = pickle.load(f)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
testVectors = testVectors / 255.0
_, predictedLabels = dbnNet.classify(testVectors)
missclassified = []
actualLabels = []
wrongPredictedLabels = []
count = 0
i = 0
while count < 10 and i < args.testSize:
if not predictedLabels[i] == testLabels[i]:
missclassified += [testVectors[i].reshape((28, 28))]
actualLabels += [testLabels[i]]
wrongPredictedLabels += [predictedLabels[i]]
count += 1
i += 1
print "worked on " + str(i) + "examples before finding 10 wrong"
misspreditctedimg = np.hstack(missclassified)
plt.imshow(misspreditctedimg, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('misspredictedMNISTdigits.png', transparent=True)
print "predicted"
print wrongPredictedLabels
print "actual"
print actualLabels
def getMissclassifiedDigits():
# First let's display the nets layer weights:
with open(args.netFile, "rb") as f:
dbnNet = pickle.load(f)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
testVectors = testVectors / 255.0
_, predictedLabels = dbnNet.classify(testVectors)
missclassified = []
actualLabels = []
wrongPredictedLabels = []
count = 0
i = 0
while count < 10 and i < args.testSize:
if not predictedLabels[i] == testLabels[i]:
missclassified += [testVectors[i].reshape((28, 28))]
actualLabels += [testLabels[i]]
wrongPredictedLabels += [predictedLabels[i]]
count += 1
i+= 1
print "worked on " + str(i) + "examples before finding 10 wrong"
misspreditctedimg = np.hstack(missclassified)
plt.imshow(misspreditctedimg, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('misspredictedMNISTdigits.png', transparent=True)
print "predicted"
print wrongPredictedLabels
print "actual"
print actualLabels
def makeNicePlots():
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=[2], bTrain=True, path=args.path)
testingVectors, testLabels =\
readmnist.read(0, args.testSize, digits=[2], bTrain=False, path=args.path)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testingVectors / 255.0
# TODO: the reconstruction for relu still looks weird
learningRate = 0.1
binary = True
activationFunction = Sigmoid()
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible,
nrHidden,
learningRate,
1,
1,
visibleActivationFunction=activationFunction,
hiddenActivationFunction=activationFunction,
rmsprop=args.rbmrmsprop,
nesterov=args.rbmnesterov)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28, 28), (10, 10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
incomingWeightVector = net.weights.T[0]
print " testingVectors[0]"
print testingVectors[0]
testVec = testingScaledVectors[0]
# reshape this vector to be 28, 28
reshapedWeightVector = incomingWeightVector.reshape((28, 28))
print "reshapedWeightVector"
print reshapedWeightVector
reshapedTestVec = testVec.reshape((28, 28))
print "reshapedTestVec"
print reshapedTestVec
overpose = reshapedWeightVector * reshapedTestVec
print "overpose"
print overpose
plt.imshow(np.absolute(reshapedWeightVector), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weightvectorreshaped.png', transparent=True)
plt.imshow(np.absolute(reshapedTestVec), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reshapedTestVec.png', transparent=True)
plt.imshow(np.absolute(overpose), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('overpose.png', transparent=True)
def rbmMainGauss(reconstructRandom=False):
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=None, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
learningRate = 0.0005
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible,
nrHidden,
learningRate,
1,
1,
visibleActivationFunction=Identity(),
hiddenActivationFunction=RectifiedNoisy(),
rmsprop=args.rbmrmsprop,
nesterov=args.rbmnesterov)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28, 28), (10, 10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
# Reconstruct an image and see that it actually looks like a digit
test = testingScaledVectors[0, :]
# get a random image and see it looks like
if reconstructRandom:
test = np.random.random_sample(test.shape)
# Show the initial image first
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(test, (28, 28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('initial7relu.png', transparent=True)
# plt.show()
# Show the reconstruction
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(recon, (28, 28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reconstruct7withallrelu.png', transparent=True)
# plt.show()
# Show the weights and their form in a tile fashion
# Plot the weights
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weights2srmsproprelu.png', transparent=True)
