Python setPlotTitle Examples

Example #1

noOfSamples = deeplearn.readCsvFile("xor.data", 4, 1, [2], 0)
print(str(noOfSamples) + " samples loaded")

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.8, 0.8])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("Xor Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(50000)

print("Training started")

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print("Training Completed")
print("Test data set performance is " + str(deeplearn.getPerformance()) + "%")

deeplearn.export("result.py")
print("Exported trained network")

Example #2

File: xor.py Project: xypan1232/libdeep-python

noOfSamples = deeplearn.readCsvFile("xor.data", 4, 1, [2], 0)
print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.8, 0.8])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("Xor Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(50000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #3

File: cancerdetect.py Project: yochju/libdeep-python

noOfSamples = deeplearn.readCsvFile("wdbc.data", 16, 3, [1], 0)
print(str(noOfSamples) + " samples loaded")

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([1.0, 1.0, 1.5, 6.0])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(2.0)

# Title of the training error image
deeplearn.setPlotTitle("Cancer Classification Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print("Training started")

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print("Training Completed")
print("Test data set performance is " + str(deeplearn.getPerformance()) + "%")

deeplearn.export("result.py")
print("Exported trained network")

Example #4

print(str(noOfSamples) + " samples loaded")

# The error threshold (percent) for each layer of the network.
# Three hidden layers, plus the final training.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([1.6, 2.05, 4.0, 9.5])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.5)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("White Wine Quality Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print("Training started")

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print("Training Completed")
print("Test data set performance is " + str(deeplearn.getPerformance()) + "%")

deeplearn.export("result.py")
print("Exported trained network")

Example #5

File: iris.py Project: HaibinLi11/libdeep-python

noOfSamples = deeplearn.readCsvFile("iris.data", 16, 3, [4], no_of_classes)
print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.5, 0.5, 0.5, 2.5])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.1)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.01)

# Title of the training error image
deeplearn.setPlotTitle("Iris Species Classification Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #6

File: concreteslump.py Project: HaibinLi11/libdeep-python

print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# Three hidden layers, plus the final training.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.5, 0.5, 0.5, 2.0])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("Concrete Slump Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(900000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%"

deeplearn.export("result.py")
print "Exported trained network"

Example #7

File: learn.py Project: JoakimSoderberg/catmuzzle

noOfSamples = deeplearn.readCsvFile("data.csv", samplesPerAxis*(axes-1), 3, [1], 0)
print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([5.0, 7.0, 7.0, 20.0])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.5)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("Catmuzzle Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(50000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #8

File: iris.py Project: xypan1232/libdeep-python

noOfSamples = deeplearn.readCsvFile("iris.data", 16, 3, [4], no_of_classes)
print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.5, 0.5, 0.5, 2.5])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.1)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.01)

# Title of the training error image
deeplearn.setPlotTitle("Iris Species Classification Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #9

File: cancerdetect.py Project: xypan1232/libdeep-python

noOfSamples = deeplearn.readCsvFile("wdbc.data", 16, 3, [1], 0)
print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([3.0, 3.0, 3.5, 3.0])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(2.0)

# Title of the training error image
deeplearn.setPlotTitle("Cancer Classification Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #10

File: winequality.py Project: xypan1232/libdeep-python

print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# Three hidden layers, plus the final training.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([1.6, 2.05, 4.0, 9.5])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.5)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("White Wine Quality Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"

Example #11

File: concreteslump.py Project: xypan1232/libdeep-python

print str(noOfSamples) + " samples loaded"

# The error threshold (percent) for each layer of the network.
# Three hidden layers, plus the final training.
# After going below the threshold the pre-training will move
# on to the next layer
deeplearn.setErrorThresholds([0.5, 0.5, 0.5, 2.0])

# The learning rate in the range 0.0-1.0
deeplearn.setLearningRate(0.2)

# The percentage of dropouts in the range 0-100
deeplearn.setDropoutsPercent(0.001)

# Title of the training error image
deeplearn.setPlotTitle("Concrete Slump Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(900000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.py")
print "Exported trained network"