# plt.show()
print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(t, f)
pickle.dump(net, f)
def rbmMain(reconstructRandom=False):
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=None, bTrain=True, path=args.path)
testingVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testingVectors / 255.0
# TODO: the reconstruction for relu still looks weird
if args.relu:
activationFunction = RectifiedNoisy()
learningRate = 5e-05
binary = False
else:
learningRate = 0.3
binary = True
activationFunction = Sigmoid()
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible,
nrHidden,
learningRate,
1,
1,
visibleActivationFunction=activationFunction,
hiddenActivationFunction=activationFunction,
rmsprop=args.rbmrmsprop,
nesterov=args.rbmnesterov,
sparsityConstraint=args.sparsity,
sparsityRegularization=0.01,
sparsityTraget=0.01)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28, 28), (10, 10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
# Reconstruct an image and see that it actually looks like a digit
test = testingScaledVectors[0, :]
# get a random image and see it looks like
if reconstructRandom:
test = np.random.random_sample(test.shape)
if args.display:
# Show the initial image first
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(test, (28, 28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('initial7.png', transparent=True)
plt.show()
hidden = net.hiddenRepresentation(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(hidden, (25, 20)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('hiddenfeatures7.png', transparent=True)
# Show the reconstruction
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(recon, (28, 28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reconstruct7withall.png', transparent=True)
plt.show()
# Show the weights and their form in a tile fashion
# Plot the weights
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weights2srmsprop.png', transparent=True)
plt.show()
print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(t, f)
pickle.dump(net, f)
def makeNicePlots():
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=[2], bTrain=True, path=args.path)
testingVectors, testLabels =\
readmnist.read(0, args.testSize, digits=[2], bTrain=False, path=args.path)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testingVectors / 255.0
# TODO: the reconstruction for relu still looks weird
learningRate = 0.1
binary = True
activationFunction = Sigmoid()
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible, nrHidden, learningRate, 1, 1,
visibleActivationFunction=activationFunction,
hiddenActivationFunction=activationFunction,
rmsprop=args.rbmrmsprop, nesterov=args.rbmnesterov)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28,28), (10,10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
incomingWeightVector = net.weights.T[0]
print " testingVectors[0]"
print testingVectors[0]
testVec = testingScaledVectors[0]
# reshape this vector to be 28, 28
reshapedWeightVector = incomingWeightVector.reshape((28, 28))
print "reshapedWeightVector"
print reshapedWeightVector
reshapedTestVec = testVec.reshape((28, 28))
print "reshapedTestVec"
print reshapedTestVec
overpose = reshapedWeightVector * reshapedTestVec
print "overpose"
print overpose
plt.imshow(np.absolute(reshapedWeightVector), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weightvectorreshaped.png', transparent=True)
plt.imshow(np.absolute(reshapedTestVec), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reshapedTestVec.png', transparent=True)
plt.imshow(np.absolute(overpose), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('overpose.png', transparent=True)
def cvadversarialMNIST():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
bestFold = -1
bestError = np.inf
params = [(5e-02, 1e-02), (1e-02, 5e-02), (5e-02, 5e-02), (1e-02, 5e-03),
(5e-02, 5e-03)]
correctness = []
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for train, test in kf:
print "cv fold", i
print "params", params[i]
net = db.DBN(5, [784, 1500, 1500, 1500, 10],
binary=False,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
save_best_weights=args.save_best_weights,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors[train],
vectorLabels[train],
maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(trainingScaledVectors[test])
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, trainLabels[test])
print "error for " + str(params[i])
print error
correct = 1.0 - error
if error < bestError:
bestError = error
bestFold = i
i += 1
correctness += [correct]
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError " + str(bestError)
for i in xrange(len(params)):
print "parameter tuple " + str(
params[i]) + " achieved correctness of " + str(correctness[i])
def deepbeliefMNISTGaussian():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.005
supervisedLearningRate = 0.005
momentumMax = 0.97
sparsityTragetRbm = 0.01
sparsityConstraintRbm = False
sparsityRegularizationRbm = 0.005
if args.train:
net = db.DBN(5, [784, 1200, 1200, 1200, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
save_best_weights=args.save_best_weights,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
weightDecayL1=0,
weightDecayL2=0,
sparsityTragetRbm=sparsityTragetRbm,
sparsityConstraintRbm=sparsityConstraintRbm,
sparsityRegularizationRbm=sparsityRegularizationRbm,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors,
vectorLabels,
maxEpochs=args.maxEpochs,
validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
print type(predicted)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def adversarialMNIST():
assert not args.relu, "do not run this method for rectified linear units"
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.01
supervisedLearningRate = 0.05
momentumMax = 0.95
if args.train:
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors, vectorLabels,
maxEpochs=args.maxEpochs, validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
def cvadversarialMNIST():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
bestFold = -1
bestError = np.inf
params = [(5e-02, 1e-02), (1e-02, 5e-02), (5e-02, 5e-02), (1e-02, 5e-03), (5e-02, 5e-03) ]
correctness = []
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for train, test in kf:
print "cv fold", i
print "params", params[i]
net = db.DBN(5, [784, 1500, 1500, 1500, 10],
binary=False,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors[train], vectorLabels[train],
maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(trainingScaledVectors[test])
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, trainLabels[test])
print "error for " + str(params[i])
print error
correct = 1.0 - error
if error < bestError:
bestError = error
bestFold = i
i += 1
correctness += [correct]
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError " + str(bestError)
for i in xrange(len(params)):
print "parameter tuple " + str(params[i]) + " achieved correctness of " + str(correctness[i])
def cvMNISTGaussian():
training = args.trainSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainVectors = np.array(trainVectors, dtype='float')
# Ensure the data has zero mean and 1 variance
trainingScaledVectors = scale(trainVectors)
vectorLabels = labelsToVectors(trainLabels, 10)
bestFold = -1
bestError = np.inf
params = [(5e-03, 1e-02), (1e-02, 5e-02), (5e-03, 5e-02), (1e-02, 5e-03), (5e-03, 5e-03), (1e-02, 1e-02) ]
correctness = []
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for train, test in kf:
# Train the net
# Try 1200, 1200, 1200
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
rmsprop=args.rmsprop,
visibleDropout=0.8,
hiddenDropout=0.5,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=args.miniBatchSize,
preTrainEpochs=args.preTrainEpochs,
sparsityConstraintRbm=False,
sparsityTragetRbm=0.01,
sparsityRegularizationRbm=None)
net.train(trainingScaledVectors[train], vectorLabels[train],
maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(trainingScaledVectors[test])
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, trainLabels[test])
print "error for " + str(params[i])
print error
correct = 1.0 - error
if error < bestError:
bestError = error
bestFold = i
i += 1
correctness += [correct]
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError " + str(bestError)
for i in xrange(len(params)):
print "parameter tuple " + str(params[i]) + " achieved correctness of " + str(correctness[i])
def deepbeliefMNIST():
assert not args.relu, "do not run this method for rectified linear units"
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.01
supervisedLearningRate = 0.05
momentumMax = 0.95
if args.train:
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors, vectorLabels,
maxEpochs=args.maxEpochs, validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
# Mistakes for digits
# You just need to display some for the report
# trueDigits = testLabels[errorCases]
# predictedDigits = predicted[errorCases]
print "correct"
print correct
# for w in net.weights:
# print w
# for b in net.biases:
# print b
# t = visualizeWeights(net.weights[0].T, trainImages[0].(28, 28), (10,10))
# plt.imshow(t, cmap=plt.cm.gray)
# plt.show()
# print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def cvMNIST():
assert not args.relu, "do not run this function for rectified linear units"
training = args.trainSize
data, labels =\
readmnist.read(0, training, bTrain=True, path=args.path)
data, labels = shuffle(data, labels)
scaledData = data / 255.0
vectorLabels = labelsToVectors(labels, 10)
activationFunction = Sigmoid()
bestFold = -1
bestError = np.inf
if args.relu:
# params =[(0.01, 0.01) , (0.01, 0.05), (0.05, 0.1), (0.05, 0.05)]
# params =[(0.0001, 0.01), (0.00001, 0.001), (0.00001, 0.0001), (0.0001, 0.1)]
params =[(1e-05, 0.001, 0.9), (5e-06, 0.001, 0.9), (5e-05, 0.001, 0.9),
(1e-05, 0.001, 0.95), (5e-06, 0.001, 0.95), (5e-05, 0.001, 0.95),
(1e-05, 0.001, 0.99), (5e-06, 0.001, 0.99), (5e-05, 0.001, 0.99)]
else:
# params =[(0.1, 0.1) , (0.1, 0.05), (0.05, 0.1), (0.05, 0.05)]
params =[(0.05, 0.05) , (0.05, 0.075), (0.075, 0.05), (0.075, 0.075)]
# params =[(0.05, 0.075, 0.1), (0.05, 0.1, 0.1), (0.01, 0.05, 0.1),
# (0.05, 0.075, 0.01), (0.05, 0.1, 0.01), (0.01, 0.05, 0.01),
# (0.05, 0.075, 0.001), (0.05, 0.1, 0.001), (0.01, 0.05, 0.001)]
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for training, testing in kf:
# Train the net
# Try 1200, 1200, 1200
trainData = scaledData[training]
trainLabels = vectorLabels[training]
# net = db.DBN(5, [784, 1000, 1000, 1000, 10],
net = db.DBN(5, [784, 500, 500, 2000, 10],
binary=1-args.relu,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
rmsprop=args.rmsprop,
visibleDropout=0.8,
hiddenDropout=0.5,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=args.miniBatchSize,
preTrainEpochs=args.preTrainEpochs,
sparsityTragetRbm=0.01,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=None)
net.train(trainData, trainLabels, maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(scaledData[testing])
testLabels = labels[testing]
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, testLabels)
print "error for " + str(params[i])
print error
if error < bestError:
bestError = error
bestFold = i
i += 1
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError" + str(bestError)
def cvMNIST():
assert not args.relu, "do not run this function for rectified linear units"
training = args.trainSize
data, labels =\
readmnist.read(0, training, bTrain=True, path=args.path)
data, labels = shuffle(data, labels)
scaledData = data / 255.0
vectorLabels = labelsToVectors(labels, 10)
activationFunction = Sigmoid()
bestFold = -1
bestError = np.inf
if args.relu:
# params =[(0.01, 0.01) , (0.01, 0.05), (0.05, 0.1), (0.05, 0.05)]
# params =[(0.0001, 0.01), (0.00001, 0.001), (0.00001, 0.0001), (0.0001, 0.1)]
params = [(1e-05, 0.001, 0.9), (5e-06, 0.001, 0.9),
(5e-05, 0.001, 0.9), (1e-05, 0.001, 0.95),
(5e-06, 0.001, 0.95), (5e-05, 0.001, 0.95),
(1e-05, 0.001, 0.99), (5e-06, 0.001, 0.99),
(5e-05, 0.001, 0.99)]
else:
# params =[(0.1, 0.1) , (0.1, 0.05), (0.05, 0.1), (0.05, 0.05)]
params = [(0.05, 0.05), (0.05, 0.075), (0.075, 0.05), (0.075, 0.075)]
# params =[(0.05, 0.075, 0.1), (0.05, 0.1, 0.1), (0.01, 0.05, 0.1),
# (0.05, 0.075, 0.01), (0.05, 0.1, 0.01), (0.01, 0.05, 0.01),
# (0.05, 0.075, 0.001), (0.05, 0.1, 0.001), (0.01, 0.05, 0.001)]
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for training, testing in kf:
# Train the net
# Try 1200, 1200, 1200
trainData = scaledData[training]
trainLabels = vectorLabels[training]
# net = db.DBN(5, [784, 1000, 1000, 1000, 10],
net = db.DBN(5, [784, 500, 500, 2000, 10],
binary=1 - args.relu,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
rmsprop=args.rmsprop,
save_best_weights=args.save_best_weights,
visibleDropout=0.8,
hiddenDropout=0.5,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=args.miniBatchSize,
preTrainEpochs=args.preTrainEpochs,
sparsityTragetRbm=0.01,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=None)
net.train(trainData,
trainLabels,
maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(scaledData[testing])
testLabels = labels[testing]
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, testLabels)
print "error for " + str(params[i])
print error
if error < bestError:
bestError = error
bestFold = i
i += 1
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError" + str(bestError)
def deepbeliefMNIST():
assert not args.relu, "do not run this method for rectified linear units"
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.01
supervisedLearningRate = 0.05
momentumMax = 0.95
if args.train:
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
save_best_weights=args.save_best_weights,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors,
vectorLabels,
maxEpochs=args.maxEpochs,
validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
# Mistakes for digits
# You just need to display some for the report
# trueDigits = testLabels[errorCases]
# predictedDigits = predicted[errorCases]
print "correct"
print correct
# for w in net.weights:
# print w
# for b in net.biases:
# print b
# t = visualizeWeights(net.weights[0].T, trainImages[0].(28, 28), (10,10))
# plt.imshow(t, cmap=plt.cm.gray)
# plt.show()
# print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def adversarialMNIST():
assert not args.relu, "do not run this method for rectified linear units"
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
activationFunction = Sigmoid()
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testVectors / 255.0
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.01
supervisedLearningRate = 0.05
momentumMax = 0.95
if args.train:
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=activationFunction,
rbmActivationFunctionVisible=activationFunction,
rbmActivationFunctionHidden=activationFunction,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
save_best_weights=args.save_best_weights,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
adversarial_training=args.adversarial_training,
adversarial_coefficient=0.5,
adversarial_epsilon=1.0 / 255,
weightDecayL1=0,
weightDecayL2=0,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors,
vectorLabels,
maxEpochs=args.maxEpochs,
validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
def cvMNISTGaussian():
training = args.trainSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainVectors = np.array(trainVectors, dtype='float')
# Ensure the data has zero mean and 1 variance
trainingScaledVectors = scale(trainVectors)
vectorLabels = labelsToVectors(trainLabels, 10)
bestFold = -1
bestError = np.inf
params = [(5e-03, 1e-02), (1e-02, 5e-02), (5e-03, 5e-02), (1e-02, 5e-03),
(5e-03, 5e-03), (1e-02, 1e-02)]
correctness = []
nrFolds = len(params)
kf = cross_validation.KFold(n=training, n_folds=nrFolds)
i = 0
for train, test in kf:
# Train the net
# Try 1200, 1200, 1200
net = db.DBN(5, [784, 1000, 1000, 1000, 10],
binary=False,
unsupervisedLearningRate=params[i][0],
supervisedLearningRate=params[i][1],
momentumMax=0.95,
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
rmsprop=args.rmsprop,
save_best_weights=args.save_best_weights,
visibleDropout=0.8,
hiddenDropout=0.5,
weightDecayL1=0,
weightDecayL2=0,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
miniBatchSize=args.miniBatchSize,
preTrainEpochs=args.preTrainEpochs,
sparsityConstraintRbm=False,
sparsityTragetRbm=0.01,
sparsityRegularizationRbm=None)
net.train(trainingScaledVectors[train],
vectorLabels[train],
maxEpochs=args.maxEpochs,
validation=args.validation)
proabilities, predicted = net.classify(trainingScaledVectors[test])
# Test it with the testing data and measure the missclassification error
error = getClassificationError(predicted, trainLabels[test])
print "error for " + str(params[i])
print error
correct = 1.0 - error
if error < bestError:
bestError = error
bestFold = i
i += 1
correctness += [correct]
print "best fold was " + str(bestFold)
print "bestParameter " + str(params[bestFold])
print "bestError " + str(bestError)
for i in xrange(len(params)):
print "parameter tuple " + str(
params[i]) + " achieved correctness of " + str(correctness[i])
def rbmMain(reconstructRandom=False):
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=None, bTrain=True, path=args.path)
testingVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
trainingScaledVectors = trainVectors / 255.0
testingScaledVectors = testingVectors / 255.0
# TODO: the reconstruction for relu still looks weird
if args.relu:
activationFunction = RectifiedNoisy()
learningRate = 5e-05
binary=False
else:
learningRate = 0.3
binary=True
activationFunction = Sigmoid()
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible, nrHidden, learningRate, 1, 1,
visibleActivationFunction=activationFunction,
hiddenActivationFunction=activationFunction,
rmsprop=args.rbmrmsprop, nesterov=args.rbmnesterov,
sparsityConstraint=args.sparsity,
sparsityRegularization=0.01,
sparsityTraget=0.01)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28,28), (10,10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
# Reconstruct an image and see that it actually looks like a digit
test = testingScaledVectors[0,:]
# get a random image and see it looks like
if reconstructRandom:
test = np.random.random_sample(test.shape)
if args.display:
# Show the initial image first
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(test, (28,28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('initial7.png', transparent=True)
plt.show()
hidden = net.hiddenRepresentation(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(hidden, (25,20)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('hiddenfeatures7.png', transparent=True)
# Show the reconstruction
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(recon, (28,28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reconstruct7withall.png', transparent=True)
plt.show()
# Show the weights and their form in a tile fashion
# Plot the weights
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weights2srmsprop.png', transparent=True)
plt.show()
print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(t, f)
pickle.dump(net, f)
def deepbeliefMNISTGaussian():
training = args.trainSize
testing = args.testSize
trainVectors, trainLabels =\
readmnist.read(0, training, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, testing, bTrain=False, path=args.path)
print trainVectors[0].shape
trainVectors, trainLabels = shuffle(trainVectors, trainLabels)
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
vectorLabels = labelsToVectors(trainLabels, 10)
unsupervisedLearningRate = 0.005
supervisedLearningRate = 0.005
momentumMax = 0.97
sparsityTragetRbm = 0.01
sparsityConstraintRbm = False
sparsityRegularizationRbm = 0.005
if args.train:
net = db.DBN(5, [784, 1200, 1200, 1200, 10],
binary=False,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
activationFunction=Rectified(),
rbmActivationFunctionVisible=Identity(),
rbmActivationFunctionHidden=RectifiedNoisy(),
nesterovMomentum=args.nesterov,
rbmNesterovMomentum=args.rbmnesterov,
rmsprop=args.rmsprop,
hiddenDropout=0.5,
visibleDropout=0.8,
rbmHiddenDropout=1.0,
rbmVisibleDropout=1.0,
weightDecayL1=0,
weightDecayL2=0,
sparsityTragetRbm=sparsityTragetRbm,
sparsityConstraintRbm=sparsityConstraintRbm,
sparsityRegularizationRbm=sparsityRegularizationRbm,
preTrainEpochs=args.preTrainEpochs)
net.train(trainingScaledVectors, vectorLabels,
maxEpochs=args.maxEpochs, validation=args.validation)
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
net = pickle.load(f)
f.close()
probs, predicted = net.classify(testingScaledVectors)
print type(predicted)
correct = 0
errorCases = []
for i in xrange(testing):
print "predicted"
print "probs"
print probs[i]
print predicted[i]
print "actual"
actual = testLabels[i]
print actual
if predicted[i] == actual:
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
if args.save:
f = open(args.netFile, "wb")
pickle.dump(net, f)
f.close()
def rbmMainGauss(reconstructRandom=False):
trainVectors, trainLabels =\
readmnist.read(0, args.trainSize, digits=None, bTrain=True, path=args.path)
testVectors, testLabels =\
readmnist.read(0, args.testSize, digits=None, bTrain=False, path=args.path)
trainVectors = np.array(trainVectors, dtype='float')
trainingScaledVectors = scale(trainVectors)
testVectors = np.array(testVectors, dtype='float')
testingScaledVectors = scale(testVectors)
learningRate = 0.0005
# Train the network
if args.train:
# The number of hidden units is taken from a deep learning tutorial
# The data are the values of the images have to be normalized before being
# presented to the network
nrVisible = len(trainingScaledVectors[0])
nrHidden = 500
# use 1 dropout to test the rbm for now
net = rbm.RBM(nrVisible, nrHidden, learningRate, 1, 1,
visibleActivationFunction=Identity(),
hiddenActivationFunction=RectifiedNoisy(),
rmsprop=args.rbmrmsprop, nesterov=args.rbmnesterov)
net.train(trainingScaledVectors)
t = visualizeWeights(net.weights.T, (28,28), (10,10))
else:
# Take the saved network and use that for reconstructions
f = open(args.netFile, "rb")
t = pickle.load(f)
net = pickle.load(f)
f.close()
# Reconstruct an image and see that it actually looks like a digit
test = testingScaledVectors[0,:]
# get a random image and see it looks like
if reconstructRandom:
test = np.random.random_sample(test.shape)
# Show the initial image first
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(test, (28,28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('initial7relu.png', transparent=True)
# plt.show()
# Show the reconstruction
recon = net.reconstruct(test.reshape(1, test.shape[0]))
plt.imshow(vectorToImage(recon, (28,28)), cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('reconstruct7withallrelu.png', transparent=True)
# plt.show()
# Show the weights and their form in a tile fashion
# Plot the weights
plt.imshow(t, cmap=plt.cm.gray)
plt.axis('off')
plt.savefig('weights2srmsproprelu.png', transparent=True)
# plt.show()
print "done"
if args.save:
f = open(args.netFile, "wb")
pickle.dump(t, f)
pickle.dump(net, f)